Srijit Mukherjee — Associate Consultant

(I am actively looking for a Summer/Fall 26 Internship in Biomedical AI! - Find my CV below) - Hey |ʘ‿ʘ)╯, I’m Srijit, a 4th-year Ph.D. student in the Department of EECS at Penn State (contact: szm6596@psu.edu), collaborating with doctors and biomedical engineers at Harvard and Yale to develop interpretable AI systems for biomedical imaging and disease detection. Before Penn State, I completed my undergraduate and graduate studies in Statistics at the Indian Statistical Institute, Kolkata. My research lies at the intersection of AI, domain knowledge, and scientific interpretability. I design domain-enriched, interpretable AI systems, embedding expert knowledge directly into data acquisition, model design, and regularization. This not only enhances accuracy but also creates robust, low-data, and explainable AI solutions that bridge the gap between algorithms and real-world science. I thrive in collaborative environments because I genuinely enjoy winning as a team. During my Ph.D., I’ve been mentoring two Master’s students: Utkarsh and Yuchen on AI solutions for Volumetric Analysis in Hydrocephalus (now graduated) and Disease Grading in Inflammatory Bowel Disease (IBD), respectively. At a deeper level, I’m fascinated by how the mind, information, and intelligence express themselves through the language of mathematics and engineering. I love learning across domains and solving problems from first principles. I also find finance deeply intriguing, perhaps because it’s a dance of psychology, probability, and unpredictability, which are the things I believe align the most with how the world works around us. Since my school days, I’ve followed what I call a “First Principles + System Principles” approach: breaking problems down to their fundamentals, then rebuilding them within context. This mindset shapes how I prove, debug, and optimize, ensuring that nothing is missed and everything connects. This has been the only guiding principle to how I learn myself, how I solve problems, how I build projects, and how I teach concepts to my students. The simple way to implement this is to ask questions (why, how, what), break down a concept till you reach what you know (not memorize!), and then build it up again to the top from scratch. It is equivalent to creating a knowledge tree, and then doing a traversal from leaves to parents, and vice-versa. This gives me immense freedom from a huge memory load. Thanks for stopping by! Feel free to reach out. I am very sparsely available on LinkedIn. Please email me at szm6596@psu.edu if you want to get in touch with me.

Stackforce AI infers this person is a Biomedical AI expert with a strong focus on healthcare applications.

Location: State College, Pennsylvania, United States

Experience: 3 yrs 9 mos