Kyanna Dagenais admitted to the Computer Science MSc program
Kyanna Dagenais, who’s been with us for 1.5 years now as an Undergraduate Researcher, has been admitted to the Computer Science MSc program of the Department. Kyanna did an impressive …
Kyanna Dagenais, who’s been with us for 1.5 years now as an Undergraduate Researcher, has been admitted to the Computer Science MSc program of the Department. Kyanna did an impressive …
Uzair Rao joins our lab as a Summer Intern through Mitacs’ Globalink Research Internship (GRI) program. Uzair is a senior Software Engineering student at NUST, Pakistan, with expertise in machine …
Kyanna Dagenais presented her work on guiding reinforcement learning agents by uncertain human advice at the seminar series of the Department of Computing and Software. Kyanna’s research focuses on the …
It is our pleasure to congratulate to our undergraduate researcher, Kyanna Dagenais on her 1st place at the ACM Student Research Competition (SRC) at this year’s MODELS. Kyanna got her …
We have five accepted papers for this year’s MODELS’ co-located and satellite events; and in addition, we lead the organization of two workshops. Accepted papers Sharon Liu, PhD candidate in …
The full pre-print of our work on Opinion-guided reinforcement learning with Kyanna Dagenais is now available on arXiv: https://arxiv.org/abs/2405.17287. Abstract. Human guidance is often desired in reinforcement learning to improve …
Kyanna Dagenais, research assistant in our lab, just got her paper “Towards Model Repair by Human Opinion-Guided Reinforcement Learning” accepted for the ACM Student Research Competition (SRC), co-located with the …
As of today, the 1st edition of the Handbook of Digital Twins (CRC Press / Taylor&Francis Group) is out, featuring a chapter on “Automated Inference of Simulators in Digital Twins“, …
Kyanna Dagenais joined our lab as an undergraduate research assistant, working on the topic of uncertainty in reinforcement learning. Kyanna’s work will help foster more performant and (resource-)efficient machine learning …