Dean Abbott is an internationally recognized thought leader and innovator in data science and predictive analytics, with more than three decades of experience solving problems in customer analytics, fraud detection, risk modeling, text mining, survey analysis, and many more. He is frequently included in lists of the top pioneering and influential data scientists in the world. Mr. Abbott is the author of Applied Predictive Analytics (Wiley, 2014) and coauthor of The IBM SPSS Modeler Cookbook (Packt Publishing, 2013). He is a popular keynote speaker and bootcamp/workshop instructor at conferences worldwide and serves on advisory boards for the UC/Irvine Predictive Analytics and UC/San Diego Data Science Certificate programs. He holds a bachelors degree in computational mathematics from Rensselaer Polytechnic Institute and a masters degree in applied mathematics from the University of Virginia.

Cheryl has 13 years experience in Statistical Physics, Computational Modeling, Data Science and Digital technologies having worked with both public and private sectors in developing strategic solutions using Machine Learning and Agent-Based Models. At AWS, Cheryl is a Principal Solution Architect for Autonomous Computing with a focus on Predictive Modeling and Simulations for Digital Twins. Prior to joining AWS, Cheryl worked at Quantum Black – McKinsey and Solvay as Data Science Lead defining digital architectures, and delivering Advanced Analytics and AI assets covering various industries including Chemicals Manufacturing, Agriculture, Aviation, R&I, Procurement, Commercial, Supply Chain, Finance and Energy. Prior to that, she worked on insider threat detection, computer vision for marketing, investment banking trading technologies and geospatial analytics for telecommunications. Cheryl obtained her Ph.D. in Physics from Nanyang Technological University in 2017 where she focused on analytical, graph and computational models for urban, linguistic and genetic systems. She also received a B.S. in Applied Physics from the University of the Philippines with specializations in Neural Network and Complex Systems.

Juan Acevedo is an Enterprise machine learning architect at Google where he helps customers optimize models for large scale distributed training and serving with accelerators such as GPUs and TPUs.

Cal Al-Dhubaib is a globally recognized data scientist, entrepreneur, and innovator in trustworthy artificial intelligence. He is the founder and CEO of Pandata, a consulting company that helps organizations operating in regulated industries design and develop AI-powered solutions to complex business problems. Cal's commitment to diversity and ethics is centered at the heart of his work.

He has gone on to be featured in multiple noteworthy publications, including Forbes,

Ohiox, Cleveland.com, the Marketing AI Institute, and Open Data Science. Cal has also received recognition in Crain's Cleveland Twenty in their 20s and Notable Immigrant Leaders, and Smart Business's Technology Awards and Smart 50 Awards. Most recently, he was named as a Cleveland 2021 Notable Entrepreneur .

Joel has worked in the insurance industry for 25 years, including three multi-billion dollar insurers, leading data science teams and doing a variety of data science work to support several business functions.  During this time, he has been involved in numerous discussions where he's seen how different people can reach different conclusions using the same data.  He will draw on these experiences to discuss a number of potential issues in analytics projects with examples from the public domain.

Bardia Beigi works at Microsoft as an Applied Scientist II in the Industry AI group delivering AI/ML based solutions to various industries within Azure. Bardia has a master's degree in Computer Science from Stanford University, as well as Bachelor of Applied Science in Engineering Physics from the University of British Columbia. In his spare time, Bardia enjoys traveling, trying out new dessert spots, and learning new life hacks.

Leo Betthauser works at Microsoft Research in Redmond, Wash. Leo has a Ph.D. in mathematics from the University of Florida where he studied applied algebraic topology. Leo is currently exploring the application of deep neural techniques to cyber security. In his spare time, Leo enjoys traveling, cooking, and skiing.

Jodi Blomberg leads the enterprise data science and machine learning teams that integrate models into applications across Waste Management. Former head of data science at Charles Schwab.  Experienced data science executive with wide track record in creating products that embed NLP, computer vision, machine learning, statistical modeling, and optimization to create demonstrable value.

Clinton Brownley, Ph.D., is a data scientist at WhatsApp, where he’s responsible for a variety of analytics projects designed to improve messaging and VoIP calling performance and reliability.  Before WhatsApp, Clinton was a data scientist at Facebook, working on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions.  As an avid student and teacher of modern analytics techniques, Clinton is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis,” and also teaches Python programming and data science courses at Facebook and in the Bay Area. Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.

Dave Coughlin currently leads a team of 10 data science professionals in the pursuit of driving greater membership and margin for CVS Health's insurance products. Prior to that he did stints in strategy consulting and population health research. A passion of his is performance improvement and he recently wrote a book to scale the insights he has used to turbocharge his own career (3 promotions in 5 years) but also the careers of his mentees. Dave currently lives just outside of Boston with his wife and three children and holds both an MS in applied math and MBA.

ucas Abreu is the co-Founder and Chief Data Scientist at DHAUZ, with an extensive background in Electronics and Computer Engineering. He holds a Master's degree in Applied Artificial Intelligence, and is deeply committed to the advancement of data science and innovative practical solutions. Lucas has a proven track record of developing critical and scalable AI models, both in leadership roles and hands-on implementation, for several industries. He is dedicated to bringing cutting-edge AI solutions to solve complex business problems and improve customer satisfaction.

Aimee DeGrauwe brings a broad background in data analysis to drive process and quality improvements in supply management, warehousing, manufacturing, and order management. She has a passion for data as a product and has worked on both the technical and business side of data engineering and analytics. In her current role as Group Product Manager, she leads multiple global teams at John Deere focused on ensuring data from our manufacturing and quality systems are easy to use for decision making.

Matt Denesuk has devoted his life to using math & data to make the world work better.  He is currently SVP of Data Analytics & AI at Royal Caribbean Group, and leads the development and deployment of data- and data-science-driven capabilities across the company, in such areas as revenue management, industrial operations, customer experience, marketing, sales, hotel operations, and supply chain. He previously was Chief Data Science Officer, and a founder, of Noodle.ai, which provides AI-as-a-Service to enterprise and industrial firms. Prior to that, he was the Chief Data Science Officer of General Electric, leading the data, analytics, and Data Science functions across GE’s industrial business units.  He also led Smarter Planet Modeling & Analytics at IBM Research, and was a Partner in IBM’s Venture Capital Group. Matt has B.S., M.S., and Ph.D. Degrees from MIT and the University of Arizona.

Dinakar Deshmukh is the VP of data science and analytics for GE Aviation. He has been working in aviation industry for the last 20 years. In his current role as data science leader for GE Aviation, he is responsible for developing AI based solutions for engine health management, supply chain and MRO operations. His team is also developing ML based applications to help airline operations to improve operational efficiencies.

Vash Doshi is the Manager of Industry Relations for UCLA Anderson's Master of Science in Business Analytics program.  Daily, Vash strives to show employers why UCLA Anderson's MSBA has been ranked the #2 place in the world for the past four years to study, build an analytics career, and recruit from. He is driven to help students and UCLA MSBA’s hundreds of partner firms find each other to create the perfect analytics fit. Vash believes in the magic of people. Whether through developing and advocating for reciprocal market access for American firms so that more Americans can find meaningful jobs and grow their businesses via internships at U.S. Departments of State and Commerce, or by counseling over 500 students to help them find their perfect career, he has over five years of experience helping people make magic happen.  He is currently finishing up his Ph.D. in Political Science at UCSB and holds an M.A. in International Studies from the University of Denver’s Josef Korbel School of International Studies.

Eric M. Dunleavy, Ph.D., is the Vice President of Employment and Litigation Support Services at DCI Consulting Group, where he is involved in equal employment opportunity audit and litigation consulting. Eric’s primary areas of expertise are in employee selection, employment testing, validation research, adverse impact analyses and other quantitative methods. His most recent work has focused on conducting independent expert reviews and related research across a wide variety of selection procedures, including those that leverage machine learning techniques to analyze data. . Eric received his M.A. (2002) and Ph.D. (2004) in Industrial/Organizational Psychology with a concentration in data analysis from the University of Houston.

John Elder chairs America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Maryland, North Carolina, Washington DC, and London. Dr. Elder co-authored 3 award-winning books on analytics, was a discoverer of ensemble methods, chairs international conferences, and is a popular keynote speaker. John is occasionally an Adjunct Professor of Systems Engineering at the University of Virginia.

Fabio is a Business Analytics Sr Executive working on the field for over 20 years applying advanced modelling and analytics tech solutions to solve complex business challenges, achieving improved customer engagement and higher operational efficiency servicing most of the top 100 companies in Latin America.

He holds a Masters of Logistics Systems Engineering and BA in Construction Engineering both from University of Sao Paulo, Brazil. Fabio is also a certified Lead of Analytics & AI from Northwestern University.

Fabio developed his professional career inside Accenture Latin America, where he started in 2002, being one of first executive in the Analytics Group back in 2010. He conducted Gapso Acquisition on Accenture side in 2015 becoming Lead for Advanced Analytics and Data Science in the region, eventually leading Supply Chain Analytics for Growth Markets until 2021.

Fabio is currently a Sr Advisor in Analytics and Data Adoption for some of the top companies in Brazil and also supports Startups growth and services in the fields of Analytics and Digital Talent Recruiting.

Jen Gennai leads Google’s Responsible Innovation team which is responsible for operationalizing Google’s AI Principles, ensuring that Google’s products have fair and ethical outcomes on individual users and the world. Her team works with product and engineering, leveraging a multidisciplinary group of experts in ethics, human rights, user research, racial justice and gender equity to validate that products and outputs align with our commitments to fairness, privacy, safety, societal benefit and more. Before she co-authored the AI Principles and founded Responsible Innovation, Jen worked on machine learning fairness and founded the Ethical ML team in Trust & Safety.

Justin Gold is a Product Manager for John Deere's Manufacturing Data Platform where he enables John Deere's Global Manufacturing Partners to make informed decisions about their business through trusted data availability.  He guides a team of data engineers as they build data pipelines to feed the generation of dashboards and analytic solutions.  Prior to this role Justin spent his career as a Manufacturing Engineer supporting day to day production of John Deere products.

