AI大牛何恺明的主页，更新了两名新弟子的信息—— 都是华人，也都是学霸履历，博士生「胡珂雅」+博士后「李宗宜 」。 至此，何恺明任教MIT以来招募的6位学生中，5名都是中国面孔。 而这一次新入组的两位中国成员，也都一路闪光。 上交「胡珂雅」 胡珂雅，本科毕业于上海交通大学。 高中时期，她就读于名声赫赫的重点中学福建师范大学附属中学。 2021年，胡珂雅入学上交知名的ACM班，修读计算机科学方向。 根据上海交大致远学院公众号，自大三起，胡珂雅便成为了上海交通大学脑机接口实验室（BCMI）的一员，师从郑伟龙教授。 那段时间，她把研究方向定在AI for Science——希望能将AI与脑科学结合，通过自监督学习去处理原始脑电信号，从而帮助抑郁症患者及其他受精神健 康困扰的群体。 经过几个月的打磨，她以第一作者完成了名为《Contrastive Self-supervised EEG Representation Learning for Emotion Classification》的论文。 这篇成果被国际生物医学计算机顶会EMBC接收，她也因此受邀赴美做口头报告。 与此同时，她还作为共同作者参与了一篇改进自监督 ...

Core Insights - The article highlights the recruitment of two new Chinese students, Hu Keya and Li Zongyi, by AI expert He Kaiming at MIT, emphasizing their impressive academic backgrounds and contributions to the field of AI [1][4]. Group 1: Hu Keya's Background and Achievements - Hu Keya graduated from Shanghai Jiao Tong University and was involved in the Brain-Machine Interface Laboratory, focusing on AI applications in neuroscience [5][7]. - She authored a paper on self-supervised EEG representation learning, which was accepted at the EMBC conference, and presented her work in the U.S. [8][10]. - Hu participated in a project that improved self-supervised learning, leading to a paper accepted at the Cognitive Science 2025 conference [10]. - During her undergraduate studies, she interned at Cornell University, contributing to a project on program synthesis and code repair, resulting in a paper accepted at NeurIPS 2024 [11][12]. - Hu Keya led her team to win the "Best Paper Award" at the ARC Prize 2024 competition, showcasing her innovative approach to AI problem-solving [15][17]. - By the end of her undergraduate studies, she had published four high-impact papers, making her a highly sought-after candidate for PhD programs, ultimately choosing MIT [21][22]. Group 2: Li Zongyi's Contributions - Li Zongyi, known for his work on the Fourier Neural Operator (FNO), published a significant paper during his PhD that enabled the large-scale application of neural operators [27][29]. - The FNO allows neural networks to learn solutions to physical equations efficiently, significantly improving computational speed in various scientific applications [30][34]. - Li Zongyi's research has made him a key figure in the field of neural operators, with over 12,000 citations of his work [36]. - Currently, he is a postdoctoral researcher at MIT and is set to join New York University as an assistant professor in the upcoming fall [38][39]. Group 3: He Kaiming's Research Focus - He Kaiming has indicated that "AI for Science" will be a primary focus of his research in the coming years, aligning with the expertise of his newly recruited team members [46][48]. - The combination of Hu Keya's background in neuroscience and Li Zongyi's expertise in neural operators strengthens the team's capabilities in advancing AI applications in scientific research [48][49].

Core Insights - A collaborative team from ETH Zurich, Caltech, and the University of Alberta has developed a deep learning framework named NOBLE, which is the first scalable framework validated by human cortical experimental data, achieving a simulation speed 4,200 times faster than traditional numerical solvers [1][3][15]. Group 1: Framework Overview - NOBLE stands for Neural Operator with Biologically-informed Latent Embeddings, designed to learn the nonlinear dynamics of neurons directly from experimental data [2][3]. - The framework constructs a unified "neural operator" that maps the continuous latent space of neuron features to a set of voltage responses without the need for separate training for each model [3][10]. Group 2: Data and Methodology - The research team created a specialized dataset containing PVALB neuron data, which includes 60 Hall of Fame (HoF) models, with 50 for training and 10 for testing, optimized through a multi-objective evolutionary framework [6][8]. - The data generation process involved a two-phase optimization strategy to fit passive subthreshold responses and capture active dynamics above spike threshold [8][12]. Group 3: Performance Evaluation - NOBLE demonstrated a relative L2 error as low as 2.18% in basic accuracy tests, indicating its ability to predict voltage trajectories closely aligned with experimental data [15][17]. - In terms of generalization, NOBLE maintained high prediction accuracy when tested on 10 unseen HoF models, showcasing its capability to learn cross-cell type electrophysiological patterns [17][18]. - The framework's computational efficiency is groundbreaking, with voltage trajectory predictions taking only 0.5 milliseconds compared to 2.1 seconds for traditional solvers, enabling real-time simulations of large neural networks [17][18]. Group 4: Innovation and Applications - NOBLE's ability to interpolate and generate new neuron models based on known features highlights its potential for innovative applications in neuroscience [18][20]. - The integration of neural operators with biological embeddings is fostering a synergy between academic research and industrial applications, enhancing the efficiency and physiological realism of neural simulations [21][24].