Real world training envs for healthcare AI models
BioStack is building the data engine for healthcare and drug discovery AI. The bottleneck is not models. It is access to high-quality biological data. Clinical and experimental data is fragmented, unstructured, and locked inside hospitals, labs, and CROs, while generating new data is slow and expensive. BioStack fixes this with proprietary clinical and preclinical data pipelines that turn real biomedical workflows into ML-ready training environments. We structure longitudinal multimodal data across imaging, EHR, and experimental assays, then package it for post-training and reinforcement learning so models can learn how research and care actually happen. Instead of static datasets, BioStack gives AI labs workflow-aligned data and environments that improve reasoning, decision-making, and real-world performance in biology and medicine.
Founder
I'm the co-founder and CEO of BioStack Platforms. In another life, I was a cancer genomics researcher at Stanford, Yale, Carnegie Mellon, and the Max-Planck Institute. I also worked as an early researcher with top AI labs on RL tasks and benchmarks across healthcare and biotech. Hailing from Mumbai, I attended IISER Mohali and Carnegie Mellon for my BS and MS degrees respectively.
Founder
I am the Co-founder and CTO at BioStack Platforms. Prior to this, I was a Gen AI scientist at AWS. Before that, I was a researcher at MIT. I have published 40+ papers with 1800+ citations across top venues like CVPR, neurIPS, EMNLP etc. in ML, GenAI, NLP. I completed my MS from UCLA.
