Javed Ahmed is a Senior Data Scientist with Metis, where he focuses on corporate training programs in Machine Learning and analytics. A financial economist by background, he has extensive experience developing analytic applications for large organizations including Amazon and the Federal Reserve Board of Governors. Javed holds a PhD in Finance and MA in Statistics from U.C. Berkeley, as well as undergraduate degrees in Finance and Systems Engineering from the University of Pennsylvania.

Muneeb Alam is a data science specialist at QuantumBlack, a McKinsey Company. He has experience in the public and social sector, healthcare, energy and basic materials. He holds a B.A. in astrophysics from Columbia University and an MSc. in business analytics from Imperial College Business School.

Since 1994, Tom Alby has been dedicated to the digital world. He financed his studies by creating digital tools and also worked at one of the first search engines. Since then, the digital expert, who was called a "data freak" by brandeins, has been driving innovation at companies such as Google, Bertelsmann and bbdo. Since 2018 he has been Chief Digital Transformation Officer at Euler Hermes. Tom Alby is author of several books and lecturer for data science at HAW Hamburg.

Experienced Data Scientist with a demonstrated history of working in the insurance industry. Skilled in Machine Learning, Databases, Big Data Analytics, Big Data, Python, R, PySpark and Tableau. Strong engineering professional with a Master's degree focused in Computer Science from University of North Carolina at Charlotte. 

Robert is a Senior Data Scientist at SAS where he builds end-to-end artificial intelligence applications.  He also researches, consults, and teaches machine learning with an emphasis on deep learning and computer vision for SAS. Robert has authored a book on computer vision and has developed several professional courses on topics including neural networks, deep learning, and optimization modeling. Before joining SAS, Robert worked under the Senior Vice Provost at North Carolina State University, where he built models pertaining to student success, faculty development, and resource management. Robert also started a private analytics company while working at North Carolina State University that focused on predicting future home sales. Prior to working in academia, Robert was a member of the research and development group on the Workforce Optimization team at Travelers Insurance. His models at Travelers focused on forecasting and optimizing resources. Robert graduated with a master’s degree in Business Analytics and Project Management from the University of Connecticut and a master’s degree in Applied and Resource Economics from East Carolina University.

Valerie Carey joined Paychex in 2018, and focuses on exploratory analysis and client-facing analytics.  Prior to Paychex, Valerie was employed as a data scientist and a business analyst in healthcare related fields.  In addition, she has worked writing automated tests for a Unix operating system, and has a PhD in biophysics from Cornell University. 

While enjoying the fun of data munging and the joy of discovery, Valerie’s deepest passion is building trust in data products and processes.  She prefers a comprehensive approach to data projects, emphasizing education and communication, automated testing, and iterative feedback.  Valerie believes that explainable AI is just one part of a journey to a comprehensible and useful model. 

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.

Mikhail Golovnya has been prototyping new machine learning algorithms and modeling automation for the past 20 years. He has been a major contributor to Salford Systems / Minitab’s on-going search for technological improvements to among the most important algorithms in Machine Learning: CART® Decision Trees, MARS® Non-linear Regression, TreeNet® gradient boosting, and Random Forests®. Mikhail has presented at multiple conferences and seminars. He has also been teaching the mathematical foundations and applications of major predictive learning algorithms, both classical and modern. Having two master’s degrees, one in rocket science from Kharkov State Polytechnic University (Ukraine) and another in statistical computing from the University of Central Florida (Orlando), he currently serves in the role of Senior Advisory Data Scientist and is leading the next generation of Minitab machine learning product development. 

Abhishek Joshi ‘AJ’ is a Sr. Director in Visa’s consulting & analytics group. He is responsible for helping financial institutions with improving growth and profitability through advanced analytics techniques. AJ has diverse experience in employing analytics to solve business problems across multiple industries – Manufacturing & Engineering, Financial Services and Telecom. 

Ian Knopke recieved his Ph.D. in Computer Science from McGill University, specializing in music search systems. He also worked as a researcher at academic institutions in the US and the UK before joining the BBC as their first data scientist, to work on applied R&D projects for TV, Radio, Sport, News, and the World Service Group. He later worked on data science problems for the Financial Times, Springer Nature, and Elsevier, as well as a couple of AI startups specializing in NLP. Ian joined Thomson Reuters as a Senior Data Scientist in the Reuters Applied Innovation group in 2020 and has also taken on an additional role as a new father in March.

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.

Carrie (Caimei) Lu is a Senior Data Scientist at Safety National Safety National Casualty Corporation. She has worked in data science domain for over 10 years. She is an expert of using data analytics and machine learning technologies to generate business insights from large amounts of industry data, and solve challenging business problems where data holds the key. At Safety National, Carrie Lu works with insurance business stakeholders to improve and optimize claim and underwriting operations by applying predictive models developed on internal and external insurance data. Carrie Lu holds Ph.D. in Information Science from Drexel University. She has over 15 published papers in the fields of Data Mining and Machine Learning.

