جستجوی استاد راهنمای دانشگاهی

Jason Liمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Algorithms Design
• Computational Complexity
• +۳ مورد دیگر

Jason Li is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. He specializes in robotics and artificial intelligence, focusing on theoretical foundations of algorithms and complexity. His research emphasizes robotic manipulation, simulation frameworks, and perception systems for real-world applications. He teaches advanced courses including 15-750 (Algorithms), 15-754 (Advanced Algorithms), 15-451 (Algorithm Design and Analysis), 15-651 (Algorithms in the Real World), and 15-850 (Special Topics in Robotics) , reflecting his expertise in algorithmic theory and practical implementation. Key research contributions include frameworks like ORBIT for surgical robotics and DexWild for dexterous human-robot interaction. His work bridges simulation and real-world deployment, with a focus on synthetic data (Synthetica) and physically consistent perception models. He currently advises PhD students Henry Fleischmann and George Li. His teaching spans both undergraduate and graduate levels, emphasizing hands-on projects and theoretical rigor.

Tai-Sing Leeمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Neuroscience
• Computer Vision
• +۵ مورد دیگر

Dr. Tai-Sing Lee is a Professor in the Computer Science Department at Carnegie Mellon University, with affiliations in the Center for the Neural Basis of Cognition (CNBC) and the Machine Learning Department. He holds adjunct roles at the University of Pittsburgh's Neuroscience Department. His research focuses on computational neuroscience, visual perception, and the intersection of biological and machine intelligence. Lee earned his S.B. from Harvard (1986), and dual Ph.D.s from Harvard (1993) and MIT (1993), followed by postdoctoral training at Harvard and MIT. He leads the Lee Lab for Biological and Machine Intelligence Research and directs programs such as the Peking University-CMU and Tsinghua-CMU summer programs in Computer Science. His research investigates computational principles of visual perception, leveraging neurophysiological, mathematical, and machine learning approaches. Key areas include statistical modeling of neural codes, learning/adaptation mechanisms in neural systems, and Bayesian inference frameworks. Lee has developed novel machine learning techniques for analyzing neural data, including studies on V1/V2/V4 neural coding and hierarchical perceptual inference. Lee has advised numerous Ph.D., M.S., and undergraduate students, many of whom pursued academic or tech careers. His honors include the NSF CAREER Award (2000) and ICCV Helmholtz Prize (2013). His lab’s projects include the MICrONS initiative to reverse-engineer brain algorithms and collaborations on predictive coding models in vision and music perception.