Growing from a diverse team of co-applicants and collaborators, AML-TN is specifically curated to offer development beyond the classroom. Our program offers multiple ways to engage with world-class research through funding, mentorship, and training. Enabling students to grow their expertise, build connections, and advance their careers.
Recognizing the challenges individuals face in STEM, AML-TN makes a conscious effort to promote Diversity in Machine Learning. Our application process dedicates a subset of funding for individuals who self-identify as being from underrepresented or marginalized groups.
Read below to learn how to participate, view past awardees, and get involved today!
Our participating faculty supervisors all play a significant role in uplifting UBC’s AI/ML community. All faculty below are eligible to receive funding from AML-TN, via their supervised students.
Grantee Team
 Frank Wood Professor, UBC Computer Science CIFAR AI Chair, Amii Program Director, AML-TN fwood@cs.ubc.ca |  Danica Sutherland Associate Professor, UBC Computer Science CIFAR AI Chair, Amii Recruitment Lead, AML-TN dsuth@cs.ubc.ca |
 Mi Jung Park Assistant Professor, UBC Computer Science CIFAR AI Chair, Amii Mobility Lead, AML-TN mijungp@cs.ubc.ca | |
 Geoff Pleiss Assistant Professor, UBC Statistics CIFAR AI Chair, Vector Institute geoff.pleiss@stat.ubc.ca |
|
 Mark Schmidt Associate Professor, UBC Computer Science CIFAR AI Chair, Amii Canada Research Chair (CRC II) in Large Scale ML schmidtm@cs.ubc.ca | |
 Leonid Sigal Associate Professor, UBC Computer Science CIFAR AI Chair, Vector Institute CRC II in Computer Vision and ML lsigal@cs.ubc.ca |
UBC Collaborators
| Jeff Clune Associate Professor, UBC Computer Science Canada CIFAR AI Chair, Vector Institute Senior Research Advisor, DeepMind jeff.clune@ubc.ca | Cristina Conati Professor, UBC Computer Science Distinguished Scholar, Sauder School of Business conati@cs.ubc.ca | Raymond Ng Professor, UBC Computer Science Director, Data Science Institute rng@cs.ubc.ca |
| David Poole Professor, UBC Computer Science Director, Laboratory for Computational Intelligence (LCI) poole@cs.ubc.ca | Helge Rhodin Assistant Professor, UBC Computer Science rhodin@cs.ubc.ca | Michiel van de Panne Professor, UBC Computer Science Research Group: Imager lab for Graphics, Visualization and HCT van@cs.ubc.ca |
| Evan Shelhamer Assistant Professor, UBC Computer Science evanesce@cs.ubc.ca | Mathias Lecuyer Assistant Professor, UBC Computer Science mathias.lecuyer@ubc.ca | Kelsey Allen Assistant Professor, UBC Computer Science & Psychology Canada CIFAR AI Chair, Vector Institute krallen@cs.ubc.ca |
| Benjamin Bloem-Reddy Assistant Professor, UBC Statistics benbr@stat.ubc.ca | Alexandre Bouchard-Côté Associate Professor, UBC Statistics bouchard@stat.ubc.ca | Purang Abolmaesumi Professor, UBC Electrical and Computer Engineering Canada Research Chair (CRC II) in Biomedical Engineering purang@ece.ubc.ca |
| Tor Aamodt Professor, UBC Electrical and Computer Engineering aamodt@ece.ubc.ca | Andre Ivanov Professor, UBC Electrical and Computer Engineering ivanov@ece.ubc.ca | Christos Thrampoulidis Assistant Professor, UBC Electrical and Computer Engineering cthrampo@ece.ubc.ca |
| Maryam Kamgarpour Professor, UBC Electrical and Computer Engineering maryamk@ece.ubc.ca | Xiaoxiao Li Assistant Professor, UBC Electrical and Computer Engineering xiaoxiao.li@ece.ubc.ca | Renjie Liao Assistant Professor, UBC Electrical and Computer Engineering rjliao@ece.ubc.ca |
| Muhammad Abdul-Mageed Associate Professor, UBC Linguistics and School of Information (Joint Appointment) Associate Member, UBC Computer Science Canada Research Chair (CRC II) in Natural Language Processing and Machine Learning muhammad.mageed@ubc.ca | Eldad Haber Professor, UBC Earth, Ocean, and Atmospheric Sciences NSERC Industrial Research Chair, Computational Geoscience ehaber@eoas.ubc.ca | Paul Cubbon Instructor and Assistant Dean, UBC Sauder School of Business paul.cubbon@sauder.ubc.ca |
AML-TN prides itself on a a wide global network. All faculty below have expressed interest in hosting UBC students for mobility stays and to collaborate on research efforts.
