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Monday, November 20, 2023 12:00 pm - 1:00 pm EST (GMT -05:00)

Master’s Thesis Presentation • Human-Computer Interaction • VisConductor: Affect-Varying Gestural Widgets for Animating Data Visualization in Augmented Video Presentations

Please note: This master’s thesis presentation will take place in DC 2310 and online.

Temiloluwa Femi-Gege, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jian Zhao

Tuesday, November 21, 2023 1:00 pm - 2:00 pm EST (GMT -05:00)

PhD Seminar • Machine Learning | Differential Privacy • DP-LFlow: Differentially Private Latent Flow for Scalable Sensitive Image Generation

Please note: The PhD seminar will take place in DC 2310 and online.

Dihong Jiang, PhD candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Yaoliang Yu, Sun Sun

Thursday, November 23, 2023 2:00 pm - 3:00 pm EST (GMT -05:00)

CrySP Speaker Series on Privacy • Private Information Retrieval: Improvements and Limitations

Please note: This CrySP Speaker Series on Privacy talk will take place in DC 1302 and online.

Kevin Yeo
Research Engineering Manager, Google
PhD candidate, Columbia University

Private information retrieval (PIR) is a very promising cryptographic tool that enables privacy-preserving data querying that has endless implications to real-world applications. Unfortunately, PIR’s high cost remains a hindrance in widespread adoption.

Friday, November 24, 2023 10:00 am - 11:00 am EST (GMT -05:00)

PhD Seminar • Software Engineering • Characterizing the Impact, Distribution, and Duration of Stale Reviewer Recommendations

Please note: This PhD seminar will take place in DC 2564 and online.

Farshad Kazemi, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Shane McIntosh

Friday, November 24, 2023 11:00 am - 12:00 pm EST (GMT -05:00)

Seminar • Artificial Intelligence • Fair and Optimal Prediction via Post-Processing

Please note: This seminar will take place in DC 1304 and online.

Han Zhao, Assistant Professor
Computer Science, University of Illinois Urbana-Champaign
Amazon Visiting Academic, Amazon AI and Search Science

To mitigate the bias exhibited by machine learning models, fairness criteria can be integrated into the training process to ensure fair treatment across all demographics, but it often comes at the expense of model performance. Understanding such tradeoffs, therefore, underlies the design of optimal and fair algorithms.