Events

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

Lunjia Hu, PhD candidate
Computer Science Department, Stanford University

Machine learning holds significant potential for positive societal impact. However, in critical applications involving people such as healthcare, employment, and lending, machine learning raises serious concerns of fairness, robustness, and interpretability. Addressing these concerns is crucial for making machine learning more trustworthy.

Please note: This seminar will take place in DC 3317 and online.

Mahsa Derakhshan, Assistant Professor
Khoury College of Computer Sciences, Northeastern University

In this talk, we discuss the stochastic vertex cover problem. In this problem, G is an arbitrary known graph, and G* is an unknown random subgraph of G containing each of its edges independently with a known probability p. Edges of G* can only be verified using edge queries. The goal in this problem is to find a minimum vertex cover of G* using a small number of queries.

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

Shubhankar Mohapatra, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Xi He

Despite several works that succeed in generating synthetic data with differential privacy (DP) guarantees, they are inadequate for generating high-quality synthetic data when the input data has missing values.

Please note: This PhD seminar will take place in DC 3317 and online.

Yiwei Lu, PhD candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Yaoliang Yu, Sun Sun

Please note: This seminar will take place online.

Philip A. Bernstein
Distinguished Scientist, Microsoft Research
Affiliate Professor, University of Washington

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

Sarah Cen, PhD candidate
Electrical Engineering and Computer Science Department, MIT

We have begun grappling with difficult questions related to the rise of AI, including: What rights do individuals have in the age of AI? When should we regulate AI and when should we abstain? What degree of transparency is needed to monitor AI systems? These questions are all concerned with AI accountability: determining who owes responsibility and to whom in the age of AI.

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

Misha Khodak, PhD candidate
Computer Science Department, Carnegie Mellon University

Advances in machine learning (ML) have led to skyrocketing demand across diverse applications beyond vision and text, resulting in unique theoretical and practical challenges. The vastness of use cases calls for general-purpose yet customizable tools for tackling large subclasses of such problems.

Please note: This seminar will take place in DC 3317 and online.

Yash Pote, PhD candidate
School of Computing, National University of Singapore

Please note: This CrySP Speaker Series on Privacy talk will take place in MC 5501 and online.

Kathrin Hövelmanns, Tenured Assistant Professor
Applied and Provable Security group, TU Eindhoven

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

Xupeng Miao, Postdoctoral Researcher
Computer Science Department, Carnegie Mellon University

In this talk, I will introduce my work on machine learning (ML) parallelization, a critical endeavor to bridge the significant gap between diverse ML programs and multitiered computing architectures. Specifically, I will explore ML parallelization at three distinct yet interconnected levels.