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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 (www.iaqf.org) and the Thalesians (www.thalesians.com).  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 info@iaqf.org

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

    • 20 May 2026
    • 6:30 PM
    • Fordham University McNally Amphitheater 140 West 62nd Street New York, NY 10023
    Register

    Wednesday May 20th, 2026

    6:30 PM Seminar Begins

    8:00 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.

    Abstract:

    We derive the optimal long-term growth rate for an agent investing in a market composed of a numéraire asset, a risky asset subject to transaction costs, and a liquidity pool within an Automated Market Maker (AMM). We first establish the necessary conditions to ensure a no-arbitrage environment within this market structure. Under these conditions, we determine the asymptotically optimal trading strategy for liquidity providers. Finally, we provide economic intuition for the strategy’s sensitivity to various market parameters, supported by numerical illustrations of our theoretical results.


    Bio:

    Maxim Bichuch holds a M.S. from NYU and a Ph.D. from Carnegie Mellon University both in Financial Mathematics. He was a Postdoctoral Research Associate & Lecturer in the ORFE department in Princeton, and an Assistant Professor at Worcester Polytechnic Institute and Johns Hopkins University, before joining the department of Mathematics at The University at Buffalo. Prior to obtaining his Ph.D. He has also gained corporate experience working for Citigroup and Bear Stearns. His research interests include optimal investment, optimal control, stochastic volatility, credit, funding and counterparty risks, and most recently electricity markets, machine learning and AI, decentralized finance and fintech.

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