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The Apple Scholars program was created to recognize and support PhD students in Computer Science and related areas who are pursuing research in artificial intelligence and machine learning, with a unique focus on work that is related to Apple’s core values.
The Apple PhD Fellowship in Artificial Intelligence and Machine Learning program supports emerging leaders in the computational sciences. Eligible fields include computer science, engineering, statistics, informatics, and related disciplines.
Each department may submit up to four nominations to the Graduate College. Each university is limited to 3 nominations.
The fellowship offers two years of support, which includes:
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- Gift amount covering full tuition and fees (enrollment fees, health insurance) for (2) academic years
- $40,000 USD gift each year to help with living expenses and related expenses
- $5,000 USD gift each year to support research-related travel and associated expenses
- 2 year mentorship with an Apple researcher
- Internship opportunities for one or both summers of their fellowship*
- Invitation to the PhD Scholars Summit in Cupertino, CA**
* Internship offers are dependent on student status, and contingent upon necessary requirements foremployment being met according to relevant employment law.
**Due to evolving travel restrictions related to COVID-19, the Summit will be postponed until it is safe for all scholars to travel internationally
The fellowship focuses exclusively on the following research areas:
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- Privacy Preserving Machine Learning (Federated Learning, Differential Privacy, Cryptographic Tools, Secure Multiparty Computation)
- On Device Machine Learning (Model Compression, Model Representation, Hardware/Software Integration, Model Personalization)
- Human-Centered Machine Learning (Social Signal Processing, ML for Multimodal Interaction, ML Design and Human Factors, Usable ML Tools and Products, Interactive ML)
- AI for Health and Wellness (ML and RL for Mobile Health, Time Series Representation Learning, Physiology- Informed Machine Learning, Modeling Multi-Modal Sensor Data)
- AI for Accessibility (Accessible User Experiences, Automatic Personalization/Adaptation, interactions via New or Combined Modalities, Participatory Design with People with Disabilities)
- AI Ethics and Fairness (Bias and Fairness in AI, Interpretable AI, Introspection)
- Speech and Natural Language (Speech Recognition, Text to Speech, Conversational and Multi-Modal Interactions, Machine Translation)
- Knowledge Graph Generation and Database Systems (Knowledge Extraction and Information Retrieval, Knowledge Inference, Large-Scale Graph Data Management, Machine Learning and Data Systems Integration)
- AI for Autonomous Systems (Reinforcement Learning, Imitation Learning, Multi-Output Models, Imbalanced Data)
- Augmented Reality and Computer Vision (3D semantic scene understanding, SLAM and Relocalization, Neural Network Architectures for 3D, Computational Photography and Videography, Visual Representative Learning, Active Sensing)
- Fundamentals of Machine Learning (Deep Learning, Reinforcement Learning, Unsupervised & Self-Supervised Learning, Optimization, Interpretability)
- AI and Manufacturing (Hardware Aware AutoML, Few-Shot Learning, Weakly-Supervised / Semi-Supervised Learning)
Eligibility
- Applicants must be full-time doctoral students pursuing research in one of the topical areas listed above.
- Applicants must have either two or three years remaining in their graduate program. Students with less than two years, or more than three years, remaining in their graduate program are not eligible.
- Students of any citizenship/nationality are eligible.
- Note: A maximum of only two nominations from a given university may be for students who do NOT self-identify as a member of a group traditionally underrepresented in the field of computing (see paragraph below).
- Applicants may NOT be supported at the time of application by another industry-sponsored full fellowship.
- A university may submit up to five nominations. However, if more than two are submitted, then in order to increase opportunities for students who are underrepresented in the field of computing, additional nominees must self-identify as a woman, non-binary, Black/African American, American Indian/Alaskan Native, Hispanic/Latinx, Native Hawaiian/Pacific Islander, and/or person with a disability. In other words, if a university chooses to nominate more than two nominees, then the third, fourth, and fifth nominees must self-identify with one of the underrepresented groups mentioned above.
- A campus review panel assembled by the Graduate College will select up to five finalists for consideration in the national competition.
A university may submit up to three nominations. However, if more than two are submitted, then in order to increase opportunities for students who are underrepresented in the field of computing, additional nominees must self-identify as a woman, non-binary, Black/African American, American Indian/Alaskan Native, Hispanic/Latinx, Native Hawaiian/Pacific Islander, and/or person with a disability. In other words, if a university chooses to nominate more than two nominees, then the third, fourth, and fifth nominees must self-identify with one of the underrepresented groups mentioned above.
A campus review panel assembled by the Graduate College will select up to five finalists for consideration in the national competition.
Please submit applications via Blackbaud by Thursday, September 16, 2021 at 11:59 PM. Contact Hannah Gorrie with any questions.
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