Team

My current fundamental and applied research interests are in reinforcement learning, human-in-the-loop AI, multi-agent systems, and robotics.

Matthew Schlegel is a Postdoctoral Associate working with Mostafa Farrokhabadi and Matt Taylor. His research focuses on applying machine learning and reinforcement learning techniques to power systems. He is especially interested in explainable AI, human-in-the-loop reinforcement learning, data-driven solutions to partial differential equations, and the broader role of AI in enhancing communities and improving human well-being.

Laura's research interests include reinforcement learning, human-robot interaction, biomechatronics, and assistive robotics. Drawing inspiration from her anatomical studies, Laura’s research aims to develop control methods for robotic manipulation with the goal of increased functionality, usability, reliability, and safety in the real-world.

I specialize in applying reinforcement learning (RL) within electronic design automation (EDA), with a focus on optimizing placement in chip design. My research is centered on leveraging AI and RL to accelerate the chip design process and reduce the excessive human involvement. Currently, I am developing model-based RL algorithms, inspired by approaches like MuZero, to enhance the quality of RL-driven placements. Additionally, I am exploring representation learning to further advance chip design methodologies.

My goal is to create AI agents that are aligned with human preferences. My research interests include reward alignment, deep reinforcement learning, and human-in-the-loop reinforcement learning.

Currently working on investigating RL to help improve Large Language Model Decision-Making and Reasoning ability. But also had an interest in Human-Computer Interaction and Human-Robot interaction.

The human brain and body have evolved over millions of years to become incredibly complex, making it difficult for us to fully understand our own capabilities. However, the challenge of replicating human-level intelligence in computers is a goal that drives many areas of research, including Machine Learning, Deep Learning, Computer Vision, Robotics, and Reinforcement Learning, which is why they intrigue me as much as they do. My ultimate aim is to contribute to this goal and gain a deeper understanding of human behavior and decision-making.

My primary research goal is to understand how human-AI interactions can help us design better, more adaptive intelligent systems. I am particularly interested in Reinforcement Learning, Embodied AI, Human-AI interaction, and how reinforcement learning can be applied to control and decision-making tasks in real-world environments.

My research focuses on the use of drones as first responders, integrating computer vision and human-in-the-loop AI to support emergency response operations.

Ph.D. student in Computing Science at the University of Alberta, researching how to integrate foundation models (large language models, vision language models, etc.) with reinforcement learning.

I am passionate about reinforcement learning, robotics, and embodied AI. My research focuses on developing intelligent robots that can cooperate with people, learn continually, and interact with complex real-world environments.

I am interested in applying reinforcement learning to real-world problems, and I am currently focusing on RL applications within the construction industry.

I am interested in Human-AI interaction, teaching and learning with AI, and multi-agent RL problems.  Currently, I am exploring metrics and reward formulations which measure and incentivize collaboration among Human-AI teams.

Thesis: Effective Reinforcement Learning with Information Reuse from Multiple Demonstrators, Summer 2025

Thesis: COVID-19 and Far-Right Engagement:An Interrupted Time Series Analysis To Investigate Online Behaviour, Fall 2024

Thesis: Continual Preference-based Reinforcement Learning with Hypernetworks, Fall 2024

Thesis: CANOR COACH:Towards Noise-Robust Human-in-the-Loop Reinforcement Learning, Summer 2024

Thesis: Iterative Large Language Models Evolution through Self-Critique, Summer 2024

Thesis: Automated Coordination of Distributed Energy Resources using Local Energy Markets and Reinforcement Learning, Spring 2024

Thesis: Human-AI Collaboration in Real-World Complex Environment With Reinforcement Learning, Winter 2023

Thesis: Ensembling Diverse Policies Improves Generalization of Deep Reinforcement Learning Algorithms to Environmental Changes in Continuous Control Tasks , Winter 2022

Thesis: Program Optimization with Local Search, Fall 2022

Thesis: Effective Transfer Learning with the Use of Distance Metrics, Winter 2021

Thesis: Methodical Advice Collection and Reuse in Deep Reinforcement Learning,

Thesis: To Ask or Explore: A Systematic Approach to Advice,

Thesis: Uncertainty Methods in Active Reinforcement Learning,

Thesis: A framework for Safe Evaluation of Offline Learning, Fall 2021

Thesis: The Impact of Different Summaries as Reinforcement Learning Explanations on Human Performance And Perception, Summer 2021

Thesis: Transfer in Deep Reinforcement Learning: How an Agent Can Leverage Knowledge From Another Agent, A Human, or Itself, Spring 2021

Thesis: Knowledge Transfer in Reinforcement Learning: How Agents Should Benefit from Prior Knowledge, Fall 2019

Thesis: Teaching Effectiveness of Intelligent Tutoring Systems, Spring 2019

Thesis: Learning from Human Teachers — Supporting How People Want to Teach in Interactive Machine Learning, Summer 2018

Thesis: TINGLE — Topic-Independent Gamification Learning Environment, Summer 2018

Thesis: Policy Advice, Non-convex and Distributed Optimization in Reinforcement Learning, Fall 2016

Thesis: Regret Minimization with Function Approximation in Extensive-Form Games, Summer 2020

Thesis: Useful Policy Invariant Shaping from Arbitrary Advice, Winter 2020

Thesis: Accelerate the Learning Speed of Deep Reinforcement Learning by Pre- training with Non-Expert Human Demonstrations, Spring 2019

Thesis: Engineering a Smart Scarecrow: Bird Deterrence with Drones, Spring 2017

Thesis: Development of the Baton: A Novel Precision Delivery Drone, Summer 2017

Thesis: Modifying Smart Home to Smart Phone Notifications using Reinforcement Learning Algorithms, Spring 2017