North america
Europe
| Host Institutions | Mobility Sponsors |
|---|---|
| Max Planck Institute for Intelligent Systems (MPI) Department of Perceiving Systems Department of Empirical Inference Computer Graphics Department Computer Vision and Machine Learning Department Visual Computing and Artificial Intelligence Department Tübingen & Stuttgart, Germany | Michael Black Bernhard Schölkopf Hans-Peter Seidel Bernt Schiele Christian Theobalt |
| ADAPT Centre for Digital Media Technology SFI Research Centre for AI Driven Digital content Technology Dublin, Ireland | Vincent Wade |
| University College London Gatsby Computational Neuroscience Unit London, England | Maneesh Sahani |
| University of Oxford Department of Statistics Oxford, England | Yee Whye Teh |
| University of Cambridge Department of Applied Mathematics and Theoretical Physics Cambridge, England | Carola-Bibiane Schönlieb |
| Toulouse Computer Science Research Institute (IRIT) Artificial Intelligence Department Toulouse, France | Nicholas Asher |
Graduate students can engage with AML-TN through Funded Trainee Awards and/or Research Mobility Awards; both offering ways to elevate their traditional academic degree.
Eligibility & Expectations
To be selected for the AML-TN program, you must be:
- An active UBC student
- Supervised and recommended by a UBC faculty member
- Actively researching Machine Learning or Artificial Intelligence
All graduate students are expected to engage in skills development and our Research Mobility Program. This is designed to encourage collaboration, innovation, and a practical setting to apply research practices.
Funded Trainee Awards
- $15,000 in award funding, complimenting their student salary.
- Guaranteed Mobility Award funding.
Awards are distributed yearly, with applications accepted in early fall. Apply via the link below!
As a training network, student researchers are at the heart of the program. All of the students listed below are members of the network and recipients of AML-TN awards.
| Award Recipient | Research Topics |
|---|---|
| Pierre Lardet Ph.D., Computer Science Kevin Leyton-Brown (Supervisor) | Machine Learning for Behavioural Game Theory, LLM Understanding, Machine Learning Metrics |
| Zheng He Ph.D., Computer Science Danica Sutherland (Supervisor) | |
| Zhejun Jiang MSc., Computer Science Mi Jung Park (Supervisor) | Trustworthy AI, Security and Privacy in Machine Learning |
| Oluwanifemi (Ayanfe) Adekanye MSc., Computer Science Frank Wood (Supervisor) | Continual Learning for Embodied AI |
| Yingchen (Charlie) He Ph.D., Computer Science Frank Wood (Supervisor) | Embodied AI |
| Weija (Owen) Zheng MSc., Computer Science Kwang Moo Yi (Supervisor) | Feed Forward 3D Reconstruction Model |
| Naitong Chen MSc., Statistics Trevor Campbell (Supervisor) | Scalable Bayesian Inference in the Large Data Regime via Bayesian Coresets |
| Yunxiang Li Ph.D., Computer Science Mark Schmidt (Supervisor) | Closing Gap Between Experimental and Theoretical Reinforcement Learning |
| Donney Fan Ph.D., Computer Science Geoff Pleiss, Mark Schmidt (Co-Supervisors) | High Dimensional Bayesian Optimization, Decision-Focused Learning |
| Vivian White Ph.D., Computer Science Evan Shelhamer (Supervisor) | Computer Vision, Test-Time Adaptation, Model Interpretability, Robustness |
| Qiaoyue Tang Ph.D., Computer Science Mathias Lecuyer (Supervisor) | Differentially Private Machine Learning |
| Award Recipient | Research Topics |
|---|---|
| Armin Boroujeni Ph.D., Electrical and Computer Engineering Purang Abolmaesumi, Michiel van de Panne (Co-Supervisors) | Active Feature Acquisition for Medical Imaging (with a focus on Deep Reinforcement Learning) |
| Cedar Turek MSc., Electrical and Computer Engineering Guy Lemieux, Frank Wood (Co-Supervisors) | Forward Mode Predictive Coding for Analog Acceleration |
| Gregory d'Eon Ph.D., Computer Science Kevin Leyton-Brown (Supervisor) | |
| Hamed Shirzad Ph.D., Computer Science Danica Sutherland (Supervisor) | Sparse Transformers for Learning on Graphs |
| Jiayun Luo Ph.D., Computer Science Leonid Sigal (Supervisor) | Vision Language Model on Visual Grounding/Segmentation Tasks |
