Summary
Sudhanva Lalit is a multidisciplinary postdoctoral researcher and data scientist with 11 years of experience applying advanced mathematical physics, machine learning, and software engineering to problems in nuclear astrophysics and transport modeling. Based at Michigan State University’s FRIB, he develops reaction networks and model-order-reduction techniques to study cooling in accreting neutron star crusts while parsing large nuclear data sets for reaction and decay rates. He combines deep theoretical training (PhD in Physics, MSc in Mathematics) with practical tooling—Python packages released on PyPI, ML pipelines (CNNs, Random Forests), and apps for teaching physics—bridging research and reproducible software. Comfortable communicating with senior stakeholders and students alike, he pairs expertise in differential equations and topology with hands-on coding in Python, Fortran and C++; an uncommon strength is his track record of shipping domain-specific libraries and educational simulators that translate complex theory into usable tools.
11 years of coding experience
Master of Science (M.Sc.), Nuclear Physics, First Class, Master of Science (M.Sc.), Nuclear Physics, First Class at The M. S. University of Baroda
Bertelsmann Tech Scholarship Challenge Course - AI Track Nanodegree Program, Artificial Intelligence, Bertelsmann Tech Scholarship Challenge Course - AI Track Nanodegree Program, Artificial Intelligence at Udacity
Master of Science (MSc), Mathematics, Master of Science (MSc), Mathematics at IIT Bombay
Doctor of Philosophy - PhD, Physics, Doctor of Philosophy - PhD, Physics at Ohio University
High School Diploma, Science, High School Diploma, Science at Rosary High School
English, Hindi, Marathi, Gujarati, Sanskrit, German, fortran, python