Apple Scholars in AI/ML

Please see the full solicitation for complete information about the funding opportunity. Below is a summary assembled by the Research & Innovation Office (RIO). Given Apple鈥檚 institutional limit of three nominees, each college or institute may nominate only two students as part of the internal competition.

Program Summary听

The Apple Scholars in AI/ML PhD fellowship program recognizes the contributions of emerging leaders in computer science and engineering at the graduate and postgraduate level. The PhD fellowship in AI/ML was created as part of the Apple Scholars program to support the work of outstanding PhD students from around the world, who are pursuing cutting edge research in machine learning and artificial intelligence.

Nominees should be pursuing research in one or more of the following research areas. The subtopics listed under each research area are not meant to be exhaustive or prescriptive, but rather highlight areas of particular interest to Apple.

Privacy Preserving Machine Learning

  • Federated Learning, Differential Privacy, Cryptographic Tools, Secure Multiparty Computation

Human Centered AI

Social Signal Processing, ML for Multimodal Interaction, ML Design and Human Factors, Usable ML Tools and Products, Interactive ML, Model Personalization, Human-in-the-loop ML

AI for Ethics and Fairness

  • Bias and Fairness in AI, Interpretable AI, Introspection, Robustness

AI for Accessibility

  • Accessible User Experiences, Automatic Personalization/Adaptation, interactions via New or Combined Modalities, Participatory Design with People with Disabilities

AI for Health and Wellness

  • ML and RL for Mobile Health, Time Series Representation Learning, Physiology- Informed Machine Learning, Modeling Multi-Modal Sensor Data, Causal modeling, Human behavior

ML Theory

  • Understanding ML, Generalization, Physics-based ML, Generative Models, Imbalanced Data Theory, Out-of-Distribution setting

ML Algorithms and Architectures

  • Auto ML, Model Compression, Architecture / Search, Optimization, Model Representation, Interpretability, Large-Scale ML, Imbalanced Data, Unsupervised and Self Supervised Representation Learning

Embodied ML

  • Imitation Learning, Multi-Output Models, Reinforcement Learning for Embodied ML, Hardware/Software Integration, Hardware Aware ML Training, Inference and Resource Constrained ML

Speech and Natural Language

  • Speech Recognition, Text to Speech, Conversational and Multi-Modal Interactions, Machine Translation, Language Modeling and Generation

Computer Vision

  • Semantic scene understanding, Video understanding , 3D scene understanding , Efficient Deep learning for computer vision, AI for content creation, Continual learning , Computer vision for AR/VR, Computer vision with Synthetic data , Language and vision, Computational photography, Vision for Robotics, Foundation model for industrial machine vision, Vision for industrial robotics

Information Retrieval, Ranking and Knowledge

  • Knowledge Extraction and Information Retrieval, Knowledge Inference, Larg