BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//College of Engineering - University of Wisconsin-Madison - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://engineering.wisc.edu
X-WR-CALDESC:Events for College of Engineering - University of Wisconsin-Madison
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20250309T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20251102T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20260308T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20261101T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20270314T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20271107T070000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260406T120000
DTEND;TZID=America/Chicago:20260406T130000
DTSTAMP:20260404T131946
CREATED:20260121T162400Z
LAST-MODIFIED:20260326T213522Z
UID:10001438-1775476800-1775480400@engineering.wisc.edu
SUMMARY:BME Seminar Series: Natasah Seybani\, PhD
DESCRIPTION:Bench-to-Bedside Engineering of Precision Immunotherapy Paradigms with Focused Ultrasound\n\n\n\n\n\n\n\nNatasha Sheybani\, PhDAssistant Professor of Biomedical EngineeringResearch Director at UVA Focused UltrasoundImmuno-Oncology (FUSION) CenterUniversity of Virginia \n\n\n\nAbstract: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. \n\n\n\nPrint PDF
URL:https://engineering.wisc.edu/event/bme-seminar-series-9/
LOCATION:1003 (Tong Auditorium) Engineering Centers Building\, 1550 Engineering Drive\, Madison\, WI\, 53706\, United States
CATEGORIES:Biomedical Engineering,Seminar
ATTACH;FMTTYPE=image/jpeg:https://engineering.wisc.edu/wp-content/uploads/2024/11/Seminar-Graphic-Fall2024-1.avif
ORGANIZER;CN="Department of Biomedical Engineering":MAILTO:bmehelp@bme.wisc.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260407T000000
DTEND;TZID=America/Chicago:20260407T000000
DTSTAMP:20260404T131946
CREATED:20251002T133320Z
LAST-MODIFIED:20260320T144535Z
UID:10001316-1775520000-1775520000@engineering.wisc.edu
SUMMARY:POSTPONED - ECE Distinguished Speaker Seminar Series: Professor Seth Ariel Tongay
DESCRIPTION:This event has been postponed. We look forward to hosting Professor Tongay for our 2026-2027 Distinguished Speaker Seminar Series. \n\n\n\n\n\n\n\nPushing the Limits of 2D Janus Layers\n\n\n\n\n\n\n\nAbstract: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. \n\n\n\nProfessor Seth Ariel Tongay\n\n\n\nBio: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.
URL:https://engineering.wisc.edu/event/ece-distinguished-speaker-seminar-series-prof-seth-ariel-tongay/
LOCATION:Wisconsin
CATEGORIES:Electrical & Computer Engineering,Seminar
ATTACH;FMTTYPE=image/jpeg:https://engineering.wisc.edu/wp-content/uploads/2025/09/Distinguished-Speaker-Seminar-Series-3.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260407T160000
DTEND;TZID=America/Chicago:20260407T170000
DTSTAMP:20260404T131946
CREATED:20260326T134206Z
LAST-MODIFIED:20260326T134209Z
UID:10001504-1775577600-1775581200@engineering.wisc.edu
SUMMARY:CBE Seminar Series: Kayla Sprenger
DESCRIPTION:Kayla SprengerUniversity of Colorado BoulderBoulder\, CO \n\n\n\nTowards Modeling Antibody–Virus Coevolution and Escape: Integrating Evolutionary Modeling\, Molecular Dynamics Simulations\, and Interpretable Machine Learning\n\n\n\n\n\n\n\nIn 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. \n\n\n\nTo 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.