| Ke Zhang Ph.D., Computer Science Frank Wood (Supervisor) | Reinforcement Learning for Autonomous Driving |
| Lyuyang (Anne) Wang MSc., Electrical and Computer Engineering Purang Abolmaesumi (Supervisor) | High-Precision Echo Assessment and Risk Triage of Heart Failure using Artificial Intelligence |
| Matthew Niedoba PhD., Computer Science Frank Wood (Supervisor) | Generative Modelling, Diffusion Models, Trajectory Modelling |
| Raymond Liu MSc., Computer Science Kevin Leyton-Brown (Supervisor) | Economic Models of Human Behaviour in Decision-Making Systems |
| Rohit Murali PhD., Computer Science David Poole (Supervisor) | Data-Driven Adaptive Support in Open-Ended Learning Environments |
| Ruinan Jin Ph.D., Electrical and Computer Engineering Xiaoxiao Li (Supervisor) | Efficient and Trustworthy Medical Machine Learning |
| Seungyeon Baek MSc., Computer Science Kwang Moo Yi (Supervisor) | |
| Tiange (Ivy) Liu MSc., Statistics Trevor Campbell (Supervisor) | MCMC Algorithms with Autonomous Kernels |
| Vala Vakilian MSc., Electrical and Computer Engineering Christos Thrampoulidis (Supervisor) | Neural Collapse and Geometry of Deep Learning Models, Structure of Language and its Impact on LLM Training |
| Vasileios Lioutas Ph.D., Computer Science Frank Wood (Supervisor) | Generative Modeling for Realistic Autonomous Driving Simulations |
| Wonho (Won) Bae Ph.D., Computer Science Danica Sutherland (Supervisor) | Learning with Limited Data Including Active Learning |
| Yang-Che (Jeff) Tseng MSc., Computer Science Kwang Moo Yi (Supervisor) | Expanding 2D Diffusion Prior for 4D Rendering |
| Yixin (Allen) Cheng Ph.D., Computer Science Leonid Sigal (Supervisor) | Computer Vision and Trustworthy ML |
| Yize Zhao MSc., Electrical and Computer Engineering Christos Thrampoulidis (Supervisor) | Interpretability and Embeddings Behavior of (Language) Models |
| Zheng He MSc., Computer Science Danica Sutherland (Supervisor) | Independence Testing and Invariant Representation Learning for Out-of-Distribution Problems |
| Award Recipient | Research Topics |
|---|---|
| Amrutha Ramesh Ph.D., Computer Science Mark Schmidt (Supervisor) | Optimization, Machine learning, Large language Models |
| Chun-Yin Huang Ph.D., Electrical and Computer Engineering Xiaoxiao Li (Supervisor) | Trustworthy AI, Federated Learning, Data Assessment, Medical Image Analysis |
| Frederik Kunstner Ph.D., Computer Science Mark Schmidt (Supervisor) | Optimization methods for Machine learning (Probabilistic Models, Deep Learning), Adaptive and Parameter-Free Methods, Impact of Data and Model Choices on Optimization Performance. |
| Harshinee Sriram Ph.D., Computer Science Cristina Conati (Supervisor) | Personalized Explainable AI, Explainable AI (XAI), Human-Computer Interaction (HCI) |
| Jinsoo (Jason) Yoo Ph.D., Computer Science Frank Wood (Supervisor) | Continual Learning, Probabilistic Machine Learning, Memory, Neuroscience |
| Nathaniel Xu Ph.D., Computer Science Danica Sutherland (Supervisor) | Approximate Inference, Generative Modelling, Kernel Methods, Machine Learning Theory |
| Shivam Chandhok M.Sc., Computer Science Leonid Sigal (Supervisor) | Computer Vision (Machine Learning, Multimodal Learning, Scene Understanding, 3D Vision) |
| Tina Behnia Ph.D., Electrical and Computer Engineering Christos Thrampoulidis (Supervisor) | Optimization in Machine Learning, Deep Learning Theory |
| Yi (Joshua) Ren M.Sc., Computer Science Danica Sutherland (Supervisor) | Machine Learning, Cognitive Science |
| Yingchen (Charlie) He M.Sc., Computer Science Frank Wood, Mijung Park (Co-Supervisors) | Privacy Preserved Machine Learning, Diffusion Models, and General ML Topics |
| Yunpeng (Larry) Liu Ph.D., Computer Science Frank Wood (Supervisor) | Generative Modeling, Reinforcement Learning, Self-Supervised Learning |
| Zhouting (Fred) Xie M.Sc., Computer Science Kwang Moo Yi (Supervisor) | Machine Learning, Statistics, and their application in Astronomy and Health Science Research. |
Undergraduate research projects are a fundamental development step for aspiring researchers. Recognizing this opportunity, our URA supports students in the early stages of their careers, allowing them to contribute to collaborative projects and gain valuable experience.
