Past events

This event is presented by the College of Science and Engineering Data Science Initiative.

This week's speaker, Hanumant Singh (Northeastern University), will be giving a talk titled, "Exploring the World's Oceans with Robotic Platforms for Mapping, Imaging and Manipulation."

Abstract

This talk looks at the role of robotic platforms for marine and polar mapping, imaging and manipulation. Using examples from research expeditions associated with Marine Geology, Archaeology, Coral Reef Ecology, Glaciology and Sea Ice we examine the hard problems in the underwater domain - where we are and where we are going in terms of geometric and machine learning techniques and algorithms.

Biography

This event is presented by the College of Science and Engineering Data Science Initiative.

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Andrew Kotz from 3M, will be giving a talk titled, "Software Defined Vehicles: The Hardware Challenge."

Abstract

Automobiles are rapidly evolving from static, hardware-centric products to software-defined platforms. Driven by advances in ADAS & autonomous driving, on-vehicle AI, and infotainment needs, vehicle software requirements are defining the hardware specifications. Such changes require expanded edge computing, increasingly complex sensing & perception systems, and high bandwidth vehicle connectivity that allows vehicles to adapt over time, delivering new features post-sale and addressing recalls through data-driven monitoring and software updates. Combining 3M’s expertise in automotive with adjacent data centers and consumer electronics technologies, 3M is helping advance the next generation of software-defined vehicles (SDVs). This presentation will cover the changes happening within the automotive industry, technical challenges & opportunities for innovation, and key 3M technologies & solutions for SDVs.

Biography

Andrew Kotz, PhD, is the Software-Defined Vehicle (SDV) Applications Specialist at 3M, where he is leading 3M’s SDV portfolio development by partnering with industry and researchers to accelerate SDV capabilities and help 3M deliver leading solutions for next generation vehicle platforms. He earned his PhD in Mechanical Engineering from the University of Minnesota, focusing on data-driven approaches to vehicle and transportation challenges. Before joining 3M, Andrew led the Commercial Vehicle Technologies team at the Department of Energy’s National Renewable Energy Laboratory, directing advanced vehicle research in the medium & heavy-duty vehicle, off-road vehicle, rail, marine, and aviation sectors. Simultaneously, Andrew was the principal investigator at Exergi Predictive, which is a start-up developing energy management & prediction software. Andrew’s expertise includes data collection & analysis, data pipelines, and on-vehicle compute, helping translate vehicle operational data into actionable insights that improve energy efficiency, reliability, and sustainability.

Interested in studying abroad as a CS&E student?

The Learning Abroad Center is partnering with the Computer Science, Data Science, and IT Infrastructure programs to showcase learning abroad opportunities recommended for CS&E and ITI students.

Learn about both academic semester and summer opportunities in Spain, Greece, Sweden, Korea, New Zealand, the Czech Republic, Ireland, the UAE and more!

This is an in-person event and free food will be provided!  

Learn more and register today!

PLUS: mark your calendars now for this fall’s Learning Abroad Fair!
Wednesday, September 23 | 10:00 AM - 2:30 PM | UMN West Bank Plaza

This event is presented by the College of Science and Engineering Data Science Initiative.

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Professor Paul Schrater, will be giving a talk titled, "Starving Our Agency: Information Loss and Cognitive Skill Collapse in Autonomous Systems (it is not me, it's you!)." Will Beaumeister will also participate in this talk. 

Abstract

As autonomous AI agents take-over increasingly complicated end-to-end execution of complex intellectual tasks, mounting empirical evidence is hammering home that passing the load is creating rapid cognitive skill degradation, mental model atrophy, and loss of debugging competence among human practitioners. 

The dominant interpretation blames the user: It's YOU! You are psychologically complacent,  or voluntarily offloading your brain (to rot). In this talk, we present a counter-hypothesis grounded in Game Theory, predictive inferential control and information theory into a theoretically more plausible alternative, 

Cognitive skill loss is the inevitable mathematical consequence of channel starvation resulting from bad system design. Connecting with the signaling-game framework of Beaumaster & Schrater (2026), we show that when human and agent incentives are not actively co-designed, spontaneous coordination and chance alignment is essentially zero as the size space of possible goals grows. A strong game theoretic consequence is that sharing of new, valid information collapses (Crawford-Sobel `no new information` pooling equilibria). We show that this equilibria manifests as both sycophancy and delusional reinforcement (Paech, 2026). The consequences of no information sharing on adaptive predictive control systems is collapse of both state observability termed equivocation (H(state|obs) -> 0) and controllability (no bits to send!).  In this regime, adaptive learning systems experience representational collapse of state and action capacities (real and information theoretic senses), as the information available is easily compressed and the reduced controllability compresses skills (policy).  In fact, our modeling results show the brain's internal generative models should suffer complete learning arrest if these kinds of interaction are exclusive, aptly represented by the measures of information complexity going to zero (K_t -> 0). On the bright side, I will show you that we can construct powerful but extremely general measures of information flow in human-human human-agent, and agent-agent interactions with results on vibe coding and delusional chatting that are powerful diagnostics for laying blame at the feet of information hiding. Redesigning vibe code workflows using simple principles of observability and control restore stability across refactoring cycles, and outline co-design principles for skill-preserving agency via a game theoretic "Design Mechanism" we term Epistemic Mediation.