Justin holds a Bachelor of Science in Engineering and Management from Clarkson University and a Master of Science in Engineering Management from Clarkson University's Beacon Institute.

Product Manager at heart. Noa's forte is creating products specially made for engineers and tech-savvy geeks. With her strong technical background as a former software engineer, her main passion is to simplify complicated technological pains into intuitive solutions and easy-to-use products that anyone can understand and use. She is currently the Lead Product Manager at Dagshub, the data scientist projects platform. She is a public speaking enthusiast and won't miss an opportunity to present her products in front of an audience.

My name is Dao Ho, I am a Senior Data Scientist at Albertsons. My current focus is personalization. At Albertsons, I have worked on several personalization models such as Buy It Again (ranking past purchases), embedding models for our products and customers. In collaboration with Ankita, I have been working on building a Wide & Deep Neural Network model for recommendations. Before Albertsons, I worked at a research lab for GAC, a Chinese car manufacture, building models to monitor the batteries of 30,000 EVs. Prior to that, I worked at a fintech company modeling market conditions. And finally, I graduated from Carnegie Mellon University with a Ph.D. degree in Physics.

Subramanian Iyer is Senior Director of Data Sciences at Target and leads AI teams for forecasting in Supply Chain and Merchandizing modernization in areas like Inventory Planning; digital placement and allocation; financial and budget planning; price optimization and so on. He has previously been Head of Analytics at First Republic Bank, where he led multiple efforts on quantitative analytics, AI modeling, data warehousing, web development for analytical applications and efficient data representation for analytical datasets. Prior to that he was VP of Investment Banking Strategies at Goldman Sachs where he worked on derivatives pricing and structuring in the Americas Financing Group, with a special focus on financial institutions. Prior to that he was in technical roles at Google, Synopsys and Fujitsu and holds several patents. He is an alumnus of Harvard where he did his executive education in General Management, of UT Austin where he received his doctorate in Computer Sciences in Formal Methods and of IIT Bombay where he got his Bachelors in Computer Science & Engineering.

Dr. Usha Jagannathan is a seasoned technologist with over 20 years of experience in driving digital transformation and building customer-centric products. With a strong focus on building new technology ecosystems, she has made significant contributions to McKinsey's Technology & Digital functions. Her expertise encompasses data strategy, leading AI/ML teams in cloud migration initiatives, driving innovation, and implementing AI ethics frameworks to develop responsible AI products. Usha's dedication to promoting DEI efforts is evident through her leadership of apprenticeship and work-study programs aimed at increasing industry diversity. Recognized by business leaders for her expertise in AI ethics and explainability, she is a sought-after speaker at top tech conferences where she shares insights on trending AI technologies and their societal impact.

Kian Katanforoosh is the CEO and cofounder of Workera.ai, the skills intelligence platform redefining how enterprises understand, develop, and mobilize talent. Workera empowers organizations to stay ahead by unlocking the skills data needed to drive innovation. Enterprises get an objective pulse of their innovation skills through a configurable skills ontology, using AI-powered measurement of data, AI, software, cloud, and cyber skills at an atomic-level.

Kian is also a lecturer at Stanford University, where he teaches Deep Learning in the Computer Science department with Prof. Andrew Ng. Kian has been acknowledged for his teaching excellence by Stanford with the Walter J. Gores Award, Stanford’s highest teaching award, and the Centennial Award for Excellence in teaching.

Additionally, Kian is a founding member of deeplearning.ai and he co-created the Deep Learning Specialization on Coursera with Andrew Ng. From 2014 to 2016, Kian co-founded and co-led Daskit, a French start-up developing in-classroom ed-tech solutions for universities.

With almost two decades of experience under his belt, Gulrez is a data wrangling extraordinaire, who has a knack for turning boring numbers into captivating stories. When he's not busy working, you can find him digging through open datasets like they're buried treasure, or passing on his data visualization skills to the next generation in the hopes of creating a world of data literate children.

A strong believer in the power of data literacy, Gulrez is on a mission to improve the way people make sense of data. He's known for delivering corporate workshops that are equal parts informative and entertaining, and is currently working on a book that promises to be a page-turner for data geeks everywhere.

Diego Klabjan is a professor at Northwestern University, Department of Industrial Engineering and Management Sciences. He is also Founding Director, Master of Science in Analytics and Director, Center for Deep Learning. His work is focused on machine learning, deep learning and analytics (modeling, methodologies, theoretical results) with concentration in finance, insurance, sports, and bioinformatics. Professor Klabjan has led projects with large companies such as Intel, Baxter, Allstate, Anthem, and many others. He is also a co-founder of Videspan, LLC after successfully starting Opex Analytics, a Coupa company.

Koga leads Planning and Business Intelligence organization at Jetson, a leader in personal mobility devices including electric bikes, electric scooters, and hoverboards. Koga joined Jetson in 2021 where is responsible for building the Data & Analytics foundation that generates insights to positively impact customer experience and improve Jetson’s operational performance. He brings extensive knowledge in the consumer electronics industry after working at different services roles at Samsung, Nokia and Microsoft and he also held a series of strategy and consulting roles earlier in her career at Accenture.

Koga prides himself on being very analytical and by connecting the dots across different parts of the customer journey. He is truly passionate about performing deep dive analysis to find the optimal spot between positively impact customer experience, and bringing positive ROI to the company.

William Komp is the Principle Data Scientist at Komplytics LLC.. He has over 2 decades of experience in academia, health care, marketing analytics, transportation, logistics, Oil&Gas, Food&Beverage, environmental management, renewable energy and public utilities. He was the technical editor for Applied Predictive Analytics (J. WIley and Sons 2014). He holds a PhD Physics with areas of research in Gravitation, Cosmology and Quantum Field Theory in Curved Spacetime.

Sam, with over 30 years experience, in the data science space, provides advanced analytic support to his many clients. His current responsibilities at Harte Hanks includes quantitative support to many industries , including retail, financial, health care, automotive, technology, consumer goods, publications, among others. His previous experiences include heading the Predictive and Targeted Modeling area at American Express. There he was responsible for strategic and tactical support for the many subsidiaries of American Express. At TMI associates, a boutique consulting firm, Sam was principal analytic consultant. At JC Penney, Sam was responsible for credit scoring and database analytics. Sam has been fortunate to train many machine learning professionals. He is a  frequent speaker at industry conferences, and has published dozens of machine learning related articles. Sam has an undergraduate degree in mathematics, and earned an MBA at New York University. He has lectured at both Columbia and New York University.

A Teaching Associate Professor in the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern work force to wisely communicate and handle a data-driven future at the nation's first Master of Science in analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management.

Previously, he was Director and Senior Scientist at Elder Research, where he mentored and lead a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government.

Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State University.

Jared P. Lander is Chief Data Scientist of Lander Analytics, the Organizer of the New York Open Statistical Programming Meetup and the New York and Government R Conferences, an Adjunct Professor at Columbia Business School, and a Visiting Lecturer at Princeton University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. Jared oversees the long-term direction of the company and acts as Lead Data Scientist, researching the best strategy, models and algorithms for modern data needs. This is in addition to his client-facing consulting and training. He specializes in data management, multilevel models, machine learning, generalized linear models, data management, visualization and statistical computing. He is the author of R for Everyone (now in its second edition), a book about R Programming geared toward Data Scientists and Non-Statisticians alike. The book is available from Amazon, Barnes & Noble and InformIT. The material is drawn from the classes he teaches at Columbia and is incorporated into his corporate training. Very active in the data community, Jared is a frequent speaker at conferences, universities and meetups around the world.

Michael Lawrence is Director, Business Intelligence, Operations, & Transformation at Tata Communications. Mr. Lawrence has 20 years of experience in telecommunications, program management and business operations.  He is an expert in change management and driving a culture of making business decisions off of data insights.  Michael has a MS in Information Science from Penn State University as well as an undergraduate degree in Mathematics from the University of Scranton.

Dr. Ankita Mangal is a Senior Data Scientist at Albertsons Companies. She has a broad experience applying data science & analytics solutions at companies like Procter & Gamble, Robert Bosch and Tata Steel Ltd. She is an accomplished leader in the field of retail and marketing science, who is championing data informed decision making in the Retail and Consumer Packaged Goods industry. Dr. Mangal is passionate about efficient cross functional collaborations for creating successful ML solutions. In her current role, she provides technical leadership, mentorship and develops interpretable and accurate models for business strategy decisions. Dr. Mangal has a doctoral degree from Carnegie Mellon University and is an alumnus of IIT Kharagpur.

Long before the buzz surrounding generative AI, Martin Musiol was already advocating for its significance in 2015. Since then, he has been a frequent speaker at conferences, podcasts, and panel discussions, addressing the technological advancements, practical applications, and ethical considerations of generative AI. Martin Musiol is a co-founder of generativeAI.net, a lecturer on AI to over 1000 students, and publisher of the newsletter 'Generative AI: Short & Sweet'. As a Data Science Manager at Infosys Consulting (previously at IBM), Martin Musiol helps companies globally harness the power of generative AI to gain a competitive advantage.

A seasoned Data leader with 16+ years of experience in Data Science delivery, product development, and consulting. Currently, he is working as a Director in Tata CLiQ (the e-commerce arm of Tata Industry for Lifestyle & Beauty business), responsible for driving Data Operations, Data Governance, Analytics & Insight practices. He is distinguished as an author of multiple papers and poster presentations at various National & International conferences. He has to his credit four patents filed in the Customer & Pricing Analytics space and has one publication in an International journal. He is also passionate about teaching and has worked as a visiting lecturer across multiple Institutes and ed-tech organizations.

Marina Petzel is a Machine Learning Engineer at Autodesk. In her role, she develops ML solutions to further improve the AutoCAD users experience; Autodesk's flagship design and drafting tool. Marina leads the data collection and labeling processes through automation using ML.

She is passionate about finding and developing efficient data driven solutions that address business needs. Marina has extensive experience in applying data science to various industries such as construction, healthcare, oil and gas, and finance.

