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Events / Upcoming

  • How I Became a Quant - Boston

    • 17 Sep 2025
    • 5:00 PM
    • Boston University Questrom School of Business 595 Commonwealth Ave Boston, MA 02215
    Register

    How I Became a Quant: Boston

    This annual event is a Panel Discussion on Careers in Quantitative Finance, as industry veterans will discuss their experiences, the current economic climate, and career paths in finance and data science.

    Wednesday September 17, 2025

    5:00 PM Program Begins: Panel Discussion

    6:30 PM Reception


    Location:

    Boston University Questrom School of Business

    595 Commonwealth Ave

    Boston, MA 02215


    Moderator: 

    Philip Sun, CEO at Adaptive Investment Solutions, LLC And Professor of Finance at Boston University


    Panelists:

    Mengjia (Sara) Tan, Quantitative Analyst at Adaptive Investment Solutions

    Ronnie Hoogerwerf, Quantitative Researcher at Squarepoint Capital

    William Zieff is Director at Northfield Information Services


    Panelist Biographies

    Moderator: Philip Sun is a fintech entrepreneur, investment manager and leader of quantitative research and investment teams with over 25 years of professional experience. Philip currently is the CEO and cofounder of a fintech startup, Adaptive Investment Solutions, LLC. Philip is also a Professor of Finance, Entrepreneurship and Data Science at Boston University and Hult International Business School. Philip began teaching Algorithmic and High-frequency class as an adjunct in the Master of Science in Mathematical Finance & Financial Technology program in the Fall of 2022, where he is working to reshape the class to combine both mathematical rigor and in-depth exposure to real world data and trading applications. Prior to starting Adaptive, Philip was the Head of Quantitative Research and Investments at Sentinel Investments, a fully owned investment management subsidiary of National Life Group. Before Sentinel, Philip held senior quantitative research and investment positions in the fixed-income and asset allocation divisions in Fidelity Investments and Wellington Management. Prior to Wellington, Philip started his career as a portfolio manager of quantitative macro strategies at Highbridge Capital Management (now part of JP Morgan Asset Management) and PanAgora Asset Management. Philip holds an MBA from the Wharton School, a PhD in Physics from Carnegie Mellon University, and dual Bachelor Degrees in Physics and Mathematics from Stony Brook University. Philip is a Chartered Financial Analyst.

    Mengjia (Sara) Tan is a Quantitative Analyst at Adaptive Investment Solutions. Her quantitative finance journey began at UC San Diego, where she studied mathematics and economics and became fascinated by the dynamics of capital markets. She later deepened that interest through Boston University’s Mathematical Finance & Financial Technology master’s program, where she focused on strategies involving index option hedging, volatility modeling, and portfolio analytics. Outside of work, she stays active in the quant community, organizing events and exchanging insights with industry professionals.

    Ronnie Hoogerwerf, PhD, is a quantitative researcher at Squarepoint Capital. Ronnie came to quantitative finance in a roundabout way; with an education as an astrophysicist and experience in academia and a couple of tech companies he ended up joining Cargometrics, a ship-tracking tech company that also started a hedge fund trading on their own information. Currently, he works as a quantitative researcher at Squarepoint Capital providing quant services to the metals and gas commodity desks.

    William Zieff is Director at Northfield Information Services, a leading provider of financial risk analytics. Bill has extensive experience in finance and investing. Prior to Northfield, he was Chief Investment Officer of Global Strategic Products, Wells Fargo Asset Management; Partner and Co-Chief Investment Officer of Global Asset Allocation at Putnam Investments; and Director of Asset Allocation at Grantham, Mayo, Van Otterloo. Bill has taught master’s and undergraduate courses in finance at Northeastern University and Boston College. Bill holds an A.B. degree in Economics and Mathematics from Brown University and MBA from Harvard Business School.


    Sponsored By:


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  • IAQF & Thalesians Seminar Series: Data-Driven Dynamic Factor Modeling via Manifold Learning - A Seminar by Jose Antonio Sidaoui

    • 21 Oct 2025
    • 6:00 PM
    • Fordham University McNally Amphitheater 140 West 62nd Street New York, NY 10023
    Register


    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.

    Abstract:

    We propose a data-driven dynamic factor framework where a response variable vector y(t) depends on a high-dimensional set of covariates x(t), without imposing any parametric model on the joint dynamics. Leveraging Anisotropic Diffusion Maps, a nonlinear manifold learning technique introduced by Singer & Coifman, our framework uncovers the joint dynamics of the covariates and responses in a purely data-driven way. We approximate the embedding dynamics using linear diffusions, and exploit Kalman filtering to predict the evolution of the covariates and response variables directly from the diffusion map embedding space. We generalize Singer’s convergence rate analysis of the graph Laplacian from the case of independent uniform samples on a compact manifold to the case of time series arising from Langevin diffusions in Euclidean space. Furthermore, we provide rigorous justification for our procedure by showing the robustness of approximations of the diffusion map coordinates by linear diffusions, and the convergence of ergodic averages under standard spectral assumptions on the underlying dynamics. We apply our method to the stress testing of equity portfolios using a combination of financial and macroeconomic factors from the Federal Reserve’s supervisory scenarios. We demonstrate that our data-driven stress testing method outperforms standard scenario analysis and Principal Component Analysis benchmarks through historical backtests spanning three major financial crises, achieving reductions in mean absolute error of up to 55% and 39% for scenario-based portfolio return prediction, respectively.


    Bio:

     J. Antonio Sidaoui is a PhD candidate at the Department of Industrial Engineering & Operations Research at Columbia University. J. Antonio joined Columbia in 2023 after studying his MS in Statistics & Data Science at Yale University, and his undergraduate degrees in Statistics and Mathematical Economics at the Wharton School, University of Pennsylvania. J. Antonio's research focuses on the discovery and design of novel Machine Learning methodologies for financial applications, most recently he has worked on Graph Machine Learning for Asset Pricing and Manifold Learning for data-driven risk management.

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