Sangeeta Srivastava is a research scientist at Meta with a decade of experience building energy-efficient deep learning systems for on-device intelligence, grounded in a PhD in Computer Science from The Ohio State University. Her research focuses on low-power models, multimodal and self-supervised learning, and physics-guided ML, with primary applications in acoustic event detection. She has blended academic rigor with industry impact through internships and roles at Facebook AI, Microsoft workshops, Micron, MediaTek, and NYU CUSP, applying model compression and quantization techniques for constrained devices. At Facebook she advanced multimodal embeddings and clustering methods to improve integrity workflows, reducing manual review overhead. Based in Seattle, she brings a rare mix of hands-on systems engineering and theoretical research, often translating publications into practical, low-latency implementations for edge devices. Outside core research, she has a background in full-stack product delivery and analytics, which helps her bridge prototype research and deployable solutions.
9 years of coding experience
2 years of employment as a software developer
Bachelor of Technology (B.Tech.), Electrical, Electronics and Communications Engineering, Bachelor of Technology (B.Tech.), Electrical, Electronics and Communications Engineering at Kalinga Institute of Industrial Technology, Bhubaneswar
Master’s Degree, Computer Science, Master’s Degree, Computer Science at The Ohio State University
Contributions:12 pushes, 1 branch in 4 years 9 months
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