College of Engineering - University of Wisconsin-Madison

BME Seminar Series: Christopher Konop, PhD
2026-03-23 12:00 - 13:00 (America/Chicago) | 1003 (Tong Auditorium) Engineering Centers Building, 1550 Engineering Drive, Madison, WI, 53706, United States
How Serious Fun (and Mild Panic) Led Me from Academia to Venture Innovation

Christopher Konop, PhDInnovation and Commercialization SpecialistIsthmus ProjectUW Health

Abstract:Most scientific careers don’t follow a straight line—and mine certainly didn’t. I’ve moved from academia to startups, consulting, and eventually venture innovation. I’ll share a few brief stories from building WiSolve, stepping into life‑science consulting, and helping develop the Isthmus Project to spark conversation and address the career-development questions that are top of mind for graduate students and postdocs. I’m glad to offer practical insights, hot tips, and lessons learned along the way. Spoiler: none of it would have been possible without great mentors and a generous network.

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ISyE-Professional Workshop
2026-03-23 14:00 - 15:00 | 2065 Mechanical Engineering, 1513 University Avenue, Madison, Wisconsin, 53706
2065 ME

Sierra Strebe-Grim, the Engineering Career Service Career Advisor for COE graduate students, will join us for a professional workshop. She’s specialized in resumes/CVs, job search strategies, job offers & negotiation, networking, and many more resources! Come with any questions you might have, or just a curious mind. Hosted by HFES.

ECE SEMICONDUCTOR MATERIALS SEMINAR SERIES: Dr. Mihir Pendharkar
2026-03-23 15:00 - 16:00 | 3609 Engineering Hall, 1415 Engineering Drive, Madison, 53711
Closing the Loop: Shrinking Materials Discovery Cycles for the Quantum Era

Abstract: As utility-scale quantum computing appears on the horizon, the field faces a scaling challenge comparable in magnitude to the pursuit of artificial general intelligence. Success in this endeavor hinges on reducing decoherence by improving materials systems at the fundamental electronic device scale — the single-qubit level — and, crucially, developing tools that enable rapid experimental feedback. This talk explores two paradigms where shrinking the characterization loop has catalyzed breakthroughs in quantum materials as well as materials for quantum hardware.

The first part focuses on the development of high-mobility III-V semiconductor quantum wells and quantum wires (nanowires). By optimizing the integration of superconductors with these low-dimensional electron systems, we have realized the high-quality hybrid interfaces necessary for topological quantum computing. I will highlight how rapid feedback was the primary driver for achieving proof-of-concept devices.

In the second part, I will address the “imaging bottleneck” in 2D moiré heterostructures. While these systems offer a rich playground for correlated quantum physics, the inability to rapidly visualize moiré superlattices has historically limited materials optimization. I will present the development of Torsional Force Microscopy (TFM), a technique that enables the visualization of moiré landscapes in minutes, bypassing the need for weeks-long cryogenic transport measurements.

Finally, I will put forward a vision for improved materials, device geometries, and rapid feedback techniques that can be ported to superconducting qubit platforms, with the hope of providing a boost to bridge the gap between laboratory prototypes and useful quantum computers.

Dr. Mihir Pendharkar

Bio: Mihir Pendharkar is a researcher at Stanford University, where he works with Prof. David Schuster on advancing materials for superconducting qubit-based quantum computing. As a Q-FARM Bloch Postdoctoral Fellow working with Prof. David Goldhaber-Gordon, Mihir developed Torsional Force Microscopy (TFM) to image moiré superlattices and atomic lattices in 2D materials. This imaging technique has since been adopted by four major AFM manufacturers and dozens of research institutions worldwide. Mihir earned his MS and PhD in Electrical and Computer Engineering from University of California, Santa Barbara working with Prof. Chris Palmstrom, where his doctoral research specialized in Molecular Beam Epitaxy (MBE) of superconductor-semiconductor hybrid heterostructures for Majorana Zero Mode-based topological quantum computation.

ISyE - Letters of Love
2026-03-23 17:00 - 18:30
Room TBD soon!

Join the students of ISyE for this special event!

Once again, our IISE students have created an opportunity to give back to their community. They invite students to join them in making hand-crafted cards to send to hospitals around the area to provide emotional support to children battling serious illnesses.

No registration needed, just come if you are able!

ECE SEMICONDUCTOR MATERIALS SEMINAR SERIES: Dr. Alex Honghyuk Kim
2026-03-24 12:00 - 13:00 | 2355 Engineering Hall, 1415 Engineering Drive, Madison, 53711
Development of Novel III–V Semiconductor Heterostructures: Overcoming Physical Limits

Alex Honghyuk Kim

Abstract: Recent advances in the epitaxial growth of III–V compound semiconductors have enabled high-performance electronic and photonic devices. However, conventional III–V and III–N material systems remain fundamentally limited by intrinsic physical and chemical constraints, including substrate-dependent lattice and bandgap properties. These limitations hinder progress in emerging applications such as neuromorphic photonics, monolithic integration with silicon photonics, and full-color micro-LED arrays. In this talk, strategies to overcome these intrinsic limitations will be discussed, with a focus on the development of novel III–V compound semiconductor material systems enabled by precise control of lattice mismatch, phase stability, and miscibility gaps. The role of metalorganic vapor phase epitaxy (MOVPE) in kinetic material design will be highlighted, together with the realization of chemically and physically metastable III–V heterostructures beyond conventional epitaxial limits.

