Past events

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.

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, Sijia Liu (Michigan State University), will be giving a talk titled "Robust and Efficient Neural Network Training: A New Bi-level Learning Paradigm".

Abstract

This talk will introduce Bi-level Machine Learning (BML), an emerging but overlooked topic rooted in bi-level optimization (BLO), to tackle the recent neural network training challenges in robust and efficient AI. In the first part, I will revisit adversarial training (AT)–a widely recognized training mechanism to gain adversarial robustness of deep neural networks–from a fresh BML viewpoint. Built upon that, I will introduce a new theoretically-grounded and computationally-efficient robust training algorithm termed Fast Bi-level AT (Fast-BAT), which can defend sign-based projected gradient descent (PGD) attacks without using any gradient sign method or explicit robust regularization.

In the second part, I will move to a sparse learning paradigm that aims at pruning large-scale neural networks for improved generalization and efficiency. As demonstrated by the Lottery Ticket Hypothesis (LTH), iterative magnitude pruning (IMP) is the predominant sparse learning method to successfully find ‘winning’ sparse sub-networks. Yet, the computation cost of IMP grows prohibitively as the sparsity ratio increases. I will show that BML provides a graceful algorithmic foundation for model pruning and helps us close the gap between pruning accuracy and efficiency. Please see the references and codes at Sijia Liu's GitHub repository.

Biography

Dr. Sijia Liu is currently an Assistant Professor at the Department of Computer Science and Engineering, Michigan State University, and an Affiliated Professor at the MIT-IBM Watson AI Lab, IBM Research. His research spans the areas of machine learning, optimization, computer vision, signal processing, and computational biology, with a recent focus on Trustworthy and Scalable ML. He received the Best Paper Runner-Up Award at the Conference on Uncertainty in Artificial Intelligence (UAI) in 2022 and the Best Student Paper Award at the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) in  2017. He has published over 60 papers at top-tier ML/AI conferences and (co-)organized several tutorials and workshops on Trustworthy AI and Optimization for Deep Learning at KDD, AAAI, CVPR, and ICML, to name a few.

The John V. Carlis Memorial Lecture is dedicated to the advancement of education and inclusion in the field of computing.

This year's speaker is Dr. Carla Brodley from Northeastern University, giving a talk titled "Broadening Participation in Computing by Opening New Pathways to the BS, MS and PhD.".

Abstract

For the last two decades professors, non-profits, philanthropists, NSF and other agencies have been working to broaden participation in computing (BPC) in higher-ed at all levels and some progress has been made, but often it is incremental and takes place in small pockets. To accelerate the rate of progress in diversifying computing nationally requires that we as a field understand and remove institutional barriers at every level of higher ed, and that we rethink the invitation to create systemic sustainable change.  

Launched in 2019 with funding from Pivotal Ventures LLC, an investment and incubation company created by Melinda French Gates, the Center for Inclusive Computing (CIC) is working in partnership with colleges and universities across the country to increase the representation of women – of all races and ethnicities – in computing. A key focus is to identify and remove institutional barriers and create new pathways to the BS, MS and PhD in computing.  

In her talk, Dr. Carla Brodley, the CIC’s Executive Director and former dean of Northeastern’s Khoury College of Computer Sciences, will explore the most common institutional barriers the CIC is seeing across its portfolio.  She will discuss the concrete measures that can be taken to address barriers to retention and the need to open new pathways to computing, such as creating a BA in computing, making CS1 a general education requirement, handling the distribution of prior computing experience in the intro sequence, creating interdisciplinary BS/BA degrees, rethinking PhD admissions, and creating and scaling the MS in CS for non-computer science majors.

Biography

Professor Carla E. Brodley is the dean of inclusive computing at Northeastern University, where she serves as the executive director for the Center for Inclusive Computing and holds a tenured appointment in Khoury College of Computer Sciences. Brodley served as dean of Khoury College from 2014-2021. Prior to joining Northeastern, she was a professor at the Department of Computer Science and the Clinical and Translational Science Institute at Tufts University (2004-2014). Before joining Tufts, she was on the faculty of the School of Electrical Engineering at Purdue University (1994-2004).

A fellow of the Association for Computing Machinery, the Association for the Advancement of Artificial Intelligence (AAAI), and the American Association for the Advancement of Science (AAAS), Brodley’s interdisciplinary machine learning research led to advances not only in computer science, but in many other fields including remote sensing, neuroscience, digital libraries, astrophysics, content-based image retrieval of medical images, computational biology, chemistry, evidence-based medicine, and predictive medicine. Brodley’s current research focus is computer science education and methods for broadening participation in computing.  In 2022 she was the recipient of the ACM Francis E. Allen Award for Outstanding Mentoring.

Brodley’s numerous leadership positions include serving as program co-chair of the International Conference on Machine Learning, co-chair of AAAI, and associate editor of the Journal of AI Research and the Journal of Machine Learning Research. She previously served on the Defense Science Study Group, the board of the International Machine Learning Society, the AAAI Council, the executive committee of the Northeast Big Data Hub, DARPA’s Information Science and Technology Board, and the NSF CISE advisory committee.  Brodley is currently vice chair of the Board of Trustees of the Jackson Laboratory (JAX).  She serves as a member of the Board of Directors of Alegion, Inc, of the Computing Research Association Board of Directors, of the Mass Technology Leadership Council, of the International Advisory Board of the Quad Fellowships, and is a strategic advisor for Science for America.

RSVP today!.

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 computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Stephanie Forrest (Arizona State University), will be giving a talk titled "The Biology of Software".

Abstract

Computer programmers like to think of software as the product of intelligent design, carefully crafted to meet well-specified goals.  In reality, large software systems evolve inadvertently through the actions of many individual programmers, often leading to unanticipated consequences.  Because software is subject to constraints similar to those faced by evolving biological systems, we have much to gain by viewing software through the lens of evolutionary biology.  The talk will highlight research applying the mechanisms of evolution quite directly to software, including repairing bugs and runtime optimization of GPU codes.  These results have implications for how we think more generally about engineering complex systems that are subject to evolutionary pressures and engineering constraints.

Biography

Stephanie Forrest is Professor of Computer Science at Arizona State University, where she directs the Biodesign Center for Biocomputation, Security and Society.  Her interdisciplinary research focuses on the intersection of biology and computation, including cybersecurity, software engineering, and biological modeling.

Prior to joining ASU in 2017, she was a Distinguished Professor at the University of New Mexico and served as Dept. Chair. She is a member of the Santa Fe Institute External Faculty and past co-Chair of its Science Board and Interim VP for Academic Affairs.  She spent 2013-2014 as a Jefferson Fellow at the U.S. Dept. of State as a Senior Science Advisor for cyberpolicy.  She was educated at St. John's College (B.A.) and the University of Michigan (M.S. and Ph.D. in Computer Science).

Some of her awards include: The 2020 Test of Time Award from the IEEE Security and Privacy Symposium; The 2019 Most Influential Paper Award from the International Conference on Software Engineering, the Santa Fe Institute Stanislaw Ulam Memorial Lectures (2013), the ACM/AAAI Allen Newell Award (2011), and the NSF Presidential Young Investigator Award (1991).  She is a Fellow of the IEEE and a member of the Computing Research Association Board of Directors.