Past events

Graduate Programs Online Information Session

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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:

CRAY Colloquium: Towards AI-Powered Data-Informed Education

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

Sihem Amer-Yahia is a Silver Medal CNRS Research Director and Deputy Director of the Lab of Informatics of Grenoble. She works on exploratory data analysis and fairness in job marketplaces. Sihem was Principal Scientist at QCRI, Senior Scientist at Yahoo! Research and Member of Technical Staff at at&t Labs. Sihem served as PC chair for SIGMOD 2023 and is the coordinator of the Diversity, Equity and Inclusion initiative for the data management community.
 

CRAY Colloquium: Towards AI-Powered Data-Informed Education

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

Sihem Amer-Yahia is a Silver Medal CNRS Research Director and Deputy Director of the Lab of Informatics of Grenoble. She works on exploratory data analysis and fairness in job marketplaces. Sihem was Principal Scientist at QCRI, Senior Scientist at Yahoo! Research and Member of Technical Staff at at&t Labs. Sihem served as PC chair for SIGMOD 2023 and is the coordinator of the Diversity, Equity and Inclusion initiative for the data management community.
 

CRAY Colloquium: Challenges in Transcatheter and Endovascular Interventions

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.

CRAY Colloquium: AI/ML at Uber: From Predictive to Generative Models

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Albert Greenberg (Uber), will be giving a talk titled "AI/ML at Uber: From Predictive to Generative Models".

Abstract

Since Uber embarked on its journey of adopting AI back in 2015, AI has become an integral part of every facet of Uber's operations, and nearly every interaction users have with Uber's applications involves AI/ML models working behind the scenes. Albert Greenberg will share Uber's journey from predictive to generative AI.

Biography

As Uber’s VP of Platform Engineering, Albert oversees Engineering and Program Management teams that include Data Center, Compute, Networking, Storage, Data, Search, Monitoring, Developer Productivity, Engineering DEI, Tooling and Corporate IT Infrastructure. Albert serves as a member of Uber’s ELT, AI/ML Law and Ethics Strike (Artificial Intelligence Law & Ethics Council), Data Governance, Privacy & Cybersecurity Council, and is the Executive Sponsor for the company’s community of senior engineers that drive the evolution of Uber’s Engineering architecture, culture, and standards to be considerably more effective, reliable, and sustainable. 

Before Uber, Albert spent 15 years at Microsoft, as Technical Fellow and CVP for Microsoft Azure Networking, leading software and hardware development and engineering for all Microsoft Azure. Within Azure, Albert founded and led the network virtualization, datapath and physical data center network teams in Azure, as well as other teams in networking and monitoring. He worked in Microsoft Research to invent and prototype the data center networking technologies now widely deployed in Microsoft services and products. Albert joined Microsoft from Bell Labs and AT&T Labs Research, where he was an AT&T Fellow and Executive Director, and where he helped build the systems and tools for engineering and managing AT&T’s networks. Albert is a member of the National Academy of Engineering, an IEEE Kobayashi Award winner, ACM Fellow, ACM Sigcomm Award winner, ACM Test of Time Paper Award winner, and distinguished alumni of UW CSE.

 

CS&E Colloquium: Junaed Sattar

This week's speaker, Junaed Sattar (University of Minnesota), will be giving a talk titled, "Addressing Perception and Interaction Challenges in Underwater Robotics for Preserving Life Underwater".

Abstract

The United Nations lists conservation and sustainable use of “Life Below Water” as the 14th global goal that will change our lives in the foreseeable future. Most aquatic and marine preservation tasks (e.g., long-term oceanographic surveys, search-and-rescue, infrastructure inspection) are performed by humans, sometimes using remotely operated vehicles (ROVs) to assist in these missions. However, in recent decades, the advent of smaller AUVs suitable for working closely with humans (termed co-AUVs) has enabled robots and humans to collaborate on many subsea tasks.  The underwater domain, nonetheless, is unique in many ways and stands out with its numerous challenges -- in sensing, control, and human-robot interaction -- that can justifiably be considered extreme. Our research at the Interactive Robotics and Vision Lab at the University of Minnesota looks into numerous issues in robust underwater human-robot collaboration. We primarily investigate computational solutions to these problems, and use methods from robotics, machine vision, stochastic reasoning, and (deep) machine learning. This talk will present a brief overview of our research and present an in-depth discussion of some recent work in underwater human-robot interaction and visual object detection challenges.

Biography

Junaed is an Associate Professor at the Department of Computer Science and Engineering at the University of Minnesota and a MnDrive (Minnesota Discovery, Research, and Innovation Economy) faculty, and a member of the Minnesota Robotics Institute. He is the founding director of the Interactive Robotics and Vision Lab, where he and his students investigate problems in field robotics, robot vision, human-robot communication, assisted driving, and applied (deep) machine learning, and develop rugged robotic systems. His graduate degrees are from McGill University in Canada, and he has a BS in Engineering degree from the Bangladesh University of Engineering and Technology. Before coming to the UoM, he worked as a post-doctoral fellow at the University of British Columbia where his research focused on human-robot dialog and assistive wheelchair robots, and at Clarkson University in New York as an Assistant Professor. Find him at junaedsattar.org, and the IRV Lab at irvlab.cs.umn.edu, @irvlab on Twitter, and their YouTube page at https://www.youtube.com/channel/UCbzteddfNPrARE7i1C82NdQ.

