Source | Machine Heart ID | almosthuman2014
Recently, JPMorgan Chase & Co. announced the winners of the 2023 Doctoral Scholarship. A total of 13 doctoral students won awards this year, including 8 Chinese doctoral students, accounting for more than 3/5.
The award was established in 2019. According to Manuela Veloso, director of AI research at JPMorgan Chase, they established the award to inspire the next generation of leading AI researchers. They hope to create an environment that can inspire researchers to produce transformative research with lasting impact in the community and across industry.
The following is the profile of the Chinese doctoral students who won the JP Morgan doctoral scholarship this year:
Dian Wang, Northeastern University
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Dian Wang is a fourth-year Ph.D. student in Khoury School of Computer Science, Northeastern University, supervised by Professor Robert Platt and Professor Robin Walters. He graduated from Sichuan University with a bachelor degree.
Dian Wang's research interests include machine learning and robotics. Recently, his research focuses on the intersection of geometric deep learning and robot learning, improving learning efficiency by applying equivariant learning methods to robotic manipulation.
Yuxi Wu, Georgia Institute of Technology
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Yuxi Wu is a PhD student in computer science at Georgia Institute of Technology under the supervision of Keith Edwards and Sauvik Das.
Yuxi Wu studies how to build collective action systems to protect user privacy. Specifically, Yuxi Wu employed a variety of design probes and hybrid approaches.
Carnegie Mellon University Ke Wu
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Ke Wu is a fourth-year Ph.D. student in the Department of Computer Science at Carnegie Mellon University under the supervision of Elaine Shi. She graduated from Fudan University with a bachelor degree.
Ke Wu's research interests lie at the intersection of cryptography, game theory, and data privacy. Her work specifically focuses on designing decentralized mechanisms with strong security guarantees and incentive compatibility. She also works on designing cryptographic primitives that provide privacy and robustness in federated learning.
Xiaoyuan Liu from UC Berkeley
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Liu Xiaoyuan is currently a doctoral student in EECS at UC Berkeley, and his supervisor is Professor Dawn Song. He graduated from the ACM (Honours) class of Shanghai Jiao Tong University.
His research focuses on the development of security and privacy protection systems through the implementation of advanced cryptography techniques, and the current projects are aimed at solving real-world security and privacy problems, which utilize homomorphic encryption, multi-party computation, zero-knowledge proof and Cutting-edge privacy enhancement technologies such as differential privacy.
Personal homepage: https://xiaoyuanliu.cn/
Imperial College Kang Gao
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Kang Gao is a doctoral student in the Department of Computer Science, Imperial College of Technology, under the tutelage of Professors Wayne Luk and Stephen Weston. He has a master's degree from Imperial College and a bachelor's degree from Shanghai University of Finance and Economics.
Kang Gao's research focuses on modeling financial markets and improving risk management practices in financial services. In his research, he takes a multidisciplinary approach covering areas such as artificial intelligence, agent-based modeling, and financial mathematics. Currently, Kang Gao focuses on agent-based modeling, working on developing innovative strategies for continuous monitoring and effective hedging of financial risks.
Academic homepage: https://scholar.google.com/citations?user=Q0W19ygAAAAJ&hl=en
Shicong Cen, Carnegie Mellon University
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Cen Shicong is a fourth-year doctoral student in the Department of Electrical and Computer Engineering at Carnegie Mellon University, under the tutelage of Professor Yuejie Chi. Graduated from Peking University in 2019 with a bachelor's degree.
Shicong Cen mainly focuses on reinforcement learning and game theory research. Through a deep understanding of the theory of policy optimization methods, he transforms heuristic methods into rigorous principles and inspires the design of new algorithms to achieve provably fast convergence. His research aims to develop efficient learning methods to improve the performance of reinforcement learning and game theory, and to provide a theoretical basis for further development in these fields.
Personal homepage: https://shicongc.me/
Northwestern University Chenkai Weng (Chenkai Weng)
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Weng Chenkai is currently a fourth-year doctoral student in computer science at Northwestern University, and his supervisor is Professor Xiao Wang. He received his Bachelor of Science degree from Xidian University in 2019.
Chenkai Weng's research interests lie in applied cryptography, with an emphasis on secure multi-party computation (MPC) and zero-knowledge proofs (ZKP). His previous work covers the design, analysis, and implementation of MPC (such as garbled circuits, oblivious transfer, and secret sharing-based protocols) and ZKP protocols (VOLE-based ZK and non-interactive ZK).
In addition, he is also working on applying cryptography-based privacy-enhancing techniques to databases, networking, formal verification, machine learning, healthcare, and Web3 systems to build secure systems.
Personal homepage: https://ckweng.github.io/
University of California, Los Angeles (UCLA) Shichang Zhang
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Shichang Zhang is a fourth-year doctoral student majoring in computer science at UCLA, supervised by Prof. Yizhou Sun. Before joining UCLA, he received a BS in statistics from UC Berkeley and an MS in statistics from Stanford University.
Shichang Zhang's research interests mainly focus on developing efficient and interpretable machine learning models, especially for graph-structured data. His research focuses on making large and black-box machine learning models accessible and trustworthy.
Personal homepage: https://shichangzh.github.io/
Reference link: https://www.jpmorgan.com/technology/artificial-intelligence/research-awards/phd-fellowship-2023