Past events

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Qianwen Wang (University of Minnesota), will be giving a talk titled "Interpreting and Steering AI Explanations with Interactive Visualizations."

Abstract

Artificial Intelligence (AI) has advanced at a rapid pace and is expected to revolutionize many biomedical applications. However, current AI methods are usually developed via a data-centric approach regardless of the usage context and the end users, posing challenges for domain users in interpreting AI, obtaining actionable insights, and collaborating with AI in decision-making and knowledge discovery.

In this talk, I discussed how this challenge can be addressed by combining interactive visualizations with interpretable AI. Specifically, I present two methodologies: 1) visualizations that explain AI models and predictions and 2) interaction mechanisms that integrate user feedback into AI models. Despite some challenges, I will conclude on an optimistic note: interactive visual explanations should be indispensable for human-AI collaboration. The methodology discussed can be applied generally to other applications where human-AI collaborations are involved, assisting domain experts in data exploration and insight generation with the help of AI.

Biography

Qianwen Wang is a tenure-track assistant professor at the Department of Computer Science and Engineering at the University of Minnesota. Before joining UMN, she was a postdoctoral fellow at Harvard University. Her research aims to enhance communication and collaboration between domain users and AI through interactive visualizations, particularly focusing on their applications in addressing biomedical challenges.

Her research in visualization, human-computer interaction, and bioinformatics has been recognized with awards and featured in prestigious outlets such as MIT News and Nature Technology Features. She has earned multiple recognitions, including two best abstract awards from BioVis ISMB, one best paper award from IMLH@ICML, one best paper honorable mention from IEEE VIS, and the HDSI Postdoctoral Research Fund. 

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, Volkan Cevher (Swiss Federal Institute of Technology Lausannes), will be giving a talk titled, "Key Challenges in Foundation Models (... and some solutions!) ".

Abstract

Thanks to neural networks (NNs), faster computation, and massive datasets, machine learning is under increasing pressure to provide automated solutions to even harder real-world tasks beyond human performance with ever faster response times due to potentially huge technological and societal benefits. Unsurprisingly, the NN learning formulations present fundamental challenges to the back-end learning algorithms despite their scalability. In this talk, we will work backwards from the "customer's" perspective and highlight these challenges specifically on the Foundation Models based on NNs. We will then explain our solutions to some of these challenges, focusing mostly on robustness aspects. In particular, we will show how the existing theory and methodology for robust training misses the mark and how we can bridge the theory and the practice.

Join the Department of Computer Science & Engineering (CS&E) for this all-alumni event to discuss a human-centered approach to AI, featuring faculty from the GroupLens Research Lab. 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. 

About the Program

What happens when you put people at the center of computing? The GroupLens Lab has some answers. Learn how systems that understand us as social beings can bring people together to solve large problems -- or to find help for one person at a time. We also will talk about how Human-Centered AI can harness the power of optimization and machine learning to solve big social challenges while protecting human values of fairness, transparency, and helpfulness. 
 

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Alon Halevy (Meta), will be giving a talk titled "Well-being, AI, and You: Developing AI-based Technology to Enhance our Well-being."

Abstract

Many applications claim to enhance our well-being, whether directly by aiding meditation and exercise, or indirectly, by guiding us to our destinations, assessing our sleep quality, or helping us manage our daily tasks. However, the truth is that the potential of technology to improve our well-being often eludes us, and this is happening at the dawn of an era where AI is supposed to usher in a new generation of personalized assistants.  Presently, we find ourselves more distracted than ever, devoting excessive time to pondering life’s minutiae, and struggling to fully embrace the present moment.  

Part of the reason that our well-being is not benefiting fully from technology is the fact that each of these apps focuses on a specific aspect of well-being, lacking coordination with other apps. This situation is reminiscent of the early days of computer programming when each program interacted directly with the computer's hardware. Drawing from this analogy, this talk will begin by describing a set of mechanisms that can facilitate better cooperation between well-being applications, effectively proposing an operating system for well-being. This operating system comprises a data repository, referred to as a personal timeline, which captures your past experiences and future aspirations. It also includes mechanisms for utilizing your personal data to provide improved recommendations and life plans, and, lastly, a module to assist in nurturing and navigating crucial relationships in your life.

