Past events

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. 

This week's talk is a part of the Cray Distinguished Speaker Series. This series was established in 1981 by an endowment from Cray Research and brings distinguished visitors to the Department of Computer Science & Engineering every year.

This week's speaker, David Evans (University of Virginia), will be giving a talk titled "On Leaky Models and Unintended Inferences".

Abstract:

Machine learning offers the promise to train models that perform surprisingly well on a wide range of tasks, merely by using massive computing power and generic training algorithms on available data sets. It is an open question, however, what else those models might learn about their training data, and how an adversary with some access to the model may be able to reveal it. In this talk, I will discuss a variety of inference risks associated with machine-trained models, with a particular focus on surprising (and potentially harmful) things a model may reveal not just about individual training records but about the overall distribution of its training data. This includes attacks an adversary may use to learn statistical properties about the training distribution and about whether certain kinds of data are or are not included, and the potential for an adversary to use a model to make sensitive inferences about individuals, even for attributes not directly related to the task and regardless of whether those individuals are included the training data. I’ll conclude with some thoughts on why defending against these types of attacks is hard, and what we might learn about how we should be training and exposing models.

Bio:

David Evans (https://www.cs.virginia.edu/evans/) is a Professor of Computer Science at the University of Virginia where he leads research on security and privacy (https://uvasrg.github.io/) with a recent focus on adversarial machine learning and inference risks in machine learning, and teaches courses on a wide variety of topics including biology, ethics, economics, and theory of computing. He is the author of an open computer science textbook (https://computingbook.org) and a children's book on combinatorics and computability (https://dori-mic.org) and co-author of a book on secure computation (https://securecomputation.org/). He won the Outstanding Faculty Award from the State Council of Higher Education for Virginia and is Program Co-Chair for the 2022 and 2023 IEEE European Symposia on Security and Privacy. He was Program Co-Chair for the 24th ACM Conference on Computer and Communications Security (CCS 2017) and the 30th (2009) and 31st (2010) IEEE Symposia on Security and Privacy, where he initiated the Systematization of Knowledge (SoK) papers (https://oaklandsok.github.io/). He has SB, SM and PhD degrees in Computer Science from MIT and has been a faculty member at the University of Virginia since 1999.

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During each session, the MSSE staff will review:

• Requirements (general)
• Applying
• Prerequisite requirements
• What makes a strong applicant
• Funding
• Resources
• Common questions
• Questions from attendees

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Wednesday from 11 a.m. - 12 p.m. during the Fall 2022 semester.

This week's speaker, Zhengyuan Zhou (New York University Stern School of Business, Department of Technology, Operations and Statistics), will be giving a talk titled "Optimal No-Regret Learning in Repeated First-Price Auctions".

Abstract

First-price auctions have very recently swept the online advertising industry, replacing second-price auctions as the predominant auction mechanism on many platforms for display ads bidding. This shift has brought forth important challenges for a bidder: how should one bid in a first-price auction, where unlike in second-price auctions, it is no longer optimal to bid one's private value truthfully and hard to know the others' bidding behaviors?

In this talk, we take an online learning angle and address the fundamental problem of learning to bid in repeated first-price auctions. We discuss our recent work in leveraging the special structures of the first-price auctions to design minimax optimal no-regret bidding algorithms. 

Biography

Zhengyuan Zhou is currently an assistant professor in New York University Stern School of Business, Department of Technology, Operations and Statistics. Before joining NYU Stern, Professor Zhou spent the year 2019-2020 as a Goldstine research fellow at IBM research. He received his BA in Mathematics and BS in Electrical Engineering and Computer Sciences, both from UC Berkeley, and subsequently a PhD in Electrical Engineering from Stanford University in 2019. His research interests lie at the intersection of machine learning, stochastic optimization and game theory and focus on leveraging tools from those fields to develop methodological frameworks to solve data-driven decision-making problems.

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During each session, the graduate staff will review:

• Requirements (general)
• Applying
• Prerequisite requirements
• What makes a strong applicant
• Funding
• Resources
• Common questions
• Questions from attendees

Students considering the following programs should attend:

• Computer Science M.S.
• Computer Science M.C.S.
• Computer Science Ph.D.
• Data Science M.S.
• Data Science Post-Baccalaureate Certificate
  
   

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Mary Czerwinski (Microsoft Research), will be giving a talk titled "The Future of Technology for Health and Wellbeing in the Workplace".

