北大校友、华人学者金驰新身份——普林斯顿大学终身副教授

Core Insights - Chi Jin, a Chinese scholar, has been promoted to tenured associate professor at Princeton University, effective January 16, 2026, marking a significant milestone in his academic career and recognition of his foundational contributions to machine learning theory [1][4]. Group 1: Academic Contributions - Jin joined Princeton's Department of Electrical Engineering and Computer Science in 2019 and has rapidly gained influence in the AI field over his six-year tenure [3]. - His work addresses fundamental challenges in deep learning, particularly the effectiveness of simple optimization methods like Stochastic Gradient Descent (SGD) in non-convex optimization scenarios [8][12]. - Jin's research has established a theoretical foundation for two core issues: efficient training of large and complex models, and ensuring these models are reliable and beneficial in human interactions [11]. Group 2: Non-Convex Optimization - One of the main challenges in deep learning is non-convex optimization, where loss functions have multiple local minima and saddle points, complicating the optimization process [12]. - Jin has demonstrated through multiple papers that even simple gradient methods can effectively escape saddle points with the presence of minimal noise, allowing for continued exploration towards better solutions [12][17]. - His findings have provided a theoretical basis for the practical success of deep learning, alleviating concerns about the robustness of optimization processes in large-scale model training [18]. Group 3: Reinforcement Learning - Jin's research has also significantly advanced the field of reinforcement learning (RL), particularly in establishing sample efficiency, which is crucial for applications with high interaction costs [19]. - He has provided rigorous regret bounds for foundational RL algorithms, proving that model-free algorithms like Q-learning can maintain sample efficiency even in complex settings [22]. - This theoretical groundwork not only addresses academic inquiries but also guides the development of more robust RL algorithms for deployment in high-risk applications [23]. Group 4: Academic Background - Jin holds a Bachelor's degree in Physics from Peking University and a Ph.D. in Electrical Engineering and Computer Science from the University of California, Berkeley, where he was mentored by renowned professor Michael I. Jordan [25]. - His academic background has equipped him with a strong foundation in mathematical and analytical thinking, essential for his theoretical research in AI and machine learning [25]. Group 5: Recognition and Impact - Jin, along with other scholars, received the 2024 Sloan Award, highlighting his contributions to the field [6]. - His papers have garnered significant citations, with a total of 13,588 citations on Google Scholar, indicating the impact of his research in the academic community [27].