Past events

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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
  
   

CSE DSI Machine Learning seminars will be held Tuesdays 11a.m. - 12 p.m. Central Time in hybrid mode. We hope facilitate face-to-face interactions among faculty, students, and partners from industry, government, and NGOs by hosting some of the seminars in-person. See individual dates for more information.

This week's speaker, Neil Gong (EE, Duke), will be giving a talk titled, "Secure Content Moderation for Generative AI".

Abstract

Generative AI–such as GPT-4 and DALL-E 3–raises many ethical and legal concerns such as the generation of harmful content, scaling disinformation and misinformation campaigns, as well as disrupting education and learning. Content moderation for generative AI aims to address these ethical and legal concerns via 1) preventing a generative AI model from synthesizing harmful content, and 2) detecting AI-generated content. Prevention is often implemented using safety filters, while detection is implemented by watermark. Both prevention and watermark-based detection have been recently widely deployed by industry. In this talk, we will discuss the security of existing prevention and watermark-based detection methods in adversarial settings. 

How do you picture a typical scientist?

The CS-IDEA Committee will be exploring this question with a screening of Picture a Scientist. The event is open to all CS&E students, faculty, and researchers. Pizza and beverages will be provided. The event is free; please RSVP by 12/1/23. 

The Computer Science & Engineering (CS&E) department is committed to supporting and recruiting a diverse community of students, staff, and faculty and helping everyone in this community to thrive. This requires deliberate work to build an inclusive and supportive environment for those from historically underrepresented and non-traditional backgrounds. The Computer Science Inclusivity, Diversity, Equity, and Advocacy (CS-IDEA) committee aims to attract and retain diverse students, staff, and faculty in computer science and engineering and help all students, staff, and faculty thrive within the Department of Computer Science & Engineering at the University of Minnesota.  

Agenda

5:30 p.m. - Pizza
6 p.m. - Movie Screening
7:45 p.m. - Discussion

PICTURE A SCIENTIST chronicles the groundswell of researchers who are writing a new chapter for women scientists. Biologist Nancy Hopkins, chemist Raychelle Burks, and geologist Jane Willenbring lead viewers on a journey deep into their own experiences in the sciences, ranging from brutal harassment to years of subtle slights. Along the way, from cramped laboratories to spectacular field stations, we encounter scientific luminaries - including social scientists, neuroscientists, and psychologists - who provide new perspectives on how to make science itself more diverse, equitable, and open to all. 

Picture a Scientist - trailer from Wicked Delicate Films on Vimeo.

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 Ben Schafer from University of Northern Iowa, giving a talk titled "What a Well-Trained Computer Scientist Should Know about Education".

Abstract

While graduate schools typically do a great job teaching students the skills to become high-quality researchers, they often ignore teaching students about the science (and art) necessary to become high-quality educators. This is an arguably significant deficiency as nearly a third of Ph.D. graduates will end up in academia, where they will be expected to teach. In this setting, most academics will simply model what they experienced as students. Unfortunately, our knowledge of the science of education has evolved while many actual classrooms have not. In this lecture, we will consider some of the more recent research about education in general, and, where applicable, computer science education. Specifically, we will focus on three major topics helpful to anyone interested in improving their computer science education skills: The Big Book of Computing Pedagogy, Understanding by Design, and Grading for Equity.

Biography

Dr. J. Ben Schafer began his career as a middle school math and science teacher while moonlighting as a developer of educational multi media software. He is a graduate of the University of Minnesota where he earned both his M.S. and Ph.D. degrees in Computer and Information Science while working in the GroupLens research lab. He is currently a Professor of Computer Science and the Program Coordinator of the CS Education program at the University of Northern Iowa in Cedar Falls. He has served on a variety of CS advisory panels and working groups for the state of Iowa, was a member of the LEGO Educational Advisory Panel, and taught almost 1400 teachers as one of the original Code.org Facilitators for the CS Fundamentals curriculum. He is an active participant with both CSTA and ACM SIGCSE having served in leadership roles and made multiple presentations at their annual conferences. He is the recent recipient of two NSF:CSForAll:RPP awards. He is a passionate and award-winning educator who firmly believes that everyone has the ability to improve their teaching.

