Core Insights - The article highlights the achievements of two Chinese scholars, Hu Keya and Li Zongyi, who have made significant contributions to the field of AI and machine learning, particularly in the context of their research at MIT under the guidance of Professor He Kaiming [1][3]. Group 1: Hu Keya's Achievements - Hu Keya, an undergraduate from Shanghai Jiao Tong University, has been involved in research at the Brain-Machine Interface Laboratory, focusing on AI applications in neuroscience to aid mental health [6][12]. - She has authored multiple high-impact papers, including one accepted at the EMBC conference and another at NeurIPS 2024, showcasing her contributions to self-supervised learning and AI for science [7][10]. - Hu Keya's team won the "Best Paper Award" at the ARC Prize 2024 competition, demonstrating her innovative approach in developing methods for data generation and model fine-tuning [10][12]. Group 2: Li Zongyi's Contributions - Li Zongyi, known for his work on the Fourier Neural Operator (FNO), has made significant strides in the application of neural operators to solve physical equations efficiently [15][18]. - His research has been widely recognized, with over 12,000 citations on Google Scholar, establishing him as a key figure in the AI for Science domain [18][20]. - Currently a postdoctoral researcher at MIT, Li Zongyi has accepted a position as an assistant professor at New York University, indicating his rising prominence in the academic field [20][22]. Group 3: He Kaiming's Research Focus - Professor He Kaiming has emphasized "AI for Science" as a primary research direction, aligning with the expertise of his newly joined team members, Hu Keya and Li Zongyi [23][24]. - His team, which includes six talented researchers, is positioned to make significant advancements in the intersection of AI and fundamental scientific research [23][24].

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].