BME Seminar Series: Dhananjay Bhaskar, PhD
2025-02-17 12:00 - 13:00 (America/Chicago) | 1003 (Tong Auditorium) Engineering Centers Building, 1550 Engineering Drive, Madison, WI, 53706, United States
Harnessing Geometry and Topology in Machine Learning for Biomedical Discovery

Dhananjay Bhaskar, PhDPostdoctoral Researcher, Yale UniversityYale – Boehringer Ingelheim FellowKavli Institute for Neuroscience FellowVisiting Scholar in Engineering, Brown University

Abstract:Understanding the shape of data through geometry and topology offers a powerful perspective with the potential to drive breakthroughs in biomedical science and engineering. This talk explores how these mathematical frameworks, integrated with machine learning, can drive innovation in drug discovery, understanding cellular communication, and unraveling the dynamics of brain activity.

I will introduce learnable geometric scattering, a novel machine learning framework that overcomes the key limitations of conventional graph neural networks. By capturing nuanced structural features, this approach opens new frontiers in drug discovery and protein conformational analysis. Building on this, I will demonstrate how combining geometric scattering with topological data analysis uncovers hidden patterns in cell signaling and neural activity, shedding light on wound healing mechanisms and schizophrenia diagnosis. Finally, I will highlight how these tools can deepen our understanding of learning in artificial neural networks, paving the way for the development of biologically inspired, more expressive, and robust machine learning models.

This talk will emphasize the practical applications of these approaches and their transformative potential for addressing real-world challenges in health and medicine.

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ISyE- IISE JEOPARDY game night
2025-02-17 18:00 - 19:00 | Location TBD (Check ME building for signage), 1415 Engineering Drive, Madison, 53711
Join UW-ISyE’s IISE student org forJeopardy night. Gift cards will be given to winning teams.

ISyE Foundations of Private Optimization for Modern Machine Learning
2025-02-18 12:00 - 13:00 | 2188 Mechanical Engineering Building, 1513 University Avenue, Madison, WI, 53706, United States
How can we develop optimization algorithms for training machine learning models that preserve the privacy of individuals’ training data? In this talk, I will present my work addressing this challenge through differential privacy (DP). Differential privacy offers a rigorous, quantifiable standard of privacy that limits potential leakage of training data. I will explore the fundamental limits of performance for differentially private optimization in modern machine learning, particularly within federated learning settings, and present scalable, efficient algorithms that achieve optimal accuracy under DP constraints. Additionally, these algorithms demonstrate strong empirical performance. Beyond privacy, I will also discuss some of my work in optimization for fair and robust machine learning, which has been deployed in industrial settings.

Bio: Dr. Andrew Lowy is a postdoctoral Research Associate at the University of Wisconsin-Madison, advised by Stephen J. Wright. He received his PhD in Applied Mathematics from the University of Southern California under the supervision of Meisam Razaviyayn, where he was awarded the 2023 Center for Applied Mathematical Sciences (CAMS) Graduate Student Prize for outstanding research.

Dr. Lowy’s research focuses on optimization for private, fair, and robust machine learning, with an emphasis on understanding fundamental limits and developing scalable algorithms that achieve these limits. His work has been published in leading venues across optimization, machine learning, and privacy, including the SIAM Journal on Optimization, NeurIPS, ICML, ICLR, ALT, ACM CCS, and the Journal of Privacy and Confidentiality. His research has also had real-world impact, with some of his algorithms deployed in industry.

ECE Discovery Panel: Quantum, Optics & Photonics
2025-02-18 12:20 - 12:50 | 1413 Engineering Hall – Cheney Room, 1415 Engineering Drive, Madison, WI, 53711, United States
Engineering undergraduates! Join us in the Cheney Room (1413 Engineering Hall) as faculty members explore the technical area of Quantum, Optics & Photonics! All undergraduate students are welcome as Assistant Professor Jennifer Choy, Professor Mikhail Kats, and Assistant Professor Ying Wang talk about application ideas, advanced course electives in this area, and future job opportunities. It’s a great place to ask your questions about classes and career paths in this growing ECE field.

Come for the insights, stay for the pizza!

ISyE Alumni Panel
2025-02-19 18:00 - 19:00
All students are invited to join us as we welcome back these ISyE alumni. Representing a variety of industries and career paths, these alumni will briefly share their stories and field questions from students in a casual Q&A format.

