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Friday, November 3, 2023 3:00 pm - 4:00 pm EDT (GMT -04:00)

Master’s Thesis Presentation • Computer Graphics • Quantum Ray Marching: Reformulating Light Transport for Quantum Computers

Please note: This master’s thesis presentation will take place in DC 3317 and online.

Logan Mosier, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Toshiya Hachisuka

Monday, November 6, 2023 9:30 am - 5:00 pm EST (GMT -05:00)

Machine Learning Theory Workshop

Please note: This workshop will take place in DC 1302 and online.

You are invited to join the Machine Learning Theory Workshop on Monday, November 6, 2023 in DC 1302 and online. 

This workshop will bring together researchers with expertise in mathematical foundations of machine learning. The purpose of this event is to foster connections and build a research community among learning theorists from various institutions.

Wednesday, November 8, 2023 11:00 am - 12:00 pm EST (GMT -05:00)

PhD Seminar • Machine Learning | Reinforcement Learning • Learning Soft Constraints From Constrained Expert Demonstrations

Please note: This PhD seminar will take place in DC 2585 and online.

Ashish Gaurav, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Pascal Poupart

Friday, November 10, 2023 9:00 am - 12:00 pm EST (GMT -05:00)

PhD Defence • Software Engineering • Adaptive Human-Chatbot Interactions: Contextual Factors, Variability Design and Levels of Automation

Please note: This PhD defence will take place in DC 2310 and virtually.

Glaucia Melo dos Santos, PhD candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Paulo Alencar, Daniel Berry, Donald Cowan

Monday, November 13, 2023 12:00 pm - 1:00 pm EST (GMT -05:00)

PhD Seminar • Artificial Intelligence • Unraveling the Dilemma of AI Errors: Exploring the Effectiveness of Human and Machine Explanations for Large Language Models

Please note: This PhD seminar will take place online.

Marvin Pafla, PhD candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Kate Larson, Mark Hancock

With the rise of large language models (LLMs) like GPT, the field of eXplainable artificial intelligence (XAI) has exploded and produced a plethora of methods  (e.g., saliency-maps) to gain insight into deep neural nets. However, human-participant studies question the efficacy of these methods, particularly when the AI output is wrong.