Biography

Paul Schrater holds the joint appointment between Psychology and Computer Science and Engineering. He received his Ph.D. in Neuroscience in 1999 from the University of Pennsylvania and has been a Post Doc with Dan Kersten in the Computational Vision Lab for the past 3 years researching human and computer vision and motor control. Schrater's research interests include statistical pattern recognition, human and computer vision, multi-modal sensory integration and motor control. 

Schrater's research domain is in the development of predictive models of human behavior, with a focus on perception, action, decision-making, learning and motivation. The approach is rooted in the idea that human behavior is a rational adaptive response to the problems of surviving and reproducing in our environment given limited information. He uses probabilistic methods like hierarchical probabilistic models, Bayesian Reinforcement learning, Bayesian decision theory, etc. to construct normative (optimal) solutions to perception, action, decision, and learning problems faced by humans. His lab uses behavioral experiments to test these ideas via one of several lab setups, including a video game lab, and through collaboration with the Multi-Sensory Perception lab.

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Professor Zhenman Fang (UMN Department of Electrical and Computer Engineering), will be giving a talk titled "Software-Defined Accelerator-Centric Systems — A Journey to AI and Big Data Acceleration."

Abstract

With the end of CPU scaling due to dark silicon limitations, customizable hardware accelerators have gained increasing attention in modern datacenters and edge devices, due to their low power, high performance, and high energy-efficiency. Unfortunately, not everyone can design hardware accelerators and there remains a substantial gap for the large community of software developers to effectively leverage the benefits of domain-specific hardware acceleration.

In this talk, I will share my research on software-defined accelerator-centric systems. First, I will give an overview of the unique advantages of hardware accelerators using the roofline model and illustrate common hardware customization techniques that can be defined by software. Next, I will share my research journey to AI and big data acceleration, and demonstrate two successful case studies of accelerator-centric systems: 1) HeatViT, a hardware-efficient image-adaptive token pruning framework for efficient yet accurate vision transformer (ViT) acceleration on edge FPGAs, with a key focus on algorithm/hardware codesign; and 2) SQL2FPGA, a compiler that automatically compiles big data queries onto datacenter FPGA accelerators, with a key focus on improving software programmability. Finally, I will discuss future research directions of software-defined accelerator-centric systems.

Biography

Dr. Zhenman Fang is an Associate Professor in Department of Electrical and Computer Engineering, University of Minnesota, Twin Cities. He is on leave from School of Engineering Science, Simon Fraser University, Canada, where he joined since Apr 2019. Zhenman founded and directs the HiAccel lab. Zhenman's recent research focuses on customizable computing with software-defined hardware acceleration, which aims to sustain the ever-increasing performance and energy-efficiency demands of important application domains in post-Moore’s law era. It spans the entire computing stack, including emerging application characterization and acceleration (including machine learning, big data analytics, high-performance computing, and precision medicine), novel accelerator-rich and near-data computing architecture designs, and corresponding programming, runtime, and tool support.

Zhenman has published over 80 papers in top-tier conferences and journals and holds two US patents, including the ICCAD 10 Year Retrospective Most Influential Paper Award in 2025, three best paper awards (FPL 2024 Stamatis Vassiliadis Best Paper, TCAD 2019 Donald O. Pederson Best Paper, and MEMSYS 2017 Best Paper), four best paper nominees (ICCAD 2025, FCCM 2025, HPCA 2017 and ISPASS 2018), and an invited paper from Proceedings of the IEEE 2019. His research has also been recognized with the inaugural SFU Research Excellence Award - Horizon Award (2025), a NSERC (Natural Sciences and Engineering Research Council of Canada) Alliance Award (2020), a CFI JELF (Canada Foundation for Innovation John R. Evans Leaders Fund) Award (2019), a Xilinx University Program Award (2019), a Team Award from Xilinx Software and IP Group (2018), and a Postdoc Fellowship from UCLA Institute for Digital Research and Education (2016-2017). Zhenman has also actively served in the organizing and program committee of top-tier conferences in the areas of FPGA and reconfigurable computing, design automation, and computer architecture. Recently, he has served as Program Vice Chair of FCCM 2026, Journal Track Chair of FPL 2026, General Chair of ASAP 2025, Program Chair of RAW 2024 and RAW 2025. More details can be found in his website: https://zhenman.github.io/.