CSE DSI Machine Learning seminars will be held Tuesdays 11a.m. - 12 p.m. Central Time in hybrid mode. We hope facilitate face-to-face interactions among faculty, students, and partners from industry, government, and NGOs by hosting some of the seminars in-person. See individual dates for more information.

This week's speaker, Yulong Lu (Math, UMN), will be giving a talk titled, "Two-scale gradient descent ascent dynamics finds mixed Nash equilibria of continuous games: A mean-field perspective".

Abstract

Finding the mixed Nash equilibria (MNE) of a two-player zero sum continuous game is an important and challenging problem in machine learning. A canonical algorithm to finding the MNE is the (noisy) gradient descent ascent (GDA) method. In this talk, we will discuss the infinite particle limit of the GDA dynamics and its convergence properties. Specifically, we show that for each finite temperature (or regularization parameter), the two-scale Mean-Field GDA with a suitable {\em finite} scale ratio converges exponentially to the unique MNE without assuming the convexity or concavity of the interaction potential. We further study the simulated annealing of the Mean-Field GDA dynamics. We show that with a temperature schedule that decays logarithmically in time the annealed Mean-Field GDA converges to the MNE of the original unregularized objective.

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Isayas Adhanom (University of Minnesota), will be giving a talk titled "Unlocking Virtual Reality’s True Potential: Addressing the Challenge of VR Sickness".

Abstract

Virtual Reality (VR) is rapidly evolving, extending its reach from entertainment and gaming to critical areas like education, healthcare, and professional training. However, its usability is significantly hindered by VR sickness, a type of motion sickness that affects a substantial portion of users. This condition not only diminishes the user experience but also limits the duration and frequency of VR usage and alters user behavior,  thereby affecting its efficacy in various fields. This challenge is particularly pronounced for certain demographic groups, especially women, making the mitigation of VR sickness a critical challenge. In this talk, I will discuss the interdisciplinary approach my research takes, leveraging insights from psychology and neuroscience to deepen our understanding of VR sickness and develop effective mitigation techniques. I will discuss environmental and perceptual adaptation techniques that my research has explored to reduce VR sickness. Environmental adaptation involves altering the virtual environment to minimize factors that contribute to VR sickness, whereas perceptual adaptation, focuses on developing systems that could help train the user's sensory system to better tolerate the disparities between visual inputs and physical sensations in VR. Furthermore, I will discuss my ongoing research that aims to develop computational models, based on insights from vision science, to get a better understanding of the relationship between the characteristics of the visual stimuli in virtual environments and their effect on user comfort in VR.  This research aims not only to enhance our theoretical understanding of VR sickness but also to provide practical tools for designing more comfortable and inclusive VR experiences.

Biography

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Marty Wolf (Bemidji State University), will be giving a talk titled "The Evolving Nature of Ethics in Computing".

Abstract

We used to do computer science for computer science’s sake. Over the past 75 years the field has increasingly focused on computing for society’s sake. Because of that evolution, computing ethics has undergone substantial change as well. In this talk, I will document how the history of the ACM Code of Ethics and Professional Conduct both reflects and anticipates that evolution. I will provide case studies that surface changes that have taken place in scholarship, publishing, higher education, and the computing industry that heighten the need for those in the field of computer science to more fully embrace better incorporation of critical ethical reflection into the practice of computing. Simply, ethics can no longer be an add on or afterthought in computing. I will present a simple framework that members of the computing community can use to incorporate ethical consideration into course delivery and research projects. This framework can be used to initiate changes that will naturally incorporate ethical reflection as a regular part of the practice of computer scientists. 

Biography

The CS&E Research Showcase is a bi-annual event that features the collective works of students and faculty in the Department of Computer Science & Engineering. The event will feature over 60 posters, as well as a keynote addresses from Eugene Spafford, the founder and executive director of the Center for Education and Research in Information Assurance and Security (CERIAS) at Purdue University, and Ed Chi, CS&E alumni award winner and Distinguished Scientist at Google. See below for more information about the speakers.

Additionally, the event will feature the Fall 2023 Data Science Poster Fair. This event is held each semester and feature the capstone project and poster presentation for graduating data science master's student.

