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DTSTART;TZID=America/Chicago:20260223T120000
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DTSTAMP:20260404T165255
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SUMMARY:BME Seminar Series: Shawn M. Gomez\, EngScD
DESCRIPTION:From Cellular Networks to Therapeutic Predictions: A Data-Driven Approach to Precision Medicine\n\n\n\n\n\n\n\nShawn M. Gomez\, EngScDProfessor and Associate Chair for ResearchCo-Executive Director\, FastTaCS\, NC TraCS InstituteLampe Joint Department of Biomedical Engineering at UNC-Chapel Hill and NC State University \n\n\n\nAbstract:Precision medicine aims to tailor prevention\, diagnosis\, and therapy to individual patients’ biological states. We pursue this as a multiscale problem\, combining molecular and systems biology approaches with translational AI methods to improve clinical decision-making. In this talk\, I focus on our systems-level efforts to predict targeted therapeutic responses in cancer. This challenge is particularly acute because despite extensive molecular profiling capabilities\, predicting how therapies affect cellular phenotypes remains a critical barrier to precision oncology. Targeted therapies produce highly variable outcomes due to the adaptive\, networked nature of cellular signaling. Comprising over 500 kinases\, the protein kinome forms the backbone of these networks and represents a central therapeutic target space. However\, predicting how kinome perturbations propagate through cellular systems to shape phenotypic outcomes is a major challenge. My research program addresses this by developing data-driven approaches that link kinase inhibition states to downstream cellular responses\, enabling the rational design of single-agent and combination therapeutic strategies. I will discuss our work building predictive models that forecast cellular responses to kinase-targeted therapies\, validated experimentally across breast and pancreatic cancer cell lines and patient-derived xenograft models. These models integrate large-scale proteomic and multi-omic data within machine learning frameworks to identify key kinases and network features driving therapeutic outcomes. This work illustrates how systems-level modeling translates molecular data into actionable insights for precision medicine. I’ll conclude by highlighting opportunities for research\, educational\, and translational innovation in BME at UW-Madison. \n\n\n\nPrint PDF
URL:https://engineering.wisc.edu/event/bme-seminar-series-5/
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
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DTSTART;TZID=America/Chicago:20260224T113000
DTEND;TZID=America/Chicago:20260224T123000
DTSTAMP:20260404T165255
CREATED:20260217T151252Z
LAST-MODIFIED:20260220T151455Z
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SUMMARY:ECE RISE-AI Seminar Series: Eshaan Nichani\, Princeton University
DESCRIPTION:Foundations of language models: scaling and reasoning\n\n\n\n\n\n\n\nEshaan Nichani\n\n\n\nAbstract: Modern deep learning methods\, most prominently language models\, have achieved tremendous empirical success\, yet a theoretical understanding of how neural networks learn from data remains incomplete. While reasoning directly about these approaches is often intractable\, formalizing core empirical phenomena through minimal “sandbox” tasks offers a promising path toward principled theory. In this talk\, Nichani will demonstrate how proving end-to-end learning guarantees for such tasks yields a practical understanding of how the network architecture\, optimization algorithm\, and data distribution jointly give rise to key behaviors. First\, they will show how neural scaling laws arise from the dynamics of stochastic gradient descent in shallow neural networks. Next\, they will study how and under what conditions transformers trained via gradient descent can learn different reasoning behaviors\, including in-context learning and multi-step reasoning. Altogether\, this approach builds theories that provide concrete insight into the behavior of modern AI systems. \n\n\n\nBio:Eshaan Nichani is a final-year Ph.D. student in the Electrical and Computer Engineering (ECE) department at Princeton University\, jointly advised by Jason D. Lee and Yuxin Chen. His research focuses on the theory of deep learning\, ranging from characterizing the fundamental limits of shallow neural networks to understanding how LLM phenomena emerge during training. He is a recipient of the IBM PhD Fellowship and the NDSEG Fellowship\, and was selected as a 2025 Rising Star in Data Science. \n\n\n\nLocation details: Discovery Building – Research’s Link\, 2nd floor of Discovery Building (access through glass doors behind information desk)
URL:https://engineering.wisc.edu/event/ece-rise-ai-seminar-series-eshaan-nichani-princeton-university/
LOCATION:Discovery Building\, 330 N. Orchard St.\, Madison\, Wisconsin\, 53715
CATEGORIES:Electrical & Computer Engineering,Seminar
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DTSTART;TZID=America/Chicago:20260226T160000
DTEND;TZID=America/Chicago:20260226T170000
DTSTAMP:20260404T165255
CREATED:20260115T155900Z
LAST-MODIFIED:20260226T172746Z
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SUMMARY:ME 903 Graduate Seminar: Professor Evangelos Theodorou
