Log in

Events / Thalesians Series

About The Series

The IAQF's Thalesians Seminar Series is a joint effort on the part of the IAQF ( and the Thalesians (  The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance.  This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion.

Call For Speakers

If you are interested in speaking at one of the upcoming seminars, please email

Past Seminars

About The Organizer

Harvey Stein is a senior VP in the Labs group at Two Sigma. From 1993 to 2022, Dr. Stein was at Bloomberg, where he served as the head of several departments including Quantitative Risk Analytics, Counterparty and Credit Risk, Interest Rates Derivatives, and Quantitative Finance R&D. Harvey is well known in the industry, having published and lectured on credit risk modeling, financial regulation, interest rate and FX modeling, CVA calculations, mortgage backed security valuation, COVID-19 data analysis, and other subjects.

Dr. Stein is on the board of directors of the IAQF, a board member of the Rutgers University Mathematical Finance program, an adjunct professor at Columbia University, and organizer of the IAQF/Thalesians financial seminar series. He's also worked as a quant researcher on the Bloomberg for President campaign.

Dr. Stein holds a Ph.D. in Mathematics from the University of California, Berkeley (1991) and a B.S. in Mathematics from Worcester Polytechnic Institute (1982).


Upcoming Seminars

    • 05 Dec 2023
    • 6:00 PM (EST)
    • Fordham University McNally Amphitheater 140 West 62nd Street New York, NY 10023

    6:00 PM Seminar Begins

    7:30 PM Reception

    Hybrid Event:

    Fordham University

    McNally Amphitheater

    140 West 62nd Street

    New York, NY 10023

    Free Registration!

    For Virtual Attendees: Please select Virtual instead of member type upon registration.


    Credit risk assessment is a multifaceted process in which lenders employ various measures to evaluate the risk associated with borrowers, ranging from individual consumers to large-scale companies. To achieve a comprehensive understanding of credit risk, lenders extensively analyze a wide array of data sources, encompassing images, text, social networks, time series data, and traditional financial variables. Deep learning methodologies offer significant advantages in leveraging diverse data from multiple sources to generate accurate predictions and provide valuable insights into the complex relationships inherent in these inputs.

    This presentation aims to explore different strategies for handling multimodal data in both consumer and corporate lending using deep learning techniques, with a particular emphasis on transformer models. The discussion will encompass the utilization of time series data, ego networks, and textual information, in conjunction with conventional financial variables. Real-world use cases will be presented to showcase the predictive gains obtained through multimodality and demonstrate the valuable insights that can be extracted from these diverse data sources.

    Furthermore, the talk will address the challenges and solutions associated with deploying these models in credit risk assessment. It will shed light on the potential pitfalls that can arise when working with multimodal data and outline effective approaches to mitigate these issues. By the end of the presentation, participants will have a better understanding of the power of deep learning techniques in analyzing multimodal data in this space, enabling them to make informed decisions and enhance their lending practices.


    Dr. Cristián Bravo is an Associate Professor and Canada Research Chair in Banking and Insurance Analytics at the University of Western Ontario, Canada. He also serves as the Director of the Banking Analytics Lab. His research lies at the intersection of data science, analytics, and credit risk, researching how techniques such as multimodal deep learning, causal inference, and social network analysis can be used to understand relations between consumers and financial institutions. He has over 75 academic works in high-impact journals and conferences in operational research, finance, and computer science. He serves as an editorial board member in Applied Soft Computing and the Journal of Business Analytics and is the co-author of the book “Profit Driven Business Analytics”, which has sold over 6,000 copies to date. Dr. Bravo has been quoted by The Wall Street Journal, WIRED, CTV, The Toronto Star, The Globe and Mail, and Global News. He is also a regular panelist at CBC News’ Weekend Business Panel where he discusses the latest news in Banking, Finance and Artificial Intelligence. He can be reached via LinkedIn, by Twitter @CrBravoR, or through his lab website at

© Copyright 2020 International Association for Quantitative Finance