Shounak Datta
Summary
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SeniorShounak 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.
8 years of coding experience
Github Skills (11)
oversampling7
nlp4
algorithm4
pytorch4
quantization4
natural-language-processing4
large-language-models4
inference3
deep-learning3
transformer3
transformers3
Programming languages (1)
Github contributions (5)
Shounak-D/Shounak-D.github.io
Jul 2018 - Sep 2025
Contributions:186 pushes, 1 branch in 7 years 3 months
Shounak-D/LexiBoost
Jan 2019 - May 2021
Contributions:6 pushes, 1 branch in 2 years 4 months
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