URL:https://engineering.wisc.edu/event/cbe-seminar-series-kayla-sprenger/
LOCATION:Wisconsin
CATEGORIES:Chemical & Biological Engineering,Seminar
ATTACH;FMTTYPE=image/jpeg:https://engineering.wisc.edu/wp-content/uploads/2023/02/2023_CBE-sem-series-web-header-scaled.webp
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260409T113000
DTEND;TZID=America/Chicago:20260409T123000
DTSTAMP:20260404T131946
CREATED:20260330T210830Z
LAST-MODIFIED:20260402T130501Z
UID:10001505-1775734200-1775737800@engineering.wisc.edu
SUMMARY:ECE RISE-AI SEMINAR SERIES: Dr. Omar Chehab
DESCRIPTION:Toward efficient inference in complex systems\n\n\n\n\n\n\n\nAbstract: 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.  \n\n\n\nIn 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. \n\n\n\nIn 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. \n\n\n\nI will conclude with open questions in the field and future directions at the intersection of generative modeling\, sampling\, and their scientific applications. \n\n\n\nOmar Chehab\n\n\n\nBio: 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. \n\n\n\nHis 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. \n\n\n\nHe 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. \n\n\n\nLocation details: Discovery Building – Room 2329\, 2nd floor of Discovery Building (access through glass doors behind information desk)
URL:https://engineering.wisc.edu/event/ece-rise-ai-seminar-series-omar-chehab/
LOCATION:Discovery Building\, 330 N. Orchard St.\, Madison\, Wisconsin\, 53715
CATEGORIES:Electrical & Computer Engineering,Seminar
ATTACH;FMTTYPE=image/jpeg:https://engineering.wisc.edu/wp-content/uploads/2025/02/Rising-Stars-Seminars-Plain.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260409T160000
DTEND;TZID=America/Chicago:20260409T170000
DTSTAMP:20260404T131946
CREATED:20260115T163008Z
LAST-MODIFIED:20260115T163011Z
UID:10001408-1775750400-1775754000@engineering.wisc.edu
SUMMARY:ME 903 Graduate Seminar: Professor Riley Barta
DESCRIPTION: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.
URL:https://engineering.wisc.edu/event/me-903-graduate-seminar-professor-riley-barta/
LOCATION:3M Auditorium\, rm 1106 Mechanical Engineering Building\, 1513 University Ave\, Madison\, 53711
CATEGORIES:Mechanical Engineering,Seminar
ATTACH;FMTTYPE=image/jpeg:https://engineering.wisc.edu/wp-content/uploads/2024/08/Event-Graphics-for-Calendar-12-jpg.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260410T120000
DTEND;TZID=America/Chicago:20260410T130000
DTSTAMP:20260404T131946
CREATED:20260120T212617Z
LAST-MODIFIED:20260324T154612Z
UID:10001425-1775822400-1775826000@engineering.wisc.edu
SUMMARY:Mechanics Seminar: Professor Ricardo Vinuesa
DESCRIPTION: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. \n\n\n\nTitle: Explainable deep learning and foundation models: control and scientific discovery \n\n\n\nAbstract: 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. \n\n\n\nBio: 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.
URL:https://engineering.wisc.edu/event/mechanics-seminar-professor-ricardo-vinuesa/
LOCATION:1227 Engineering Hall\, 1415 Engineering Drive\, Madison\, WI\, 53706\, United States
CATEGORIES:Mechanical Engineering,Seminar
ATTACH;FMTTYPE=image/jpeg:https://engineering.wisc.edu/wp-content/uploads/2024/08/Event-Graphics-for-Calendar-11-jpg.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260410T120000
DTEND;TZID=America/Chicago:20260410T130000
DTSTAMP:20260404T131946
CREATED:20260402T131700Z
LAST-MODIFIED:20260402T131702Z
UID:10001513-1775822400-1775826000@engineering.wisc.edu
SUMMARY:ECE RISE-AI SEMINAR SERIES: Associate Professor Salman Asif
DESCRIPTION:Learning to See\, Adapt\, and Forget: From Computational Imaging to TrustworthyMultimodal AI\n\n\n\n\n\n\n\nAbstract: 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. \n\n\n\nIn 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.  \n\n\n\nI 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. \n\n\n\nSalman Asif\n\n\n\nBio: 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. \n\n\n\nLocation details: Discovery Building – Room 2329\, 2nd floor of Discovery Building (access through glass doors behind information desk)
URL:https://engineering.wisc.edu/event/ece-rise-ai-seminar-series-associate-professor-salman-asif/
LOCATION:Discovery Building\, 330 N. Orchard St.\, Madison\, Wisconsin\, 53715
CATEGORIES:Electrical & Computer Engineering,Seminar
ATTACH;FMTTYPE=image/jpeg:https://engineering.wisc.edu/wp-content/uploads/2025/02/Rising-Stars-Seminars-Plain.avif
END:VEVENT
END:VCALENDAR