Marina is an AI Influencer in social media where she shares AI/ML tips and best practices. She coaches and mentors young professionals in data science, engineering and career strategy.

Marina graduated from the University of Rochester, NY with a Master of Science degree in Data Science. She received her Bachelor degree in Computer Science from Moscow State University.

Kumaran Ponnambalam is a technology leader with 20+ years of experience in AI, Big Data, Data Processing & Analytics. His focus is on creating robust, scalable AI models and services to drive effective business solutions.  He is currently leading AI initiatives in the Emerging Technologies & Incubation Group in Cisco. In his previous roles, he has built data pipelines, analytics, integrations, and conversational bots around customer engagement. He has also authored several courses on the LinkedIn Learning Platform in AI and Big Data.

Steven J. Ramirez is the chief executive officer of Berkeley, Calif.-based Beyond the Arc, Inc., a firm recognized as a leader in helping companies transform their customer experiences by leveraging advanced analytics.

In addition to developing and executing the vision for Beyond the Arc, Ramirez leads teams of data and strategy consultants committed to client success. They analyze customer and social media data, combined with text analysis, to drive customer growth, improve customer retention, understand service breaks and build stronger customer loyalty.

Prior to leading Beyond the Arc, Ramirez served as an executive with Time Warner, where he was responsible for creating and successfully implementing marketing and corporate development strategies.

Ramirez earned a bachelor's degree and master's in Business Administration from the University of California at Berkeley. He as also created and taught courses in business management for UC Berkeley and been a guest speaker at the university's Haas School of Business.

Karl Rexer founded Rexer Analytics in 2002. He and his teams have built an outstanding reputation providing predictive modeling and analytic consulting to clients across many industries. Recent clients include OneBlood, PwC, Boston Scientific, Redbox, ADT Security, Interamericana University, MIT, Forward Financing, SharkNinja, and many smaller companies. In addition to leading client engagements and hands-on data work, Karl is a predictive analytics evangelist, frequently speaking at conferences, colleges, and other events. He also serves on Advisory Boards for the Business Analytics programs at both Babson College and Bentley University. Since 2007 Rexer Analytics has conducted surveys of analytic professionals, asking them about their algorithms, tools, behaviors and  views. Summary reports from these surveys are available as a free download from the Rexer Analytics website. Prior to founding Rexer Analytics, Karl held leadership positions at several consulting firms and two multi-national banks. Karl holds a PhD from the University of Connecticut.

Brian Sampsel is a Vice President with the International Institute for Analytics, where he leads delivery of the company's services such as the Research and Advisory Network and the Analytics Leadership Consortium. He possesses a wide breadth of experience in the data and analytics field across a variety of industries and functions, holding leadership roles on both the client and vendor sides. Brian seeks to help his clients through a consultative and solutions focused mindset, with expertise across a number of tools and techniques including business analytics, data science, cognitive services and robotic process automation. Brian has a Bachelor's degree in Mathematics from Cedarville University and a Master's degree in Statistics from Miami University.

Dr. Jennifer Schaff leads Elder Research's Commercial business unit where she advises organizational decision makers to help them realize maximum value from their analytics investments. In her 20 years of management, data analysis, and business experience, Dr. Schaff has built her reputation as a "Barricade Buster" in the data analytics industry. Her experience has uncovered that business barriers -- defining the right opportunity and consumer, delivering the pertinent results to guide data driven decisions, and implementing the change processes -- rather than model building, is where analytics initiatives often fail. Dr. Schaff channels this approach into a passion for guiding organizations through these business challenges to maximize their analytics ROI.

Dan works as the Head of Machine Learning at Abnormal Security, where he builds cybercrime detection systems to keep people and businesses safe. Cybercrime is always changing as attackers innovate, and the detection organization at Abnormal Security must stay ahead of these innovations. As the Head of Machine Learning, Dan works to optimize the detection organization's ability to anticipate these innovations, iterate and improve.

Before joining Abnormal Dan worked at Twitter: first as an ML researcher working on recommendation systems, and then as the engineering manager for the web ads machine learning team. Before Twitter Dan built smartphone sensor algorithms at TrueMotion and Computer Vision systems at the Serre Lab. Dan's PhD at the University of Oxford focused on the applications of Category Theory to Machine Learning.

Eric Siegel, Ph.D. is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series, a frequent keynote speaker, and executive editor of The Machine Learning Times. Eric authored the forthcoming book The AI Playbook: Mastering the Rare Art of Machine Learning Deployment and the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at hundreds of universities. He won the Distinguished Faculty award when he was a professor at Columbia University, where he taught the graduate courses in machine learning and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice.

Prerna Singh is currently working as an Applied Scientist II in the Industry AI group @Microsoft where she develops machine learning-based solutions for different industrial verticals including finance and sustainability. Before joining Microsoft, she obtained her master's degree in Electrical and Computer Engineering with a concentration on Machine Learning from Carnegie Mellon University (CMU). Prerna is passionate about machine learning, NLP and deep Reinforcement Learning. Besides work, Prerna enjoys traveling, Zumba and hiking in her free time.

Corwin Smith is an accomplished data & analytics strategist and practitioner. In his role, Corwin focuses on designing and implementing predictive and statistical models to forecast future business performance by leveraging advanced machine learning platforms. Additionally he analyzes internal & external data to understand performance drivers to identify improvement opportunities to streamline and automate the analytical processes and data pipelines that feed predictive models and dashboards necessary for decision-making. Corwin spent 20 years in the financial and insurance product industry spanning sales, digital, marketing and data & analytics (D&A). He spent the last 5 of those years focused on D&A, where he enabled delivery of over $60 million in quantifiable value for organizational partners by owning the development and deployment of the artificial intelligence and machine learning data product solution strategies.

Brandon Southern, MBA, is a Director of Analytics with over 20 years' experience across analytics, software engineering, quality assurance, data engineering, and leadership. As a business and technical leader, he has generated millions of dollars in revenue and cost reductions through advanced analytics.  Brandon is also the founder of Analytics Mentor, where he specializes in training, coaching, and elevating analytics professionals.

David Talby is the Chief Technology Officer at John Snow Labs, helping companies apply artificial intelligence to solve real-world problems in healthcare and life science. David is the creator of Spark NLP the world's most widely used natural language processing library in the enterprise. He has extensive experience building and running web-scale software platforms and teams – in startups, for Microsoft's Bing in the US and Europe, and to scale Amazon's financial systems in Seattle and the UK. David holds a Ph.D. in Computer Science and Masters degrees in both Computer Science and Business Administration. He was named USA CTO of the Year by the Global 100 Awards and GameChangers Awards in 2022.

James Taylor is the CEO of Decision Management Solutions and is a leading expert in how to use business rules and analytic technology to build decision management systems. He is passionate about using decision management systems to help companies improve decision-making and develop an agile, analytic and adaptive business. He provides strategic consulting to companies of all sizes, working with clients in all sectors to adopt decision-making technology. James is an expert member of the International Institute for Analytics and is the author of multiple books and articles on decision management, decision modeling, predictive analytics and business rules, and writes a regular blog at JT on EDM. James also delivers webinars, workshops and training. He is a regular keynote speaker at conferences around the world.

Annette Tyman is a Partner at the law firm Seyfarth Shaw LLP.  She chairs the People Analytics practice group and regularly advises employers on diversity, equity and inclusion workforce analytics and the legally compliant use of machine learning in the workplace.  Annette collaborates with employers and vendors to design, develop and implement machine learning while mitigating the legal risks associated with its use.   Annette has also provided testimony on proposed labor and employment legislation and regulations pending before the United States Senate, House of Representatives, and the Equal Employment Opportunity Commission.  Most recently, Annette testified before the New York City Department of Consumer and Worker Protection on proposed rules implementing Local Law 144 of 2021 on the use of automated employment decision tools and hearings regarding the implementation of proposed laws in Washington D.C.

Dr. Vazirabadi (Vazeerabaadi), received her PhD in Psychological Measurement and Quantitative Research Methods from University of Denver, in Denver, CO in 2010. She has been contributing to the workforce analytics field since 2005 and was directly responsible for her team's achievement of their first industry award in 2008 from IQPC for Best HR Metrics and Analytics. Since then, she has been key in the analytics strategy, development, and execution of number of mid-size and large organization where through her work she was responsible for significant cost savings, business process improvement and HR effectiveness measures. She has received multiple industry awards for her work, published in peer reviewed journals and taught at colleges and universities in her field of expertise. For the past six years she has been working for Bristol Myers Squibb as a subject matter expert in HR Analytics, where she designs measurement techniques, analyzes data, interprets results, and provides action plans and next steps for her HR executives.

Evan brought his military experience to Elder Research to deliver client solutions as a Data Scientist. He now serves as the Director of Analytics Strategy, helping to marry the exciting things that analytics can do with the myriad of challenges that people are facing. Evan almost always has a smile on his face, and that smile is the widest when he is helping organizations use data in new ways to solve unique problems. Evan earned his BS in Management from Georgia Tech, his MS in Economics from East Carolina University and his MS in Analytics from the Institute for Advanced Analytics at NC State University.  He loves teaching and learning and can often be found exploring the outdoors with his wife and children.

Cheryl has 13 years experience in Statistical Physics, Computational Modeling, Data Science and Digital technologies having worked with both public and private sectors in developing strategic solutions using Machine Learning and Agent-Based Models. At AWS, Cheryl is a Principal Solution Architect for Autonomous Computing with a focus on Predictive Modeling and Simulations for Digital Twins. Prior to joining AWS, Cheryl worked at Quantum Black – McKinsey and Solvay as Data Science Lead defining digital architectures, and delivering Advanced Analytics and AI assets covering various industries including Chemicals Manufacturing, Agriculture, Aviation, R&I, Procurement, Commercial, Supply Chain, Finance and Energy. Prior to that, she worked on insider threat detection, computer vision for marketing, investment banking trading technologies and geospatial analytics for telecommunications. Cheryl obtained her Ph.D. in Physics from Nanyang Technological University in 2017 where she focused on analytical, graph and computational models for urban, linguistic and genetic systems. She also received a B.S. in Applied Physics from the University of the Philippines with specializations in Neural Network and Complex Systems.

