Machine learning in business: modern approaches and applications
Centre for a Responsible Wealth Transition - Workshop Series
This workshop is part of the CRWT workshop series held by the Centre for a Responsible Wealth Transition (CRWT) at the Telfer School of Management. This event is open to the public and is available in-person or online, however registration is required. For more information about the Centre or the Workshop Series, please contact Jonathan Li.
Machine learning has emerged as a powerful technology in today’s business world. It provides new analytics solutions to many pressing research problems in areas such as finance, operations management, healthcare, and many others. The question of how to best leverage this technology to offer new business insights has been a focal point of study in the past decade. The vast volume and variety of data available today has motivated the development of new approaches to harness the power of machine learning. This workshop is aimed at:
- introducing several business applications advanced by the latest developments of machine learning,
- introducing modern machine learning approaches, including natural language processing (NLP), large language models, deep learning, and reinforcement learning,
- providing a tutorial to those who seek to apply machine learning in their own work or research
This workshop consists of a distinguished lecture and two tutorial sessions.
1:00 pm - 2:50 pm - Distinguished lecture by Dacheng Xiu, Chicago Booth School of Business
News Text Mining: The lecture provides two examples of applying natural language processing techniques in finance. The first part focuses on sentiment analysis (classification), whereas the second part on topic modeling (clustering).
3:00 pm - 4:20 pm - Tutorial by Rafid Mahmood, NVIDIA
Modern Practices in Deep Learning: Bigger Models, Better Data: Foundation Models are an increasingly popular family of deep learning models that, by training on troves of unlabeled data, demonstrate incredible generalization to new applications. More and more deep learning applications simply adapt a general-purpose Foundation Model using custom curated data sets. This seminar will first present a tutorial of Foundation Models: how they work, and how to adapt them for our applications. In the second half of the seminar, we step back to look at the big picture of deep learning from a data-centric perspective: given the task, how do we curate, annotate, and use the appropriate data to build our model.
4:30 pm - Tutorial by Jonathan Li, Telfer School of Management
Deep reinforcement learning and business applications: This tutorial provides an overview of deep reinforcement learning (DRL) and its applications in portfolio management, financial derivatives hedging, and other dynamic decision problems. It discusses how to identify tasks that can be solved by DRL and how to implement a solution.
6:00 pm - Reception
About the Speakers
Dacheng Xiu, Chicago Booth School of Business
Dacheng Xiu is Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His current research focuses on developing machine learning solutions to big-data problems in empirical finance. Xiu’s work has appeared in the Journal of Finance, Review of Financial Studies, Econometrica, Journal of Political Economy, the Journal of the American Statistical Association, and the Annals of Statistics. He is a Co-Editor for the Journal of Financial Econometrics, an Associate Editor for the Review of Financial Studies, Journal of Econometrics, Management Science, Journal of Business & Economic Statistics, etc. He has received several recognitions for his research, including the Fellow of the Society for Financial Econometrics, Fellow of the Journal of Econometrics, AQR Insight Award, EFA Best Paper Prize, and Swiss Finance Institute Outstanding Paper Award. Xiu earned his PhD and MA in applied mathematics from Princeton University.
Jonathan Li, Telfer School of Management
Jonathan Li is Associate Professor at the Telfer School of Management. His research examines a wide array of operational, financial, and technological problems from a risk management perspective, and is currently focused on developing data-driven and analytics-powered methodologies using optimization, machine learning, and financial econometrics. His work has appeared in the leading academic journals, Management Science and Operations Research. Li earned his PhD in operations research and financial engineering from the University of Toronto.
Rafid Mahmood, NVIDIA
Rafid Mahmood is a Senior Research Scientist at NVIDIA and an incoming Assistant Professor at the Telfer School of Management. He is interested in operational solutions to improve data collection and model tuning for AI systems. He received his PhD in Industrial Engineering from the University of Toronto. From 2019-2021, he was a Postgraduate Affiliate of the Vector Institute for Artificial Intelligence.