Theron (TJ) Dodson

Maria Palma

Ed Kopetsky

TJ Dodson

Maria Palma

Ed Kopetsky

ISyE Accounting Individual Cognition and Social Influence for Better Human-AI Collaboration
2025-02-20 12:00 - 13:00 | 2188 Mechanical Engineering Building, 1513 University Avenue, Madison, WI, 53706, United States
Artificial intelligence (AI) technologies are increasingly integrated into our lives, enabling a new paradigm of human-AI decision-making where AI provides recommendations, and humans make the final decision. However, such human-AI collaboration often yields mixed results in reality, highlighting the need to understand how humans engage with AI-based systems and optimize human-AI collaboration’s effectiveness. In this talk, I address this challenge from two perspectives. First, I explore how individual cognitive processes shape human-AI interactions and how these insights can be modeled and incorporated into the design of AI models optimized for human-AI teams. Second, I examine the role of social influence—information humans receive beyond AI recommendations—in decision-making and discuss how it can be strategically designed in AI-based systems to foster critical reflections of AI recommendations and enhance human-AI collaboration.

Bio: Zhuoran Lu is a Ph.D. candidate in Computer Science at Purdue University, advised by Prof. Ming Yin. His research lies at the intersection of human-computer interaction (HCI) and artificial intelligence (AI), with a focus on human-AI interaction, human-centered AI, and applied machine learning. His work has been published in top-tier venues across HCI (e.g., CHI, CSCW) and AI (e.g., AAAI, IJCAI), and was recognized with the Best Paper Award at CSCW 2022. His research has been supported by the Frederick N. Andrews Fellowship and the Bilsland Dissertation Fellowship from Purdue University.

ECE RISE-AI and RISE-EARTH Seminar Series - Seah Kim
2025-02-20 12:00 - 13:00 | 4610 Engineering Hall, 1415 Engineering Drive, Madison, 53711
4610 Engineering Hall

Scalable SoC Architectures for Domain-Specific Computing: From Algorithms to Silicon

Abstract:Modern software stacks contain concurrent and heterogeneous workloads with bespoke constraints. This is especially crucial for emerging edge applications, such as AR/VR, robotics, and autonomous vehicles. In response to these demands, hardware has shifted towards pervasive specialization, making development and cross-stack integration increasingly challenging. This shift raises the pressing question at the core of modern computing: How do we enable scalable specialization in modern SoCs? How do we design and integrate heterogeneous accelerators while ensuring performance scalability through efficient resource management and adaptability across system layers?In this talk, I will present my research addressing these interconnected challenges of scalable specialization through full-stack, system approaches. (1) First, I will introduce Gemmini, an award-winning, widely used DNN accelerator generator that enables agile, full-stack accelerator evaluation. Gemmini allows researchers to explore the specialized accelerator design space under a full SoC. (2) Next, I will present AuRORA, the award-winning, novel virtualized accelerator integration approach with dynamic resource allocation, paving the foundation for accelerator-rich SoCs. AuRORA redesigns a novel CPU-accelerator interface that enables fast and flexible resource repartitioning, along with a runtime system that abstracts physical accelerators into a unified virtualized resource pool. (3) ​​Then, I will introduce SuperNoVA, an algorithm-hardware co-design for real-time, dynamic workloads on resource-constrained platforms, using SLAM as a target workload. SuperNoVA tackles the challenge of balancing accuracy and real-time execution with an adaptive algorithm for large-scale SLAM. (4) Finally, I will showcase a silicon test chip I taped out that embodies my research by integrating these innovations. Silicon validation with real workloads successfully proves the feasibility of scalable specialization.By bridging hardware design, system software, application algorithms, and silicon validation, my research enables adaptive, accelerator-rich computing platforms for modern edge applications. I will revolutionize edge SoC design by combining design-time hardware-software co-optimization with runtime adaptive resource management, achieving the best of both static specialization and dynamic flexibility to address the evolving demands of future edge platforms.