Bio: Alex Honghyuk Kim is an Assistant Professor in the School of Semiconductor Convergence Engineering at Hanyang University, South Korea. He received his Ph.D. in Electrical and Computer Engineering from the University of Wisconsin–Madison, where his research focused on the epitaxial growth of III–V compound semiconductors for advanced optoelectronic applications. His research interests include MOVPE-based epitaxy of III–V compound semiconductor materials, metastable heterostructures, and the design and characterization of advanced optoelectronic devices. Prior to joining Hanyang University, he held research positions at Lumileds LLC, Northwestern University, and the Korea Photonics Technology Institute. He has authored and coauthored over 30 peer-reviewed journal papers and currently serves as a co-principal investigator on multiple nationally funded semiconductor research projects.

CBE Seminar Series: Carl D. Laird
2026-03-24 16:00 - 17:00
Carl D. LairdJohn E. Swearingen Professor and Department HeadCarnegie Mellon UniversityPittsburgh, Pennsylvania

Systems, Surrogates, Solutions: Optimization and Machine Learning for Decision-Making at Scale

Emerging global challenges are pushing the limits of today’s scientific computing tools. To overcome these barriers, our group develops open-source solutions for large-scale optimization problems. At the intersection of data science and mathematical programming, new capabilities support optimization-based decision-making with embedded machine-learning and data-driven models. Leveraging high-level languages like Python, we are democratizing these capabilities, placing powerful tools in the hands of a broader research community. Two vignettes illustrate the effectiveness of these capabilities to tackle challenging science and engineering problems at scale.

The first vignette highlights our rapid-response work during COVID-19. The pandemic exposed significant challenges in mitigating emerging infectious diseases. I will discuss our work to efficiently estimate county-level transmission parameter dynamics using a fully-coupled, national-scale model. With full spatio-temporal transmission parameter profiles, we were able to estimate the impact of non-pharmaceutical interventions on the spread of COVID-19. Our current work focuses on developing accessible, advanced optimization capabilities that enable inference on very large-scale, nonlinear dynamic systems.

Machine learning (ML) models are increasingly used as surrogates for complex processes within engineering. Here, I will discuss the need for surrogates in large-scale decision-making and introduce the Optimization and Machine Learning Toolkit (OMLT), a Python framework developed in collaboration with Imperial College London and Sandia National Laboratories. This package supports solution of mathematical programming problems with embedded ML models. I will showcase several applications that illustrate the use of machine learning surrogates, including for example, process design and operations, bioprocess modeling, and process family design. We will discuss our most current work on the use of conformal methods for optimization under uncertainty and advanced decomposition approaches for training hybrid models.

ISyE - Bonfire
2026-03-25 17:00 - 19:00 | Picnic Point, Fire Pits - fire circle #4, Madison, 53711
Picnic Point Fire Pits – Fire Circle #4

Join the students of HFES for a spring bonfire! Connect with fellow students, relax, and enjoy the outdoors!

ECE RISE-AI SEMINAR SERIES: Dr. Andrew Wagenmaker
2026-03-26 12:00 - 13:00 | Orchard View Room – Third Floor – Discovery Building, 330 N. Orchard St., Madison, 53715
Physical Agents that Learn from Experience

Abstract: Humans fundamentally learn through interaction with the physical world, yet modern AI-based approaches in robotics rely primarily on learning from static, offline sources of data. While this approach has enabled exciting capabilities in some domains, it has proven notoriously difficult to scale to the demands of fully open-world autonomy.

Dr. Andrew Wagenmaker

In this talk, I will investigate how we can overcome the limitations of learning with only static data sources, and enable robots to learn from experience as they interact with the physical world. In particular, I will consider how we can collect the experience—explore—that allows for learning and improvement, and how the limited sources of data that are often available to us in the physical world—simulators and human demonstrations—can enable this. I will consider how simulators, even coarse simulators that are insufficient for obtaining effective task-solving policies, can enable efficient exploration, and how the resulting exploration allows for learning performant task-solving robotic behaviors. I will then show how generative robot policies trained on human demonstrations can be utilized to achieve highly focused exploration and enable fast online improvement, and how we can pretrain generative policies on human demonstrations that can themselves collect the experience necessary to learn and improve. Across these examples, I will argue that the insights gained through rigorous analysis are key to uncovering the algorithmic approaches that enable learning from experience, and ultimately bringing AI to the physical world.

Bio: Andrew Wagenmaker is a postdoctoral scholar in Electrical Engineering and Computer Sciences at UC Berkeley working with Sergey Levine. Previously, he completed a PhD in Computer Science at the University of Washington, where he was advised by Kevin Jamieson. Andrew’s research focuses on learning in dynamic, interactive settings, spanning fundamental algorithm development to practical approaches for real-world learning and decision-making, particularly toward enabling efficient learning in robotic systems. His work has been recognized by a Best Paper nomination at the Conference on Robot Learning, and he is a recipient of the NSF Graduate Research Fellowship.

ME 903 Graduate Seminar: Laura Grossenbacher
2026-03-26 16:00 - 17:00 | 3M Auditorium, rm 1106 Mechanical Engineering Building, 1513 University Ave, Madison, 53711
The ME 903: Graduate Student Lecture Series features campus and visiting speakers who present on a variety of research topics in the field of mechanical engineering. Professor Laura Grossenbacher is the Director of Technical Communication with the College of Engineering at UW-Madison.

Title: Navigating Ethical Challenges in Mechanical Engineering Research and Industry Contexts: Strategies for Ethical Leadership – and Followership

Abstract: This workshop will provide a brief background on engineering ethics challenges and the behavioral science that suggests we must think both with and beyond a Code of Ethics to deal with the moral ambiguities that can emerge in complex workplace contexts, including in university research labs and large engineering organizations. We will explore questions that engineering leaders should ask themselves – but also some “followership” strategies for those engineers who are not yet leaders.My hope is to engage the ME 903 students in discussion of a couple of unique cases to practice voicing, listening, and productively responding to the values of their peers.