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 Evangelos Theodorou is a professor at Georgia Tech University. \n\n\n\nPresentation Title: Optimization for Decision-Making in the Era of Artificial Intelligence. \n\n\n\nAbstract: Optimization-based decision-making is at the core of autonomy and planning systems with applications in various domains of science and engineering from aerospace systems and robotics to networked and large-scale control. In this talk\, I will give an overview of algorithms for decision-making and discuss use-cases and relevant applications. The topics include stochastic optimization algorithms such as Model Predictive Path Integral Control and its variations with applications to single agent system control\, Distributed Optimization Architectures for multi-agent swarm control in the presence of uncertainty\, and Deep-Learning Aided optimization algorithms for fast and scalable distributed optimization problems. If time permits\, I will also cover stochastic optimal control algorithms with applications in the areas of Generative Artificial Intelligence and diffusions models on graphs. \n\n\n\nBio: Evangelos A. Theodorou is an Associate Professor with the Daniel Guggenheim School of Aerospace Engineering at Georgia Institute of Technology. He is also the director of the Autonomous Control and Decision Systems Laboratory and an Amazon Scholar. Dr. Theodorou is affiliated with the Institute of Robotics and Intelligent Machines and the Center for Machine Learning Research at Georgia Institute of Technology. He holds a BS in Electrical Engineering\, from the Technical University of Crete (TUC)\, Greece in 2001 and three MSc degrees in Production Engineering from TUC in 2003\, Computer Science and Engineering from University of Minnesota in 2007\, and Electrical Engineering from the University of Southern California (USC) in 2010. In 2011\, he graduated with his PhD in Computer Science from USC. From 2011 to 2013\, he was a Postdoctoral Research Fellow with the department of Computer Science and Engineering\, University of Washington. Dr. Theodorou is the recipient of the King-Sun Fu best paper award of the IEEE Transactions on Robotics in 2012 and recipient of several best paper awards and nominations in machine learning and robotics conferences. His research spans the areas of stochastic optimal control theory\, machine learning\, dynamic and distributed optimization with applications to robotics\, autonomy\, and large-scale systems.
URL:https://engineering.wisc.edu/event/me-903-graduate-seminar-professor-evangelos-theodorou-2/
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
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DTSTART;TZID=America/Chicago:20260226T160000
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DTSTAMP:20260404T165255
CREATED:20260220T141854Z
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SUMMARY:ECE Semiconductor Materials Seminar Series: Dr. Kuangye Lu
DESCRIPTION:Seamless Monolithic 3D Heterogeneous Integration Enabled by Advanced Epitaxy\n\n\n\n\n\n\n\nAbstract: Three-dimensional heterogeneous integration (3D heterointegration) is emerging as the leading approach to enhancing performance in the field of microelectronics. However\, this method often relies on complex wafer-to-wafer bonding processes\, which introduce alignment challenges and interfacial defects. Alternatively\, heteroepitaxy offers another route for implementing 3D heterointegration but suffers from material degradation due to defects and strain caused by lattice and thermal mismatches.In this talk\, I will introduce three new epitaxy paradigms designed to address the key limitations of current 3D heterointegration processes. First\, I will discuss Remote Epitaxy\, which enables wafer-scale exfoliation of ultra-thin membranes across a broad range of materials. By leveraging a 2D interlayer\, these membranes can be transferred and monolithically 3D (M3D) integrated onto arbitrary substrates with ultra-high throughput and low cost\, effectively addressing the challenges associated with wafer-to-wafer bonding. I will then present 2D-Assisted Heteroepitaxy\, a technique that significantly reduces and\, in some cases\, eliminates defects in heteroepitaxy through strain relaxation mechanism at the 2D/3D interface. This advancement enhances materials quality and device performance over conventional heteroepitaxy\, broadening opportunities for M3D heterointegration. Lastly\, I will introduce single-crystal materials growth on amorphous substrates\, which is made possible with a bold substrate design and carefully engineered materials growth conditions\, offering an entirely new scheme of M3D heterointegration.Building on these epitaxy paradigms\, I will demonstrate various novel (opto)electronic devices as examples of their applications\, including fabrication of world’s smallest micro-LED pixels (based on Remote Epitaxy)\, defect-free direct growth of III-V on silicon for next-generation optoelectronic applications (based on 2D-Assisted Heteroepitaxy)\, and advanced 3D stacking of 2D transistors (based on single-crystal materials growth on amorphous substrates). I will conclude the talk with a perspective on future materials development that could enable innovations across advanced 3D logic/memory\, XR\, energy\, and quantum information\, driven by new devices built upon advances in M3D heterointegration. \n\n\n\nDr. Kuangye Lu\n\n\n\nBio: Dr. Kuangye Lu is currently a Postdoctoral Associate at the Research Laboratory of Electronics\, Massachusetts Institute of Technology (MIT). He earned his Ph.D. in Mechanical Engineering from MIT in 2023 under the supervision of Prof. Jeehwan Kim\, and earned a B.S. with honors in Physics from Zhejiang University (ZJU) in 2018.His research focuses on the invention and development of advanced epitaxy techniques for compound semiconductors and 2D materials\, as well as their heterointegration for device fabrication and applications. These efforts include the monolithic 3D integration of high-quality III-V optoelectronic devices on silicon\, reconfigurable AI chips\, and transistors engineered for next-generation advanced nodes.Dr. Lu has authored peer-reviewed articles in high-impact journals\, including Nature\, Nature Nanotechnology\, and Nature Electronics. He is the recipient of the Chu Ko-Chen Scholarship\, the highest honor for graduates of ZJU\, and the MIT Shangzhi Wu Fellowship. Additionally\, Dr. Lu has served as a conference organizer of Advanced Epitaxy of Freestanding Membranes and 2D Materials (AEFM) Conference and a Review Editor for Frontiers in Energy Research. He also serves as a reviewer for journals including Nature Chemical Engineering\, Science Advances\, and Nano Letters.