The second half of the talk will delve into the technical challenges involved in building the components of the operating system. In particular, we will focus on the creation of your life experiences timeline from the digital data you create on a daily basis. In this context, we will identify opportunities for language models to be a core component on which we build systems for querying personal timelines and for supporting other components of the operating system. In particular, the challenge of answering questions about your timeline raises important challenges in the intersection of large language models and structure data.  

Biography

Alon Halevy is a director at Meta’s Reality Labs Research, where he works on Personal Digital Data,  the combination of neural and symbolic techniques for data management and on Human Value Alignment. Prior to Meta, Alon was the CEO of Megagon Labs (2015-2018) and led the Structured Data Group at Google Research (2005-2015), where the team developed WebTables and Google Fusion Tables. From 1998 to 2005 he was a professor at the University of Washington, where he founded the database group. Alon is a founder of two startups, Nimble Technology and Transformic (acquired by Google in 2005). Alon co-authored two books: The Infinite Emotions of Coffee and Principles of Data Integration. In 2021 he received the Edgar F. Codd SIGMOD Innovations Award. Alon is a Fellow of the ACM and a recipient of the PECASE award and Sloan Fellowship. Together with his co-authors, he received VLDB 10-year best paper awards for the 2008 paper on WebTables and for the 1996 paper on the Information Manifold data integration system.

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, Benjamin Grimmer (John Hopkins), will be giving a talk titled, "Accelerated Gradient Descent via Long Steps".

Abstract

This talk will discuss recent work establishing provably faster convergence rates for gradient descent in smooth convex optimization via semidefinite programming and computer-assisted analysis techniques. We do this by allowing nonconstant stepsize policies with frequent long steps potentially violating descent. This is managed by analyzing the overall effect of many iterations at once rather than the typical one-iteration inductions used in most first-order method analyses. We prove a O(1/T^{1.02449}) convergence rate, beating the classic O(1/T) rate simply by periodically including longer steps (no momentum needed!).

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Dinesh Manocha (University of Maryland), will be giving a talk titled "Robot Navigation in Complex Indoor and Outdoor Environments".

Abstract

In the last few decades, most robotics success stories have been limited to structured or controlled environments. A major challenge is to develop robot systems that can operate in complex or unstructured environments corresponding to homes, dense traffic, outdoor terrains, public places, etc. In this talk, we give an overview of our ongoing work on developing robust planning and navigation technologies that use recent advances in computer vision, sensor technologies, machine learning,  and motion planning algorithms. We present new methods that utilize multi-modal observations from an RGB camera, 3D LiDAR, and robot odometry for scene perception, along with deep reinforcement learning  for reliable planning.  The latter is also used to compute dynamically feasible and spatial aware velocities for a robot navigating among mobile obstacles and uneven terrains. We have integrated these methods with wheeled robots, home robots, and legged platforms and highlight their performance in crowded indoor scenes, home environments and dense outdoor terrains.

Biography

RSVP today!.

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, Sihem Amer-Yahia (Grenoble Alpes), will be giving a talk titled "Towards AI-Powered Data-Informed Education".

Abstract

The Covid-19 health crisis has seen an increase in the use of digital work platforms from videoconferencing systems to MOOC-type educational platforms and crowdsourcing and freelancing marketplaces. These levers for sharing knowledge and learning constitute the premises of the future of work. Educational technologies coupled with AI hold the promise of helping learners and teachers. However, they are still limited in terms of learner and teacher agency and learning opportunities. I will describe research at the intersection of data-informed recommendations and education theory and discuss with ethical considerations in building educational platforms.

Biography

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Sihem Amer-Yahia (Grenoble Alpes), will be giving a talk titled "Towards AI-Powered Data-Informed Education".