Bio: Laura Grossenbacher is Director of Undergraduate Program Review and Director of the Technical Communication Program in the College of Engineering at the University of Wisconsin-Madison. She holds a Ph.D. from the University of Texas at Austin and has been teaching courses in engineering communication and ethics for over twenty-five years to both undergraduates and graduate students in the UW Madison College of Engineering. Since year 2012 she has been developing ethics cases for use with a variety of different Professional Engineering groups, including engineers working for the Wisconsin Department of Transportation, the Wisconsin Department of Natural Resources, American Transmission Company, Madison Gas and Electric, WE Energies, Realtime Utility, the Milwaukee Metropolitan Sewerage District, and the American Water Resources Administration; she has also held ethics workshops for the Wisconsin Structural Engineering Code Refresher Annual Conference, the Wisconsin Concrete Pavement Association, the Wisconsin Society for Landscape Architects, and the American Society for Heating, Refrigerating and Air Conditioning Engineers.

Her ethics workshops are designed to engage engineers and other professionals in discussing and applying codes of ethics, moral theory, and behavioral science to practical cases.She is a member of the Association for Practical and Professional Ethics (APPE), and a current co-chair, with Rider Foley, of the Online Ethics Center Community of Practice in Teaching Engineering Ethics. Her most recent conference workshops have been for the annual ABET Symposium and at the American Society for Engineering Education on using applied ethics cases to interrogate challenges with power and inclusivity.

ECE QUANTUM ENGINEERING SEMINAR SERIES: Dr. Yun Zhao
2026-03-27 10:00 - 11:00 | 2534 Engineering Hall, 1415 Engineering Drive, Madison, Wisconsin, 53706, United States
Microresonator-based quantum photonics

Yun Zhao

Abstract: As the only quantum information carrier at atmospheric pressure and temperature, photons play a versatile role in the quantum information ecosystem. Recent progress in fabricating high-quality-factor microresonators has enabled unprecedented control of photons through nonlinear optical interactions. Here, I will focus on optical squeezing, which is a foundational process in both photonic quantum metrology and computing. I will first discuss the generation of squeezed vacuum states on a CMOS-compatible platform. Then I will present a fundamentally new way of applying optical squeezing in optical frequency metrology, with applications in optical frequency division and narrow-linewidth lasers. Finally, I will briefly discuss other micro- resonator-based applications, including quantum frequency conversion and spatial light modulation.

Bio: Yun Zhao is currently a postdoc at Stanford University in the Applied Physics department, advised by Prof. Amir Safavi-Naeini. He earned his PhD in Electrical Engineering from Columbia University, advised by Prof. Alexander Gaeta. He has broad research interests in quantum and nonlinear photonics. His work spans optical squeezing, Kerr frequency comb, frequency conversion, optical frequency division, and spatial light modulation, etc. He served as the postdoctoral community chair for the DOE Codesign Center for Quantum Advantage in 2023 and 2024 and hosted a webinar series for the center’s graduate students and postdocs.

Mechanics Seminar: Professor Xiangru Xu
2026-03-27 12:00 - 13:00 | 1227 Engineering Hall, 1415 Engineering Drive, Madison, WI, 53706, United States
The Mechanics Seminar Series is a weekly seminar given by campus and visiting speakers on topics across the spectrum of mechanics research (solids, fluids, and dynamics). Professor Xiangru Xu is a professor at UW-Madison.

Title: Safe Control for Learning-Enabled Autonomous Systems

Abstract: This talk presents recent advances in provably safe control for learning-enabled autonomous systems. In the first part, I will discuss reachability analysis and controlled invariance of neural network control systems. I will introduce methods for safety verification and safe control synthesis based on forward and backward reachable set computations using constrained and hybrid zonotopes, together with an interval-based invariance operator for computing the maximum controlled invariant set. In the second part, I will present hierarchical safe control architectures that integrate a high-level optimization-based motion planner with a low-level safety filter, providing formal guarantees of continuous-time safety constraint satisfaction. I will demonstrate their effectiveness through two case studies: safe trajectory planning and tracking for quadrotors, and occlusion-free visual servoing of robotic manipulators.

Bio: Xiangru Xu is an Assistant Professor in the Department of Mechanical Engineering at the University of Wisconsin-Madison. He received his Ph.D. from the Chinese Academy of Sciences and held postdoctoral positions at the University of Michigan and the University of Washington. He is a recipient of the NSF CAREER Award and the Best New Application Paper Award from IEEE Transactions on Automation Science and Engineering. He currently serves as an Associate Editor for IEEE Transactions on Automatic Control, Control Theory and Technology, and Autonomous Intelligent Systems, and is a member of the IEEE Control Systems Society Conference Editorial Board.

BME Seminar Series: Natasah Seybani, PhD
2026-04-06 12:00 - 13:00 | 1003 (Tong Auditorium) Engineering Centers Building, 1550 Engineering Drive, Madison, WI, 53706, United States
Bench-to-Bedside Engineering of Precision Immunotherapy Paradigms with Focused Ultrasound

Natasha Sheybani, PhDAssistant Professor of Biomedical EngineeringResearch Director at UVA Focused UltrasoundImmuno-Oncology (FUSION) CenterUniversity of Virginia

Abstract:Immunotherapy has revolutionized cancer treatment, but significant limitations remain across solid tumor indications. This talk will highlight advances in the use of image-guided focused ultrasound (FUS) as a non-invasive, multi-pronged interventional tool for potentiating multiple classes of immunotherapy, including vaccine adjuvants, checkpoint inhibitors, and CAR T cells. We will showcase integration of non-invasive surveillance approaches such as positron emission tomography (PET) and liquid biopsy with FUS to inform precision, adaptation, and de-intensification of combinatorial treatment regimens. We will also showcase development of novel image-guided ultrasound instrumentation toward these objectives. Applications spanning high-risk breast cancer and adult/pediatric brain cancers will be discussed. Finally, this talk will overview clinical translation and insights from first-in-human trials investigating FUS for immuno-oncology applications.