Michael Lyons manages a Data Science team at Paychex.  The team produces predictive models used throughout the company and is responsible for the Paychex | IHS Markit Small Business Employment Watch which gauges small business wage and employment trends on a national, regional, state, metro, and industry basis.  He had a long career in information systems at Xerox Corporation prior to joining the Buffalo Bills as their first Director of Analytics, building the department and capabilities over the course of five seasons which culminated with the team making their first playoff appearance in 18 years.

Keith McCormick is an independent data miner, trainer, speaker, and author. For the last several years, his  emphasis has been working with analytics management to more efficiently run their teams and to nurture new hires as they expand their teams. Keith is skilled at explaining complex methods to new users or decision-makers and can do so at any level of technical detail. He specializes in predictive models and segmentation analysis including classification trees, neural nets, general linear model, cluster analysis, and association rules.

Syed Mehmud, ASA, MAAA, FCA, is a Principal and Senior Consulting Actuary in the Denver office of Wakely. Syed is a recognized expert on risk adjustment and actuarial applications of predictive modeling. Through the combination of large scale actuarial projects and developing popular product offerings, Syed has served most health plans in the United States in some capacity. He has worked on a variety of healthcare related projects, particularly involving the application of risk adjustment tools and implementation of risk adjustment methodologies.

Syed has worked on risk adjustment with clients in Medicare, Medicaid, and Commercial settings. His recent work includes a large-scale actuarial consulting engagement where the Wakely team simulated the HHS risk adjustment methodology in over 30 individual and small group markets across the United States. His other works include the conception and development of the Wakely Risk Assessment (WRA) model, advanced Risk Score Optimization (RSO) analytics, the Wakely RAPID program, and the Wakely Risk Insight (WRI) program.

Most recently, Syed and his team have executed an on-going national-scale project aimed at understanding the drivers of success and challenges in the Affordable Care Act (ACA) program. The Wakely Risk Insight – National Reporting (WRINR) project is a unique lens on the ACA program in that it uses detailed data on millions of ACA lives in order to uncover insights related to succeeding in this program.

Syed co-authored (with Ross Winkelman) the 2007 Society of Actuaries' study on the comparative assessment of risk assessment models. Syed led a 2012 Society of Actuaries’ study on Uncertainty in Risk Adjustment. His other major published works include Society of Actuaries research project titled 'Non-Traditional Predictors in Risk Assessment' (SOA, 2013), and Risk Scoring in Health Insurance – A Primer (SOA, 2016). He is also in the process of writing a book on predictive modeling.

Natalia Modjeska is a Research Director at Omdia (part of Informa Tech) where she leads the team of analysts covering Artificial Intelligence and Intelligent Automation from processors and software to enterprise deployments.

Natalia’s journey into AI started in the late 1990’s with a PhD in NLP from the University of Edinburgh in Scotland. Since then she has worked in range of roles developing, deploying and evangelizing analytics and AI. Her diverse career includes R&D, product and program management, sales, consulting, and client advisory. She has worked with organizations of all sizes and levels of maturity across many industries and geographies helping clients to harness the power of data, advanced analytics and AI for transformative change. In the past four years she has advised 200+ organizations around the globe on topics ranging from strategy and use cases, to execution, best practices, governance, ethics, emerging trends, and vendor due diligence.

Natalia is passionate about helping clients to demystify AI/ML, deploy these technologies responsibly and achieve sustainable business benefits. As part of this effort, she also serves as an AI expert on the ISO/IEC JTC 1/SC 42 - AI Standards working group and volunteers with several non-profits to develop responsible AI certification; and to increase AI literacy and improve government through innovative technologies.

Mei Najim currently works as an Applied Analytics Manager at HSBC and teaches part-time as a Data Analytics Lecturer at University of Chicago. Mei has over 18 years of hands-on analytics experience in banking (collections, agent performance, and financial crime), insurance (claim management, underwriting, pricing, reserving, and catastrophe risk management), and consulting.

Since 2007, Mei has mainly been working, leading, and implementing various large scale data analytics and predictive analytics projects to develop analytics capability for financial organizations. She has extensive statistics, machine learning, and data mining experience dealing with large and complex data sets.  She is an analytics thought leader with a positive influence and a clear vision for how analytics can transform business strategy through techniques, communication, and leadership to devise innovative data-driven solutions. 
Mei has been a frequent speaker at various industry conferences to share her expertise in predictive analytics, machine learning, and data science. She holds a BS in actuarial science from Hunan University and two MS degrees in applied mathematics and in statistics, from Washington State University. She is a member of the American Statistical Association and a Certified Specialist in Predictive Analytics (CSPA) of the Casualty Actuarial Society.

George Papaioannou, is a Senior Trading Strategist within the Scientific Implementation Group of Bank of America Merrill Lynch. A Global quantitative team employing systematic, quantitative and scientifically informed methodologies around execution, portfolio management, and risk management, with emphasis on development of client solutions. George joined BAML in May 2018, following 12 years in energy major Shell, where he worked on a variety of functions. His latest role was in a team of computational science specialists, advising on machine learning, data, cloud, and high performance computing projects. He has previously worked in production operations, oil and gas forecasting, production optimization, reservoir management, development and project execution, for offshore fields in Brunei. The first 5 years of his industry career he worked in R&D as a scientific software developer focusing on scalable solvers and high performance computing.  George holds a PhD in Computational Fluid Mechanics from the Massachusetts Institute of Technology, where he also completed two MSc degrees and worked as a post-doctoral associate for a year. He has authored academic articles and acted as referee for several scientific journals.

