I am a Staff ML Researcher and Engineer at PathAI, with a focus on foundation models, representation learning, computer vision and applications of ML in real-world systems. Some examples of past work -
- Novel ML methods research -
- Foundation Models - PLUTO: a scalable & performant Foundation Model for pathology (accepted in following workshops in ICML 2024 - ML for life and material science, Foundation models in the wild and Accessible and efficient foundation models for biological discovery )
- Domain generalization - ContriMix (Best-performing official submission in Camelyon17 Stanford WILDS leaderboard)
- Label Imbalance - Supervised Contrastive MIL (WACV 2024)
- Interpretability - Additive Multiple Instance Learning (NeurIPS 2022)
- Interpretability analysis on a pathology foundation model reveals biologically relevant embeddings across modalities (ICML Mechanistic Interpretability Workshop 2024)
- Agentic evaluation framework
- AI agents as ML engineers - ML-Dev-Bench: Comparative Analysis of AI Agents on ML development workflows (ICLR 2025 Deep Learning for Code Workshop) Github
- Application of ML to medical imaging -
- Clinical trials for liver disease - AI-based automation of enrollment criteria and endpoint assessment in clinical trials in liver diseases (Nature Medicine 2024)
- Talks and interviews -
- PyTorch Conference 2023 - Lightning talk on domain generalization in medical imaging
- ML Seminar Series, University of Minnesota - AI for digital pathology
- Fung Institute of Engineering Leadership, UC Berkeley - Alumnus interview
Posts
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How to think with images
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Observations on self-supervised learning for vision
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Foundation models for vision
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