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POSTPONED - ECE Distinguished Speaker Seminar Series: Professor Seth Ariel Tongay
2026-04-07 00:00
This event has been postponed. We look forward to hosting Professor Tongay for our 2026-2027 Distinguished Speaker Seminar Series.

Pushing the Limits of 2D Janus Layers

Abstract:Named after the two faced Roman God Janus, 2D Janus layers contain two different atomic types on its top and bottom faces. Previous theoretical studies have shown that broken mirror symmetry together with large change transfer across the top and bottom face opens up completely new quantum properties including Rashba effect, colossal Janus field, dipolar excitons, and Skyrmion formation. Despite the theoretical advances in the field, experimental results are still limited due to limitations in high quality 2D Janus layer synthesis. In this talk, I will introduce recent discoveries made at Arizona State University towards different types of Janus layers. The growth process relies on Plasma enhanced low pressure chemical vapor deposition (PE-LPCVD). With this all room temperature technique, our team can synthesize different Janus layers as well as their vertical / lateral heterojunctions, and Janus nanoscrolls. Further studies from our team will introduce on-demand fabrication of 2D Janus layers with unique in-situ growth capabilities that allows us to collect spectroscopy data during the course of Janus material growth. Results are presented along with microscopy, spectroscopy, high – pressure studies, and electronic transport datasets for complete understanding of these systems.

Professor Seth Ariel Tongay

Bio:Professor Seth Ariel Tongay is an internationally recognized materials scientist and engineer whose research bridges fundamental discoveries and real-world manufacturing of next-generation semiconductors. He serves as one of the research directors of College of Engineering at Arizona State University, home to the largest engineering college in the United States.Prof. Tongay’s research focuses on lab-to-fab integration of emergent semiconductor materials, addressing key challenges in metal interconnects, stress liner technologies, and advanced device architectures such as FinFETs and gate-all-around (GAA) transistors. He is particularly known for his seminal contributions to two dimensional (2D) materials, including Janus semiconductors and the discovery of quasi-one- dimensional (quasi-1D) layered systems.He has published over 350 peer-reviewed papers and holds an h-index of 86, reflecting his high impact across materials science, nanotechnology, and semiconductor physics. His work has been recognized with the Presidential Early Career Award for Scientists and Engineers (PECASE), NSF CAREER Award, and fellowships from the American Physical Society, Royal Society of Chemistry, and the Institute of Physics.Prof. Tongay is also an associate editor for Applied Physics Reviews (AIP) and npj 2D Materials and Applications (Nature). His research is supported by the CHIPS Act, NSF, DOE, ARO, and industry leaders including Intel and Applied Materials.

CBE Seminar Series: Kayla Sprenger
2026-04-07 16:00 - 17:00
Kayla SprengerUniversity of Colorado BoulderBoulder, CO

Towards Modeling Antibody–Virus Coevolution and Escape: Integrating Evolutionary Modeling, Molecular Dynamics Simulations, and Interpretable Machine Learning

In the Rationally Designed Immunotherapeutics and Interfaces (RDI) Lab, we integrate computational modeling, immuno-engineering, molecular biophysics, and machine learning to understand—and ultimately control—how immune systems respond to rapidly evolving viral pathogens. A key challenge in designing vaccines against such pathogens is engineering immunogens that elicit broadly neutralizing antibodies (bnAbs), which target conserved regions of viral surface proteins and thereby bind diverse viral variants. Yet, even the most potent bnAbs isolated from infected individuals to date have proven susceptible to escape by viral mutations that weaken or abolish bnAb binding. Notably, neutralization datasets frequently reveal escape mutations at sites distal to the antibody-bound epitope, suggesting that allosteric and epistatic effects play a key role in modulating binding. In most cases, the mechanistic basis by which these distal mutations confer escape remains poorly understood, limiting our ability to design vaccine immunogens or antibody-based therapeutics that are resistant to escape mutations.

To address this gap, this talk will highlight our use of evolutionary frameworks to model B cell affinity maturation against static sequences of HIV-1–derived immunogens administered via time-varying immunization protocols. This approach enables us to understand how different vaccine strategies shape antibody lineages and guide them toward broadly neutralizing responses. In parallel, coupling these models of immune evolution with dynamic viral fitness landscapes enables identification of escape pathways that may be exploited in vivo, thus informing the iterative design of immunogens and immunization strategies capable of eliciting fully escape-resistant bnAbs. To further resolve the mechanistic basis of escape, this talk will also describe our use of atomistic molecular dynamics simulations and interpretable machine learning to characterize how distal mutations propagate dynamical changes through the structure of HIV-1’s Envelope (Env) spike protein to abrogate antibody binding and neutralization. Complementing these structural insights, we have developed an interpretable protein language model framework trained on HIV-1 sequence data and bnAb neutralization profiles. This framework identifies context-dependent mutational effects that rewire long-range residue-level communication networks governing antibody sensitivity. Together, our work provides a mechanistic foundation for designing next-generation immunogens against highly mutable pathogens like HIV-1, as well as antibody-based therapeutics that are more robust to rapid viral evolution.

ISyE - Industry info session
2026-04-08 18:00 - 19:00 | 3127 Mechanical Engineering Building, 1513 University Avenue, Madison, 53706
3127 ME

Join the students of IISE as they welcome industry guest Greenheck Group to campus.

