As quantitative research evolves from small-scale backtests to large-scale, AI-driven experimentation, leading firms are rethinking how they build, track, and scale alpha generation. Modern quant teams are no longer constrained by ideas; they are constrained by their ability to run, evaluate, and iterate on experiments at scale. Compute, data, and experimentation infrastructure have become the primary drivers of research velocity and model performance, and emerging research suggests the same scaling laws that drive the LLM revolution now hold in finance. In this session, we’ll break down how top quantitative teams are architecting their research stack, from initial signal discovery to large-scale simulation and production deployment, and how advances in compute and machine learning are reshaping the path to alpha.
What to expect
Firsthand perspectives from researchers (Luka Vulicevic, Allan Timmermann) on how quant labs and hedge funds are evolving, specifically how increasing compute, data scale, and simulation volume are reshaping research practices and expectations for tooling
New academic research on whether scaling laws, the compute–performance curves driving the LLM revolution, also hold in finance, and whether the resulting compute arms race is paying off
An overview of the modern quant research lifecycle (idea → experiment → evaluation → production)
A demo of scalable, agent-driven research workflows
A discussion of what these shifts mean for tooling: from lightweight backtesting setups to full systems of record for experiments, lineage, and large-scale evaluation
Real-world insights on how compute impacts model performance and training stability
Practical patterns for managing thousands of experiments and simulations
How leading teams are moving from fragmented workflows to integrated, production-ready research platforms
What you'll learn
Why compute is now a primary driver of alpha, not just infrastructure
How increasing compute improves both prediction quality and training reliability
What “training beyond interpolation” means and why it leads to better performance in practice
How leading quant labs are evolving their research workflows (from small backtests to large-scale, simulation-driven experimentation)
What these shifts mean for tooling: why systems of record, experiment tracking, and evaluation infrastructure are becoming critical
How to manage and compare thousands of experiments with full lineage and reproducibility
How to reduce training risk and variability through better model scaling and infrastructure
How agentic and multi-model systems are accelerating research iteration and signal discovery
How to move from fragmented research workflows to scalable, production-ready quant systems
Who should attend
Quantitative researchers and quants
Heads of ML, AI, quant research, and research leads
MLOps, platform, and infrastructure engineers supporting quant teams
Employees from quant funds and quant labs, hedge funds, and major financial institutions
Featured speakers
Allan Timmermann
Dr. Harry M. Markowitz Endowed Chair in Finance and Investing
Webinar
