Author | Sisi
Editor | Chen Caixian
Not long ago, IBM’s official website announced the list of winners of the 2022 IBM Doctoral Scholarship: a total of 19 students around the world have won awards, of which 8 are Chinese students, nearly half.
According to IBM, the IBM Doctoral Scholarship was established in 1951. The IBM Doctoral Scholarship is mainly for doctoral students from research fields such as artificial intelligence, hybrid cloud technology, quantum computing, data science, security, etc. It has been 71 years and has supported Thousands of outstanding doctoral students from around the world.
The following is the introduction of the winning students:
Bo Zhang
Bo Zhang graduated from Shanghai Jiaotong University with a bachelor’s degree in electrical engineering in 2018, and is currently studying for a doctorate in EE at Columbia University. The fourth generation disciple of Niu Hu Zhengming.
His research focuses on computer architectures applied to machine learning algorithms, as well as ultra-low-power large-scale integrated circuits and system design. In recent years, the communication between artificial intelligence and large-scale integrated circuits has become more and more close, and VLSILab, to which Bo Zhang belongs, has also done a lot of work in related directions. Currently, he has published 5 research papers in the field of integrated circuits.
Catherine Chen
Catherine Chen graduated from Princeton University with a bachelor’s degree in computer science in 2018, with a minor in neuroscience, statistics and machine learning, and applied mathematics, and then did a year of research on causal reasoning and neuroscience at the University of Munich, Germany. Studying for a PhD in EECS at UC Berkeley, under the tutelage of NLP master Dan Klein (just won the ACL 2022 Best Paper Award) and Jack Gallant.
She is affiliated with Berkeley’s famous artificial intelligence laboratory-BAIR laboratory, and her doctoral career was funded by the NSF Graduate Research Fellowship. Her main research interests are the intersection of natural language processing, perceptual neuroscience and machine learning. Previously, she interned at Google and Airbnb.
Personal homepage: https://ift.tt/R0L4USA
Ching-Yao Chuang
Ching-Yao Chuang graduated from National Tsing Hua University in Taiwan with a bachelor’s degree in electrical engineering in 2017. He is currently studying for a Ph.D. in EECS at MIT, under the tutelage of Stefanie Jegelka and Antonio Torralba.
According to reports, his research goal is to build a robust and general machine learning system with minimal supervision. In addition, he is particularly interested in representation learning, unsupervised learning and statistical learning theory. In addition to his school work, he has interned in the machine learning group of the University of Toronto, IBM’s artificial intelligence research group and Microsoft Research. He has published ten papers at the International Artificial Intelligence Summit, of which seven papers have been published. .
Personal homepage: https://chingyaoc.github.io
Fangzheng Xu
Fangzheng Xu received a bachelor’s and master’s degree in computer science from Shanghai Jiaotong University in 2016 and 2019, respectively. The master’s tutor is Zhu Qili. He is currently studying for a doctorate at the Institute of Language Technology at Carnegie Mellon University (the world’s No. 1 NLP), under the tutelage of Graham Neubig. . Previously, Xu worked as an intern at Microsoft Research Asia, Microsoft Redmond Headquarters and Google Research.
According to his personal homepage, Fangzheng Xu’s current research content combines natural language and software engineering. The research goal is to enable machine learning systems to capture the procedural and intent semantics of natural language commands, and to combine these two distinct domain languages in a Together, users are expected to use natural language to guide computers in the future. At present, Xu has published more than 20 papers at top conferences and was nominated for the best poster in WWW 2018.
Personal homepage: https://frankxfz.me
Ka Ho Chow
Ka Ho Chow graduated with a bachelor’s degree and a master’s degree in computer science from Hong Kong Science and Technology. He has done scientific research in the Multimedia Technology Research Center of the Hong Kong University of Science and Technology. His supervisor is S.-H. Gary Chan. Ling Liu is affiliated with the Distributed Data Intensive Systems Laboratory (DiSL). Previously, he worked in the storage systems research group of the Almaden branch of IBM Research, promoting the implementation of machine learning on industrial systems.
His research goals are to make applied machine learning robust, privacy-preserving, and trustworthy. At present, he has published 8 related works and won the best paper award at the 2020 ACM Symposium on Edge Computing.
Personal homepage: https://khchow.com
Yangruibo Ding
Yangruibo Ding graduated from the University of Electronic Science and Technology of China in Chengdu, and is currently studying for a CS PhD at Columbia University, under the tutelage of Baishakhi Ray and Gail Kaiser. Previously intern at IBM Research Institute.
His main research interests are machine learning applied to software engineering automation, with particular interest in self-supervised source code and deep learning applied to program analysis. Currently, he has published about ten conference and journal papers in related fields.
Personal homepage: https://robin-y-ding-columbia.github.io
Yufeng Bright Ye
Yufeng Ye graduated with a bachelor’s degree in engineering physics from the University of Toronto, and is currently pursuing a Ph.D. at MIT under the tutelage of Kevin P. O’Brien. At present, he has completed nearly 20 research papers in related fields.
Zexue He
Zexue He graduated from Beijing Normal University with a bachelor’s degree in computer science in 2019, and is currently pursuing a PhD in CS at the University of California, San Diego, under the tutelage of Julian McAuley. Previously intern at Google and Microsoft Research Asia. Her research interests include debiasing and fairness in NLP, and trustworthy machine learning. At present, she has published 9 papers in related fields. In addition, it is worth noting that she has participated in ACM/ICPC competitions many times and won silver and bronze medals. Personal homepage: https://zexuehe.github.io
Reference link:
https://ift.tt/Wgpe6uI 
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