While school is in session, EECS invites a distinguished researcher or practitioner in a computer science or electrical and computer engineering-related field to present their ideas and/or work. Talks are generally targeted to electrical engineering and computer science graduate students. This colloquium series is free and open to everyone.

Upcoming Colloquia

Surveillance at the margins of the U.S. carceral system

Monday, May 23, 2022 - 12:00pm to 1:00pm
KEC 1007

Speaker Information

Kentrell Owens
PhD Candidate
Allen School of Computer Science & Engineering
University of Washington


Courts have ruled that incarcerated people have a diminished right to privacy. But what does that mean for people who are not incarcerated who are also caught up in the ever-widening net of surveillance? In this talk I will discuss prior work on how carceral surveillance impacts two communities: families members of incarcerated people and people under community supervision (e.g., release from immigrant detention, probation, parole).

Surveillance of communication between incarcerated and non-incarcerated people has steadily increased, enabled partly by technological advancements. Third-party vendors control communication tools for most U.S. prisons and jails and offer surveillance capabilities beyond what individual facilities could realistically implement. Frequent communication with family improves mental health and post-carceral outcomes for incarcerated people, but does discomfort about surveillance affect how their relatives communicate with them? To explore this question and others we conducted 16 semi-structured interviews with participants who have incarcerated relatives. Among other findings, we learned that participants communicate despite privacy concerns that they felt helpless to address. We also observed inaccuracies in participants’ beliefs about surveillance practices. We discussed implications of inaccurate understandings of surveillance, misaligned incentives between end-users and vendors, how our findings enhanced ongoing conversations about carceral justice, and recommendations for more privacy-sensitive communication tools. In a subsequent project I focused on the rise in the use of smartphone applications (apps) to monitor people under community supervision. Electronic monitoring is the use of technology to track individuals accused or convicted of a crime (or civil violation) as an "alternative to incarceration." Traditionally, this technology has been in the form of ankle monitors, but recently federal, state, and local entities around the U.S. are shifting to using smartphone applications for electronic monitoring. These apps purport to make the monitoring simpler and more convenient for both the community supervisor and the person being monitored. However, due to the multipurpose nature of smartphones in people's lives and the amount of sensitive information (e.g., sensor data) smartphones make available, this introduces new risks to people coerced to use these apps.

To understand what type of privacy-related and other risks might be introduced to people who use these applications, we conducted a privacy-oriented analysis of 16 Android apps used for electronic monitoring. We analyzed the apps first technically, with static and (limited) dynamic analysis techniques. We also analyzed user reviews in the Google Play Store to understand the experiences of the people using these apps, and also the privacy policies. We found that the apps contain numerous trackers, the permissions requested by them vary widely (with the most common one being location), and the reviews indicate that people find the apps invasive and frequently dysfunctional. We ended our paper by encouraging mobile app marketplaces to reconsider their role in the future of electronic monitoring apps, and computer security and privacy researchers to consider their potential role in auditing carceral technologies. We hope that this work will lead to more transparency in this obfuscated ecosystem.

Speaker Bio

Kentrell Owens is a PhD student in the Allen School of Computer Science & Engineering at the University of Washington and a member of the Security and Privacy Research Lab. He is co-advised by Franziska Roesner and Tadayoshi Kohno. He is specifically interested in the computer security and privacy needs of underserved communities. He has recently published work on web authentication, the surveillance of the communication of incarcerated people and their families, and the risks of using smartphone applications for electronic monitoring (e.g., as a condition of probation/parole).

AI Seminar: Toward Addressing Evaluation and Explanation Challenges in Scientific ML Applications

Wednesday, May 25, 2022 - 1:00pm to 2:00pm
Zoom: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5UT09

Speaker Information

Shusen Liu
Research Scientist
Machine Intelligence Group
Lawrence Livermore National Laboratory


Although the influence of deep learning in scientific domains is unmistakable, there are still fundamental barriers to utilizing these complex models for scientific discovery due in part to our inability to directly translate their predictive capabilities into scientific understanding. The root of the problem is twofold: 1) the challenge to evaluate the model in the context of the application; 2) the difficulties of reasoning about such models in terms that domain scientists can easily understand for knowledge extraction. This talk will provide a closer look at these unique challenges associated with applying deep learning to scientific applications. And cover some of our works for addressing the evaluation and explanation challenges in Scientific ML. Specifically, we will show how topological data analysis plays a crucial role in evaluating deep surrogate models for fusion science, and how deep generative models allow us to explore hypothetical materials and obtain actionable explanations that lead to improved material performance.

Speaker Bio

Shusen Liu is a research scientist with the Machine Intelligence Group at the Lawrence Livermore National Laboratory (LLNL).  His interests include fundamental research in explainable AI and high-dimensional data visualization, as well as their impact on scientific applications for advancing domain understanding. He received a Ph.D.in computing from the University of Utah in 2017.

AI Seminar: Enabling Humans and Robots to Predict the Other’s Behavior from Small Datasets

Wednesday, June 1, 2022 - 1:00pm to 2:00pm
Zoom: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5UT09

Speaker Information

Vaibhav Unhelkar
Assistant Professor of Computer Science
Rice University


We are steadily moving towards a future where humans work with robotic assistants, teammates, and even trainers. Towards realizing this future and mitigating its adverse side effects, I will share two techniques that together enable humans and robots to predict the other’s task-oriented behavior. First, I will present AI Teacher: an interactive machine teaching framework to assist humans in acquiring mental models of robots. By building upon human’s natural ability to model others (Theory of Mind), the AI Teacher framework reduces the number of interactions it takes for humans to arrive at predictive models of robot behavior. Second, I will discuss BTIL, an imitation learning approach for enabling robots to arrive at predictive models of humans’ collaborative behavior. Discussions of both these techniques will highlight the need as well as solutions for sample-efficient learning in settings of human-robot collaboration.

Speaker Bio

Vaibhav Unhelkar is an Assistant Professor of Computer Science at Rice University, where he leads a research group in the emerging area of Human-Centered AI. Ongoing research in his group includes development of algorithms and systems that enable “robots to work with humans” and “humans to become informed users of robots.” Unhelkar received his doctorate in Autonomous Systems at MIT (2020) and completed his undergraduate education at IIT Bombay (2012). He serves as an Associate Editor for IEEE Robotics and Automation Letters and is the recipient of JPMC AI Early Career Researcher Award and AAMAS 2022 Best PC Member Award. Before joining Rice, Unhelkar worked as a robotics researcher at X, the Moonshot Factory (formerly, Google X).

Past Colloquia

Sinisa Todorovic
Wednesday, May 18, 2022 - 1:00pm to 2:00pm
Karthika Mohan and Chi Zhang
Wednesday, May 11, 2022 - 1:00pm to 2:00pm
Tawfik Rahal-Arabi
Monday, May 9, 2022 - 1:00pm to 2:00pm
Roni Khardon
Wednesday, May 4, 2022 - 1:00pm to 2:00pm
Balaji Lakshminarayanan
Wednesday, April 27, 2022 - 1:00pm to 2:00pm
Brian Wood
Wednesday, April 20, 2022 - 1:00pm to 2:00pm
Bolei Zhou
Wednesday, April 13, 2022 - 1:00pm to 2:00pm
Heather Knight
Wednesday, April 6, 2022 - 1:00pm to 2:00pm
Kai Ming Ting
Wednesday, March 30, 2022 - 5:00pm to 6:00pm
Sharon Yixuan Li
Wednesday, March 9, 2022 - 1:00pm to 2:00pm