I am a machine learning research scientist at Apple AIML 🍎. I earned my PhD in the Machine Learning Program at Georgia Tech, working in the Neural Data Science Lab led by Prof. Eva L. Dyer. Over the past few years, I interned at Apple AIML, Cajal Neuroscience, and Meta. Previously, I got my Bachelor’s degree from Fudan University majoring in physics working on Quantum Hall effect and Superconductors.

My ultimate research goal is to create a next-generation deep learning framework that incorporates logic. I believe that the critical path toward achieving this involves developing large-scale learning methods that are both more generalizable and explainable. To this end, I have been focusing on the following topics:

• Large-scale Pretraining and Alignment for Time Series and Beyond: Bias in large models (ICML’24), Group-Aware Embeddings for transformers (ICLR’24), Frequency-Aware MAE (ICLR’24 TS4H), Foundation model for neurons (NeurIPS’22), Content-Style separation for time-series (NeurIPS’21).
• Representation Learning, Contrastive Methods, and Data Augmentation: Alignment theory for contrastive learning (coming soon), Sample-aware augmentation (WACV’24), Upsampling augmentation for graphs (ICML’23), Cross-sample augmentation in SSL (NeurIPS’21 SSLTP).
• Generative Modeling and Segmentation of Medical Images: Open-source dataset for multiscale brain modeling (NeurIPS’22 DnB), Multiscale brain modeling (ICIP’21), Population-level variability in brain (MICCAI’20).

Outside of research, I am a fan of indoor climbing, fine dining, and playing with my two lovely cats🐱.

I am also somewhat of a hunter.

Contact Me

• My email address is rliu361{at}gatech{dot}edu.
• My full CV is here. (Updated Feb, 2024)