The annual Apple Doctor Scholarship is back to the draw!
Just now, Apple Machine Learning Research released the list of "Apple Scholars" who will receive doctoral scholarships in the field of artificial intelligence/machine learning in 2023.
This year, a total of 22 students won Apple scholarships, of which Chinese students accounted for half of the total, 11.
22 doctoral students were selected, half of whom were Chinese
Just now, Apple Machine Learning Research released the list of "Apple Scholars" who won doctoral scholarships in the field of artificial intelligence/machine learning in 2021.
Nominees are honored based on their innovative research, resumes as thought leaders and collaborators, and commitment to advancing their fields.
This fellowship program recognizes the contributions of computer science and engineering researchers at the graduate and graduate levels.
A total of 22 students were selected this year, of which 11 were Chinese PhD students.
Yutong Bai
Yutong Bai is a fourth-year Ph.D. student in computer science at Johns Hopkins University majoring in computer vision, supervised by Alan Yuille, the Bloomberg Distinguished Professor.
She has interned at Meta AI and Google Brain. Previously, she was a finalist for the CVPR Best Paper Finalist.
Her research aims to integrate knowledge into computer vision problems and build powerful AI with less supervision, enabling deep learning models to be safely deployed in the real world. To achieve this goal, she explores directions including representation learning, self-supervised learning, and scalable multimodal learning.
Yifan Jiang
Yifan Jiang is a fourth-year doctoral student at the University of Texas at Austin, under the tutelage of Professor Zhangyang (Atlas) Wang.
His research interests lie in the fields of generative models, neural rendering, and computational photography. His goal is to build machine intelligence that links generative models and neural rendering techniques for powerful applications in 2D and 3D scenes, including image editing/processing, view compositing, and 3D creation.
He received his undergraduate studies at Huazhong University of Science and Technology and has interned at Adobe (Marc Levoy's team), Google Research (GCam), Adobe (applied research team), and Bytedance AI Lab (CV Lab, USA).
Xisen Jin
Xisen Jin is a PhD student at the University of Southern California under the supervision of Professor Xiang Ren.
His research seeks to enable language models to continuously learn from the never-ending flow of text data. His research reduces the cost of updating models, making it easier to build personalized models and keep the knowledge in the models up to date.
He graduated from Fudan University with a bachelor's degree and has interned on the Bloomberg NLP Platform Team, AWS AI Lab, Snap, Microsoft Research Asia, and JD.com's Data Science Lab.
Yong Lin
Lin Yong is a PhD student at the Hong Kong University of Science and Technology, supervised by Zhang Tong, former director of Tencent AI Lab.
Lin Yong's research is at the intersection of causality, machine learning algorithms and theory. Specifically, his goal is to build stable models that can generalize to unseen test distributions.
Jessy Lin
Jessy Lin is a PhD student at the Institute of Artificial Intelligence at the University of California, Berkeley, supervised by Dan Klein and Anca Dragan.
She graduated from the Massachusetts Institute of Technology with a major in computer science and philosophy. Prior to that, she worked in research and products at Lilt, exploring these issues in the context of human-machine translation.
Her research focuses on building AI agents that use language to collaborate with people and learn from human feedback. Her research interests include interactive code-generating models, dialogue, and language-led reinforcement learning.
Jingping No
Jingping Nie is a fourth-year doctoral student in electrical engineering at Columbia University, supervised by Professors Xiaofan Jiang and Matthias Preindl. Graduated from Smith College with a bachelor's degree in engineering.
Her research focuses on the co-design of software and hardware for future human-centered intelligent and privacy-aware wearables in AI systems.
Yifu Qiu
Yifu Qiu is a PhD student in the ELIS programme at the University of Edinburgh and the University of Cambridge, supervised by Dr Shay Cohen, Professor Anna Korhonen and Dr Edoardo M. Ponti.
Previously, he obtained his Master's degree from the University of Edinburgh, working on text summarization and structured forecasting. He also worked as a research intern in Baidu NLP, conducting research on open-domain question answering and intensive search.
His research focuses on the field of language generation in multilingual environments, including improving basic capabilities such as generalization of models, small-shot learning, and controllability.
Zijie Jay Wang
Zijie J. Wang is a doctoral student at Georgia Tech, supervised by Polo Chau.
His research makes AI accountable, interpretable, and accessible by developing novel interactive interfaces that enable people to easily interact with machine language systems at scale.
Yi Wei
Yi Wei is a PhD student at Tsinghua University, supervised by Professor Lu Jiwen. He has worked at Xilinx, SenseTime, Microsoft Research Asia, ByteDance, and Gaussian Robotics.
His research interests are in 3D vision, with a particular focus on 3D reconstruction and 3D scene understanding. His work combines traditional geometry with recent developments in deep learning to help AI systems better understand the 3D real world.
Kaichao You
You Kaichao is a third-year doctoral student at the School of Software of Tsinghua University, under the tutelage of Professor Long Mingsheng.
His research aims to develop machine learning algorithms in open and dynamic environments, with a focus on transfer learning in machine learning, specifically adapting various forms of knowledge in deep learning from one source to another. His recent work includes domain adaptation, model adaptation, and model-centric adaptation.
Jiaqi Zhang
Jiaqi Zhang is a PhD student at MIT under the supervision of Caroline Uhler. He previously received his bachelor's degree in mathematics and statistics from Peking University.
Spurred by biomedical applications, her research aims to develop statistical and algorithmic foundations at the intersection of sequential decision-making and causality, or rather, the design of strategies for making decisions in a system created by latent causal rules.
Full list
Here is the full list of selected students:
Resources:
https://machinelearning.apple.com/updates/apple-scholars-aiml-2023