Matin Moezzi
About Me
As a 2nd-year M.A.Sc. student at the University of Toronto, my research aims to address two critical Reinforcement Learning challenges as applied to complex real-world environments: sample complexity and generalization using deep generative models.
Research Interests
- Reinforcement Learning (RL): Model-based RL, Offline RL
- Deep Generative Models: VAEs, Diffusion models, Score-based Generative Models
- Imitation Learning (IL): Behavioral Cloning, Adversarial IL, Inverse RL, Sim-to-Real Transfer
News
- [Aug. 2023] Our paper titled “A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control” is accepted to IEEE SWC 2023.
- [Jan. 2023] I joined Vector Institute as an Applied Machine Learning Intern.
- [May 2022] I was admitted to the NSERC Alliance-Mitacs Accelerate Internship program.
Publication
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IEEE Smart World Congress 2023
Research Experiences
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Graduate RA, University of Toronto, Jan 2022 - Present -
Graduate RA, University of Toronto, Jan 2022 - Present -
Teaching Experiences
Graduate Teaching Assistant, University of Toronto
- CSC2516: Neural Networks and Deep Learning [Winter by Prof. Jimmy Ba, Fall 2023]
- MIE567: Dynamic and Distributed Decision Making [Winter 2023]
- APS1080: An Introduction to Reinforcement Learning [Fall 2022, Winter, Summer 2023]
- MIE1615: Markov Decision Processes [Winter 2023]
- Probability [Fall 2022, 2023]
- CSC369: Operating Systems [Fall 2022]
Selected Courses and Certificates
University of Toronto
- Imitation Learning for Robotics - A
- Probabilistic Learning & Reasoning - A+
- Markov Decision Processes - A
- Cloud-Based Data Analytics - A
Coursera
- Reinforcement Learning Specialization, University of Alberta - See the Certificate
- Deep Learning Specialization, Andrew Ng, deeplearning.ai - See the Certificate
- Natural Language Processing Specialization, deeplearning.ai - See the Certificate
- TensorFlow Developer Specialization, deeplearning.ai - See the Certificate