Apple Scholars in AI/ML Fellowship
2025-26 Cohort Call for Nominations
UC San Diego has been invited to submit nominations for the Apple Scholars in AI/ML PhD Fellowship. 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.
Award Details
All monetary gifts are disbursed to the nominating university annually at the beginning of each academic year and are conditional upon the Apple Scholar’s full-time enrollment with the university for the upcoming year to which the monetary gifts would apply and continuing to meet the eligibility criteria.
Gift amount covering full tuition and fees (enrollment fees, health insurance, and books) for two (2) academic years
Up to $40,000 USD gift each year to help with actual living expenses and related expenses
$5,000 USD gift each year to support research-related travel and associated expenses
Mentorship with an Apple researcher
Nominees must meet all of the following criteria to be considered:
Nominee must be enrolled full time at the nominating university at the start of Fall 2026, and expect to be enrolled through the end of the 2027/2028 academic year;
Nominee should be entering their last two to three (2-3) years of study as of Fall 2025; and
Nominee must not hold another industry-sponsored full fellowship while they are an Apple Scholar in AI/ML (Fall 2026 to Summer 2028).
Departments may submit multiple nominations. Departments should determine and approve as a whole which students will be nominated to avoid duplicate submissions and to ensure each nominee is a strong fit.
Nominations are reviewed and moved forward based on:
The strength of the research proposal
The impact the nominee has had on the field thus far (both as a researcher and community citizen)
And their demonstrated potential as a leader and collaborator in the field.
When reviewing the research proposal, we consider the following:
Clearly stated research issue and proposed direction
Novelty of the proposal
Scientific merit of the proposed approach, potential for impact, and alignment with research areas highlighted by Apple.
We also consider, in addition to the aforementioned research acumen, the unique perspective and experience each nominee brings to the field.
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. When entering a nomination, the nominator will be asked to select up to two (2) research areas that the nominee feels are most aligned with their work. There is no single research area that Apple prioritizes over another.
Research Area | Description |
|---|
Research Area | Description |
|---|---|
Privacy Preserving Machine Learning | Privacy Preserving Machine Learning focuses on developing techniques to analyze data without compromising the privacy of individuals. Sub topics: Federated Learning, Differential Privacy, Cryptographic Tools, Secure Multiparty Computation |
Human Centered AI | Human Centered AI seeks to design, develop, and deploy AI systems that are aligned with human values and needs.
|
AI for Ethics and Fairness | AI for Ethics and Fairness focusses on developing AI systems that are unbiased and ethical. Sub topics: Mitigating Bias, Fairness in AI, Interpretable AI, Introspection, Robustness |
AI for Accessibility | AI for Accessibility focuses on developing AI systems to help people with disabilities to interact with the world in new ways. Sub topics: Accessible User Experiences, Automatic Personalization/Adaptation, Interactions via New or Combined Modalities, Participatory Design with People with Disabilities |
AI for Health and Wellness | AI for Health and Wellness on developing AI systems to improve healthcare outcomes and promote personal wellness. Sub topics: Foundation models and LLMs for Health, ML and RL for Mobile Health, Time Series Representation Learning, Physiology-Informed Mach |
ML Theory | ML Theory focusses on understanding the mathematical foundations and theoretical properties of machine learning algorithms. Sub topics: Understanding ML, Generalization, Optimization, Foundations of Generative Models, Imbalanced Data Theory, Out-of-Distribution setting |
ML Algorithms and Architectures | ML Algorithms and Architectures focuses on developing new algorithms, models, and architectures to improve the performance and efficiency of machine learning. Sub topics: Foundation Models, Diffusion Models, Hallucinations, Debiasing Data and Models, Auto ML, Model Compression, Architecture / Search, Optimization, Model Representation, Interpretability, Large-Scale ML, Imbalanced Data, Unsupervised and Self Supervised Representation Learning |
Interactive ML and Agents | Interactive ML and Agents Interactive ML and Agents focuses on developing intelligent agents that can learn to interact with the physical world through trial-and-error learning. Sub topics: Inference and Resource Constrained ML, Embodied Foundation Models, Imitation Learning, Multi-Output Models, Reinforcement Learning, Hardware/Software Integration, Hardware Aware ML Training |
Speech and Natural Language | Speech and Natural Language focuses on developing algorithms and models to understand or generate spoken or written human language. Sub topics: Large Language Models, Conversational and Multi-Modal Interactions, Speech Recognition, Text to Speech, Machine Translation, Language Modeling and Generation |
Computer Vision | Computer Vision is a field that focuses on developing algorithms and models to analyze and interpret visual information captured through digital interfaces. Sub topics: 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 | Information Retrieval, Ranking and Knowledge focuses on developing algorithms and models to extract, organize and infer meaningful information from large amounts of data and serve such information to satisfy user needs. Sub topics: Knowledge Extraction, Knowledge Inference & Reasoning, Large-Scale Graph Data Management, Machine Learning and Data Systems Integration, Information Retrieval, Recommendation System |
Data-Centric AI | Data-Centric AI focuses on developing machine learning techniques to effectively manage, process, and analyze large volumes of data, while also ensuring data efficacy, efficiency, and fairness. Sub topics: Multi Modal Language Models, Data Efficacy, Data Efficiency, Data Generation, Data Fairness, Synthetic Data Generation, Dataset Creation, Data and Annotation, Active Learning, ML-Enabled Data Annotation, Augmentation and Curation, Transfer Learning with Limited Data, Unsupervised and Weakly-supervised Anomaly Detection, Synthetic Defect Generation and Simulation, Sim2real Transfer Learning |
Universities should submit the following materials for each student’s nomination:
• Student CV and publication list
• Research Abstract (200 word maximum)
• Research statement covering past work and proposed direction for next 2 years (4 page maximum, including citations, in a legible font size) clearly stating the hypothesis and expected contributions to the chosen research area. We recommend not including personally identifiable information in the main body of the research statement in order to maintain research statement clarity for reviewers of the redacted copy.
• 2 letters of recommendation, one from current advisor (1 page maximum per letter)
• Link to most recent published work (optional)
Nominee’s estimated tuition and fees (including enrollment, health insurance, and books) for the nominee’s 2025-2026 academic year (or PhD student bursary stipend, if regionally appropriate)
The Apple AI/ML Fellowship is a gift under unique Fund TBD. GEPA's Interim Dean, Judy Kim, serves as the PI on this award.
Since this is a gift, please work with GEPA's Financial Analyst and UCSD's Gift Processing Office to complete the appropriate award acceptance documents.
This fellowship should be used as per the intent of the Apple Fellowship written in the award letter.
Chart of Accounts:
Each awardee will receive a unique project tied to their financial unit under unique Fund TBD.
Once the project has been established, please request appropriate AIDID's in order to use the fellowship