Lunch is available beginning at 12 PM

Speaker to begin promptly at 12:30 PM

Co-sponsored by AI² and the Center for Statistics and Machine Learning

Abstract: In this talk, I will delve into the exciting research opportunities and unique challenges of building large foundation models for high-frequency trading. Our focus centers on the high-stakes problem of alpha signal extraction from massive streams of order book and transaction data. Key themes include why high-frequency markets remain predictably exploitable despite their complexity, how data-driven approaches are outperforming traditional economic models, and the potential of foundation models trained on semi-structured data such as time series, tables, and graphs. I will share insights on scaling model sizes in extremely noisy data settings, the surprising robustness of learned features compared to hand-crafted ones, and the critical role of strict causality — offering a rare opportunity to measure true “intelligence” progress by avoiding the train-test contamination often seen in modern benchmarks. I aim to shed light on the intersections between academia and industry, highlighting opportunities for cross-disciplinary innovation, collaboration, and the creation of scalable AI systems for high-impact applications.

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