Abstract

How can we create technologies to help us reflect on and potentially change our behavior, as well as improve our health and overall wellbeing, both at work and at home? In this talk, I will briefly describe the last several years of work our research team has been doing in this area. We have developed wearable technology to help families manage tense situations with their children, mobile phone-based applications for handling stress and depression, as well as automatic stress sensing systems plus psychologically efficacious interventions to help users just in time. The overarching goal in all of this research is to develop intelligent systems that work with and adapt to the user so that they can maximize their personal health goals and improve their wellbeing.

Biography

Dr. Mary Czerwinski is a Partner Research Manager of the Human Understanding and Empathy (HUE) Research Group at Microsoft Research. Mary's latest research focuses primarily on behavior change and intervention design, health and wellness for individuals and productivity at work. Her research background is in visual attention and multitasking. She holds a Ph.D. in Cognitive Psychology from Indiana University in Bloomington. Mary received the ACM SIGCHI Lifetime Service Award, was inducted into the CHI Academy and received the Distinguished Alumni award from Indiana University's College of Arts and Sciences. Mary is a Fellow of the ACM and the American Psychological Science Association. This year, Mary was inducted into the National Academy of Engineering. More information about Dr. Czerwinski can be found at her website: https://www.microsoft.com/en-us/research/people/marycz/.

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Wednesday from 11 a.m. - 12 p.m. during the Fall 2022 semester.

This week's speaker, Xiaoran Sun (FSS, UMN), will be giving a talk titled "Machine Learning for Human Development and Family Research: An Overview and an Example".

Abstract

This talk will first provide a brief overview about the utility of machine learning (ML) in research on developmental and family science by presenting what ML can offer in the face of theories and research questions in this field. Then the talk will introduce a study using a literature-driven supervised ML approach for empirical synthesis on how family experiences during adolescence predict future educational outcomes in adulthood. Based on the utility and the empirical synthesis example, there will be a discussion about future steps for how we can expand on the use of ML in social science research. Note that this talk will be focused on the applications of ML instead of technical details of advancing  ML itself. Questions, discussions, and comments will all be super appreciated given the project is still in its development stage.

Biography

Xiaoran Sun is an assistant professor in the Department of Family Social Science at the University of Minnesota. She is also a faculty affiliate of the Learning Informatics Lab in the College of Education and Human Development and of the Data Science Initiative. She obtained her PhD in Human Development and Family Studies from the Pennsylvania State University with an NSF traineeship on Big Data Social Science. Before joining UMN she was a postdoctoral scholar at Stanford University in the Departments of Pediatrics and Communication and a Stanford Data Science scholar. She uses ML in her research on family systems and adolescent development.

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. More details about the fall 2022 series will be provided at the beginning of the semester. This week's speaker, Fernando Maestre (UMN CS&E), will be giving a talk titled "Participatory Design as a Method for More Ethical Computer Science". 

Abstract

Technology can have unintended negative impacts or consequences in people’s lives. For example, the design of a user interface may exclude certain populations by having a text-field data entry for gender including only the binary option of man and woman. More recently, technologies which use machine learning and artificial intelligence may reflect and even exacerbate systemic bias and inequalities experienced by racial and gender minorities and other vulnerable groups. In my work, I aim to reduce these unintended consequences in the design of technologies through participatory design (PD) methods. During the talk, I will discuss how PD methods as well as value-sensitive and speculative design approaches can help include and amplify the voices of study participants and stakeholders throughout the design process. This has been particularly important in my research as I have been working with vulnerable and marginalized populations such as people living with stigmatized conditions like HIV, or those with non-normative gender identities. I will go over a few examples of my prior work with these populations where I used PD in both in-person and online settings. I will end the talk with next steps for ongoing and future work that explores potential ways in which PD could be used in a more ethical design of algorithm-based technology that would take in account multi-stakeholder values and that could be more sensitive and reactive to historical and systemic inequalities.

Biography

Fernando Maestre (he/him/his) is an Ecuadorian researcher and educator. After moving to the United States in 2013, he obtained a Master’s degree in Informatics from the University of Iowa and a PhD degree in Human-computer Interaction Design from the Luddy School of Informatics, Computing, and Engineering at Indiana University. Fernando conducts Human-Computer Interaction (HCI) research with stigmatized and marginalized groups. He applies participatory design methods to conduct research in in-person and online settings regarding technology design for stigma management, health informatics, and transportation access. Fernando is currently a President’s Postdoctoral Fellow and member of the GroupLens Lab in the Department of Computer Science and Engineering at the University of Minnesota.

