Building AI systems that help us understand, predict, and improve complex real-world systems

ML Intern @ AIBT & Regev Lab, Genentech, South San Francisco
2nd year PhD student @ AIMM, EPFL under Prof. Charlotte Bunne
Bachelor's + Master's @ IIT Bombay, Institute Silver Medalist (Rank #2)
Exchange student @ EPFL, Switzerland in Computer Science for Autumn 2022-23
Contact: eeshaan (dot) jain (at) epfl (dot) ch
[ Eeshaan Jain ] [ eeshaanjain ] [ eeshaan_jain ] [ eeshaanjain ]

I have a keen interest in applications of machine learning to life sciences, especially multi-scale representation learning and drug discovery along with geometric deep learning. Previously, I have worked on optimization, graph neural networks, graph retrieval, and fair learning under Prof. Abir De and Prof. Soumen Charkabarti at IIT Bombay. I have also worked with Google Research for a year, and with AWL, Inc. and Sony, Japan.

I have acted as a Teaching Assistant in the following courses:

  1. CS 461: Foundation models and generative AI (250+ students) [EPFL]
  2. CS 119: Information, calculation, communication (300+ students) [EPFL]
  3. CS 419M (Introduction to Machine Learning) (100+ students) [IIT Bombay]
  4. CS 768 (Learning with Graphs) (50+ students) [IIT Bombay]
  5. CS 769 (Optimization for Machine Learning: 100+ students) [IIT Bombay]
  6. MA 207 (Partial Differential Equations: 200+ students) [IIT Bombay]
  7. MA 108 (Ordinary Differential Equations: 400+ students) [IIT Bombay]
  8. CH 107 (Quantum Chemistry: 400+ students) [IIT Bombay]

I have received scholarships and grants from:

  • Swiss AI Initiative (42000$)
  • Broad School of MIT and Harvard for ICLR 2025 (3500$)
  • NeurIPS Travel Grant for NeurIPS 2023 (2000$+)
  • Google Research (Google Conference Scholarship) (2500$)
  • IIT Bombay (Institute Academic Prize)
  • Govt. of India (Institute Silver Medal)
  • Heyning-Roelli Foundation (Semeseter Exchange Scholarship)(6000$+)

I have given the following invited talks:

  • Agents and Benchmarks for Molecular Tumor Board @ AstraZeneca, Applied Data Science Group, Feb 2026
  • Test-Time View Selection for Multi-Modal Decision Making @ MLGenX Workshop, ICLR 2025

I have reviewed for: NeurIPS 2025, ICLR 2026, ICML 2026, NeurIPS 2026

Highlights

VirTues
The Virtual Tissues foundation model resolves spatial proteomics across scales Nature, 2026

VirTues is the first foundation model for spatial proteomics. Pretrained on 12,000+ multiplexed images from 5,300+ patients across 30+ cohorts and four imaging technologies, it runs cell segmentation and typing zero-shot in a single pass.

Nature Code
Measure Less, Know More
Measure Less, Know More: Self-Supervised Test-Time Feature Acquisition NeurIPS 2026

A self-supervised approach for deciding which features to acquire at test time, measuring less while retaining the information needed for accurate prediction.

Paper Code
MTBBench
MTBBench: A Multimodal Sequential Clinical Decision-Making Benchmark in Oncology NeurIPS 2025

An agentic benchmark simulating Molecular Tumor Board decision-making through multimodal, longitudinal oncology questions, paired with a foundation-model tool framework that improves LLM reasoning and reliability.

Paper Code

News

  • Measure Less, Know More — Our work Measure Less, Know More: Self-Supervised Test-Time Feature Acquisition was accepted to NeurIPS 2026 🎉
  • spora — Our work spora: A Unified Multimodal Dataset for Spatial Proteomics was accepted to the NeurIPS 2026 Evaluations & Datasets Track 🎉
  • Virtual Tissues (VirTues) — Our foundation model for spatial proteomics was published in Nature 🎉
  • MTBBench — Our benchmark on multimodal sequential clinical decision-making (MTBBench: A Multimodal Sequential Clinical Decision-Making Benchmark in Oncology) was accepted to NeurIPS 2025 🎉
  • AI-powered Virtual Tissues — Awarded Best Paper at ICLR 2025 MLGenX Workshop 🎉
  • Test-Time View Selection for Multi-Modal Decision Making — Accepted as an Oral (top 4%) at MLGenX Workshop @ ICLR 2025 🎉
  • Clique Number Estimation via Differentiable Adjacency Permutations — Accepted to ICLR 2025 🎉
  • Co-organizing the Learning Meaningful Representations of Life (LMRL) Workshop @ ICLR 2025
  • Two extended abstracts (Graph Edit Distance Evaluation Datasets, Graph Edit Distance with General Cost) accepted at LoG 2024
  • Graph Edit Distance with General Costs Using Neural Set Divergence — Accepted to NeurIPS 2024 🎉
  • Started my PhD in the AI in Molecular Medicine (AIMM) Lab at EPFL under Prof. Charlotte Bunne
  • Best Poster Award at Eastern European ML School by Google DeepMind
  • Winner — Kinase Selectivity Hackathon @ ML for Drug Discovery Summer School
  • Efficient Data Subset Selection to Generalize Training Across Models — NeurIPS 2023 (with Google AI & UT Dallas)
  • Research internship at Aalto University under Prof. Vikas Garg