Past events

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Alex Kamenev, will be giving a talk titled "Quantum Optimization and Image Recognition."

Abstract

Abstract

Designers today have access to a growing number of advanced computational tools across the design process. While many tools have greatly improved design work efficiency, most have digitized previously done processes or have automated specific tasks. New tools and techniques are emerging that shift the tools from digital versions of hand-done processes to systems that take on their own agency and facilitate co-creation with the designer. As we develop more tools that act like design collaborators, we need to understand better how such digital collaborators can work effectively with designers and how designers can learn to collaborate. In this talk, I will describe some of my team’s prior and current work, exploring how new design tools can act more as design collaborators. I’ll then conclude with a vision of future design tools and systems that can further augment designers’ capabilities toward solving increasingly challenging problems.

Biography

Nikolas Martelaro is an Assistant Professor at Carnegie Mellon's Human-Computer Interaction Institute. His lab focuses on augmenting designer's capabilities through new technology and design methods. His goal to develop new ways to support designers stems from his experience creating interactive and intelligent products. Nik blends a background in product design methods, interaction design, human-robot interaction, and mechatronic engineering to build tools and methods that allow designers to understand people better and create more human-centered products. Before moving to the HCII, he was a researcher in the Digital Experiences group at the Accenture Technology Labs. Nik graduated with a Ph.D. in Mechanical Engineering from Stanford's Center for Design Research, where he was co-advised by Larry Leifer and Wendy Ju.

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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 computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Catherine Qi Zhao (University of Minnesota), will be giving a talk titled, "Attention in Vision-based AI Systems".

Abstract

Imagine you are at a bus stop in an unfamiliar city. You quickly glance around, gather and synthesize the information, and decide your next steps. While this process may seem intuitive, it represents a highly sophisticated ability to select and process information effectively. Our research is focused on developing machine intelligence that can understand and interact with the environment in a way that mimics human cognitive abilities.

I will begin by discussing attention prediction—specifically, the capability of machines to identify the most relevant information. I will delve into our computational models and experimental methods for attention prediction and understanding. Following this, I will explore new approaches that utilize attention to enhance interpretability and task performance. I will also present our research that builds upon attention the reasoning and knowledge integration capabilities to elucidate and refine the opaque and bias-prone decision-making processes in learning-
based AI systems. Lastly, I will discuss practical applications of our models and data in healthcare and manufacturing, where we enabled translations by clearing roadblocks of AI deployment which in turn amplified the impact of our AI innovations.

Biography

Catherine Qi Zhao is an Associate Professor in the Department of Computer Science and Engineering at the University of Minnesota, specializing in computer vision and machine learning with a strong multidisciplinary focus. Her research is currently supported by the NSF, NIST, and NIH. Dr. Zhao has published over 100 papers in peer-reviewed conferences and journals and held significant roles in the academic community. She serves as an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems and the IEEE Transactions on Multimedia. Additionally, she is the Program Chair at WACV 2022 and serves as an organizer or Area Chair at prestigious conferences such as CVPR, NeurIPS, and ICCV regularly. Dr, Zhao received the 2024 George W. Taylor Award for Distinguished Research.

Abstract

Recent high-profile scenarios have demonstrated that, in spite of the “data” in data-driven decision making, analysis practices nonetheless can lead to poor outcomes, given numerous junctures where bias can be introduced. Data may contain culturally embedded biases, algorithms may propagate or exacerbate those biases, and people’s decisions can be influenced by their own cognitive biases. While it is not yet possible to completely remove these varying biases from data analysis, some techniques exist to mitigate the effects by providing guidance or other forms of intervention. In this talk, I describe the development of one approach: novel visualization-oriented strategies that, rather than prescribing appropriate analysis behaviors or decisions, instead promote metacognitive awareness. 

Metacognition, or thinking about one’s own thinking, can provide insight on internal philosophies and promote people’s ability to effectively learn, i.e., by identifying their own deficits and strategizing to correct them. Importantly, metacognitive strategies can be taught and, when successful, has led to better education outcomes and has been shown to reduce bias and political polarization. Metacognitive training thus represents a promising approach to improve visual data analysis processes through in-situ interventions. This talk will detail recent and ongoing work in the Cognition and Visualization Lab at Emory toward designing metacognitive interventions that promote reflection on individuals’ decision making processes. I posit that, in addition to increasing metacognitive awareness of technical components of visual data analysis, these interventions may also lead to more socially responsible and conscientious data analysis practices. 

Biography

Emily Wall is an Assistant Professor in the Computer Science Department at Emory University where she directs the Cognition and Visualization Lab. Her research interests lie at the intersection of cognitive science and data visualization. Particularly, her research has focused on increasing awareness of unconscious and implicit human biases through the design and evaluation of (1) computational approaches to quantify bias from user interaction and (2) interfaces to support visual data analysis. Her research has been funded by the National Science Foundation and Emory Office of the Provost on Racial Justice and Racial Equity.

Join the Department of Computer Science & Engineering (CS&E) for this all-alumni event to explore the latest research advancements in virtual reality (VR), augmented reality (AR), and visualization. The evening will feature discussions with CS&E faculty members Zhu-Tian Chen, Victoria Interrante, Daniel Keefe, and Qianwen Wang. 

Enjoy hosted beverages and appetizers, and the chance to reconnect with former classmates, colleagues, instructors, and friends. All alumni of the University of Minnesota CS&E programs (Computer Science, Data Science, MSSE) are invited to attend, and guests are welcome. 

