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Monday, February 8, 2021 12:00 pm - 12:00 pm EST (GMT -05:00)

Seminar • Machine Learning — New Advances in (Adversarially) Robust and Secure Machine Learning

Please note: This seminar will be given online.

Hongyang Zhang, Postdoctoral Fellow
Toyota Technological Institute at Chicago

Deep learning models are often vulnerable to adversarial examples. In this talk, we will focus on robustness and security of machine learning against adversarial examples. There are two types of defenses against such attacks: 1) empirical and 2) certified adversarial robustness.

Wednesday, February 10, 2021 10:00 am - 10:00 am EST (GMT -05:00)

Master’s Thesis Presentation • Recommender Systems | Information Retrieval — People Know How Diverse Their Music Recommendations Should Be; Why Don’t We?

Please note: This master’s thesis presentation will be given online.

Kyle Robinson, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Dan Brown

University Professor Ming Li holding Kolmogorov complexity book
Tuesday, February 2, 2021

University Professor Ming Li receives 2020 Lifetime Achievement Award in Computer Science

University Professor Ming Li has received the 2020 Lifetime Achievement Award in Computer Science from CS-Can|Info-Can, the non-profit professional society dedicated to representing all aspects of computer science and the interests of the discipline across the nation. Conferred annually since 2014, the prestigious lifetime achievement award recognizes faculty members in departments, schools and faculties of computer science who have made outstanding and sustained achievement in research, teaching and service.

Tuesday, February 2, 2021 12:00 pm - 12:00 pm EST (GMT -05:00)

Seminar • Natural Language Processing | Machine Learning — Reliable Machine Learning for Computational Social Science

Please note: This seminar will be given online.

Dallas Card, Postdoctoral scholar
NLP Group and the Data Science Institute, Stanford University

Machine learning and natural language processing have become increasingly influential, both in commercial applications and as key tools for research in the natural and social sciences. In both cases, however, research in these fields raises numerous concerns related to bias, transparency, robustness, and how we communicate information.