Advancing Molecules and Materials via Data Science

Register now! (free)

Event Website

About the workshop

The goal of the workshop is to bring together experts working at the intersection of data science and materials science and explore promising data science approaches and techniques that could support major advances in materials science in the coming years. The registration is free and required. Lunch will be provided for registered attendees.

Speakers

The forum will include sessions and panel discussions led by experts from UMN, MIT, UIUC, UT Dallas, Argonne, NIST, Google, and NSF. The full schedule of the workshop can be found on the workshop website.

Poster session

Students and postdocs are encouraged to attend and to make contributions in the form of poster presentations (please submit an abstract on the registration form).

Organizing committee

Vuk Mandic, Chris Bartel, Sapna Sarupria, Ellad Tadmor, Ke Wang

For more information, please reach out to Prof. Vuk Mandic at vuk@umn.edu. 

Cray Colloquium: Geometry and Latent Representations in Machine Learning

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, Daniel D. Lee (Cornell Tech), will be giving a talk titled "Geometry and Latent Representations in Machine Learnings".

Abstract

The advent of deep neural networks has brought significant advancements in the development and deployment of novel AI technologies. Recent large-scale neural network architectures have shown significantly better performance for object classification, segmentation, scene understanding and multimodal representations.  How can we understand how the representations of sensor input signals are transformed by deep neural networks? I will show how statistical insights can be gained by analyzing the high-dimensional geometrical structure of these representations as they are reformatted in neural network hierarchies.

Biography

Dr. Daniel D. Lee is the Tisch University Professor in Electrical and Computer Engineering at Cornell Tech and recently served as Global Head of AI for Samsung Research. He received his B.A. summa cum laude in Physics from Harvard University and his Ph.D. in Condensed Matter Physics from the Massachusetts Institute of Technology. He was also a researcher at Bell Labs in the Theoretical Physics and Biological Computation departments. He is a Fellow of the IEEE and AAAI and has received the NSF CAREER award and the Lindback award for distinguished teaching. He was also a fellow of the Hebrew University Institute of Advanced Studies in Jerusalem, an affiliate of the Korea Advanced Institute of Science and Technology, and organized the US-Japan National Academy of Engineering Frontiers of Engineering symposium and Neural Information Processing Systems (NeurIPS) conference. His group focuses on understanding general computational principles in biological systems and on applying that knowledge to build autonomous systems.

ML Seminar: Ziyue Xu (Nvidia)

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, Ziyue Xu (Nvidia), will be giving a talk titled, "Federated Learning: Image, Language, and Beyond".

Abstract

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. In this talk, we will discuss two major aspects of FL: the research towards FL model development, and the tool needed to perform a real-life multi-institute FL study. Specifically, we will cover recent works on personalized FL, vertical FL, and client-contribution, and will illustrate the implementation of FL under various model settings using NVFlare - the NVIDIA Federated Learning Application Runtime Environment.

Biography

Dr. Ziyue Xu, IEEE Senior Member, is a Senior Scientist at Nvidia. His research interests lie in the area of image analysis and machine learning with applications in biomedical and clinical imaging, and is among the earliest researchers in adopting deep learning in this field. Before joining Nvidia, he was a Staff Scientist at National Institutes of Health.

Dr. Xu obtained his B.S. from Tsinghua University, and M.S./Ph.D. from the University of Iowa. He is an Associate Editor for the journals of International Journal of Computer Vision (IJCV), IEEE Transactions on Medical Imaging (TMI), IEEE Journal of Biomedical and Health Informatics (JBHI), Computerized Medical Imaging and Graphics (CMIG), and Computers in Biology and Medicine (CBM).

ML Seminar: Zhiqi Bu

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, Zhiqi (Woody) Bu (Amazon AWS AI), will be giving a talk titled, "On the Computational Efficiency of Differentially Private Deep Learning".

Abstract

Differentially private (DP) optimization is the standard paradigm to learn large neural networks that are accurate and privacy-preserving. The computational cost for DP deep learning, however, is notoriously heavy due to the per-sample gradient clipping. Existing DP implementations are 2−1000× more costly in time and space complexity than the standard (non-private) training. In this work, we develop a novel Book-Keeping (BK) technique that implements existing DP optimizers (thus achieving the same accuracy), with a substantial improvement on the computational cost. Specifically, BK enables DP training on large models and high dimensional data to be roughly as efficient as the standard training, whereas previous DP algorithms can be inefficient or incapable of training due to memory error. The computational advantage of BK is supported by the complexity analysis as well as extensive experiments on vision and language tasks. Our implementation achieves state-of-the-art (SOTA) accuracy with very small extra cost: on GPT2 and at the same memory cost, BK has 1.0× the time complexity of the standard training (0.75× training speed in practice), and 0.6× the time complexity of the most efficient DP implementation (1.24× training speed in practice). We will open-source the codebase for the BK algorithm.

Biography

Dr. Zhiqi Bu is an Applied Research Scientist at Amazon AWS AI, focusing on the optimization of large-scale deep learning, especially with differential privacy. Dr. Bu obtained his Ph.D. in the Applied Math and Computational Science program (AMCS) at the University of Pennsylvania in 2021, under Benjamin Franklin Fellowship, where he also obtained his M.A. in Statistics from Wharton School. Dr. Bu completed his B.A. (Honors) in Mathematics at the University of Cambridge in 2015.