This event is open to the public and all interested undergraduate and graduate students, alumni, staff, faculty, and industry professionals are encouraged to attend.  To let us know you'll be joining us, please fill out our RSVP form below. We ask those who plan to attend to RSVP by Friday, November 10. 
 

Keynote Speakers

Eugene Spafford - Professor, Executive Director of CERIAS Emeritus

A Perspective on Cybersecurity History and Futures

Abstract  
Cybersecurity is about 60 years old.  As such, it is a relatively new field, with much of its early history being centered in computing.  As technology and computing uses have advanced, new challenges, threats, and solutions have appeared.  Today’s cybersecurity landscape includes issues related to people, laws, privacy, safety, and fundamental questions of ethics, in addition to issues of technology.

In this talk, I will recap some of the history and developments of computing that have had implications for cybersecurity and related areas.  I will discuss some of the current challenges and some of what I see as developments and challenges over the next few decades.  Many of these are more general issues in computing, developing as we adapt to new technologies and constraints.

About
Eugene H. Spafford is a professor of Computer Sciences at Purdue University. He is also the founder and Executive Director Emeritus of the Center for Education and Research in Information Assurance and Security (CERIAS). He has worked in computing as a student, researcher, consultant, and professor for more than 45 years. Some of his work is at the foundation of current security practice, including intrusion detection, incident response, firewalls, integrity management, and forensic investigation. His most recent work has been in cybersecurity policy, security of real-time systems, and future threats. He has also been a pioneer in education, including starting and heading the oldest degree-granting cybersecurity program.

Dr. Spafford has been recognized with significant honors from various organizations. These include being elected as a Fellow of the American Academy of Arts and Sciences (AAA&S), and the Association for the Advancement of Science (AAAS); a Life Fellow of the ACM, the IEEE, and the (ISC)2; a Life Distinguished Fellow of the ISSA; and a member of the Cyber Security Hall of Fame — the only person to ever hold all these distinctions. In 2012 he was named one of Purdue’s inaugural Morrill Professors — the university’s highest award for the combination of scholarship, teaching, and service. In 2016, he received the State of Indiana’s highest civilian honor by being named as a Sagamore of the Wabash.

Among many other activities, he is editor-in-chief of the journal Computers & Security, serves on the Board of Directors of the Computing Research Association, and is a member of the National Security Advisory Board for Sandia Laboratories.

Ed Chi - Distinguished Scientist at Google and Alumni Award Winner (Ph.D., 1999; M.S., 1998; B.S., 1994)

The LLM (Large Language Model) Revolution: Implications from Chatbots and Tool-use to Reasoning

Abstract
Deep learning is a shock to our field in many ways, yet still many of us were surprised at the incredible performance of Large Language Models (LLMs). LLM uses new deep learning techniques with massively large data sets to understand, predict, summarize, and generate new content.  LLMs like ChatGPT and Bard have seen a dramatic increase in their capabilities---generating text that is nearly indistinguishable from human-written text, translating languages with amazing accuracy, and answering your questions in an informative way. This has led to a number of exciting research directions for chatbots, tool-use, and reasoning:

- Chatbots: LLM chatbots that are more engaging and informative than traditional chatbots. First, LLMs can understand the context of a conversation better than ever before, allowing them to provide more relevant and helpful responses.  Second, LLMs enable more engaging conversations than traditional chatbots, because they can understand the nuances of human language and respond in a more natural way. For example, LLMs can make jokes, ask questions, and provide feedback.  Finally, because LLM chatbots can hold conversations on a wide range of topics, they can eventually learn and adapt to the user's individual preferences.  

- Tool-use, Retrieval Augmentation and Multi-modality: LLMs are also being used to create tools that help us with everyday tasks. For example, LLMs can be used to generate code, write emails, and even create presentations.  Beyond human-like responses in Chatbots, later LLM innovators realized LLM’s ability to incorporate tool-use, including calling search and recommendation engines, which means that they could effectively become human assistants in synthesizing summaries from web search and recommendation results.  Tool-use integration have also enabled multimodal capabilities, which means that the chatbot can produce text, speech, images, and video.

