Speakers

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 Wimpey was a Financial Management Officer in the Marine Corps and an Operations Associate at Goldman Sachs prior to joining Elder Research as a Data Scientist. He holds master's degrees in Economics from East Carolina University and in Analytics from North Carolina State University. He is currently the Director of Strategic Analytics at Elder Research. Evan has a passion for cryptocurrency and exploring more equitable access to credit markets. Evan lives in Cary, NC with his wife and two sons.

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).