Abstract

The Covid-19 health crisis has seen an increase in the use of digital work platforms from videoconferencing systems to MOOC-type educational platforms and crowdsourcing and freelancing marketplaces. These levers for sharing knowledge and learning constitute the premises of the future of work. Educational technologies coupled with AI hold the promise of helping learners and teachers. However, they are still limited in terms of learner and teacher agency and learning opportunities. I will describe research at the intersection of data-informed recommendations and education theory and discuss with ethical considerations in building educational platforms.

Biography

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Jaydev Desai (Georgia Tech), will be giving a talk titled "Challenges in Transcatheter and Endovascular Interventions".

Abstract

Mitral regurgitation is a common heart valve disease. Current approaches for mitral valve repair include open heart surgery (which carries the risk of post-operative complications) and transcatheter mitral valve repair (TMVR). TMVR is a relatively new approach that is performed on a beating heart using a catheter that is guided to the target location to implant the device to reduce or eliminate mitral regurgitation. Given the tortuosity of the path that needs to be taken to reach the mitral valve, TMVR is a clinically challenging procedure. The first part of the talk will focus on our work in developing a highly articulated, intravascular meso-scale robot that can be guided to deploy the mitral valve implant under image guidance. 

The second part of the talk will focus on the area of micro-scale robotic systems involving steerable guidewires. One of the primary requirements of an endovascular robotic system is to be able to successfully steer the guidewire towards the target location with minimal or no harm to the vessel. Chronic total occlusions (CTOs) remain the riskiest, most challenging, and least successful vascular lesions to treat with traditional endovascular devices. Peripheral artery disease (PAD) in particular, is one of the most common causes of cardiovascular deaths worldwide. Procedural complexity in treating CTOs are attributed to multiple causes. The second part of the talk will present our work on the development of 400 microns (~0.016”) robotically steerable guidewire as a potential solution to this challenging clinical problem.

Biography

Dr. Jaydev P. Desai is currently a Professor at Georgia Tech in the Wallace H. Coulter Department of Biomedical Engineering and holds the G.P. “Bud” Peterson and Valerie H. Peterson Faculty Professorship in Pediatric Research. He is the founding Director of the Georgia Center for Medical Robotics (GCMR) and an Associate Director of the Institute for Robotics and Intelligent Machines (IRIM). He completed his undergraduate studies from the Indian Institute of Technology, Bombay, India, in 1993. He received his MA in Mathematics in 1997 and MSE and Ph.D. in Mechanical Engineering and Applied Mechanics in 1995 and 1998 respectively, all from the University of Pennsylvania. He was also a Post-Doctoral Fellow in the Division of Engineering and Applied Sciences at Harvard University. 

He is a recipient of several NIH R01 grants, NSF CAREER award, and was the lead inventor on the “Outstanding Invention in the Physical Science Category” at the University of Maryland, College Park, where he was formerly employed. He is also the recipient of the Ralph R. Teetor Educational Award and the 2021 IEEE Robotics and Automation Society Distinguished Service Award. He has been an invited speaker at the National Academy of Sciences “Distinctive Voices” seminar series and also invited to attend the National Academy of Engineering’s U.S. Frontiers of Engineering Symposium. He has over 200 publications, is the founding Editor-in-Chief of the Journal of Medical Robotics Research, and Editor-in-Chief of the four-volume Encyclopedia of Medical Robotics. At 2018 ICRA, his prior work was the finalist for “IEEE RAS Award for the Most Influential Paper from ICRA 1998”. His research group has received several accolades including the best student paper award, best symposium paper award, cover image of IEEE Transactions on Biomedical Engineering, and featured article in the IEEE Transactions on Biomedical Engineering. His current research interests are primarily in the areas of image-guided surgical robotics, pediatric robotics, endovascular robotics, and rehabilitation and assistive robotics. He is a Fellow of IEEE, ASME, and AIMBE.