ECE RISE-AI SEMINAR SERIES: Dr. Omar Chehab
2026-04-09 11:30 - 12:30 | Discovery Building, 330 N. Orchard St., Madison, Wisconsin, 53715
Toward efficient inference in complex systems

Abstract: I will present a line of work on efficient inference in complex systems, spanning both the foundations of machine learning and applications to brain imaging data. The talk is organized around two complementary directions.

In the first part, I will study modern algorithms for sampling, estimating normalizing constants, and estimating likelihoods. These methods often rely on a probability path that connects a complex target distribution to a simple base distribution, such as a Gaussian. I will highlight fundamental limitations of classical approaches, and show how path-guided algorithms can substantially improve efficiency. I will also discuss principled strategies for designing these probability paths, explaining when and why such methods succeed.

In the second part, I will turn to machine learning algorithms that are applied in neuroscience, presenting recent results on learning representations and discovering causal structure from brain imaging data. This line of work is a step toward using machine learning to obtain new scientific insights.

I will conclude with open questions in the field and future directions at the intersection of generative modeling, sampling, and their scientific applications.

Omar Chehab

Bio: Omar Chehab is a postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University. He completed his graduate training in France, earning a PhD in Mathematical Computer Science at Inria under the supervision of Aapo Hyvärinen and Alexandre Gramfort, followed by a postdoctoral position in the Statistics Department of ENSAE/CREST with Anna Korba.

His research focuses on principled methods for efficient inference from complex probability distributions. This includes estimating likelihoods from data, generating samples from unnormalized densities, as well as learning representations and discovering causal structure from brain imaging data. His work draws on a range of modern methods, including diffusion models, annealed MCMC, score matching, multi-view independent component analysis, and noise-contrastive estimation. More broadly, he studies these algorithms through the lens of computational and statistical efficiency, aiming to understand their fundamental limits and guide their design.

He regularly publishes at leading machine learning conferences such as NeurIPS, ICML, and ICLR, where his work has been recognized with a spotlight and top reviewer awards.

Location details: Discovery Building – Room 2329, 2nd floor of Discovery Building (access through glass doors behind information desk)

ME 903 Graduate Seminar: Professor Riley Barta
2026-04-09 16:00 - 17:00 | 3M Auditorium, rm 1106 Mechanical Engineering Building, 1513 University Ave, Madison, 53711
The ME 903: Graduate Student Lecture Series features campus and visiting speakers who present on a variety of research topics in the field of mechanical engineering. Professor Riley Barta is a professor at Purdue University.

Title: Bridging Scales: High-Fidelity Characterization and Modeling to Deploy Sustainable Working Fluids

Abstract: Climate change is driving the decarbonization of energy systems, where reducing direct and indirect CO₂ emissions is critical to lowering the environmental impact of cooling, heating, and power conversion technologies. Approximately 40% of U.S. energy is consumed by buildings, and interest in decarbonizing commercial cooling and industrial processes through heat pumps and waste heat recovery is rapidly increasing. At the same time, the environmental impact of the working fluids themselves cannot be overlooked, and evolving regulations phasing out working fluids further complicate the development of sustainable thermal systems. This talk will address these themes in the context of two research efforts: High-temperature heat pumps and expansion work recovery in transcritical CO₂ refrigeration systems. In these efforts, experimental and numerical methods were employed across a range of fidelities, from empirical correlations to high-frequency pressure pulsation measurements. The challenges and opportunities associated with low-global-warming-potential working fluids will be discussed, along with examples of ongoing research, infrastructure development, and future directions.

Bio: Riley Barta has been an Assistant Professor of Mechanical Engineering at Purdue University’s Ray W. Herrick Laboratories since 2023. His research area is thermal systems technology, with a focus on the characterization and implementation of environmentally friendly working fluids and the development of equipment to utilize them in vapor compression systems, waste heat utilization, refrigerant property characterization, data center and electronics cooling as well as aircraft thermal management. In 2024, Dr. Barta received the ASHRAE New Investigator Award to focus on practical challenges surrounding zeotropic mixtures in heat pumps.

Before starting joining the faculty at Purdue University, Dr. Barta received an Alexander von Humboldt Postdoctoral Fellowship while working for several years as the Refrigeration Research Group Leader at the TU Dresden in Germany. Additionally, he received the DKV (German Refrigeration Society) Young Talent Award for his Ph.D. Dissertation on efficiency increasing measures for transcritical CO2 refrigeration systems. He holds a bachelor’s degree in mechanical engineering and a Ph.D. in mechanical engineering from Purdue University.

Reinforcement Learning for Digital Health Interventions in the Dyadic Setting
2026-04-10 12:00 - 13:00 | 1163 Mechanical Engineering, 1513 Engineering Dr., Madison, WI, 53706, United States
UW-ISyE looks forward to welcoming Susan Murphy Professor of Statistics and of Computer Science and Associate Faculty at the Kempner Institute, Harvard University.

We present our ongoing work on the development of an online reinforcement learning (RL) algorithm for dyadic digital intervention settings in which the task for the RL algorithm is to assist the target person with a difficult illness be adherent to behavioral activities. To achieve this goal the RL algorithm will not only deliver digital interventions to the target person but also deliver interventions to assist the carepartner to manage caregiving burden and help the two individuals improve their relationship. That is, different RL components target different elements of the dyad. The RL algorithm is a multi-agent RL algorithm in which the 3 agents make decisions on the 3 elements of the dyad. We incorporate domain knowledge in the form of approximal causal directed acyclic graphs to speed up online learning in this sparse data setting. This work is motivated by our development of the ADAPTS-HCT multi-agent RL algorithm, designed to improve medication adherence by young adults who have undergone a blood and bone marrow transplant. The RL algorithm will be deployed in summer 2026.

