Shantanu Ghosh is a PhD candidate in Electrical Engineering at Boston University specializing in representation learning, explainable AI, and robust multimodal models, with a focus on detecting and mitigating systematic neural network errors. He combines nine years of industry-grade software engineering experience with academic research, publishing in top venues such as ICML, ACL, MICCAI, JAMIA and Radiology: AI, and has interned at Amazon Science exploring failures in self-supervised models. His work bridges medical imaging, causal inference, and deep learning—often collaborating across BU’s medical campus and prior labs at Pitt and UF—bringing practical domain awareness to algorithmic robustness. A former senior software developer, he is fluent in production tooling and ML stacks (Python, PyTorch, TensorFlow) and has a knack for turning theoretical insights into reproducible, deployable solutions. Notably, he followed his advisor’s lab relocation from Pitt to BU, reflecting adaptability and continuity in long-term research programs.
9 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy - PhD, Artificial Intelligence, Doctor of Philosophy - PhD, Artificial Intelligence at University of Pittsburgh
Master of Science - MS, Computer Science, 3.88/4, Master of Science - MS, Computer Science, 3.88/4 at University of Florida
PCHE Cross-Registered Machine Learning Department, Robotics, 3.83/4, PCHE Cross-Registered Machine Learning Department, Robotics, 3.83/4 at Carnegie Mellon University
Doctor of Philosophy - PhD, Electrical Engineering, Doctor of Philosophy - PhD, Electrical Engineering at Boston University
Contributions:21 commits, 19 pushes, 1 branch in 2 months
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