Internship - Generative modeling for synthetic time-series via stochastic interpolant

Description du poste

Founded in 1991, we are a global quantitative and systematic asset management firm applying a scientific approach to finance to develop alternative investment strategies that create value for our clients.
We value innovation, dedication, collaboration, and the ability to make an impact. Together, we create a stimulating environment for talented and passionate experts in research, technology, and business to explore new ideas and challenge existing assumptions.

Summary

Build and stress-test generative models based on flow matching/stochastic interpolants for synthetic time-series. You will create controllable synthetic datasets (with tunable noise, nonlinearity, regime changes, and exogenous controls) and/or adapt benchmarks from the literature, compare neural architectures (MLPs, Transformers, etc.) and training strategies, and quantify:

1. Sample complexity and data efficiency across architectures
2. Whether learning a full conditional distribution improves accuracy when we only care about conditional means
3. Out-of-support generalization with control variables (how far can we extrapolate?)

Motivation

Diffusion-style generative models (flow matching / stochastic interpolants) work remarkably well for images and text, but their behavior on structured time-series—especially with controls and mixed scalar/sequence inputs—is less well understood. Also, in realistic settings we are in a limited data regime, which is not encountered in vision or NLP. This project asks: which architectures and training choices are most data-efficient, when is generative modeling worth it versus simple regressions, and how robust are these models to shifts in inputs statistics?

These are the main questions of interest; the internship will prioritize a subset based on progress and the intern’s interests.

Prerequisites: comfort with Python/PyTorch and basic probability/ML. Curiosity about generative modeling and careful experimental design.

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Prerequisites: comfort with Python/PyTorch and basic probability/ML. Curiosity about generative modeling and careful experimental design.