Summary
Shounak Datta is a Senior Machine Learning Research Engineer with a decade of experience focused on making deep learning—especially LLMs and Vision Transformers—practical for deployment on memory- and compute-constrained devices. He combines academic rigor (PhD from the Indian Statistical Institute and an ICML 2023 first-author paper on few-shot learning that preserves data manifolds) with industry impact, having built low-latency, resource-efficient models at Arm and production-scale NLP agents at Amazon. His work spans model design, training, and optimization for edge scenarios, producing Pareto-efficient trade-offs between accuracy and latency. Earlier research includes medical imaging for glaucoma, causal inference methods, and robust ML techniques for noisy and imbalanced data, reflecting a breadth from theory to applied systems. Based in Austin, he brings both deep research credentials and hands-on engineering experience to bridge cutting-edge algorithms and real-world constrained deployments.
7 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at Indian Statistical Institute
Master of Engineering - MEng, Electrical, Electronics and Communications Engineering, Master of Engineering - MEng, Electrical, Electronics and Communications Engineering at Jadavpur University
BTech - Bachelor of Technology, Electrical, Electronics and Communications Engineering, BTech - Bachelor of Technology, Electrical, Electronics and Communications Engineering at Techno India
Bengali, English, Hindi