Bio: Susan A. Murphy is Mallinckrodt Professor of Statistics and of Computer Science and Associate Faculty at the Kempner Institute, Harvard University. Her research focuses on improving sequential decision making via the development of online, real-time reinforcement learning algorithms. Her lab is involved in multiple deployments of these algorithms in digital health. She is a member of the US National Academy of Sciences and of the US National Academy of Medicine. In 2013 she was awarded a MacArthur Fellowship for her work on experimental designs to inform sequential decision making. She is a Fellow of the College on Problems in Drug Dependence, Past-President of Institute of Mathematical Statistics, Past-President of the Bernoulli Society and a former editor of the Annals of Statistics.

Mechanics Seminar: Professor Ricardo Vinuesa
2026-04-10 12:00 - 13:00 | 1227 Engineering Hall, 1415 Engineering Drive, Madison, WI, 53706, United States
The Mechanics Seminar Series is a weekly seminar given by campus and visiting speakers on topics across the spectrum of mechanics research (solids, fluids, and dynamics). Professor Ricardo Vinuesa is a professor at Michigan University.

Title: Explainable deep learning and foundation models: control and scientific discovery

Abstract: In this seminar we discuss a unified framework that combines explainable deep learning, deep reinforcement learning (DRL) and foundation models to advance both understanding and control of turbulence, with direct implications for accelerated design and discovery. First, we will show how explainable deep learning techniques can be used to identify the flow features that are truly responsible for key turbulent processes in wall-bounded flows. By systematically interrogating trained neural networks, we uncover the most influential coherent structures driving momentum transport and drag. Our results reveal that classically studied structures (while important) provide only a partial and sometimes misleading perspective, motivating a more data-driven and physics-aware view of turbulence organization. Building on these insights, we will demonstrate how deep reinforcement learning can be used to actively control turbulent flows by targeting the dynamically relevant structures identified through explainability. This approach achieves over 30% drag reduction in canonical wall-bounded turbulence and extends naturally to more complex configurations, including turbulent wings, highlighting the scalability of learning-based control strategies. Finally, we will introduce a foundation-model-based framework for accelerated design, optimization and scientific discovery. By learning compact, interpretable latent representations of high-dimensional flow physics, these models (combined with agentic-AI systems) enable rapid exploration of design spaces, causal reasoning and closed-loop optimization, bridging the gap between expensive simulations, control and engineering decision making. Together, these results illustrate how explainable and agentic AI are becoming essential for turbulence physics, flow control and next-generation engineering design.

Bio: Dr. Ricardo Vinuesa is the Associate Chair for Research and an Associate Professor at the Department of Aerospace Engineering, University of Michigan. He studied Mechanical Engineering at the Polytechnic University of Valencia (Spain), and he received his PhD in Mechanical and Aerospace Engineering from the Illinois Institute of Technology in Chicago. His research combines numerical simulations and data-driven methods to understand, control and predict complex wall-bounded turbulent flows, such as the boundary layers developing around wings and the flow in urban environments. Dr. Vinuesa has received, among others, an ERC Consolidator Grant, the Harleman Lecture Award, the TSFP Kasagi Award, the MST Emerging Leaders Award, the Goran Gustafsson Award for Young Researchers, the IIT Outstanding Young Alumnus Award and the SARES Young Researcher Award. He received the Outstanding Reviewer Prize of the Journal of Fluid Mechanics and he is also a member of the Young Academy of Science of Spain.

ECE RISE-AI SEMINAR SERIES: Associate Professor Salman Asif
2026-04-10 12:00 - 13:00 | Discovery Building, 330 N. Orchard St., Madison, Wisconsin, 53715
Learning to See, Adapt, and Forget: From Computational Imaging to TrustworthyMultimodal AI

Abstract: A central challenge in modern AI is that the world at test time does not match what was assumed at training time. Physical sensors operate under constraints, modalities go missing, data shift out of distribution, and models retain information they were never meant to keep. Building systems that remain robust and reliable under incomplete, shifted, or misaligned information is the organizing question of my research program.

In this talk, I will present our research spanning physically grounded inverse problems to large-scale trustworthy AI, showing how robust behavior across different applications can be achieved through principled, low-dimensional representations and adaptations. I will begin with computational imaging, where we seek robust recovery of multidimensional data from indirect or incomplete measurements. I will discuss domain expansion and wavefront sensing, showing how principled algorithmic innovations lead to robust models for challenging inverse problems. I will then discuss multimodal learning, where we seek robustness against missing and imbalanced modalities at train or test time via parameter-efficient adaptation, proxy token generation, and model merging across modalities. Finally, I will discuss targeted adversarial attacks and unlearning, where we seek to exploit model vulnerabilities or remove targeted information (e.g., identities, concepts, unsafe content) without affecting unrelated capabilities.

I will close with a discussion of ongoing work and open problems spanning robust multimodal AI at scale, continual learning with efficient unlearning, and AI-guided sensing for medical, agricultural, and scientific applications.

Salman Asif

Bio: M. Salman Asif is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California, Riverside. Dr. Asif received his Ph.D. from the Georgia Institute of Technology, Atlanta, Georgia. He worked as a Senior Research Engineer at Samsung Research America, Dallas (2012–2014) and as a Postdoctoral Researcher at Rice University (2014–2016). He has received an NSF CAREER Award (2021), Google Faculty Research Award (2019), Hershel M. Rich Outstanding Invention Award (2016), and UC Regents Faculty Fellowship (2017) and Faculty Development (2021) Awards. Dr. Asif currently serves as Senior Associate Editor for the IEEE Transactions on Computational Imaging and as Area Chair for several top-tier venues including CVPR, NeurIPS, ICLR, and AAAI. His research interests lie at the intersection of machine learning, signal processing, and computational imaging, with a focus on building robust and trustworthy AI systems that perform reliably under incomplete, shifted, or misaligned information. Current research directions include robust multimodal learning, model editing and unlearning, and domain adaptation and generative models for computational imaging and inverse problems.