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Alex Elder (Linaro), will be giving a talk titled "The Linux Kernel Development Model".

Abstract

The Linux kernel began in 1991 as one person's toy project "just for fun." Linus Torvalds wanted only to share and get feedback on his work. Today, there are thousands of Linux contributors, and the kernel underlies a vast number of computing systems, including microcontrollers, mobile phones, laptops, and the largest supercomputers.

Linux development has always been in the open. Proposed changes, and discussions about them, occur predominantly via e-mail.  This allows people from around the world to participate. The source code is managed and protected by a hierarchy of subsystem "maintainers," who ensure that changes to the system are not allowed without proper review and justification. Maintainers thus serve a critical and trusted role in preserving integrity of the kernel (both its design, and the quality of its source code). People who regularly contribute establish a reputation based on the quality of their changes (and feedback).  A good reputation builds trust, and this can lead to recognition and increased responsibility within the community. In addition to the core kernel, contributors build tools, automation, and documentation that improve and streamline the development process itself.

This talk presents the Linux kernel development model. It will highlight important parts of the model that help ensure the quality of the kernel remains high, while permitting the flexibility to adapt and evolve. It will close with some discussion of the relationship between the University of Minnesota and the Linux community.

Biography

Alex Elder is an operating system developer who has been working on the Linux kernel since 2000. He first used Unix in college, and began his professional career maintaining Unix systems used by faculty and students. He studied parallel computing in graduate school, and went on to work with high-performance computers, and to develop software for highly secure operating systems. He developed an expertise in Unix and Linux storage, including distributed and scalable clustered storage. Since 2013, Alex has been working for Linaro, developing Linux kernel software for systems that use the Arm architecture.

The Minnesota Natural Language Processing (NLP) Seminar is a venue for faculty, postdocs, students, and anyone else interested in theoretical, computational, and human-centric aspects of natural language processing to exchange ideas and foster collaboration. The talks are every other Friday from 2 - 3 p.m. during the fall 2022 semester.

This week's speaker, Juho Kim (KAIST), will be giving a talk titled "Interaction-Centric AI".

Abstract

Remarkable model performance makes news headlines and compelling demos, but these advances rarely translate to a lasting impact on real-world users. A common anti-pattern is overlooking the dynamic, complex, and unexpected ways humans interact with AI, which in turn limits the adoption and usage of AI in practical contexts. To address this, I argue that human-AI interaction should be considered a first-class object in designing AI applications.

In this talk, I present a few novel interactive systems that use AI to support complex real-life tasks. I discuss tensions and solutions in designing human-AI interaction, and critically reflect on my own research to share hard-earned design lessons. Factors such as user motivation, coordination between stakeholders, social dynamics, and user’s and AI’s adaptivity to each other often play a crucial role in determining the user experience of AI, even more so than model accuracy. My call to action is that we need to establish robust building blocks for “Interaction-Centric AI”—a systematic approach to designing and engineering human-AI interaction that complements and overcomes the limitations of model- and data-centric views.

Biography

Juho Kim [juhokim.com] is an Associate Professor in the School of Computing at KAIST, affiliate faculty in the Kim Jaechul Graduate School of AI at KAIST, and a director of KIXLAB (the KAIST Interaction Lab) [kixlab.org]. His research in human-computer interaction and human-AI interaction focuses on building interactive and intelligent systems that support interaction at scale, with the goal of improving the ways people learn, collaborate, discuss, make decisions, and take action online.  He earned his Ph.D. from MIT in 2015, M.S. from Stanford University in 2010, and B.S. from Seoul National University in 2008. In 2015-2016, he was a Visiting Assistant Professor and a Brown Fellow at Stanford University. He is a recipient of KAIST’s Songam Distinguished Research Award, Grand Prize in Creative Teaching, and Excellence in Teaching Award, as well as 14 paper awards from ACM CHI, ACM CSCW, ACM Learning at Scale, ACM IUI, ACM DIS, and AAAI HCOMP. He is currently spending his sabbatical year at Ringle Inc., a startup building an online language tutoring platform, to transfer his research on automatically analyzing and diagnosing learners’ English proficiency into a real product.