Triangle Computer Science Distinguished Lecturer Series

4:00 PM – 5:00 PM ET

Title: Balancing multiple objectives in contextual multi-armed bandits

Host School: N.C. State University

Abstract

Contextual multi-armed bandits are a popular framework for learning to make good decisions under uncertainty, and are applicable to areas ranging from ad placement to optimizing flu shot reminders. The majority of work in this space assumes the goal is to learn a decision policy to map from contexts to decisions in a way that maximizes the cumulative sum of outcomes of interest, such as total clicks or flu shot appointments. However in many real world settings there are multiple objectives of interest: for instance, a stakeholder may have a limited budget, care about fairness to subpopulations, or may wish to balance the experience for those participating in a study with the generalizable knowledge that might be learned and of use for other situations. In this talk I’ll discuss some of our work on learning to make decisions under uncertainty given multiple objectives of interest, and highlight motivating settings in education, healthcare and public policy.

Short Biography

Emma Brunskill is an associate professor in the Computer Science Department at Stanford University where she and Brunskill’s lab aim to create AI systems that learn from few samples to robustly make good decisions. Their work spans algorithmic and theoretical advances to experiments, inspired and motivated by the positive impact AI might have in education and healthcare. Brunskill’s lab is part of the Stanford AI Lab, the Stanford Statistical ML group, and AI Safety @Stanford. Brunskill has received a NSF CAREER award, Office of Naval Research Young Investigator Award, a Microsoft Faculty Fellow award and an alumni impact award from the computer science and engineering department at the University of Washington. Brunskill and her lab have received multiple best paper nominations and awards both for their AI and machine learning work (UAI best paper, Reinforcement Learning and Decision Making Symposium best paper twice) and for their work in Ai of education (Intelligent Tutoring Systems Conference, Educational Data Mining conference x3, CHI).

4:00 PM – 5:00 PM ET

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Title: Storing and Querying Video Data

Host School: Duke University

Abstract

The proliferation of inexpensive high-quality cameras coupled with recent advances in machine learning and computer vision have enabled new applications on video data. This in turn has renewed interest in video data management systems (VDMSs). In this talk, we explore how to build a modern data management system for video data. We focus, in particular, on the storage manager and present several techniques to store video data in a way that accelerates queries over that data. We then move up the stack and discuss different types of data models that can be exposed to applications. Finally, we discuss how it’s possible to support users in expressing queries to find events of interest in a video database.

Short Biography

Magdalena Balazinska is Professor, Bill & Melinda Gates Chair, and Director of the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Magdalena’s research interests are in the field of database management systems. Her current research focuses on data management for data science, big data systems, cloud computing, and image and video analytics. Prior to her leadership of the Allen School, Magdalena was the Director of the eScience Institute, the Associate Vice Provost for Data Science, and the Director of the Advanced Data Science PhD Option. She also served as Co-Editor-in-Chief for Volume 13 of the Proceedings of the Very Large Data Bases Endowment (PVLDB) journal and as PC co-chair for the corresponding VLDB’20 conference. Magdalena is an ACM Fellow. She holds a Ph.D. from the Massachusetts Institute of Technology (2006). Shortly after her arrival at the University of Washington, she was named a Microsoft Research New Faculty Fellow (2007). Magdalena received the inaugural VLDB Women in Database Research Award (2016) for her work on scalable distributed data systems. She also received an ACM SIGMOD Test-of-Time Award (2017) for her work on fault-tolerant distributed stream processing and a 10-year most influential paper award (2010) from her earlier work on reengineering software clones.

4:00 PM – 5:00 PM ET

Title: Mitigating Technology Abuse in Intimate Partner Violence and Encrypted Messaging

Host School: Duke University

Abstract

Computer security is traditionally about the protection of technology, whereas trust and safety efforts focus on preventing technology abuse from harming people. In this talk, I’ll explore the interplay between security and tech abuse, and make the case that trust and safety represents an important frontier for computer security researchers. To do so, I’ll draw on examples from two lines of my recent work.

First, I’ll overview our work on technology abuse in the context of intimate partner violence (IPV). IPV is a widespread social ill affecting about one in our women and one in ten men at some point in their lives. Via interviews with survivors and professionals, online measurement studies, and reverse engineering of malicious tools, our research has provided the most granular view to date of technology abuse in IPV contexts. This has helped educate our efforts on intervention design, most notably in the form of what we call clinical computer security: direct, expert assistance to help survivors navigate technology abuse. Our work led to establishing the Clinic to End Tech Abuse, which has so far worked to help hundreds of survivors of IPV in New York City.

Second, I’ll discuss how basic security tools like encrypted messaging need to be adapted in light of tech abuse. Here we find a fundamental tension between the desire for messaging service providers to help moderate malicious content and the confidentiality goals of encryption, which prevent the platform from seeing content. I’ll show how we end up reconceptualizing and redesigning basic cryptographic tools to more securely support abuse mitigation.

The talk will include content on abuse, including discussion of physical, sexual, and emotional violence.

Short Biography

Thomas Ristenpart is an Associate Professor at Cornell Tech and a member of the Computer Science department at Cornell University. Before joining Cornell Tech in May, 2015, he spent four and a half years as an Assistant Professor at the University of Wisconsin-Madison. He completed his PhD at UC San Diego in 2010. His research spans a wide range of computer security topics, with recent focuses including digital privacy and safety in intimate partner violence, anti-abuse mitigations for encrypted messaging systems, improvements to authentication mechanisms including passwords, and topics in applied and theoretical cryptography. His work is routinely featured in the media and has been recognized by a number of distinguished paper awards, two ACM CCS test-of-time awards, an Advocate of New York City award, an NSF CAREER Award, and a Sloan Research Fellowship.

Dates and titles are subject to change.