Juan Acevedo is an Enterprise machine learning architect at Google where he helps customers optimize models for large scale distributed training and serving with accelerators such as GPUs and TPUs.

Rohit works as a Chief Data Officer at Mobisy Technologies), where he focuses on retail analytics by applying state of the art ML & Deep Learning techniques. His retail insights are published in all the leading financial publications in India every week. Prior to Mobisy, he was with GE for 11 years. He has spoken in multiple international conferences including PAW Munich (2019), Berlin (2020), Las Vegas (2022). Rohit has a Masters degree in Information Technology & Bachelors degree in Computer Science.

Natesh Babu Arunachalam is a Director of Data Science at Mastercard, specifically focused on building innovative AI applications leveraging open banking data. Prior to Mastercard, Natesh was part of the risk management org at JP Morgan Chase where he specialized in underwriting, fraud detection and marketing targeting AI applications for the bank’s consumer division.

Bardia Beigi works at Microsoft as an Applied Scientist II in the Industry AI group delivering AI/ML based solutions to various industries within Azure. Bardia has a master's degree in Computer Science from Stanford University, as well as Bachelor of Applied Science in Engineering Physics from the University of British Columbia. In his spare time, Bardia enjoys traveling, trying out new dessert spots, and learning new life hacks.

Eric Ben-Artzi is a researcher in the team that develops client-tailored algorithms for LightSolver’s super-computer. Prior to joining LightSolver, he developed complex derivatives and portfolio risk models at Goldman Sachs and Citibank. Eric holds a PhD in mathematics from the Courtant Institute at NYU, and is an adjunct professor for financial engineering and fintech at the Technion - Israel Institute of Technology.

Jie Chen is the head of Decision Science and Artificial Intelligence Model Validation in Corporate Model Risk at Wells Fargo. Her team covers model validation in decision science, natural language processing (NLP), fair lending, financial crime detection, fraud detection and marketing. Prior to that, she led the Cross-Functional Model Validation team and the Statistical Modeling and Machine Learning R&D team in Corporate Model Risk at Wells Fargo.  Before joining Wells Fargo, Jie was a vice president at Bank of America working on market risk model development and consumer credit risk solutions.  She has over fifteen-year experience on machine learning, artificial intelligence, and advanced statistics in the banking industry.  Jie holds a Ph.D. in statistics from the Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology.

Vash Doshi is the Manager of Industry Relations for UCLA Anderson's Master of Science in Business Analytics program.  Daily, Vash strives to show employers why UCLA Anderson's MSBA has been ranked the #2 place in the world for the past four years to study, build an analytics career, and recruit from. He is driven to help students and UCLA MSBA’s hundreds of partner firms find each other to create the perfect analytics fit. Vash believes in the magic of people. Whether through developing and advocating for reciprocal market access for American firms so that more Americans can find meaningful jobs and grow their businesses via internships at U.S. Departments of State and Commerce, or by counseling over 500 students to help them find their perfect career, he has over five years of experience helping people make magic happen.  He is currently finishing up his Ph.D. in Political Science at UCSB and holds an M.A. in International Studies from the University of Denver’s Josef Korbel School of International Studies.

John Elder chairs America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Maryland, North Carolina, Washington DC, and London. Dr. Elder co-authored 3 award-winning books on analytics, was a discoverer of ensemble methods, chairs international conferences, and is a popular keynote speaker. John is occasionally an Adjunct Professor of Systems Engineering at the University of Virginia.

Jen Gennai leads Google’s Responsible Innovation team which is responsible for operationalizing Google’s AI Principles, ensuring that Google’s products have fair and ethical outcomes on individual users and the world. Her team works with product and engineering, leveraging a multidisciplinary group of experts in ethics, human rights, user research, racial justice and gender equity to validate that products and outputs align with our commitments to fairness, privacy, safety, societal benefit and more. Before she co-authored the AI Principles and founded Responsible Innovation, Jen worked on machine learning fairness and founded the Ethical ML team in Trust & Safety.

Dr. Usha Jagannathan is a seasoned technologist with over 20 years of experience in driving digital transformation and building customer-centric products. With a strong focus on building new technology ecosystems, she has made significant contributions to McKinsey's Technology & Digital functions. Her expertise encompasses data strategy, leading AI/ML teams in cloud migration initiatives, driving innovation, and implementing AI ethics frameworks to develop responsible AI products. Usha's dedication to promoting DEI efforts is evident through her leadership of apprenticeship and work-study programs aimed at increasing industry diversity. Recognized by business leaders for her expertise in AI ethics and explainability, she is a sought-after speaker at top tech conferences where she shares insights on trending AI technologies and their societal impact.

Kian Katanforoosh is the CEO and cofounder of Workera.ai, the skills intelligence platform redefining how enterprises understand, develop, and mobilize talent. Workera empowers organizations to stay ahead by unlocking the skills data needed to drive innovation. Enterprises get an objective pulse of their innovation skills through a configurable skills ontology, using AI-powered measurement of data, AI, software, cloud, and cyber skills at an atomic-level.

Kian is also a lecturer at Stanford University, where he teaches Deep Learning in the Computer Science department with Prof. Andrew Ng. Kian has been acknowledged for his teaching excellence by Stanford with the Walter J. Gores Award, Stanford’s highest teaching award, and the Centennial Award for Excellence in teaching.

Additionally, Kian is a founding member of deeplearning.ai and he co-created the Deep Learning Specialization on Coursera with Andrew Ng. From 2014 to 2016, Kian co-founded and co-led Daskit, a French start-up developing in-classroom ed-tech solutions for universities.

With almost two decades of experience under his belt, Gulrez is a data wrangling extraordinaire, who has a knack for turning boring numbers into captivating stories. When he's not busy working, you can find him digging through open datasets like they're buried treasure, or passing on his data visualization skills to the next generation in the hopes of creating a world of data literate children.

A strong believer in the power of data literacy, Gulrez is on a mission to improve the way people make sense of data. He's known for delivering corporate workshops that are equal parts informative and entertaining, and is currently working on a book that promises to be a page-turner for data geeks everywhere.

William Komp is the Principle Data Scientist at Komplytics LLC.. He has over 2 decades of experience in academia, health care, marketing analytics, transportation, logistics, Oil&Gas, Food&Beverage, environmental management, renewable energy and public utilities. He was the technical editor for Applied Predictive Analytics (J. WIley and Sons 2014). He holds a PhD Physics with areas of research in Gravitation, Cosmology and Quantum Field Theory in Curved Spacetime.

Dr. Li is the leading Research Scientist for AI Risks of Munich Re in the US. As part of a team developing insurance solutions around AI/ML underperformance, she focuses on model uncertainty quantification in an applied and theoretical way. This extends her academic research at UC Davis where she received her PhD in statistics. Munich Re is a leading global risk and insurance solutions providers.

Long before the buzz surrounding generative AI, Martin Musiol was already advocating for its significance in 2015. Since then, he has been a frequent speaker at conferences, podcasts, and panel discussions, addressing the technological advancements, practical applications, and ethical considerations of generative AI. Martin Musiol is a co-founder of generativeAI.net, a lecturer on AI to over 1000 students, and publisher of the newsletter 'Generative AI: Short & Sweet'. As a Data Science Manager at Infosys Consulting (previously at IBM), Martin Musiol helps companies globally harness the power of generative AI to gain a competitive advantage.

Karl Rexer founded Rexer Analytics in 2002. He and his teams have built an outstanding reputation providing predictive modeling and analytic consulting to clients across many industries. Recent clients include OneBlood, PwC, Boston Scientific, Redbox, ADT Security, Interamericana University, MIT, Forward Financing, SharkNinja, and many smaller companies. In addition to leading client engagements and hands-on data work, Karl is a predictive analytics evangelist, frequently speaking at conferences, colleges, and other events. He also serves on Advisory Boards for the Business Analytics programs at both Babson College and Bentley University. Since 2007 Rexer Analytics has conducted surveys of analytic professionals, asking them about their algorithms, tools, behaviors and  views. Summary reports from these surveys are available as a free download from the Rexer Analytics website. Prior to founding Rexer Analytics, Karl held leadership positions at several consulting firms and two multi-national banks. Karl holds a PhD from the University of Connecticut.

Dr. Jennifer Schaff leads Elder Research's Commercial business unit where she advises organizational decision makers to help them realize maximum value from their analytics investments. In her 20 years of management, data analysis, and business experience, Dr. Schaff has built her reputation as a "Barricade Buster" in the data analytics industry. Her experience has uncovered that business barriers -- defining the right opportunity and consumer, delivering the pertinent results to guide data driven decisions, and implementing the change processes -- rather than model building, is where analytics initiatives often fail. Dr. Schaff channels this approach into a passion for guiding organizations through these business challenges to maximize their analytics ROI.

Eric Siegel, Ph.D. is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series, a frequent keynote speaker, and executive editor of The Machine Learning Times. Eric authored the forthcoming book The AI Playbook: Mastering the Rare Art of Machine Learning Deployment and the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at hundreds of universities. He won the Distinguished Faculty award when he was a professor at Columbia University, where he taught the graduate courses in machine learning and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice.

Prerna Singh is currently working as an Applied Scientist II in the Industry AI group @Microsoft where she develops machine learning-based solutions for different industrial verticals including finance and sustainability. Before joining Microsoft, she obtained her master's degree in Electrical and Computer Engineering with a concentration on Machine Learning from Carnegie Mellon University (CMU). Prerna is passionate about machine learning, NLP and deep Reinforcement Learning. Besides work, Prerna enjoys traveling, Zumba and hiking in her free time.

James Taylor is the CEO of Decision Management Solutions and is a leading expert in how to use business rules and analytic technology to build decision management systems. He is passionate about using decision management systems to help companies improve decision-making and develop an agile, analytic and adaptive business. He provides strategic consulting to companies of all sizes, working with clients in all sectors to adopt decision-making technology. James is an expert member of the International Institute for Analytics and is the author of multiple books and articles on decision management, decision modeling, predictive analytics and business rules, and writes a regular blog at JT on EDM. James also delivers webinars, workshops and training. He is a regular keynote speaker at conferences around the world.