Seah Kim

Bio:Seah Kim is a Ph.D. Candidate at UC Berkeley, specializing in Computer Architecture and VLSI. Her research spans the full computing stack, from chip design and hardware development to system software and application algorithms, with a focus on scalable domain-specific SoC design. She has been awarded the IEEE Micro Top Pick in Computer Architecture (MICRO 2023), the Best Paper Award (DAC 2021), and the Distinguished Artifact Award (ISCA 2023). Prior to UC Berkeley, she earned a B.S. in Electrical and Computer Engineering from Seoul National University. Seah was selected as a 2024 Rising Star in EECS and a 2023 ML and Systems Rising Star.

NEEP Seminar: Tara Righetti, University of Wyoming
2025-02-20 12:00 - 13:30
Thursday, February 20 | 12:00pmEngineering Hall 1610

Speaker: Tara Righetti, University of Wyoming

Title: Climate, Clean Energy, and the Social Contract

Abstract: Advanced Nuclear and CCUS feature prominently in the U.S. plans for net-zero energy and industry and will become increasingly important with expanding energy demand. Despite their potential contributions to commercial scale decarbonization, however, they are each subject to extensive and time consuming federal regulatory programs which advocates of these technologies argue pose a major barrier to deployment. The rigor of these programs reflects the sentiment that the magnitude of potential societal and environmental harms–however however small the probability–outweigh the need for expediency in climate action. At the same time, the Trump Administration has called for emergency permitting authority for energy projects. With dueling calls for greater efficiency in permitting and communities fervently opposing strong-handed approaches to infrastructure development, clean-industry technologies are at a crossroads. Moving forward requires consideration about how we evaluate and allocate risk — questions that are central to the social contract between the people and places that produce energy and those that consume it. This talk contemplates these factors in light of the shifting energy policy priorities of the new administration.

Bio: Tara Righetti is the Occidental Chair of Energy and Environmental Policies at the University of Wyoming, the co-director of the Nuclear Energy Research Center, and the director of the Jurisprudence of Underground Law and Energy (JOULE) research group. Her research focuses on property and administrative law issues associated with decarbonization and development of the energy and industrial sectors.

This seminar is presented by the Nuclear Engineering & Engineering Physics Department and the University of Wisconsin Law School, in collaboration with the Clean Energy Community Initiative and the Grainger Institute for Engineering

Remote Participation is available through Zoom: https://uwmadison.zoom.us/j/99289578680?pwd=[REDACTED]

ME 903 Graduate Seminar: Professor Sastry Pamidi
2025-02-20 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 Sastry Pamidi is a professor at Florida State University.

ISyE - "IISE on ICE"
2025-02-21 16:45 - 21:00 | Memorial Union, 800 Langdon St, Madison, Wisconsin, 53706
Join IISE for an evening of ice skating at Memorial Union. Don’t have your own skates? Reserve ahead of time at: https://union.wisc.edu/events-and-activities/event-calendar/event/throwbackskatenight-2025-ouw-3/

BME Seminar Series: Mehmet Orman, PhD
2025-02-24 12:00 - 13:00 | 1003 (Tong Auditorium) Engineering Centers Building, 1550 Engineering Drive, Madison, WI, 53706, United States
Metabolic Adaptations of Drug-Tolerant Persister Cells

Mehmet A Orman, PhDAssociate ProfessorChemical and Biomolecular Engineering DepartmentUniversity of Houston

Abstract:My research focuses on understanding drug-tolerant phenotypes, particularly persister cells, in both prokaryotic and eukaryotic systems. Persister cells are temporarily tolerant to drugs and contribute significantly to disease recurrence. We investigate their molecular makeup, including metabolite profiles, RNA expression, protein composition, and regulatory networks, to identify key survival mechanisms and potential therapeutic targets. Our ongoing projects include investigating self-digestion-mediated tolerance in bacterial persisters, host-pathogen interactions in drug tolerance, metabolic and repair pathways in bacterial and cancer persisters, and the immunomodulatory potential of microbial cells to harness host-pathogen interactions for cancer therapy.

In this talk, I will highlight our strategies to study and target persister cell metabolism in bacteria and cancer. Contrary to the belief that persister cells in bacteria have suppressed metabolism, we show that their survival depends on energy metabolism, particularly oxidative phosphorylation. In bacteria, the cyclic-adenosine monophosphate and its receptor protein redirects metabolism from anabolism to oxidative phosphorylation, highlighting the importance of the tricarboxylic acid cycle, electron transport chain, and ATP synthase. Similarly, in melanoma persisters, chemotherapy-induced stress increases mitochondrial activity, revealing a dual role of treatment—inducing apoptosis in most cells while promoting a dormant, drug-tolerant subpopulation. Understanding these metabolic adaptations opens new avenues for therapeutic interventions, aiming to eradicate persister cells and prevent disease recurrence.