Andreas is an Executive Director at Goldman Sachs Quantitative Execution Services, focusing on signal research for execution algorithms. Andreas has received a PhD in Information Engineering at the University of Cambridge, working on the interface of stochastic control theory and Bayesian machine learning. Andreas also holds a BA and an MEng degree in Electrical and Information Sciences from Trinity College, University of Cambridge, during which he has received the G-Research and TTP awards.

Viktoriia Samatova is a Director of Applied Innovation team of Data Scientists within Reuters Technology division focused on discovering and applying new technologies to enhance Reuters products and improving efficiency of news content production and discoverability. Prior to joining Reuters, Viktoriia spent over 5 years at State Street Bank’s Global Exchange division where she was managing product development of academic and financial industry content ingestion platform, which was the first company-wide product application utilizing emerging technologies of AI and machine learning.

With over 20 years of professional expertise, Harphajan has led global business transformations across Wealth Management, Insurance, Retail and Institution Asset Management as well as Corporate Business Strategy in a diverse set of markets 

Tarun Sood is head of data and analytics in Vanguard Institutional Investor Group. He leads four different teams; Data Management and Governance: Business Intelligence and Reporting; Data Science and AI; Data and ML Engineering. The main goal of his group is to deliver actionable insights to the business in a timely manner. Prior to Joining Vanguard, Tarun worked as a strategy and analytics leader in a consulting capacity for over 15 years. He has implemented various solutions: Big Data & predictive Analytics, Advanced visualizations & Business Intelligence, Data Lakes both on premise and on cloud, Internet of Things (IoT), Enterprise Data Warehousing, Enterprise Data Governance, and multi domain Master Data Management. He has a track record of establishing and maintaining successful partnerships with Business and IT executives. He has successfully led large global teams with multi-million dollar portfolios across the Americas and Asia with a proven record of accomplishments.

Michael Steliaros is the global head of Quantitative Execution Services at Goldman Sachs. He is responsible for the research, development and implementation of quantitative processes for portfolio and electronic trading as well as managing the bank's relations with the quantitative client base. Previously, Michael held a variety of senior roles at BofAML in London and New York, most recently running the global agency portfolio trading and quantitative equity businesses. Earlier in his career, he spent a decade on the buy-side (most notably BGI and Winton) building quant stock-selection models and managing global market neutral equity portfolios. Michael received a bachelor's degree in Economics & Econometrics from the University of Nottingham, and an MSc and PhD in Finance from City University (CASS) Business School in London.

David Stephenson has over 20 years of experience leading analytics initiatives across a wide range of industries, including as Head of Global Business Analytics at eBay Classifieds Group. In addition to consulting for dozens of companies, including Adidas, Miro, ABN Amro, and Sky Germany, Dr. Stephenson also serves as part time faculty at the University of Amsterdam Business School and has developed and delivered data science trainings for hundreds of analytics professionals around the globe.

Previously Nathan was a data scientist on the Wells Fargo Enterprise Analytics and Data Science team where he led a small team as head of Natural Language Processing (NLP) and Speech Capabilities Development, and was focused on building out Wells Fargo's capabilities in areas related to NLP, deep learning and data science product design. Nathan holds a Masters in Predictive Analytics from Northwestern University and is working on his second Masters in Computer Science from Georgia Tech. He has been with Wells Fargo for the past five years and worked in marketing analytics prior to his current role.

Edo van Uitert started working for ABN AMRO in 2018 as data scientist for the Non-Financial Risk grid, where he worked on a variety of topics, including a text mining analysis of customer complaints and a predictive model for credit fraud. In August 2019 he moved to the Detecting Financial Crime Data Science Innovation team, where he develops novel advanced analytics methods to improve transaction monitoring. In 2012 Edo obtained his PhD in astronomy from Leiden University on the topic of weak gravitational lensing. After six years of research at the University of Bonn and University College London, he decided to change careers and transitioned into data science.

Min Yu is a Lead Data Scientist at Axis Capital. She specializes in AI transformation of the insurance business including personal, commercial, and specialty lines by developing and deploying machine learning models within the business process. Min holds a Ph.D. in Physics from the University of Illinois at Urbana-Champaign.

Jing Zhu is a Risk Modeling Analyst at Paychex Inc., a leading provider of payroll, human resource, insurance, and benefits outsourcing solutions for small- to medium-sized businesses. Jing's main responsibility is developing models to help with strategic decisions across all aspects of the business, including effectively targeting retention strategies, assisting in dynamic cross-sell initiatives, and improving collection targets. Jing holds a PhD in Biology from the University of Rochester, where she applied statistics in biological research.