Mack Wallace is Head of Financial Products at MPOWER Financing. His area of expertise is the use of new methods in data and analytics in highly regulated areas of consumer finance, including credit underwriting. Mr Wallace also serves as an external Advisor at both the Aspen Institute and the World Bank where he researches and publishes on the use of data and technology to advance financial inclusion and consumer protection.

William (Bill) Wilkins is a multi-purpose actuary for the Safety National Casualty Corporation(SNCC). He is a CERA, FCAS, ASA and MAAA.  While his main focus is on Enterprise Risk Management and Predictive Analytics, Bill has experience in pricing insurance, reinsurance, credit risk products, risk management, reserving and broker management.  He is currently Co-Chair of the SNCC Data Analytics Committee.  The Data Analytics Committee is a cross-functional group tasked with all aspects of data for SNCC.  The Committee works on data to be collected, how it is collected, where it is collected from, how it stored, standards and practices of usage, etc.  This includes the populating items like benchmarking reports or how best to create and use predictive analytics.  The goal is to establish a process that is repeatable and adaptable so SNCC can deliver both internally and externally.

Evan brought his military experience to Elder Research to deliver client solutions as a Data Scientist. He now serves as the Director of Analytics Strategy, helping to marry the exciting things that analytics can do with the myriad of challenges that people are facing. Evan almost always has a smile on his face, and that smile is the widest when he is helping organizations use data in new ways to solve unique problems. Evan earned his BS in Management from Georgia Tech, his MS in Economics from East Carolina University and his MS in Analytics from the Institute for Advanced Analytics at NC State University.  He loves teaching and learning and can often be found exploring the outdoors with his wife and children.

Gordon Yang is a Fellow of the Society of Actuaries (FSA) with 15 years of experience serving the finance and insurance industry. Currently, he is leading the advanced analytics and AUW effort in the underwriting department at Pacific Life. Gordon is a frequent speaker in the industry, providing thought leadership on utilizing machine learning techniques in the actuarial and financial risk management space.

Prior to being an actuary, Gordon obtained a master's degree in Applied Mathematics from Columbia University. Gordon is also a certified AWS practitioner with an analytics specialty and a certified Financial Risk Manager (FRM).

Juan Acevedo is an Enterprise machine learning architect at Google where he helps customers optimize models for large scale distributed training and serving with accelerators such as GPUs and TPUs.

Cal Al-Dhubaib is a globally recognized data scientist, entrepreneur, and innovator in trustworthy artificial intelligence. He is the founder and CEO of Pandata, a consulting company that helps organizations operating in regulated industries design and develop AI-powered solutions to complex business problems. Cal's commitment to diversity and ethics is centered at the heart of his work.

He has gone on to be featured in multiple noteworthy publications, including Forbes,

Ohiox, Cleveland.com, the Marketing AI Institute, and Open Data Science. Cal has also received recognition in Crain's Cleveland Twenty in their 20s and Notable Immigrant Leaders, and Smart Business's Technology Awards and Smart 50 Awards. Most recently, he was named as a Cleveland 2021 Notable Entrepreneur .

Assistant Professor Michael Albert is a faculty member in the Quantitative Analysis area of the University of Virginia Darden School of Business with joint appointments in Systems Engineering and Computer Science in the School of Engineering and Applied Sciences (SEAS). His research focuses on combining machine learning and algorithmic techniques to automate the design of markets. His work has appeared in leading artificial intelligence and machine learning venues such as the Association for the Advancement of Artificial Intelligence (AAAI) and the International Joint Conference on Artificial Intelligence (IJCAI) as well as leading operations research journals such as Operations Research.

Prior to joining Darden in 2018, Albert received his Ph.D. in financial economics at Duke University’s Fuqua School of Business. He has also worked as a visiting assistant professor of finance at the Ohio State University, as a postdoctoral researcher at the Learning Agents Research Group at the University of Texas at Austin under Peter Stone, and as a postdoctoral researcher in the artificial intelligence group headed by Vincent Conitzer at Duke University. 

Steve Anderson is an analytics executive who has lead analytics teams for several years and has wrangled petabytes of data to drive positive outcomes/impact. He is currently the VP of Analytics at Outlook Amusement, which is a company based in Los Angeles. Mr. Anderson specializes in: Database Marketing, Data Analysis, Data Warehousing, Report Development, Business Intelligence and Machine Learning. His expertise expands across several industries – sports, entertainment, financial services, gaming, and retail. Additionally, he received a Master of Engineering Management degree from Christian Brothers University and a Bachelor of Science in Computer Engineering from The University of Memphis.

Andrew Bate is VP & Head of Safety Innovation & Analytics at GSK and leads the development of next generation Safety capability as a member of the Global Safety Leadership team. Previously Andrew was in the Global Epidemiology Leadership team at Pfizer and prior to that led the Research function at the WHO Collaborating Centre for International Drug Monitoring.

Andrew has contributed to many international initiatives and partnerships including membership of the FDA Science Board Subcommittee on Pharmacovigilance, CIOMS VIII on signal detection of adverse effects of medicinal products and Transcelerate Pharmacovigilance Steering Committee where he is sponsor of the Intelligent Automation work stream. He has served on both the Executive Committee of ISoP and the Board of ISPE. He is currently a member of the international, non-governmental, non-profit organization established jointly by WHO and UNESCO Council for International Organizations of Medical Sciences (CIOMS)’s Working Group on Artificial Intelligence.

Bardia Beigi works at Microsoft as an Applied Scientist II in the Industry AI group delivering AI/ML based solutions to various industries within Azure. Bardia has a master's degree in Computer Science from Stanford University, as well as Bachelor of Applied Science in Engineering Physics from the University of British Columbia. In his spare time, Bardia enjoys traveling, trying out new dessert spots, and learning new life hacks.

Ben Cleveland has led data science at UnityPoint Health since 2014. He and his team design and
operationalize data products that use advanced data modeling techniques for the purposes of clinical
excellence, strategic planning, and improvement within a large health system. Ben is a nationally
recognized data science practitioner with speaking invitations at analytics, IT, and data science events
across the country. He’s also received several awards for healthcare data science innovation, including
most recently the “Most Needed in Your Health System” at the 2022 Healthcare Analytics Summit and
runner-up in the 2022 Innovation Awards by Healthcare Innovation.

Dave Coughlin currently leads a team of 10 data science professionals in the pursuit of driving greater membership and margin for CVS Health's insurance products. Prior to that he did stints in strategy consulting and population health research. A passion of his is performance improvement and he recently wrote a book to scale the insights he has used to turbocharge his own career (3 promotions in 5 years) but also the careers of his mentees. Dave currently lives just outside of Boston with his wife and three children and holds both an MS in applied math and MBA.

ucas Abreu is the co-Founder and Chief Data Scientist at DHAUZ, with an extensive background in Electronics and Computer Engineering. He holds a Master's degree in Applied Artificial Intelligence, and is deeply committed to the advancement of data science and innovative practical solutions. Lucas has a proven track record of developing critical and scalable AI models, both in leadership roles and hands-on implementation, for several industries. He is dedicated to bringing cutting-edge AI solutions to solve complex business problems and improve customer satisfaction.

Fabio is a Business Analytics Sr Executive working on the field for over 20 years applying advanced modelling and analytics tech solutions to solve complex business challenges, achieving improved customer engagement and higher operational efficiency servicing most of the top 100 companies in Latin America.

He holds a Masters of Logistics Systems Engineering and BA in Construction Engineering both from University of Sao Paulo, Brazil. Fabio is also a certified Lead of Analytics & AI from Northwestern University.

Fabio developed his professional career inside Accenture Latin America, where he started in 2002, being one of first executive in the Analytics Group back in 2010. He conducted Gapso Acquisition on Accenture side in 2015 becoming Lead for Advanced Analytics and Data Science in the region, eventually leading Supply Chain Analytics for Growth Markets until 2021.

Fabio is currently a Sr Advisor in Analytics and Data Adoption for some of the top companies in Brazil and also supports Startups growth and services in the fields of Analytics and Digital Talent Recruiting.

Chris is the Chair of Predictive Analytics World Healthcare and the Vice President of Advanced Analytics for OSF Healthcare where he leads a team of data scientists and automation engineers who create and deploy industry leading advanced analytics solutions.  He has more than 16 years of experience working in healthcare and holds a MS in Predictive Analytics from Northwestern University, where he focused on advanced modeling techniques and predictive text mining.

Jen Gennai leads Google’s Responsible Innovation team which is responsible for operationalizing Google’s AI Principles, ensuring that Google’s products have fair and ethical outcomes on individual users and the world. Her team works with product and engineering, leveraging a multidisciplinary group of experts in ethics, human rights, user research, racial justice and gender equity to validate that products and outputs align with our commitments to fairness, privacy, safety, societal benefit and more. Before she co-authored the AI Principles and founded Responsible Innovation, Jen worked on machine learning fairness and founded the Ethical ML team in Trust & Safety.

Kian Katanforoosh is the CEO and cofounder of Workera.ai, the skills intelligence platform redefining how enterprises understand, develop, and mobilize talent. Workera empowers organizations to stay ahead by unlocking the skills data needed to drive innovation. Enterprises get an objective pulse of their innovation skills through a configurable skills ontology, using AI-powered measurement of data, AI, software, cloud, and cyber skills at an atomic-level.

Kian is also a lecturer at Stanford University, where he teaches Deep Learning in the Computer Science department with Prof. Andrew Ng. Kian has been acknowledged for his teaching excellence by Stanford with the Walter J. Gores Award, Stanford’s highest teaching award, and the Centennial Award for Excellence in teaching.

Additionally, Kian is a founding member of deeplearning.ai and he co-created the Deep Learning Specialization on Coursera with Andrew Ng. From 2014 to 2016, Kian co-founded and co-led Daskit, a French start-up developing in-classroom ed-tech solutions for universities.

