M&SOM Practice Platform

Managing Ambiguity at Scale: Lessons Learned During the Pandemic

Abstract

The purpose of this talk is to highlight some of the challenges faced while using AI approaches to tackle problems encountered in the post-pandemic world. Specifically, we want to address the issue of risk vs ambiguity and how we had to change the learning mechanisms of various systems and the objective functions of the optimizers to deal with uncertainty we encountered once the pandemic hit. One of the learnings was that dealing with risks and ambiguity requires different learning paradigms and that there is a need for a more comprehensive unified framework to deal with all types of uncertainty. We offer some examples from our experience at Walmart.

Prakhar Mehrotra

Vice President, Machine Learning, Walmart

Dr. Prakhar Mehrotra is a Vice President of Machine Learning at Walmart. He is leading team of 300+ Data Scientist and overseeing research and development of AI efforts related to Supply Chain, Merchandising and Store Operations. He has 10+ years of experience across ridesharing, social media, renewable energy, eCommerce, and retail. Prior to joining Walmart, he was Head of Data Science at Uber Technologies, San Francisco. At Uber, he built the Data Science arm for pricing, finance and forecasting and led a full stack global team of data scientists and machine learning engineers spread across Amsterdam, Hyderabad and San Francisco. He led the research and development of Machine Learning Algorithms related to Financial Forecasting (Supply & Demand), Budget Planning, Economic Simulations for Autonomous Vehicles. In his role, he has also worked on research and development related to payment analytics and treasury financial simulations. Prior to Uber, Dr. Mehrotra worked as Sr. Data Scientist at Twitter, Inc in San Francisco as part of Sales & Monetization team. He has Advanced Engineer's degree in Aeronautics from California Institute of Technology (Caltech), Pasadena, and dual Masters in Aeronautics and Applied Mechanics from Ecole Polytechnique, Paris and Caltech. He did is undergraduate in Mechanical Engineering from National Institute of Technology, Trichy, India. He is a recipient of the Edelman Laureate Medal by INFORMS, NASA Leadership Medal, Rajiv Gandhi Award, and top data scientist award. He is also IEEE Senior Member and has a track record of publications and patents across the broader field of Artificial Intelligence. He also sits on the peer review panel for top conferences like AAMAS, CVPR, AAAI, NeurIPS, ICML etc. He has given numerous keynotes talks and invited guest lectures across top industry, academic and government organized conferences.

Monika Shrivastav

AI Strategy and Operations Lead, Walmart

Monika Shrivastav is an AI Strategy and Operations lead at Walmart, managing portfolio of AI initiatives and their incubation to implementation strategy. She has 10+ years of experience in investment banking, fintech and retail. Prior to Walmart, she worked in a Fintech startup, Henry Labs, designing features for IOS applications. Prior to that Monika was part of Nomura Investment bank and worked on several projects of Trade Lifecycle Management in Global Market Business Services domain. In her academics, she has done master's in Engineering Management from International Technological University, CA, USA and dual Master of Business Administration in Finance and Marketing from Prestige Institute of Management and Research, Indore, India. She is recipient of the Edelman Laureate medal-2020 from INFORMS. She is an IEEE Senior Member and has published journals on her passion to drive and implement AI in enterprises. She is one of the finalists of Transform- Rising Star in AI 2022 award. Her professional journey is published in SWE blog.

This seminar took place on Wednesday, November 16, 2022. The PDF of the webinar chat log is available here. An online discussion forum has been set up through the INFORMS YouTube channel in order to facilitate further interactions with the seminar presenters and organizers for a period of one month following the seminar (i.e., until December 17, 2022).

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