Victor has managed teams of quantitative analysts in multiple organizations. He is currently Head of Data Science and Artificial Intelligence in Workplace Solutions at Fidelity Investments. Previously he managed advanced analytics teams in Personal Investing, Corporate Treasury, Managerial Finance, and Healthcare and Total Well-being at Fidelity Investments. Prior to Fidelity, he was VP and Manager of Modeling and Analysis at FleetBoston Financial (now Bank of America), and Senior Associate at Mercer Management Consulting (now Oliver Wyman).
For academic services, Victor has been a visiting research fellow and corporate executive-in-residence at Bentley University. He has also been serving on the steering committee of the Boston Chapter of the Institute for Operations Research and the Management Sciences (INFORMS) and on the editorial board for two academic journals. Victor earned a master’s degree in Operational Research and a Ph.D. in Statistics and was a Postdoctoral Fellow in Management Science. He has co-authored a graduate level econometrics book and published numerous articles in Data Mining, Marketing, Statistics, and Management Science literature.
Randomized experiments allow us to determine the overall treatment effect of a program (e.g. marketing, medical, social, education, political, economic). Uplift modeling takes a further step to identify individuals who are truly positively influenced by treatment or intervention through machine learning and predictive modeling by uncovering heterogeneous treatment effects in available data. This technique enables us to identify the “persuadables” and thus optimize target selection in order to maximize treatment impact. This important subfield of data science or business analytics has gained tremendous attention in recent years in application areas such as personalized marketing, personalized medicine, political election, and healthcare programs with plenty of publications and presentations from both industry practitioners and academics across the world.
In this workshop, I will introduce the concept of Uplift, compare with traditional response modeling, and review various approaches to Uplift Modeling. The discussion will include approaches to handling a general situation where only observational data are available, i.e. without randomized experiments, integrating causal inference with uplift modeling. We will then extend to the multiple treatment cases to determine the best treatment for each individual. Various prescriptive analytics (optimization) methods will be introduced to handle the uncertainty of lift estimates. While the talk is geared towards marketing type applications (“personalized marketing”), the same methodologies can be readily applied in other fields such as insurance, medicine, education, political, and social programs. Examples from multiple industries will be used to illustrate its application and methodologies.