Summary
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Rockstar๐
Top SchoolNobin Sarwar is a PhD student and Graduate Research Assistant at UMBC specializing in post-training optimization and system-level methods for trustworthy NLP and multimodal models. With a decade of experience, he blends hands-on research in federated learning, differential privacy, and compressive sensing with practical system design for privacy-preserving and robust AI. His prior work at UTRGV contributed to a $600k NSF grant and produced peer-reviewed publications on biometric presentation attack detection and privacy-preserving data publishing. Nobinโs focus spans both model-level techniques for LLMs/VLMs and distributed learning frameworks, signaling a rare combination of algorithmic depth and deployment awareness. Based in Washington, DC, he brings a research-first mentality to applied problems, often reframing system challenges to unlock provable privacy and trust guarantees. Colleagues note his ability to translate complex theory into reproducible systems that scale beyond toy datasets.
10 years of coding experience
2 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at The University of Texas Rio Grande Valley
UMBC