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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

    • 07 May 2024
    • 6:00 PM (EDT)
    • 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.


    Topological Data Analysis (TDA) has emerged as a powerful methodology in time-series analysis and signal processing. TDA is able to provide detailed descriptions of complex data which complements statistical methods. Recent applications include detection of critical transitions in financial time series, particularly of financial bubbles. The methodology relies on time-delay coordinate embedding, which is used to construct, from the time-series, a point-cloud in some space. The dynamics on the point-cloud unveils patterns in the time-series. Most of the evidence so far on the adeptness of TDA to detect financial bubbles has been empirical. We present, for the first time, a heuristic argument for why TDA can detect financial bubbles. We use models from economics that assert that the time series exhibit certain oscillatory patterns when approaching a tipping point. These oscillations determine holes in the point-clouds, which can be quantified by TDA. When approaching the tipping point of a bubble, there are significant changes in the nature of the oscillations, and consequently in the TDA output. These changes can be captured via persistence homology and yield early warning signals. As an application, we illustrate this approach on a sample of positive and negative bubbles in the Bitcoin price.


    Marian Gidea is a professor of mathematics at Yeshiva University in New York City. He held previous appointments at the Mathematical Sciences Research Institute in Berkeley, the Institute for Advanced Study in Princeton, Centre de Recerca Matemàtica in Barcelona, Northeastern Illinois University, Northwestern University, and Loyola University Chicago. He also served at the National Science Foundation as a program director in the Mathematical Sciences Division. His research interests include Dynamical Systems, Topological Data Analysis, and Financial Mathematics.

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