Sheila McIlraith is a Professor in the Department of Computer Science, University of Toronto, a Canada CIFAR AI Chair (Vector Institute), and an Associate Director and Research Lead at the Schwartz Reisman Institute for Technology and Society. Prior to joining the University of Toronto, Prof. McIlraith spent six years as a Research Scientist at Stanford University, and one year at Xerox PARC. McIlraith’s research is in the area of sequential decision making, broadly construed, with a focus on human-compatible AI. She also has a particular interest in the ethics of AI and the impact of AI on society. McIlraith is a Fellow of the Association for Computing Machinery (ACM), a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), Associate Editor for the Journal of AI Research (JAIR), and a past Associate Editor of the Journal of Artificial Intelligence. McIlraith is past program Co-Chair of the 32nd AAAI Conference on Artificial Intelligence (AAAI), the 13th International Conference on Principles of Knowledge Representation and Reasoning (KR2012), and the International Semantic Web Conference (ISWC2004). Her work on semantic web services has had notable impact. In 2011 she and her co-authors were honoured with the SWSA 10-year Award, recognizing the highest impact paper from the International Semantic Web Conference, 10 years prior.
Professor, Department of Computer Science, Faculty of Arts & Science, University of Toronto
Canada CIFAR Artificial Intelligence Chair
Associate Director and Research Lead at Schwartz Reisman Institute for Technology and Society
- Knowledge representation & Automated Reasoning
- Reinforcement Learning
- Human-compatible AI and AI Safety
- Sequential decision making
- Fellow of the Association for Computing Machinery (ACM).
- Fellow of the Association for the Advancement of Artificial Intelligence (AAAI).
- Associate Editor of the Journal of Artificial Intelligence Research (JAIR).
Reward machines: Exploiting reward function structure in reinforcement learning
Ltl2action: Generalizing ltl instructions for multi-task rl
Learning reward machines: A study in partially observable reinforcement learning
Efficient multi-agent epistemic planning: Teaching planners about nested belief
BLAST: Latent Dynamics Models from Bootstrapping
Embedding Ethics in Computer Science Courses: Does it Work?
Knowledge-based programs as building blocks for planning
Explaining the Plans of Agents via Theory of Mind
Be Considerate: Objectives, Side Effects, and Deciding How to Act