URL:https://engineering.wisc.edu/event/ece-semiconductor-materials-seminar-series-dr-kuangye-lu/
LOCATION:4610 Engineering Hall\, 1415 Engineering Drive\, Madison\, 53711
CATEGORIES:Electrical & Computer Engineering,Seminar
ATTACH;FMTTYPE=image/jpeg:https://engineering.wisc.edu/wp-content/uploads/2026/02/2026-Faculty-Recruiting-Seminars-Plain-for-website-2.avif
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DTSTART;TZID=America/Chicago:20260227T120000
DTEND;TZID=America/Chicago:20260227T130000
DTSTAMP:20260404T165255
CREATED:20260120T211424Z
LAST-MODIFIED:20260226T172842Z
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SUMMARY:Mechanics Seminar: Professor Wonmo Kang
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 Wonmo Kang is a professor at Arizona State University. \n\n\n\nPresentation Title: Mechanisms Behind Enhanced Electrical and Mechanical Properties in Graphene–Metal Composites \n\n\n\nAbstract: Graphene–metal composites are widely regarded as promising candidates for high-performance electrical conductors as well as advanced structural and functional applications\, owing to graphene’s exceptional electron mobility\, mechanical strength\, and thermal conductivity. To leverage these attractive properties\, small graphene flakes are often dispersed within a macroscopic metal matrix to form bulk composites. However\, this approach intrinsically introduces discontinuous interfaces between the nanoscale carbon reinforcements and the much larger metal matrix\, which hinder efficient load transfer and limit performance gains. In this regard\, this talk investigates how both graphene continuity and quality influence the electrical and mechanical performance of graphene–metal composites. Using axially bi-continuous graphene–copper wires\, we achieve a 41% reduction in electrical resistivity and a 450% increase in current-carrying capacity compared to pure copper. We further show that this architecture enables enhanced mechanical\, thermal\, and anti-oxidation performance. Notably\, axially bi-continuous graphene–nickel wires break the traditional strength–ductility trade-off\, achieving an exceptional combination of both. Finally\, I will discuss our ongoing efforts toward high-throughput\, cost-effective manufacturing of macroscopic graphene–metal composites with continuous graphene networks. \n\n\n\nBio: Wonmo Kang is an associate professor in the School for Engineering of Matter\, Transport and Energy at Arizona State University (ASU). He received his Ph.D. in 2012 with the Outstanding Mechanical Engineering PhD Award from the University of Illinois at Urbana-Champaign. Before joining ASU\, he was a research scientist at the US Naval Research Laboratory. His current research includes graphene-metal composites for multifunctional applications\, in situ material characterization\, nano/bio-mechanics\, and NEMS/MEMS/bioMEMS. Dr. Kang has published his work in leading scientific journals including Advanced Materials\, Advanced Functional Materials\, Small\, Nano Letters\, and Acta Biomaterialia. Dr. Kang is the recipient of several awards/fellowships including the National Science Foundation CAREER Award\, the ASME Rising Stars of Mechanical Engineering Award\, the postdoctoral fellowship from the American Society for Engineering Education\, and the Leidos technical publication awards.
URL:https://engineering.wisc.edu/event/mechanics-seminar-professor-wonmo-kang/
LOCATION:1227 Engineering Hall\, 1415 Engineering Drive\, Madison\, WI\, 53706\, United States
CATEGORIES:Mechanical Engineering,Seminar
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