CS Colloquium speakers Speaker: Xiang Lisa Li, Stanford University Date: Monday, February 24 Time: 12:30pm EST Location: CS 105 Host: Sanjeev Arora Event page: https://www.cs.princeton.edu/events/controlling-language-models Register for live-stream online here: https://princeton.zoom.us/webinar/register/WN_HscNKtVWSfiVBw0zM6q_ag Title: Controlling Language Models Abstract: Controlling language models is key to unlocking their full potential and making them useful for downstream tasks. Successfully deploying these models often requires both task-specific customization and rigorous auditing of their behavior. In this talk, I will begin by introducing a customization method called Prefix-Tuning, which adapts language models by updating only 0.1% of their parameters. Next, I will address the need for robust auditing by presenting a Frank-Wolfe-inspired algorithm for red-teaming language models, which provides a principled framework for discovering diverse failure modes. Finally, I will rethink the root cause of these control challenges, and propose a new generative model for text, called Diffusion-LM, which is controllable by design. Bio: Xiang Lisa Li is a PhD candidate at Stanford University, where she is advised by Percy Liang and Tatsunori Hashimoto. Her research focuses on developing methods to make language models more capable and controllable. Lisa is supported by the Two Sigma PhD fellowship and Stanford Graduate Fellowship and is the recipient of an EMNLP Best Paper award. Speaker: Zhen Dong, University of California, Berkeley Date: Tuesday, February 25 Time: 12:30pm EST Location: CS 105 Host: Kai Li Event page: https://www.cs.princeton.edu/events/make-ai-more-accessible-and-run-faster Register for live-stream online here: https://princeton.zoom.us/webinar/register/WN_yrwYu8XfT2-UxV-2RVKGvg Title: Make AI More Accessible and Run Faster Abstract: LLMs and diffusion models have achieved great success in recent years. However, many AI models, particularly those with state-of-the-art performance, have a high computational cost and memory footprint. This impedes the development of pervasive AI in scenarios lacking sufficient computational resources (e.g., IoT devices, lunar rovers), requiring ultra-fast inference (e.g., AI4Science), or demanding real-time interaction under constrained computation (e.g., AR/VR, Embodied AI). Model compression (quantization, pruning, distillation, etc) and hardware-software co-design are promising approaches to achieving Efficient AI, which makes AI more accessible and run faster. In this talk, I will first introduce my work on 1) mixed-precision quantization based on Hessian analysis (HAWQv1v2, ZeroQ, Q-BERT) and 2) hardware-software co-design (HAWQv3, CoDeNet, HAO). Then I will talk about my ongoing and future works in the era of LLMs and GenAI, including SqueezeLLM, Q-Diffusion, efficient AI agent systems, advanced CoT distillation, efficient deep thinking for OpenAI-o1 and Deepseek-R1, etc. My research vision is that efficient AI is becoming indispensable both at the edge where increasingly powerful sensors with diverse modalities generate huge volumes of local data, and in the cloud where reducing costs is essential to bridge the speed gap between inference scaling laws and Moore’s law for hardware. Bio: Dr. Zhen Dong is currently a Postdoc at UC Berkeley. He obtained his Ph.D. from Berkeley AI Research advised by Prof. Kurt Keutzer. Before Berkeley, Zhen received B.E. from Peking University. Zhen’s research focuses on efficient AI, model compression, hardware-software co-design, and AI systems. Zhen has received Berkeley University Fellowship and SenseTime Scholarship. Zhen has published over 10 papers as the first or co-first author at top AI conferences. He won the best paper award at AAAI Practical-DL workshop, and Zhen is also a winner of the DAC 2024 PhD forum and CVPR 2024 doctoral consortium. Speaker: Yuke Zhu, University of Texas, Austin Date: Thursday, February 27 Time: 12:30pm EST Location: CS 105 Host: Jia Deng Event page: https://www.cs.princeton.edu/events/pathway-generalist-robot-autonomy-data-c... Register for live-stream online here: https://princeton.zoom.us/webinar/register/WN_p4RA6oDFTiCVquo7_GGJdg Title: Pathway to Generalist Robot Autonomy — A Data-Centric Approach Abstract: In an era of rapid AI progress, leveraging accelerated computing and big data has unlocked new possibilities to develop general-purpose AI models. As AI systems like ChatGPT showcase remarkable performance in the digital realm, we are compelled to ask: Can we achieve similar breakthroughs in the physical world — to create generalist robots capable of performing everyday tasks? In this talk, I will present my data-centric research principles and methodologies for building general-purpose robot autonomy in open-world environments. I will discuss my recent work on building compositional robot autonomy stacks with diverse data sources. I will also present a human-in-the-loop framework for trustworthy robot deployment and continual learning. Combining these advances with cutting-edge developments in humanoid robotics, I will outline a roadmap toward the next generation of autonomous robots. Bio: Yuke Zhu is an Assistant Professor in the Computer Science Department of UT-Austin, where he directs the Robot Perception and Learning (RPL) Lab. He also co-leads the Generalist Embodied Agent Research (GEAR) lab at NVIDIA Research, which builds foundation models for embodied agents in virtual and physical worlds, particularly for humanoid robots. He focuses on developing intelligent algorithms for generalist robots and embodied agents to reason about and interact with the real world. His research spans robotics, computer vision, and machine learning. He received his Master's and Ph.D. degrees from Stanford University. His work has won various awards and nominations, including the Best Conference Paper Award in ICRA 2019, 2024, the Outstanding Learning Paper Award at ICRA 2022, and the Outstanding Paper Award at NeurIPS 2022. He received the NSF CAREER Award and faculty awards from Amazon, JP Morgan, and Sony Research.