I work at the intersection of software engineering and research, solving challenging problems in ML systems and reinforcement learning.
Currently: Math & CS at Stanford · ML & genomics · agentic systems, infrastructure, and developer tools.
About
I'm a software engineer and researcher with experience building ML systems and working on research problems. I'm particularly interested in tackling difficult and challenging problems in machine learning systems and reinforcement learning—from architecture design to optimization and deployment.
Outside of work, I'm a huge football and basketball fan and a proud Wisconsinite. I love reading and am working on getting better at it; I'll be documenting my reading journey and insights on this website.
Selected Work
FiberFold: Predicting 3D Chromatin Organization
Worked on improving a deep learning model combining CNNs and transformers to predict 3D genome organization. Focused on model evaluation and refinement using Hi-C maps, developing metrics to assess prediction accuracy across different genomic regions. Also tackled other challenging genomics problems in the lab, working with single-molecule sequencing data to understand chromatin structure and function.
Agentic Systems for Enterprise
Built end-to-end agentic workflows for enterprise systems like Salesforce and Workday. Developed a vendor-agnostic multi-agent platform that provides a single view across IT architecture with enterprise-grade governance and secure execution.
Projects
Priori AI
Local intelligence for clinical confidence. Simplifies prior authorization by bringing AI to the point of care—runs locally on physicians' devices for maximum privacy while dramatically improving insurance approval workflows.
LiDAR Sim2Real Translation
Range-view diffusion model for translating synthetic LiDAR data to real-world distributions. Physics-aware approach incorporating beam angles, dropout patterns, and intensity falloff for improved sim-to-real transfer.
Paper Notes
Paper notes coming soon...
Writing
More writing coming soon...
Contact
The best way to reach me is by email. I'm always open to talking about ML research, infrastructure for agents, and interesting collaborations.
Email: krishs04@stanford.edu