Scholar
Ran Liu
Google Scholar ID: vBEAxZgAAAAJ
Apple AIML, Georgia Tech
Citations & Impact
Citations
661
H-index
10
i10-index
10
Publications
20
Co-authors
11
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Papers published: ICML'24 (Bias in large models), ICLR'24 (Group-Aware Embeddings for transformers), ICLR'24 TS4H (Frequency-Aware MAE), NeurIPS'22 (Foundation model for neurons), NeurIPS'21 (Content-Style separation for time-series), WACV'24 (Sample-aware augmentation), ICML'23 (Upsampling augmentation for graphs), NeurIPS'21 SSLTP (Cross-sample augmentation in SSL), NeurIPS'22 DnB (Open-source dataset for multiscale brain modeling), ICIP'21 (Multiscale brain modeling), MICCAI'20 (Population-level variability in brain).
Research Experience
- Interned at Apple AIML, Cajal Neuroscience, and Meta; worked in the Neural Data Science Lab.
Education
- PhD: Machine Learning Program at Georgia Tech, advisor Prof. Eva L. Dyer; Bachelor's degree: Physics from Fudan University, working on Quantum Hall effect and Superconductors.
Background
- Machine learning research scientist with the goal of creating a next-generation deep learning framework that incorporates logic. Research interests include large-scale pretraining and alignment, representation learning, contrastive methods, data augmentation, generative modeling, and segmentation of medical images.
Miscellany
- Enjoys indoor climbing, fine dining, and playing with two lovely cats, also somewhat of a hunter.
