Summary
Moshir Harsh is a specialist postdoctoral research fellow with nine years of experience at the intersection of statistical physics, machine learning, and quantitative biology, now based in Boston. He develops AI-driven methods—including LLMs, VAEs, diffusion and flow-matching models—for early clinical disease risk assessment and multi-omics-driven discovery of cancer drug targets. His work spans end-to-end pipelines from LC-MS and sequencing data to uncertainty-aware inference and deployable surveillance programs in collaboration with hospitals and federal partners. Earlier research introduced memory-aware statistical physics tools for stochastic biochemical dynamics and shipping a Python package that demonstrated state-of-the-art performance. Moshir combines deep theoretical rigor with practical engineering to operationalize ML in biomedical settings, and his track record includes interdisciplinary projects across Max Planck, EMBL, DESY and Harvard/Broad. He is seeking opportunities to translate quantitative research into scalable solutions that uncover early disease signals and novel therapeutic strategies.
9 years of coding experience
1 year of employment as a software developer
Master of Science - MS, Physics, Master of Science - MS, Physics at Ecole normale supérieure
Bachelor of Science (BS), Physics, Bachelor of Science (BS), Physics at Indian Institute of Science (IISc)
Step by Step High School
Doctor of Philosophy - PhD, Theoretical Physics, Doctor of Philosophy - PhD, Theoretical Physics at The University of Göttingen
Hindi, German, English