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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
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DTSTART;TZID=America/Chicago:20260409T113000
DTEND;TZID=America/Chicago:20260409T123000
DTSTAMP:20260413T144026
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
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DTSTART;TZID=America/Chicago:20260410T120000
DTEND;TZID=America/Chicago:20260410T130000
DTSTAMP:20260413T144026
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
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