Location details: Discovery Building – Room 2329, 2nd floor of Discovery Building (access through glass doors behind information desk)

ISyE - Sheep shearing and animal petting
2026-04-11 11:00 - 13:00 | Haven’s Petting Farm, 2894 Erbe Road, Blue Mounds, Wisconsin, 53517
Take a trip to the Havens Petting Farm for their annual Sheep Shearing Day! Watch their farm hands give the sheep their much-needed haircut and pet some animal friends. Ride sharing available. Hosted by the HFES student organization.

Admission fee: $10/personHFES dues-paying members get 50% off!

Register now!

ISyE - Volunteering at Ronald McDonald House
2026-04-11 16:00 - 18:00 | Ronald McDonald House, 2716 Marshall Court, Madison, Wisconsin, 53705
Join fellow students to volunteer at our local Ronald McDonald House!

ISyE students will be preparing and serving meals for families in need at our local Ronald McDonald House on Saturday, April 11. All students are welcome to participate. Hosted by the UW-Madison INFORMS student organization.

Register now – details will be sent after your registration is received.

Register to volunteer at Ronald McDonald House

ISyE - Industry info session
2026-04-13 18:00 - 19:00 | 2535 Mechanical Engineering Building, 1513 University Avenue, Madison, 53706
2535 ME Building

Join the students of IISE as they welcome industry guest GRAINGER.

ECE Undergraduate Research Symposium
2026-04-14 11:30 - 13:00 | Discovery Building, 330 N. Orchard St., Madison, Wisconsin, 53715
The Department of Electrical and Computer Engineering invites you to join us for the second annual ECE Undergraduate Research Symposium. The symposium will be an exciting showcase of undergraduate research beyond the classroom. Selected students and teams will present their innovative work during a poster session where all are encouraged to talk with the undergraduate researchers about their discoveries.

This event is open to the public—come support our students and explore their cutting-edge research!

Event schedule:11:30 – 1:00 Poster session with light refreshments – Discovery Building Town CenterAwards to follow

Bird, Stewart and Lightfoot Lecture: Daniel Hammer
2026-04-14 16:00 - 17:00
Room 1610 Engineering Hall

Daniel HammerAlfred G. and Meta A. Ennis Professor of BioengineeringUniversity of PennsylvaniaPhiladelphia, PA

A Tale of Two Motilities

In the spirit of Bird, Stewart and Lightfoot, we have been studying an important problem in transport phenomena, the biological and bio-inspired motility of cells.

Motility is important for the functioning of the immune system, mostly because immune recognition requires molecular transfer by direct cell to cell contact. We have been studying a fascinating form of cell motility in which T-lymphocytes can migrate against the direction of flow, much like a salmon can swim upstream. Upstream migration is solely due to interactions between a specific lymphocyte receptor, LFA-1, and its natural ligand; no other receptor can support upstream migration. Our lab has found that many actively motile cells in the immune system have the ability to migrate upstream. Using CRISPR-Cas9 deletion, we have identified several molecules in cells that are critical for upstream migration; deletion using Cas9 reverses the direction of migration. We have also made the first traction maps of forces exerted by upstream migrating cells. We found that during upstream migration, cells maintain their “architecture,” with active forces in the front and rear, but the magnitude of the forces greatly increases, allowing cells to exert sufficient traction to overcome the applied hydrodynamic forces.

Our laboratory is also interested in making synthetic cells, or protocells, that can mimic the behavior of biological cells. In collaboration with Daeyeon Lee at Penn, we have been making inert capsules using microfluidic assembly that can display motility in solution. By attaching urease to the surface of a capsule, we can drive autonomous motion of the capsule in a field of urea. We find that asymmetry of the capsule, in geometry or chemistry, or both, greatly enhances capsule motion. In gradients of urea, our capsules display negative phoresis (move down the gradient). We have preliminary results for the urease-driven motion of Janus capsules, made by microfluidic assembly from mixtures of phase-separating polymers, as a function of the geometry of the capsules.

In the end, we draw analogies between our biological and bio-inspired motile systems, ultimately finding they have little in common.

ISyE - Paper airplane social!
2026-04-15 16:00 - 18:00 | 3126 Mechanical Engineering, 1513 University Avenue, Madison, 53706
3126 Mechanical Engineering

Join IISE members for a paper airplane social! If you’ve been searching for an excuse to throw paper airplanes in a classroom, here it is! We’ll host a competition on the side with three fun challenges for those feeling competitive – but no pressure! Just drop in, experiment, and enjoy a chill social event with fellow engineers.

ME 150th Celebration: Distinguished Alumni, Dr. Kelly Senecal
2026-04-16 16:15 - 17:00 | 3M Auditorium, rm 1106 Mechanical Engineering Building, 1513 University Ave, Madison, 53711
To celebrate 150 years of Mechanical Engineering at the University of Wisconsin – Madison, the Department of Mechanical Engineering will feature distinguished alumni in mechanical engineering and engineering mechanics who have made a lasting impact on the field. Kelly Senecal, who received his master’s (’97) and PhD (’00) in mechanical engineering, is the cofounder of Convergent Science. Kelly also received the 2025 Luminary Award! To learn more about Dr. Kelly Senecal’s experience, please join us for this installment of our ME 903: Graduate Student Lecture series.

Title: From Graduate Research to Global Impact: Building a CFD Company That Challenged Convention

Abstract: This seminar reflects on the journey from graduate student at the University of Wisconsin–Madison to co-founding a company built on the idea that even established engineering fields can be reimagined. I will share formative experiences from my time at UW–Madison, the early vision behind building a different kind of CFD company, and the challenges of pursuing an approach that did not always align with conventional thinking.

