Applications are now being accepted for the 2023-2024 Apple Scholars in AI/ML competition.

Student Deadline to Graduate Unit: August 9, 2022
Value/Duration: 2 years of funding: Full tuition, fees and $40,000 USD living stipend for each academic year, plus $5,000 USD travel grant for each year
Level of Study: Doctoral
Required Legal Status: Domestic or International
Results: January 2023

Purpose

The Apple Scholars in AI/ML 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. Nominations are only reviewed from invited institutions.

Value & Duration

2 years* of funding, starting in the Fall of 2023:

  • Full tuition and fees (including UHIP if applicable);
  • $40,000 USD stipend for each academic year;
  • $5,000 USD travel grant for each year of the fellowship;
  • 2-year mentorship with Apple researcher;
  • Internship opportunities for one or both summers of their fellowship**; and
  • Invitation to the PhD Scholars Summit in Cupertino, CA***.

*Award is distributed on an annual basis at the beginning of each academic year, conditional upon the recipient’s full-time enrolment in their program. If a student is not enrolled full-time in their second year, the award amount may be reduced or omitted at Apple’s discretion.

**Internship offers are dependent on student status, and contingent upon necessary requirements for employment 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.

Eligibility

Nominees:

  • Must be registered as a full-time PhD student at the nominating university at the start of fall 2023;
  • Should be entering their last 2-3 years of study as of fall 2023;
  • Should have demonstrated strong research through publication in one of the research areas listed below; and
  • Must not hold another industry-sponsored full fellowship while they are an Apple Scholar in AI/ML.

Research Areas
Nominees should be pursuing research in one of the following areas. The subtopics listed below are not meant to be exhaustive, 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
  • 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 behaviour
  • 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 AI/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, Large-Scale Graph Data Management, Machine Learning and Data Systems Integration, Search and Ranking
  • Data-Centric AI: 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

Underrepresented groups

The University is strongly encouraged by the funding program to use at least one of the three (3) slots to nominate students who identify as a member of a traditionally underrepresented group in the technology industry.

An underrepresented group is typically defined as a group whose representation in a particular context is significantly lower than their group size in the wider population. In the North American technology industry, underrepresented groups generally refer to those who identify as Black, Hispanic, Native American (Indigenous), women and non-binary individuals.

Note that an individual nominee’s underrepresented group status will not be collected or reviewed by Apple; applications will be reviewed based solely on the strength of the submitted materials.

Selection Criteria

Nominations are reviewed and selected based on the strength and relevance 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, the Apple Selection Committee considers the following:

  • Novelty and relevance of the proposal;
  • Scientific merit of the proposed approach;
  • Potential for impact; and
  • Alignment with research areas highlighted by Apple.

They also consider, in addition to the aforementioned research acumen, the unique perspective and experience each nominee brings to the field.

Application Process

Applicants must submit an electronic copy of their completed application as a single PDF file via email to awards.dlsph@utoronto.ca by the application deadline. The email must have the subject title “Apple 2022 – NAME OF APPLICANT.”

Students applying for both Google and Apple fellowships must submit two separate applications as the required list of items are different for each competition.

Application Package

A complete application package will include all of the following items in the order listed:

  • Student CV and publication list;
  • Research Abstract (200 word maximum);
  • Research statement covering past work and proposed direction for next 2 years (maximum 5 pages, including citations, in a legible font size), clearly stating the hypothesis and expected contributions to the chosen research area;
  • 2 letters of recommendation, one from current advisor (1 page maximum per letter). Letters must be emailed by each referee as a PDF attachment directly to dlsph@utoronto.ca with the subject title “Apple 2022 Ref – NAME OF APPLICANT” by the application deadline; and
  • Optional: Link to most recent published work.
  • New in 2022: Transcripts are no longer required and should not be included with applications.

Application packages should not contain confidential or proprietary research. Additionally, the applicant’s birth date and/or photographs should be removed or redacted if they appear on submitted materials.

Underrepresented group applicants only: please indicate within the body of the submission email that you self-identify as being a member of an underrepresented group. Your underrepresented group status will not be disclosed during the SGS Committee scoring of the applications, and applications will be reviewed based solely on the strength of the submitted materials.

Applicants must not include their birth date, photograph, or confidential or proprietary research in the application materials.

Results

The SGS Graduate Awards Office will notify applicants of the University competition results in late-September. The results of the international competition are communicated directly by the funding agency in January 2023.

For questions, please email awards.dlsph@utoronto.ca