Stanford University
Showing 2,101-2,135 of 2,135 Results
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Xingyuan Zhang
Ph.D. Student in Chemistry, admitted Autumn 2023
Ph.D. Minor, Computer ScienceBioPhD candidate in Chemistry and Computer Science, affiliated with the Wu Tsai Neurosciences Institute and the ChEM-H Institute at Stanford. Investigating the molecular mechanisms underlying chronic diseases, cancer and fibrosis, with interest on applying ML/DL approaches to drug discovery and disease modeling.
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Zhuo Zheng
Postdoctoral Scholar, Computer Science
BioMy research interests are Earth Vision and AI4Earth, especially multi-modal and multi-temporal remote sensing image analysis and their real-world applications.
First-author representative works:
- Our Change family: ChangeStar (single-temporal learning, ICCV 2021), ChangeMask (many-to-many architecture, ISPRS P&RS 2022), ChangeOS (one-to-many architecture, RSE 2021), Changen (generative change modeling, ICCV 2023)
- Geospatial object segmentation: FarSeg (CVPR 2020) and FarSeg++ (TPAMI 2023), LoveDA dataset (NeurIPS Datasets and Benchmark 2021)
- Missing-modality all weather mapping: Deep Multisensory Learning (first work on this topic, ISPRS P&RS 2021)
- Hyperspectral image classification: FPGA (first fully end-to-end patch-free method for HSI, TGRS 2020) -
Richard Zhuang
Masters Student in Computer Science, admitted Autumn 2025
BioI’m broadly interested in understanding and improving the capabilities of Large Language Models (LLMs) in a data-centric way. Specifically, I’m intrigued by how certain data “foster” skills that are essential for LLM agents (e.g. reasoning and planning). I have also had a long-standing passion in Sports Analytics. Outside the realm of AI, you will usually find me playing basketball!
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James Zou
Associate Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering
Current Research and Scholarly InterestsMy group works on both foundations of statistical machine learning and applications in biomedicine and healthcare. We develop new technologies that make ML more accountable to humans, more reliable/robust and reveals core scientific insights.
We want our ML to be impactful and beneficial, and as such, we are deeply motivated by transformative applications in biotech and health. We collaborate with and advise many academic and industry groups.
