Roxie (Ruoxi) Jiang

I am a Computer Science PhD student at the University of Chicago advised by Professor Rebecca Willett.

Previously, I received my master's degree in Operations Research at Columbia University and bachelor's degree at Xi'an JiaoTong University.

Email  /  Github

My research interests lie in machine learning for dynamical systems and its applications in scientific computing. In particular, I work on learning structural hidden representations of the high-dimensional data. My work has been applied to addressing inverse problems with uncertainty quantification and designing practical algorithms to achieve data efficiency in decision-making problems (i.e., bandits). Currently, I am interested in predicting high-dimensional chaotic systems with deep learning.

We introduce a novel likelihood-free inference method based on contrastive learning that efficiently handles high-dimensional data and complex, multimodal parameter posteriors.

We introduce a novel neural network parameterization that induces low-rank simplicity bias and enhances semantic feature learning in generative representation learning frameworks. By incorporating a weighting factor to reduce the strength of identity shortcuts within residual networks, our modification increases the MAE linear probing accuracy on ImageNet from 67.8% to 72.7%, while also boosting generation quality for diffusion models.

We introduce two novel approaches: Optimal-transport(OT) based method with prior knowledge of the phsycial property; and Contrastive learning (CL) based method in absence of prior knowledge, to match long-term statistics of chaotic dynamical systems with noisy observations.

We develop a novel deep-learning-based approach to solve time-evolving Schrödinger equation, with inspiration from stochastic mechanics and diffusion models.

A novel contrastive framework for learning feature embeddings of observed dynamics jointly with an emulator that can replace high-cost simulators for parameter estimation.

To overcome the curse of dimensionality, we propose to adaptively embed the feature representation of each arm into a lower-dimensional space and carefully deal with the induced model misspecifications.

Source code from Dr.Jon Barron.