Internal CS Deadline: 9/10/20 11:59P
Grad College Deadline: September 16, 2020 by 5:00P
Apple Deadline: 9/30/2020

Apple PhD Fellowship in AI/ML (Fall 2020)	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 5 nominations. The fellowship offers two years of support, which includes: $40,000 annual stipend. Full coverage of tuition and fees. $5,000 travel grant each year. 2-year mentorship with an Apple researcher. Internship offer for one or both summers of the fellowship. Invitation to the PhD Scholars Summit in Cupertino, CA (may be held virtually because of Covid-19). The fellowship focuses exclusively on the following research areas: 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) Additional Information Links: Grad College Fellowship Finder database: https://apps.grad.illinois.edu/fellowship-finder/index.cfm?action=main.fellowship&fid=4868 Read about the first cohort of Apple Scholars: https://machinelearning.apple.com/updates/introducing-apple-scholars-aiml Learn more about Apple's work in AI/ML: https://machinelearning.apple.com/

Apple PhD Fellowship in AI/ML (Fall 2020)

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 5 nominations.

The fellowship offers two years of support, which includes:

- $40,000 annual stipend.
- Full coverage of tuition and fees.
- $5,000 travel grant each year.
- 2-year mentorship with an Apple researcher.
- Internship offer for one or both summers of the fellowship.
- Invitation to the PhD Scholars Summit in Cupertino, CA (may be held virtually because of Covid-19).

The fellowship focuses exclusively on the following research areas:

- 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)

Additional Information Links:

Grad College Fellowship Finder database: https://apps.grad.illinois.edu/fellowship-finder/index.cfm?action=main.fellowship&fid=4868
Read about the first cohort of Apple Scholars: https://machinelearning.apple.com/updates/introducing-apple-scholars-aiml
Learn more about Apple's work in AI/ML: https://machinelearning.apple.com/

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.

Students must be nominated by their department, and each department may submit up to four nominations. Note that if more than two nominations are submitted, however, the third and fourth nomination must be of a student who self-identifies as underrepresented (see list in paragraph above).

Required Materials (9/10/20 by 11:59P)

Required Materials:

Name of the research area: (from above list) in which student is being nominated.
Student CV: (including publications list).
Graduate transcript: Graduate transcript (unofficial are sufficient). (Unofficial Illinois transcripts can be obtained for free at: https://apps.uillinois.edu/selfservice/)
Research abstract: (maximum 200 words). Must be in Times New Roman, 12 point font, with one-inch margins.
Research statement: covering past work and proposed direction for the next two years (maximum five pages, including citations). Must be in Times New Roman, 12 point font, with one-inch margins, and include a Title.
Two letters of recommendation: (one of which must come from the current advisor). Each letter must be no more than one page.
If applicable, which of these groups do you self-identify as?: woman, non-binary, Black/African American, American Indian/Alaskan Native, Hispanic/Latinx, Native Hawaiian/Pacific Islander, and/or person with a disability.
Optional: arXiv link to most recent published work.

Materials should be submitted by 11:59PM on 9/10/20 to Michelle Wellens.

Note: Students, submit ONE PDF named "20-21. APPLE - Last Name, First Name." Letters of recommendation should be sent from the letter writer directly to mwellens@illinois.edu.

I recommend that students submit their CVs and drafts of their statement(s) to their letter writers at least a week prior to the deadline.

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