About

Hi! 👋 #

I’m Aneesh Sathe. I work at the intersection of AI, data science, and biomedical R&D — mostly on systems that help scientists turn messy biological and clinical data into clearer decisions.

📍 San Diego, CA | 📧 [email protected] | 📚 Google Scholar

Over the last decade, I’ve built and led AI/ML, data science, and product teams across drug discovery, cancer diagnostics, digital pathology, and mechanobiology. My work tends to sit in the space between research and real-world use: computer vision pipelines, knowledge graphs, LLM/RAG and agentic workflows, Bayesian and causal models, FAIR data products, and cloud-scale infrastructure for scientific teams.

I’m currently VP of AI & Data Science at Qureator, where I help build interoperable data and AI systems for target discovery, experiment design, and molecule design. A recent highlight: leading AI-driven image-analysis and efficacy pipelines that supported the world’s first animal-data-free FDA IND approval in oncology, using human vascularized organoid efficacy data.

Before Qureator, I co-founded and led Qritive, an AI pathology company. We built regulatory-approved cancer-diagnostics AI products, raised venture funding, expanded across Singapore, India, and the US, and partnered with organizations including Dell, GE, AWS, and Mayo Clinic. Qritive also became a founding member of the White House-supported Cancer Moonshot initiative.

The common thread in my work is practical scientific AI: systems that reduce weeks of analysis to days, make complex evidence easier to reason about, and help teams ask better questions. Sometimes that means deep learning on microscopy images. Sometimes it means knowledge graphs and AI agents. Sometimes it means the less glamorous but essential work of data governance, MLOps, and reliable infrastructure.

I also advise and consult on applied AI systems, especially in healthcare, biotech, computer vision, LLM/RAG applications, knowledge graphs, cloud products, and AI strategy.

I write with the Protocol Institute on World Machines, a project whose conceit is to invent psychohistory. My piece of that is the Prime Radiant: an attempt to make civilizational machinery visible enough that we can reason about what institutions might emerge next.

I also keep a couple of art-project-ish experiments running:

• Prime Radiant — structural prediction, psychohistory, and world-machine diagrams you can walk around in.
• Invisible Cities — an atlas of small-web sites reimagined as Calvino-style cities: real websites, described as places.

My academic background is in mechanobiology and computational image analysis: a PhD from the National University of Singapore, and M.Tech. and M.S. degrees from Savitribai Phule Pune University.

Outside the professional bio, this site is where I write about technology, books, leadership, music, family, startups, and whatever else I’m trying to understand.

Connect #

You can find me on LinkedIn, Bluesky, or by email at [email protected].