Advancing Data Strategies for AI success

Nadine Chang is a senior research scientist at NVIDIA in the Autonomous Vehicles Applied Research Group. She obtained her PhD under Martial Hebert and Michael Tarr at CMU, where she focused on long-tail learning and vision language model applications. She was the recipient of the NSF Graduate Research Fellowship in 2020. Her current interests include multimodal language models (MLLMs) for data curation and pruning, pursuit of hallucination free and robust MLLMs, and data centric approaches for physical AI.

Maying Shen is a Senior Research Engineer at NVIDIA in the Autonomous Vehicles Applied Research Group, where she focuses on data-centric strategies for enabling robust and efficient physical AI systems. Her current work explores the use of multimodal large language models (MLLMs) to understand data value, guide data selection, and mitigate hallucinations. Previously, she worked extensively on model-centric efficiency techniques such as pruning, quantization, and distillation. Maying graduated with a Masters from Carnegie Mellon University, where she specialized in computer vision.

Zhiding Yu is a principal research scientist and research lead at the Learning and Perception Research Group, NVIDIA Research. Before joining NVIDIA, he received his Ph.D. in ECE from Carnegie Mellon University in 2017. His research interests include Transformers, foundation models, and multimodal LLMs, with their applications to building the next-generation general intelligence. He is a recipient of multiple best paper awards and challenge winners. At NVIDIA, he led numerous efforts to develop state-of-the-art multimodal LLMs, data engines and autonomous systems.

Yan Chang is a senior engineering manager and principal engineer at NVIDIA, where she leads the Isaac Robotics Mobility team. Her current work focuses on developing advanced cognition, navigation, and whole-body control technologies for embodied AI and robotics. She obtained her Ph.D. from University of Michigan.

Sugandha Sharma is a senior generative AI architect and scientist at NVIDIA, specializing in generative models for robotics and embodied AI. Prior to joining NVIDIA, she was a research scientist at Microsoft Research, where she worked on GPT-based gaming AI agents and their alignment with humans. She holds a PhD in theoretical neuroscience from MIT, where she developed generative AI agents for 3D spatial planning, a memory model without catastrophic forgetting and the first neural circuit model linking memory and spatial navigation, significantly advancing the theoretical understanding of brain circuits.

Kaleb Smith is a Senior Data Scientist at NVIDIA on the Higher Education and Research team. Based in Florida, he specializes in artificial intelligence, deep learning, and machine learning, supporting higher education research and collaborating with top-tier university researchers to advance AI/ML applications using NVIDIA technologies. He earned his Ph.D. in computer engineering from Florida Institute of Technology, with a focus on generative AI in the time series domain.