- Reasoning: LLMs are also being used to develop new AI systems that can reason and solve problems. Using Chain-of-Thought approaches, we have shown LLM's ability to break down problems, and then use logical reasoning to solve each of these smaller problems, and then combine the solutions to reach the final answer.  LLMs can answer common-sense questions by using their knowledge of the world to reason about the problem, and then use their language skills to generate text that is both creative and informative.

In this talk, I will cover recent advances in these 3 major areas, attempting to draw connections between them, and paint a picture of where major advances might still come from.  While the LLM revolution is still in its early stages, it has the potential to revolutionize the way we interact with AI, and make a significant impact on our lives.

About
Ed H. Chi is a Distinguished Scientist at Google DeepMind, leading machine learning research teams working on large language models (LaMDA/Bard), neural recommendations, and reliable machine learning. With 39 patents and ~200 research articles, he is also known for research on user behavior in web and social media.  As the Research Platform Lead, he helped launched Bard, a conversational AI experiment, and delivered significant improvements for YouTube, News, Ads, Google Play Store at Google with >660 product improvements since 2013.

Prior to Google, he was Area Manager and Principal Scientist at Xerox Palo Alto Research Center's Augmented Social Cognition Group in researching how social computing systems help groups of people to remember, think and reason. Ed earned his 3 degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota. Inducted as an ACM Fellow and into the CHI Academy, he also received a 20-year Test of Time award for research in information visualization. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press.  An avid golfer, swimmer, photographer and snowboarder in his spare time, he also has a blackbelt in Taekwondo.

CSE DSI Machine Learning seminars will be held Tuesdays 11a.m. - 12 p.m. Central Time in hybrid mode. We hope facilitate face-to-face interactions among faculty, students, and partners from industry, government, and NGOs by hosting some of the seminars in-person. See individual dates for more information.

This week's speaker, Aldo Scutari (IE, Purdue), will be giving a talk titled, "Statistical Inference over Networks: Decentralized Optimization Meets High-Dimensional Statistics".

Abstract

There is growing interest in solving large-scale statistical machine learning problems over decentralized networks, where data are distributed across the nodes of the network and no centralized coordination is present (we termed these systems as “mesh” networks). Inference from massive datasets poses  a fundamental challenge at the nexus of the computational and statistical sciences: ensuring the quality of statistical inference when computational resources, like time and communication, are constrained.   While statistical-computation tradeoffs have been largely explored in the centralized setting, our understanding over mesh networks is limited: (i) distributed schemes, designed and performing well in the classical low-dimensional regime, can break down in the high-dimensional case; and (ii) existing convergence studies may fail to predict algorithmic behaviors, with some findings directly contradicted by empirical tests. This is mainly due to the fact that the majority of distributed algorithms  have been designed and studied only from the optimization perspective, lacking the statistical dimension. This talk will discuss some vignettes from  high-dimensional statistical inference suggesting  new analyses (and designs) aiming at bringing statistical thinking in distributed optimization.

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Klara Nahrstedt (University of Illinois Urbana-Champaign), will be giving a talk titled "Digital Transformations of Cleanrooms in Academic Scientific Environments".

Abstract

Computer science and engineering made tremendous advances to enable digital transformations in many domains via computing technologies such as data processing and management, Internet of Things (IoT) systems, wired and wireless networks, machine-learning and multi-modal analytics, and others.

These computing advances are coming now to academic scientific environments, used in physical and life sciences, enabling digital transformations not seen before. These digital transformations are and will enable speed-up of materials discovery, shortening the span between materials discovery and their usage in device, circuit and computer architectures development, and other scientific discoveries on our campuses. However, it is a non-trivial task to achieve these digital transformations in academic environments in comparison to industrial scientific environments due to highly diverse groups who work in academic cleanrooms, heterogeneous scientific equipment spanning very different lifespans, and major cost and other resource constraints. In this talk, I will discuss the challenges of academic cleanrooms and the diversity of computing technologies that can and are contributing to the digital transformations in the cleanrooms and other academic scientific environments. I will discuss dealing with data acquisitions, processing, and management from diverse microscopes, and handling older scientific instruments and their security concerns. Furthermore, I will present IoT systems that enable access to much finer granularity of state information in cleanrooms to the scientists and lab managers when it comes to micro-climate information, maintenance information of scientific instruments, and visualization of anomaly and alert
information in case of failures.  

Biography