Researcher in Artificial Intelligence & Applied Machine Learning
I work on developing new machine learning and optimization methods in applications that typically interface with people, such as retail and next-generation transportation. My current and prior work experiences include working with/at the University of Illinois at Chicago, Xerox Research, Massachusetts Institute of Technology, Citadel Investments, Apple, State Street Global Advisors, and Texas Instruments to name a few.
Some examples research outcomes are:
- Enhancing supervised learning in the presence of operational information
- Solving transportation problems such as scheduling, crowdshipping and enabling fair ride-shares
- Optimizing and personalizing recommendations and prices assuming realistic user behavior models
- Product pricing and learning in economic and competitive settings (e.g., capturing loyalty)
- Designing theoretically well-behaved online and reinforcement learning methods for applications involving personalization by exploiting the underlying application specific structure.
- PhD in Electrical Engineering and Computer Science (thesis topic area: Machine Learning and Optimization), 2014, Massachusetts Institute of Technology, Cambridge, USA.
- Dual Degree in Electrical Engineering (Prime Minister’s Gold Medal for being ranked 1 GPA-wise across the university), 2009, Indian Institute of Technology Kharagpur, India.
I am broadly interested in pursuing practical solutions to applied problems, which make use of the right set of statistical models and data driven decision making methods, and have a significant impact on business and societal outcomes.
Specific interests include:
- Modeling human behavior and enhancing their decision making prowess quantitatively
- Developing application specific machine learning/statistical methods
- Developing special-purpose optimization and reinforcement learning methods for real life deployment
- Tech entrepreneurship (e.g., ML Ops: see my recent course playlist)