Along the way, I will highlight how computational fluid dynamics has grown from a specialized research tool into a technology with global impact across transportation, energy, and other critical industries. More importantly, I will reflect on the lessons learned: that meaningful innovation often requires patience, resilience, and the courage to trust fundamentals over trends.

For students and researchers, the message is simple: protect your curiosity, question assumptions, and be willing to take the long path. The most impactful ideas are not always the most popular at the start, `but they are the ones worth pursuing.

Bio: Dr. Kelly Senecal is a co-founder of Convergent Science, an industry-leading computational fluid dynamics software company. He is a visiting professor at the University of Oxford and a co-founder and director of the Computational Chemistry Consortium (C3). Dr. Senecal is a Fellow of the Society of Automotive Engineers (SAE), the American Society of Mechanical Engineers (ASME), and the Combustion Institute (CI). He is the Chair of the executive committee of the ASME Transportation Systems Division and a member of the board of directors of the Combustion Institute. Recent accolades include the 2019 ASME ICE Award, the 2023 SAE John Johnson Diesel Engine Research Medal, the 2023 ASME Dedicated Service Award, the 2025 ASME Soichiro Honda Medal, and the 2025 University of Wisconsin Alumni Association Luminary Award.

Dr. Senecal has long been an advocate of creating cleaner propulsion systems, with a particular focus on using CFD and HPC to enable faster design. Starting with his TEDx talk in late 2016, he has promoted a diverse mix of transportation technologies through invited talks, articles, and social media. Dr. Senecal is co-author of the book Racing Toward Zero: The Untold Story of Driving Green, winner of the 2022 Independent Press Award for Environment.

Midwest Mechanics Seminar: Professor Laura de Lorenzis
2026-04-17 12:00 - 13:00 | Engineering Research Building, Room 106, 1500 Engineering Dr., Madison, Wisconsin, 53706
The Mechanics Seminar Series is a weekly seminar given by campus and visiting speakers on topics across the spectrum of mechanics research (solids, fluids, and dynamics). This specific seminar is hosted in conjunction with the Midwest Mechanics Seminar Series. Professor Laura de Lorenzis is a professor at Institute for Mechanical Systems – Zurich.

Title: Material model discovery: from sparse regression to the language of material laws

Abstract: The lecture provides an overview of recent research by the speaker’s group and collaborators on the automated discovery of material models. This research advocates a paradigm shift: moving beyond the traditional approach of calibrating unknown parameters within a preselected material model, towards the new objective of model discovery.

Model discovery entails the simultaneous selection, generation, or encoding of the most suitable model to interpret experimental data, along with the calibration of its parameters. To this end, a variety of tools are employed—ranging from sparse regression to Bayesian learning, and from formal grammars to symbolic regression. While each tool has distinct features, they share the common aim of enforcing physics constraints and ensuring interpretability of the discovered models.

Initially developed to discover a specific model within a given category (e.g. hyperelasticity, viscoelasticity, or plasticity), the approach has more recently been extended to the general case of materials belonging to an unknown class of constitutive behavior. Additional aspects such as data type, specimen design, and experimental validation are also discussed

Bio: Laura De Lorenzis received her Engineering degree and her PhD from the University of her hometown Lecce, in southern Italy, where she first stayed as Assistant and later as Associate Professor of Solid and structural mechanics. In 2013 she moved to the TU Braunschweig, Germany, as Professor and Director of the Institute of Applied Mechanics. There she was founding member and first Chair (2017-2020) of the Center for Mechanics, Uncertainty and Simulation in Engineering. Since 2020 she is Professor of Computational Mechanics at ETH Zürich, in the Department of Mechanical and Process Engineering. She was visiting scholar in several renowned institutions, including Chalmers University of Technology, the Hong Kong Polytechnic University, the Massachusetts Institute of Technology (as holder of a Fulbright Fellowship in 2006), the Leibniz University of Hannover (with an Alexander von Humboldt Fellowship in 2010-2011), the University of Texas at Austin and the University of Cape Town. She is the recipient of several prizes, including the RILEM L’Hermite Medal 2011, the AIMETA Junior Prize 2011, the IIFC Young Investigator Award 2012, the Euromech Solid Mechanics Fellowship 2022, the IACM Fellowship 2024, two best paper awards and two student teaching prizes. In 2011 she was awarded a European Research Council Starting Researcher Grant. She has delivered over 30 plenary lectures at international conferences and authored or co-authored more than 170 papers on international journals on different topics of computational and applied mechanics. Since 2023 she is Editor of Computer Methods in Applied Mechanics and Engineering.

BME Seminar Series: Fabian Voigt, PhD
2026-04-20 12:00 - 13:00 | 1003 (Tong Auditorium) Engineering Centers Building, 1550 Engineering Drive, Madison, WI, 53706, United States
Expanding the optical bag of tricks for (neuro)biology

Fabian Voigt, PhDPostdoctoral Researcher & Branco Weiss Fellow (2024-2029)Biolabs 2072Engert LaboratoryHarvard University

Abstract:Seeing is believing and thus, optical imaging techniques are extremely useful to study brain structure and function. I will present several projects aimed at providing the neuroscience community with better imaging instrumentation: These range from open-source light-sheet microscopes for imaging cleared tissue (http://mesospim.org) to novel multi-immersion microscope objectives that take inspiration from scallops and astronomical telescopes. In addition, I will present recent projects aimed at rapid 3D tracking of freely behaving fish larvae and for increasing the light-collection efficiency of single objective light-sheet microscopes.

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ISyE - Industry info session
2026-04-20 18:00 - 19:00
Room tbd

Join the students of IISE as they welcome industry guest ABB.