Long before the buzz surrounding generative AI, Martin Musiol was already advocating for its significance in 2015. Since then, he has been a frequent speaker at conferences, podcasts, and panel discussions, addressing the technological advancements, practical applications, and ethical considerations of generative AI. Martin Musiol is a co-founder of generativeAI.net, a lecturer on AI to over 1000 students, and publisher of the newsletter 'Generative AI: Short & Sweet'. As a Data Science Manager at Infosys Consulting (previously at IBM), Martin Musiol helps companies globally harness the power of generative AI to gain a competitive advantage.

Lauren Rost, PhD is a Translational Informatics Analyst at Mayo Clinic focused on bringing artificial intelligence into clinical practice with careful intention, strong rigor, and appropriate monitoring. Lauren completed her doctoral studies in biomedical informatics at the University of Pittsburgh, where she focused on electronic health record mining, machine learning, and prescribing patterns for both antibiotics and antidepressants.  

Eric Siegel, Ph.D. is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series, a frequent keynote speaker, and executive editor of The Machine Learning Times. Eric authored the forthcoming book The AI Playbook: Mastering the Rare Art of Machine Learning Deployment and the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at hundreds of universities. He won the Distinguished Faculty award when he was a professor at Columbia University, where he taught the graduate courses in machine learning and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice.

Prerna Singh is currently working as an Applied Scientist II in the Industry AI group @Microsoft where she develops machine learning-based solutions for different industrial verticals including finance and sustainability. Before joining Microsoft, she obtained her master's degree in Electrical and Computer Engineering with a concentration on Machine Learning from Carnegie Mellon University (CMU). Prerna is passionate about machine learning, NLP and deep Reinforcement Learning. Besides work, Prerna enjoys traveling, Zumba and hiking in her free time.

David Talby is the Chief Technology Officer at John Snow Labs, helping companies apply artificial intelligence to solve real-world problems in healthcare and life science. David is the creator of Spark NLP the world's most widely used natural language processing library in the enterprise. He has extensive experience building and running web-scale software platforms and teams – in startups, for Microsoft's Bing in the US and Europe, and to scale Amazon's financial systems in Seattle and the UK. David holds a Ph.D. in Computer Science and Masters degrees in both Computer Science and Business Administration. He was named USA CTO of the Year by the Global 100 Awards and GameChangers Awards in 2022.

Dr. Nephi Walton is a biomedical informaticist and clinical geneticist with experience in machine learning and genomics. He had led several research initiatives in genomics and informatics at Geisinger prior to joining Intermountain Healthcare. At Geisinger he successfully completed a pilot integration of genomics data into the EPIC electronic health record system for both pharmacogenomics and CDC tier one genetic conditions. He currently serves as the Associate Medical Director of Intermountain Precision Genomics where he leads the HerediGene Genomic Sequencing Return of Results program. He also serves as the Associate Medical Director of Intermountain’s sequencing laboratory.  He is the Chair of Genomics and Translational Bioinformatics for the American Medical Informatics Association and has presented at several meetings on translating the use of genomics into general medical practice, something he is actively pursuing at Intermountain Healthcare.

Glenn has always been interested in creative problem solving. He got his PhD in Computer Science from the University of Virginia is 1999 and has been looking for the signal in the noise ever since. Currently he serves as the Administrator of Analytics for UVA Health where he oversees the way UVA curates and analyzes data for patient care and research. In this role, he works on everything from at-the-bedside predictive care to population risk modeling to untangling hospital ranking algorithms. Glenn is particularly interested in how to create problem solving cultures, but in his off-time you're most likely to find him in his art studio culturing new problems.

Evan brought his military experience to Elder Research to deliver client solutions as a Data Scientist. He now serves as the Director of Analytics Strategy, helping to marry the exciting things that analytics can do with the myriad of challenges that people are facing. Evan almost always has a smile on his face, and that smile is the widest when he is helping organizations use data in new ways to solve unique problems. Evan earned his BS in Management from Georgia Tech, his MS in Economics from East Carolina University and his MS in Analytics from the Institute for Advanced Analytics at NC State University.  He loves teaching and learning and can often be found exploring the outdoors with his wife and children.

Dean Abbott is an internationally recognized thought leader and innovator in data science and predictive analytics, with more than three decades of experience solving problems in customer analytics, fraud detection, risk modeling, text mining, survey analysis, and many more. He is frequently included in lists of the top pioneering and influential data scientists in the world. Mr. Abbott is the author of Applied Predictive Analytics (Wiley, 2014) and coauthor of The IBM SPSS Modeler Cookbook (Packt Publishing, 2013). He is a popular keynote speaker and bootcamp/workshop instructor at conferences worldwide and serves on advisory boards for the UC/Irvine Predictive Analytics and UC/San Diego Data Science Certificate programs. He holds a bachelors degree in computational mathematics from Rensselaer Polytechnic Institute and a masters degree in applied mathematics from the University of Virginia.

Cheryl has 13 years experience in Statistical Physics, Computational Modeling, Data Science and Digital technologies having worked with both public and private sectors in developing strategic solutions using Machine Learning and Agent-Based Models. At AWS, Cheryl is a Principal Solution Architect for Autonomous Computing with a focus on Predictive Modeling and Simulations for Digital Twins. Prior to joining AWS, Cheryl worked at Quantum Black – McKinsey and Solvay as Data Science Lead defining digital architectures, and delivering Advanced Analytics and AI assets covering various industries including Chemicals Manufacturing, Agriculture, Aviation, R&I, Procurement, Commercial, Supply Chain, Finance and Energy. Prior to that, she worked on insider threat detection, computer vision for marketing, investment banking trading technologies and geospatial analytics for telecommunications. Cheryl obtained her Ph.D. in Physics from Nanyang Technological University in 2017 where she focused on analytical, graph and computational models for urban, linguistic and genetic systems. She also received a B.S. in Applied Physics from the University of the Philippines with specializations in Neural Network and Complex Systems.

Juan Acevedo is an Enterprise machine learning architect at Google where he helps customers optimize models for large scale distributed training and serving with accelerators such as GPUs and TPUs.

Bardia Beigi works at Microsoft as an Applied Scientist II in the Industry AI group delivering AI/ML based solutions to various industries within Azure. Bardia has a master's degree in Computer Science from Stanford University, as well as Bachelor of Applied Science in Engineering Physics from the University of British Columbia. In his spare time, Bardia enjoys traveling, trying out new dessert spots, and learning new life hacks.

Dr. Chakrabarti is a Senior Research Scientist in Hitachi's Financial Innovation Lab. in Santa Clara. He works at the intersection of data science and finance, and has been doing industrial research for over ten years . He did his MS-PhD in Electrical and Computer Engineering at Rice University, and afterwards did his Master of Financial Engineering at U C Berkeley. Dr. Chakrabarti's research publications and patents have collectively been cited over one thousand times.

Vash Doshi is the Manager of Industry Relations for UCLA Anderson's Master of Science in Business Analytics program.  Daily, Vash strives to show employers why UCLA Anderson's MSBA has been ranked the #2 place in the world for the past four years to study, build an analytics career, and recruit from. He is driven to help students and UCLA MSBA’s hundreds of partner firms find each other to create the perfect analytics fit. Vash believes in the magic of people. Whether through developing and advocating for reciprocal market access for American firms so that more Americans can find meaningful jobs and grow their businesses via internships at U.S. Departments of State and Commerce, or by counseling over 500 students to help them find their perfect career, he has over five years of experience helping people make magic happen.  He is currently finishing up his Ph.D. in Political Science at UCSB and holds an M.A. in International Studies from the University of Denver’s Josef Korbel School of International Studies.

John Elder chairs America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Maryland, North Carolina, Washington DC, and London. Dr. Elder co-authored 3 award-winning books on analytics, was a discoverer of ensemble methods, chairs international conferences, and is a popular keynote speaker. John is occasionally an Adjunct Professor of Systems Engineering at the University of Virginia.

Jen Gennai leads Google’s Responsible Innovation team which is responsible for operationalizing Google’s AI Principles, ensuring that Google’s products have fair and ethical outcomes on individual users and the world. Her team works with product and engineering, leveraging a multidisciplinary group of experts in ethics, human rights, user research, racial justice and gender equity to validate that products and outputs align with our commitments to fairness, privacy, safety, societal benefit and more. Before she co-authored the AI Principles and founded Responsible Innovation, Jen worked on machine learning fairness and founded the Ethical ML team in Trust & Safety.

Sarah Kalicin is a HR Measurement Data Scientist with Intel Corporation, who is passionate about how organizations gain more business value through Data Science. In her extensive data career at Intel, she develops strategic plans for analytic adoption to increase business impact. Working in various industry and government positions, such as Becton Dickinson (BD), Kraft Foods, Nabisco, Ford Motor Company, and USDA/National Agriculture Statistics Service, she has gained insight and experience on how successful organizations function that allow them to succeed in their analytic initiatives.  She holds a Master’s degree in Applied Statistics from the University of Michigan and Bachelors’ degrees in Mathematics and Psychology from the SUNY at Oswego.

Kian Katanforoosh is the CEO and cofounder of Workera.ai, the skills intelligence platform redefining how enterprises understand, develop, and mobilize talent. Workera empowers organizations to stay ahead by unlocking the skills data needed to drive innovation. Enterprises get an objective pulse of their innovation skills through a configurable skills ontology, using AI-powered measurement of data, AI, software, cloud, and cyber skills at an atomic-level.

Kian is also a lecturer at Stanford University, where he teaches Deep Learning in the Computer Science department with Prof. Andrew Ng. Kian has been acknowledged for his teaching excellence by Stanford with the Walter J. Gores Award, Stanford’s highest teaching award, and the Centennial Award for Excellence in teaching.

Additionally, Kian is a founding member of deeplearning.ai and he co-created the Deep Learning Specialization on Coursera with Andrew Ng. From 2014 to 2016, Kian co-founded and co-led Daskit, a French start-up developing in-classroom ed-tech solutions for universities.

