Introducing Apple Scholars in AIML

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