Apple Scholars is a program created to recognize the contributions of emerging leaders in computer science and engineering at the graduate and postgraduate level. As part of Apple Scholars, Apple is proud to announce the recipients of PhD fellowships in AIML. In recognition of these outstanding PhD students, each will receive support for their research and academic travel for two years, internship opportunities, and a two-year mentorship with an Apple researcher in their field. Nominated students were selected based on their innovative research, record as thought leaders and collaborators in their fields, and unique commitment to take risks and push the envelope in machine learning and AI.
Apple is excited to amplify cutting-edge machine learning research worldwide, covering a wide range of topics including health, on device and private machine learning, human centered design, and more. The selected Apple Scholars are advancing the field of machine learning and AI to push the boundaries of what’s possible, and Apple is committed to supporting the academic research community and their invaluable contributions to the world.
The PhD fellowship in AIML is currently open to invited institutions. For more information, please email aiml_scholars@apple.com.
Ishwarya Ananthabhotla
Massachusetts Institute of Technology
Speech and Natural Language
Yahav Bechavod
The Hebrew University of Jerusalem
Privacy Preserving Machine Learning
Graham Gobieski
Carnegie Mellon University
On Device Machine Learning
Mitchell Gordon
Stanford University
Human Centered Machine Learning
Jaya Narain
Massachusetts Institute of Technology
AI for Health and Wellness
Jeong Joon Park
University of Washington
Augmented Reality and Computer Vision
Yang Song
Stanford University
Fundamentals of Machine Learning
Xinyi Wang
Carnegie Mellon University
Speech and Natural Language
Yifan Wang
ETH Zurich
Augmented Reality and Computer Vision
Bingzhe Wu
Peking University
Privacy Preserving Machine Learning
Yiren “Aaron” Zhao
University of Cambridge
On Device Machine Learning
Tijana Zrnic
University of California, Berkeley
Fundamentals of Machine Learning