There is no charge to attend our event, but pre-registration is required. 

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Ruofei Du (Google), will be giving a talk titled "Computational Interaction for a Universally Accessible Metaverse."

Abstract

The emergent revolution of generative AI and spatial computing will fundamentally change the way we work and live. However, it remains a challenge how to make information universally accessible, and specifically, how to make generative AI and spatial computing useful in our daily life. In this talk, we will delve into a series of innovations in augmented programming, augmented interaction, and augmented communication, that aim to make both the virtual metaverse and the physical world universally accessible.

With Visual Blocks and InstructPipe, we empower novice users to unleash their inner creativity, by rapidly building machine learning pipelines with visual programming and generative AI. With Depth Lab, Ad hoc UI, Artificial Object Intelligence, and Finger Switches, we present real-time 3D interactions with depth maps, objects, and micro-gestures. Finally, with CollaboVR, GazeChat, Visual Captions, ThingShare, and ChatDirector, we enrich communication with mid-air sketches, gaze-aware 3D photos, LLM-informed visuals, object-focused views, and co-presented avatars.

We conclude the talk with highlights of the Google I/O Keynote, offering a visionary glimpse into the future of a universally accessible metaverse.

Biography

Ruofei Du serves as Interactive Perception & Graphics Lead / Manager at Google and works on creating novel interactive technologies for virtual and augmented reality. His research focuses on interactive perception, computer graphics, and human-computer interaction. He serves on the program committees of ACM CHI and UIST, and is an Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology. He holds 6 US patents and has published over 40 peer-reviewed publications in top venues of HCI, Computer Graphics, and Computer Vision, including CHI, SIGGRAPH, UIST, TVCG, CVPR, and ICCV. His work won a Distinguished Paper Award in ACM IMWUT, a Best Paper Award at SIGGRAPH Web3D 2016, a Best Student Paper Award & a Best Student Poster Award at I3D, two CHI Honorable Mentions Awards, and one TVCG Honorable Mentions Award. Dr. Du holds a Ph.D. and an M.S. in Computer Science from University of Maryland, College Park; and a B.S. from ACM Honored Class, Shanghai Jiao Tong University. Website: https://duruofei.com

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Guoliang Xue (Arizona State), will be giving a talk titled, "Inferring Device Events and User Activities from IoT Network Traffic".

Abstract

The availability of ubiquitous and heterogeneous Internet-of-Things (IoT) devices in smart homes and their interactions with users provide a unique opportunity to monitor, understand, recognize, learn, and infer device events and user activities for safety monitoring, connected health, as well as other disruptive services. In this talk, we discuss recent studies in inferring device events and user activities from IoT network traffic in a smart home environment. First, we study what kind of information can be extracted from mostly encrypted IoT network packets. It turns out that information in an IP packet such as timestamp, address of IoT device, server canonical name, remote port, protocol, packet length, etc., can be used to identify an IoT device event, with varying degrees of accuracy. Second, efficient algorithms are designed to infer a sequence of device events from the IoT network traffic log. Next, we explore the possibilities of identifying user activities from a sequence of IoT device events. Unsupervised learning algorithms are designed to infer a subset of user activities from the IoT network traffic log. These algorithms are validated using the network traffic of heterogeneous IoT devices collected at the router of a real-world smart home testbed.

Biography

Abstract

Social ventures that are fundraising through crowdfunding often involve major donors to influence the contributions of smaller donors. We theorize that male and female donors will respond differently to alternative major-donor contribution schemes that are commonly used by social ventures. In a field experiment, donors were randomly assigned to receive one of three solicitation messages about a pair of projects that were seeking funds through crowdfunding. All donors received identical messages, with the exception that information on major-donor involvement was varied across conditions. The message to the control group made no mention of a major donor. The seed group was informed that a major donor had unconditionally funded the first 50% of the projects' costs and that the projects were collecting the remaining 50% from other donors; we hypothesize that this scheme is likely to appeal to female donors, who tend to be more uncertainty and risk-averse. Finally, the challenge-match group was informed that a major donor would provide the remaining 50% of the projects' costs after the projects secure the first 50% from other donors; we hypothesize that this scheme is likely to appeal to male donors, who tend to respond more positively to challenges. The major donor in the field experiment-Forschungsförderungsgesellschaft-is a government agency that promotes and funds innovation in Austria. We find that females in the seed group are ~50% and ~35% more likely to exhibit an interest in contributing, as compared to females in the control or challenge-match conditions, respectively. Among males, we do not observe significant differences in interest in contributing across the three groups. A subsequent survey indicates that females are more responsive to the seed scheme because it yields a perception that the organization conducting the project is of high quality, is likely to reach its funding goal, and is likely to achieve its implementation goals.

Biography

Dr. Bapna is Associate Professor and Lawrence Fellow at the Carlson School of Management, University of MN. Dr. Bapna's research interests lie in the areas of digital platforms, entrepreneurship, and gender gaps. Her work is inherently multidisciplinary in nature and engages with industry partners. Dr. Bapna’s research has been published in Management Science and MIS Quarterly, and is funded by the National Science Foundation and the Kauffman Foundation. She is a recipient of the INFORMS Information Systems Society’s Gordon B. Davis Young Scholar Award, and the Association for Information Systems’ Early Career Award.