Ankita Pasad is a research scientist at NVIDIA with nine years of experience at the intersection of speech representation, semi-supervised learning, and multimodal spoken language understanding. Her work focuses on interpretability of pre-trained speech models and leveraging unlabeled data through semi-supervised and consistency-driven objectives, a thread that connects internships at Google (video-based spatio-temporal consistency for segmentation) and ASAPP (SLUE benchmark). She holds a PhD from Toyota Technological Institute at Chicago and dual B.Tech/M.Tech degrees from IIT Bombay in communications and signal processing, bringing strong theoretical grounding to applied problems. Based in California, she blends academic rigor with industry impact, often translating evaluation insights into benchmarks and practical model analysis. An underappreciated strength is her cross-domain fluency—applying ideas from vision and video consistency to advance robustness and interpretability in speech systems.
9 years of coding experience
Doctor of Philosophy Computer Science, Doctor of Philosophy Computer Science at Toyota Technological Institute at Chicago
A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
Contributions:48 pushes, 9 branches in 4 months
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