Eligibility & Expectations
To be selected for the AML-TN program, you must be:
- An active UBC student
- Supervised and recommended by a UBC faculty member
- Actively researching Machine Learning or Artificial Intelligence
- Spend 16+ weeks working with their project
- Receive, at least, a minimum wage salary
Undergraduate Research Award (URA)
- $5,000 in award funding, complimenting their student salary.
Awards are distributed yearly, with applications accepted in early spring. Apply via the link below!
| Award Recipient | Research Project |
|---|---|
| Alex Yao BSc, Computer Science Jiarui Ding (Supervisor) | Evaluating and Optimizing Metrics for Single-Cell Data Integration |
| Alan Cho BSc, Computer Science Frank Wood (Supervisor) | PLAICraft: Embodied AI in a Game Environment |
| Christopher Tardy BSc, Computer Science Frank Wood (Supervisor) | Time-aligned multimodal student-teacher reasoning datasets for desktop environments |
| Robin Yadav BSc, Computer Science Mark Schmidt (Supervisor) | Understanding and Extending the Adam Optimizer |
| Taegyoem (Andrew) Ahn BSc, Computer Science Mi Jung Park (Supervisor) | Latent-Factor Consistency Learning for Adversarial Robustness in LLMs |
| Rose Samaeian BSc, Electrical and Computer Engineering Purang Abolmaesumi (Supervisor) | Agentic AI methodologies for echocardiographic image analysis |
| Yingru (Shelly) Ji BSc, Computer Science Leonid Sigal (Supervisor) | Enhancing VLM Alignment with Generative Models |
| Lucas Eisenberg BSc, Computer Science Peter Yichen Chen (Supervisor) | Using AI to Simulate Metallic Plate Deformations |
| Robert Hoang BSc, Computer Science Peter Yichen Chen (Supervisor) | Physics Informed Diffusion Models |
| Han (Adam) Zhou BSc, Computer Science Kevin Leyton-Brown (Supervisor) | Predicting Human Strategic Behaviour in Video Games |
| Award Recipient | Research Project |
|---|---|
| Aaron Wei BSc, Computer Science Danica Sutherland (Supervisor) | Fundamental Limits on Measuring Conditional Dependence |
| Bi Xiang (Jack) Wu BSc, Statistics Trevor Campbell (Supervisor) | Flexible, Efficient Approximations for High-Dimensional Distributed Bayesian Inference |
| Geo Lee BSc, Computer Science Frank Wood (Supervisor) | Embodied AI |
| Gunbir Singh Babeja BSc, Computer Science Mark Schmidt (Supervisor) | Mitigating Loss of Plasticity in Continual Learning Systems |
| Kaiwen Liu BSc, Electrical and Computer Engineering Renjie Liao (Supervisor) | Resource-Constrained Test-Time Steering of Large Language Models with RLHF through Product of Expert (PoE) |
| Maziyar Dowlatabadibazaz BSc, Computer Science Kevin Leyton-Brown (Supervisor) | Using LLMs to Optimize TA Resource Allocation in Educational Peer Review Systems |
| Mercury Mcindoe BSc, Computer Science Leonid Sigal (Supervisor) | Human-Like Attention in Vision-Language Models |
| Raul Vazquez Guerrero BSc, Electrical and Computer Engineering Andre Ivanov (Supervisor) | Machine Learning Models to Reduce Electronic Systems Reliability Tests |
| Yash Mali BSc, Computer Science Raymond Ng (Supervisor) | Natural Language Interface for Medical Guidelines |
| Zimeng (Mabel) Wang BSc, Electrical and Computer Engineering Christos Thrampoulidis (Supervisor) | Next-Token Prediction in Large Language Models |
| Award Recipient | Research Project |
|---|---|
| Alyssa Zhang BSc, Computer Science Mark Schmidt (Supervisor) | Data Science for Medium-Rare Multi-Cause Diseases |
| Hala Murad BSc, Computer Science Kevin Leyton-Brown (Supervisor) | Deep Learning in Behavioral Game Theory |
| Malcolm Zhao BSc, Computer Science Mark Schmidt (Supervisor) | Newton-Like Methods For Modern Machine Learning Problems |
| Yujia Ma BSc, Statistics Trevor Campbell (Supervisor) | Advancing Distributed Bayesian Inference with Pigeons |