Apple Scholars is a program created to recognize the contributions of emerging computer and engineering leaders at graduates and third cycle. As part of Apple researchers, Apple is proud to announce the recipients of doctoral scholarships in AIML. In recognition of these exceptional doctoral students, everyone will receive support for their research and their university trips for two years, internship opportunities and two -year mentoring with an Apple researcher in their field. Nominated students were selected according to their innovative research, recordings as opinion leaders and employees in their fields and a unique commitment to make risks and push the limits of automatic learning and AI.
Apple is delighted to amplify the search for advanced automatic learning worldwide, covering a wide range of subjects, including health, device and private automatic learning, human -centered design, etc. Apple selected researchers advance the field of automatic learning and AI to push the limits of what is possible, and Apple undertakes to support the community of university research and their invaluable contributions to the world.
The doctoral scholarship in Aiml is currently open to invited institutions. For more information, please send an email Aiml_scholars@apple.com.
Ishwarya Annanthabhotla
Massachusetts Institute of Technology
Speech and natural language
YAHAV BECHAVOD
Hebrew University of Jerusalem
Privacte preserving learning
Graham Gobieski
Carnegie Mellon University
On the appliance learning
Mitchell Gordon
University of Stanford
Automatic learning focused on humans
Jaya Narain
Massachusetts Institute of Technology
IA for health and well-being
Jeong Joon Park
Washington University
Augmented reality and computer vision
Yang song
University of Stanford
Fundamentals of automatic learning
Xinyi Wang
Carnegie Mellon University
Speech and natural language
Yifan Wang
Eth Zurich
Augmented reality and computer vision
Bingzhe Wu
Beijing University
Privacte preserving learning
Yiren “Aaron” Zhao
University of Cambridge
On the appliance learning
Tijana Zrnic
University of California, Berkeley
Fundamentals of automatic learning