Diego Klabjan is a professor at Northwestern University, Department of Industrial Engineering and Management Sciences. He is also Founding Director, Master of Science in Analytics and Director, Center for Deep Learning. His work is focused on machine learning, deep learning and analytics (modeling, methodologies, theoretical results) with concentration in finance, insurance, sports, and bioinformatics. Professor Klabjan has led projects with large companies such as Intel, Baxter, Allstate, Anthem, and many others. He is also a co-founder of Videspan, LLC after successfully starting Opex Analytics, a Coupa company.

Wes is a machine learning expert with over a decade of experience delivering business value with AI.  Wes's experience spans multiple industries, but always with an MLOps focus.  His recent areas of focus and interest are graphs, distributed computing, and scalable feature engineering pipelines.

Terry Miller is founder and director of the applied AI startup Bōwdee. Previously, he led a global team of data scientists and data engineers to solve problems in the Services business for Johnson Controls. Deploying the best practices of Machine Learning and Predictive Analytics, he facilitates outcomes in Customer Retention, Pricing, and other enterprise applications.

Long before the buzz surrounding generative AI, Martin Musiol was already advocating for its significance in 2015. Since then, he has been a frequent speaker at conferences, podcasts, and panel discussions, addressing the technological advancements, practical applications, and ethical considerations of generative AI. Martin Musiol is a co-founder of generativeAI.net, a lecturer on AI to over 1000 students, and publisher of the newsletter 'Generative AI: Short & Sweet'. As a Data Science Manager at Infosys Consulting (previously at IBM), Martin Musiol helps companies globally harness the power of generative AI to gain a competitive advantage.

Ayush is the co-founder of TwelveFold, an AI start-up studio, where he manages a portfolio of MLOps and Generative AI companies with entrepreneurs. He also works as the CEO of Censius, an AI Observability platform that helps to optimize AI models' real-world performance.  As a seasoned professional, he has closely worked with customers across industry verticals, AI teams, and research projects to build reliable and compliant AI solutions to solve everyday business problems and scale models at production.

Marina Petzel is a Machine Learning Engineer at Autodesk. In her role, she develops ML solutions to further improve the AutoCAD users experience; Autodesk's flagship design and drafting tool. Marina leads the data collection and labeling processes through automation using ML.

She is passionate about finding and developing efficient data driven solutions that address business needs. Marina has extensive experience in applying data science to various industries such as construction, healthcare, oil and gas, and finance.

Marina is an AI Influencer in social media where she shares AI/ML tips and best practices. She coaches and mentors young professionals in data science, engineering and career strategy.

Marina graduated from the University of Rochester, NY with a Master of Science degree in Data Science. She received her Bachelor degree in Computer Science from Moscow State University.

Steven J. Ramirez is the chief executive officer of Berkeley, Calif.-based Beyond the Arc, Inc., a firm recognized as a leader in helping companies transform their customer experiences by leveraging advanced analytics.

In addition to developing and executing the vision for Beyond the Arc, Ramirez leads teams of data and strategy consultants committed to client success. They analyze customer and social media data, combined with text analysis, to drive customer growth, improve customer retention, understand service breaks and build stronger customer loyalty.

Prior to leading Beyond the Arc, Ramirez served as an executive with Time Warner, where he was responsible for creating and successfully implementing marketing and corporate development strategies.

Ramirez earned a bachelor's degree and master's in Business Administration from the University of California at Berkeley. He as also created and taught courses in business management for UC Berkeley and been a guest speaker at the university's Haas School of Business.

Karl Rexer founded Rexer Analytics in 2002. He and his teams have built an outstanding reputation providing predictive modeling and analytic consulting to clients across many industries. Recent clients include OneBlood, PwC, Boston Scientific, Redbox, ADT Security, Interamericana University, MIT, Forward Financing, SharkNinja, and many smaller companies. In addition to leading client engagements and hands-on data work, Karl is a predictive analytics evangelist, frequently speaking at conferences, colleges, and other events. He also serves on Advisory Boards for the Business Analytics programs at both Babson College and Bentley University. Since 2007 Rexer Analytics has conducted surveys of analytic professionals, asking them about their algorithms, tools, behaviors and  views. Summary reports from these surveys are available as a free download from the Rexer Analytics website. Prior to founding Rexer Analytics, Karl held leadership positions at several consulting firms and two multi-national banks. Karl holds a PhD from the University of Connecticut.

Alejandro Jesús Castañeira Rodríguez is the Principal Data Scientist atJanzz.technology a leading developing and consulting company in jobmatching solutions, international classification systems, multilingualknowledge graphs, and processing of Human Resources(HR) information.Janzz offers SaaS/Cloud solutions as well as standard on-premise andwhite-label products in up to 40 languages for multinational jobportals, social and career networks, governmental or state-levelorganizations and public employment services(PES) worldwide. In hiscurrent role, Alejandro leads multiple stages of the product developmentcycle, from data collection, training of deep learning models,evaluation, deployment, maintenance, and customer support. He alsosupports and mentors other members of its team, providing guidance ontechnical issues, setting performance goals, and identifying areas forprofessional growth. Before coming to the HR industry, Alejandro was alecturer of Applied Mathematics and researcher, for more than 5 years atone of the most prestigious and beloved universities in Latin America,the Universidad de Oriente, Cuba. Where he graduated from a Master'sDegree in Computer Science specializing in the field of ArtificialIntelligence and Natural Language Processing.

Eric Siegel, Ph.D. is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series, a frequent keynote speaker, and executive editor of The Machine Learning Times. Eric authored the forthcoming book The AI Playbook: Mastering the Rare Art of Machine Learning Deployment and the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at hundreds of universities. He won the Distinguished Faculty award when he was a professor at Columbia University, where he taught the graduate courses in machine learning and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice.

Prerna Singh is currently working as an Applied Scientist II in the Industry AI group @Microsoft where she develops machine learning-based solutions for different industrial verticals including finance and sustainability. Before joining Microsoft, she obtained her master's degree in Electrical and Computer Engineering with a concentration on Machine Learning from Carnegie Mellon University (CMU). Prerna is passionate about machine learning, NLP and deep Reinforcement Learning. Besides work, Prerna enjoys traveling, Zumba and hiking in her free time.

Evan brought his military experience to Elder Research to deliver client solutions as a Data Scientist. He now serves as the Director of Analytics Strategy, helping to marry the exciting things that analytics can do with the myriad of challenges that people are facing. Evan almost always has a smile on his face, and that smile is the widest when he is helping organizations use data in new ways to solve unique problems. Evan earned his BS in Management from Georgia Tech, his MS in Economics from East Carolina University and his MS in Analytics from the Institute for Advanced Analytics at NC State University.  He loves teaching and learning and can often be found exploring the outdoors with his wife and children.

Juan Acevedo is an Enterprise machine learning architect at Google where he helps customers optimize models for large scale distributed training and serving with accelerators such as GPUs and TPUs.

Bardia Beigi works at Microsoft as an Applied Scientist II in the Industry AI group delivering AI/ML based solutions to various industries within Azure. Bardia has a master's degree in Computer Science from Stanford University, as well as Bachelor of Applied Science in Engineering Physics from the University of British Columbia. In his spare time, Bardia enjoys traveling, trying out new dessert spots, and learning new life hacks.

Leo Betthauser works at Microsoft Research in Redmond, Wash. Leo has a Ph.D. in mathematics from the University of Florida where he studied applied algebraic topology. Leo is currently exploring the application of deep neural techniques to cyber security. In his spare time, Leo enjoys traveling, cooking, and skiing.

Christopher Brossman leads the enterprise ML/AI teams and Data Engineering teams that enable scale and profitability at The RealReal. Experienced ML/AI engineer executive with wide track record in creating products that embed computer vision, machine learning, statistical modeling, and optimization to create demonstrable value.

Clinton Brownley, Ph.D., is a data scientist at WhatsApp, where he’s responsible for a variety of analytics projects designed to improve messaging and VoIP calling performance and reliability.  Before WhatsApp, Clinton was a data scientist at Facebook, working on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions.  As an avid student and teacher of modern analytics techniques, Clinton is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis,” and also teaches Python programming and data science courses at Facebook and in the Bay Area. Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.

Pranjal helped set up Cisco Innovation Labs after dropping out of his PhD and handles Machine Learning/Product there. He also serves as an Entrepreneur-in-Residence at Vonzos Partners. Previously he conducted ML research at Adobe Research, IBM Research, University of Alberta, Purdue University and Northwestern University. He attended Stanford GSB in the Ignite program and is an On Deck Fellow.

John Elder chairs America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Maryland, North Carolina, Washington DC, and London. Dr. Elder co-authored 3 award-winning books on analytics, was a discoverer of ensemble methods, chairs international conferences, and is a popular keynote speaker. John is occasionally an Adjunct Professor of Systems Engineering at the University of Virginia.

Jen Gennai leads Google’s Responsible Innovation team which is responsible for operationalizing Google’s AI Principles, ensuring that Google’s products have fair and ethical outcomes on individual users and the world. Her team works with product and engineering, leveraging a multidisciplinary group of experts in ethics, human rights, user research, racial justice and gender equity to validate that products and outputs align with our commitments to fairness, privacy, safety, societal benefit and more. Before she co-authored the AI Principles and founded Responsible Innovation, Jen worked on machine learning fairness and founded the Ethical ML team in Trust & Safety.

Sami is the co-founder and CTO of Forethought, the leading generative AI platform for customer experience. Founded in 2017, Forethought integrates with most popular helpdesk and CRM solutions, and uses a proprietary AI engine dubbed SupportGPT™ to provide automation, agent augmentation, and advanced analytics and insights solutions. Forethought's customers include Instacart, Upwork, Marriott, and many others. Prior to founding Forethought, Sami was a senior machine learning engineer on the LinkedIn Feed AI team. Sami received his undergraduate and graduate degrees in computer science from Harvard University. He was named to the Forbes 30u30 "Enterprise Technology" list in 2018.

My name is Dao Ho, I am a Senior Data Scientist at Albertsons. My current focus is personalization. At Albertsons, I have worked on several personalization models such as Buy It Again (ranking past purchases), embedding models for our products and customers. In collaboration with Ankita, I have been working on building a Wide & Deep Neural Network model for recommendations. Before Albertsons, I worked at a research lab for GAC, a Chinese car manufacture, building models to monitor the batteries of 30,000 EVs. Prior to that, I worked at a fintech company modeling market conditions. And finally, I graduated from Carnegie Mellon University with a Ph.D. degree in Physics.

Vanja Josifovski is the Co-Founder and CEO of Kumo. Prior to Kumo, Vanja was the CTO of Homes at Airbnb. Here Vanja led an organization that included horizontal groups such as Homes Engineering and Homes Data Science, as well as GM groups such as Marketplace, Relevance and Personalization, and Regulatory Frameworks. Before Airbnb, Vanja was the CTO & VP Eng at Pinterest, responsible for the overall technical vision and strategy of the company and communicating that to leadership and the teams; hiring and development of technical talent. As the head of Engineering he managed some core engineering teams. Vanja has also served as an advisor and investor for multiple startups and was the founder for Kosei which was acquired by Pinterest.

Kian Katanforoosh is the CEO and cofounder of Workera.ai, the skills intelligence platform redefining how enterprises understand, develop, and mobilize talent. Workera empowers organizations to stay ahead by unlocking the skills data needed to drive innovation. Enterprises get an objective pulse of their innovation skills through a configurable skills ontology, using AI-powered measurement of data, AI, software, cloud, and cyber skills at an atomic-level.

Kian is also a lecturer at Stanford University, where he teaches Deep Learning in the Computer Science department with Prof. Andrew Ng. Kian has been acknowledged for his teaching excellence by Stanford with the Walter J. Gores Award, Stanford’s highest teaching award, and the Centennial Award for Excellence in teaching.

Additionally, Kian is a founding member of deeplearning.ai and he co-created the Deep Learning Specialization on Coursera with Andrew Ng. From 2014 to 2016, Kian co-founded and co-led Daskit, a French start-up developing in-classroom ed-tech solutions for universities.

Sagar is an accomplished Big Data and Machine Learning Architect with a passion for designing innovative solutions that accelerate digital transformation and simplify data management and analytics. With extensive experience combining multiple transactional and operational systems, Sagar excels at implementing simplified Data Lake, Lake Houses, MLOps Platform, Customer Data Platform, and Data Mesh environments. His focus is on delivering actionable insights through descriptive and predictive analytics. Sagar specializes in collaborating with strategic clients delivering actionable insights through descriptive and predictive analytics.

Sagar has held numerous positions across Data Engineering, Platform Management, and Enterprise Architecture teams. He is also an enthusiastic public speaker at conferences and events, sharing his expertise and insights with fellow industry professionals.

Diego Klabjan is a professor at Northwestern University, Department of Industrial Engineering and Management Sciences. He is also Founding Director, Master of Science in Analytics and Director, Center for Deep Learning. His work is focused on machine learning, deep learning and analytics (modeling, methodologies, theoretical results) with concentration in finance, insurance, sports, and bioinformatics. Professor Klabjan has led projects with large companies such as Intel, Baxter, Allstate, Anthem, and many others. He is also a co-founder of Videspan, LLC after successfully starting Opex Analytics, a Coupa company.

Jared P. Lander is Chief Data Scientist of Lander Analytics, the Organizer of the New York Open Statistical Programming Meetup and the New York and Government R Conferences, an Adjunct Professor at Columbia Business School, and a Visiting Lecturer at Princeton University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. Jared oversees the long-term direction of the company and acts as Lead Data Scientist, researching the best strategy, models and algorithms for modern data needs. This is in addition to his client-facing consulting and training. He specializes in data management, multilevel models, machine learning, generalized linear models, data management, visualization and statistical computing. He is the author of R for Everyone (now in its second edition), a book about R Programming geared toward Data Scientists and Non-Statisticians alike. The book is available from Amazon, Barnes & Noble and InformIT. The material is drawn from the classes he teaches at Columbia and is incorporated into his corporate training. Very active in the data community, Jared is a frequent speaker at conferences, universities and meetups around the world.

Michelle Li is a Data Scientist at Paychex and work on predictive modeling, text analytics and business intelligence. Before joining Paychex, Michelle received a PhD degree in Earth Science and spent over 10 years analyzing and modeling data in a variety of fields including satellite imagery, weather prediction, environmental protection, and child welfare.  

Dr. Ankita Mangal is a Senior Data Scientist at Albertsons Companies. She has a broad experience applying data science & analytics solutions at companies like Procter & Gamble, Robert Bosch and Tata Steel Ltd. She is an accomplished leader in the field of retail and marketing science, who is championing data informed decision making in the Retail and Consumer Packaged Goods industry. Dr. Mangal is passionate about efficient cross functional collaborations for creating successful ML solutions. In her current role, she provides technical leadership, mentorship and develops interpretable and accurate models for business strategy decisions. Dr. Mangal has a doctoral degree from Carnegie Mellon University and is an alumnus of IIT Kharagpur.

Mrinal Mohit heads machine learning at Glean, a Sequoia-backed unicorn building the next generation of enterprise search. Mrinal previously worked as a Machine Learning engineer at Facebook, where he built conversational systems powering Facebook Portal, Oculus Quest, and Ray-Ban Stories. His research has been published research at NeurIPS and EMNLP, and he co-created the open source library PyText. He holds a masters from MIT, and a bachelors from IIT.

Long before the buzz surrounding generative AI, Martin Musiol was already advocating for its significance in 2015. Since then, he has been a frequent speaker at conferences, podcasts, and panel discussions, addressing the technological advancements, practical applications, and ethical considerations of generative AI. Martin Musiol is a co-founder of generativeAI.net, a lecturer on AI to over 1000 students, and publisher of the newsletter 'Generative AI: Short & Sweet'. As a Data Science Manager at Infosys Consulting (previously at IBM), Martin Musiol helps companies globally harness the power of generative AI to gain a competitive advantage.

Ayush is the co-founder of TwelveFold, an AI start-up studio, where he manages a portfolio of MLOps and Generative AI companies with entrepreneurs. He also works as the CEO of Censius, an AI Observability platform that helps to optimize AI models' real-world performance.  As a seasoned professional, he has closely worked with customers across industry verticals, AI teams, and research projects to build reliable and compliant AI solutions to solve everyday business problems and scale models at production.

Rishab is the co-founder & CTO of Openlayer. Openlayer is a YC-backed company that aims to make it easy for ML teams to find failures and biases in their models, figure out their root causes and use better data to fix them. Rishab graduated from Yale in 2019, and subsequently worked at Apple on a variety of projects within their AI/ML org before founding Openlayer.

Eric Siegel, Ph.D. is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series, a frequent keynote speaker, and executive editor of The Machine Learning Times. Eric authored the forthcoming book The AI Playbook: Mastering the Rare Art of Machine Learning Deployment and the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at hundreds of universities. He won the Distinguished Faculty award when he was a professor at Columbia University, where he taught the graduate courses in machine learning and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice.

Prerna Singh is currently working as an Applied Scientist II in the Industry AI group @Microsoft where she develops machine learning-based solutions for different industrial verticals including finance and sustainability. Before joining Microsoft, she obtained her master's degree in Electrical and Computer Engineering with a concentration on Machine Learning from Carnegie Mellon University (CMU). Prerna is passionate about machine learning, NLP and deep Reinforcement Learning. Besides work, Prerna enjoys traveling, Zumba and hiking in her free time.

David Talby is the Chief Technology Officer at John Snow Labs, helping companies apply artificial intelligence to solve real-world problems in healthcare and life science. David is the creator of Spark NLP the world's most widely used natural language processing library in the enterprise. He has extensive experience building and running web-scale software platforms and teams – in startups, for Microsoft's Bing in the US and Europe, and to scale Amazon's financial systems in Seattle and the UK. David holds a Ph.D. in Computer Science and Masters degrees in both Computer Science and Business Administration. He was named USA CTO of the Year by the Global 100 Awards and GameChangers Awards in 2022.

James Taylor is the CEO of Decision Management Solutions and is a leading expert in how to use business rules and analytic technology to build decision management systems. He is passionate about using decision management systems to help companies improve decision-making and develop an agile, analytic and adaptive business. He provides strategic consulting to companies of all sizes, working with clients in all sectors to adopt decision-making technology. James is an expert member of the International Institute for Analytics and is the author of multiple books and articles on decision management, decision modeling, predictive analytics and business rules, and writes a regular blog at JT on EDM. James also delivers webinars, workshops and training. He is a regular keynote speaker at conferences around the world.

Huy Vo is a Data Science and Analytic Manager at Otrafy Technologies Inc where he spends time researching and converting Machine learning from academia into production that is helpful for Food Safety Supply Chain Management and Document Understanding. He is passionate about understanding how machine learning can leverage and understand information from Complex Documents and create valuable insights for supply chain stakeholders. Not only that, but he is also interested in solving the problem of using Small Datasets to Solve Big Problems through Data Augmentation techniques for Document AI. He held a degree in Computer Science from the University of South Australia and worked as a Research Assisstant for the University of South Australia's Industrial AI Research Centre.

Devanshi is the co-founder of Censius, an AI Observability platform that helps to optimize AI models' real-world performance. As a seasoned ML practitioner, she recognizes the significance of enterprise-level Observability to help companies solve complex challenges in AI development and incorporate AI models into production successfully. She has worked with customers across industry verticals alongside AI teams and is a strong advocate for building transparent, reliable, and compliant AI solutions.

Evan brought his military experience to Elder Research to deliver client solutions as a Data Scientist. He now serves as the Director of Analytics Strategy, helping to marry the exciting things that analytics can do with the myriad of challenges that people are facing. Evan almost always has a smile on his face, and that smile is the widest when he is helping organizations use data in new ways to solve unique problems. Evan earned his BS in Management from Georgia Tech, his MS in Economics from East Carolina University and his MS in Analytics from the Institute for Advanced Analytics at NC State University.  He loves teaching and learning and can often be found exploring the outdoors with his wife and children.