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MS&E Faculty Search Presentation: Dr. Jiale Shi
2025-02-24 16:00 - 17:00
UW-Madison Department of Materials Science and Engineering welcomes Dr. Jiale Shi. Their presentation on “AI-driven Computational Predictions for Polymeric Materials” will be on Monday, Feb. 24 in MS&E 265 from 4 p.m. to 5 p.m.

Abstract

Polymeric materials, an integral part of modern life, permeate various aspects of daily living, including clothing, food, transportation, construction, and healthcare, due to their versatile macroscopic properties. The macroscopic properties of polymers depend on their microscopic design, including chemical structure, composition, sequence, topology, chain length, and molecular mass distribution. However, due to the complex nature and gigantic design space of functional polymeric materials, many important quantitative structure-property relationships remain unknown. Artificial intelligence (AI) can not only efficiently explore the vast polymer design space but also provide data-driven solutions to uncover quantitative structure-property relationships that are difficult to identify through conventional computational methods. In this talk, I will present how we develop AI-driven computational methods for polymer property prediction and design. First, I will discuss the development of AI models for predicting polymer–surface adhesive free energy and guiding the optimal design of polymer sequences with a genetic algorithm, as well as the use of transfer learning techniques to improve AI model predictions when data is insufficient. Second, I will highlight the development of similarity-informed explainable AI for polymer search, embedding, and property prediction.

Bio

Dr. Jiale Shi is currently a postdoctoral associate in the Department of Chemical Engineering at MIT working with Prof. Bradley Olsen and Dr. Debra Audus (NIST). His postdoc research projects focus on developing similarity-informed explainable AI for polymer search, embedding and property predictions. He is a core member of the Community Resource for Innovation in Polymer Technology (CRIPT). He received his Ph.D. in Chemical Engineering at the University of Notre Dame with Prof. Jonathan Whitmer. His Ph.D. research focused on facilitating optimal design of new soft materials via integrating AI, molecular simulations, and statistical physics. He was a graduate participant of the Midwest Integrated Center for Computational Materials (MICCoM), focusing on developing and applying advanced sampling methods for free energy calculations. He received his B.S. in Chemistry at Peking University. His future research program will focus on AI-driven computational design for functional polymeric materials. His research accomplishments have been recognized by awards and honors, including (1) ACS PMSE Future Faculty Scholar, (2) ACS POLY Big Data Award, (3) Outstanding Paper Award from Department of Chemical and Biomolecular Engineering at University of Notre Dame, (4) Best Poster Award at Notre Dame-Purdue Soft Matter & Polymers Symposium.

ISyE - Letters of Love Volunteering
2025-02-24 18:00 - 19:00 | 1610 Engineering Hall, 1415 Engineering Drive, Madison, 53706
Spend a night with the IISE student org volunteering for Letters of Love.

Letters of Love aims to provide emotional support to children in hospitals by sending them handwritten cards and letters. Use this hour to give back to your community, connect with other industrial engineers, and unleash your creativity. All supplies will be provided.

ECE RISE SEMINAR SERIES: AI for Next-Generation Electronics and Chip Design - Olivia Hsu
2025-02-25 12:00 - 13:00 | 1240 Computer Sciences, 1210 W. Dayton St., Madison, 53711
1240 Computer SciencesLIVE STREAM: https://uwmadison.zoom.us/j/93718552231?pwd=[REDACTED]

From Language to Silicon: Programming Systems for Sparse Accelerators

Abstract:In this era of specialization, modern hardware development focuses on domain-specific accelerator design due to the plateau in technology scaling combined with a continual need for performance. However, domain-specific programming systems for these accelerators require extreme engineering effort, and their complexity has largely caused them to lag behind. Fundamentally, the widespread usability, proliferation, and democratization of domain-specific accelerators hinge on their programming systems, especially when targeting new domains.This talk presents research on accelerator programming systems for the emerging domain of sparsity targeting emerging sparse accelerators. The first system, the Sparse Abstract Machine (SAM), introduces a unified abstract machine model and compiler abstraction for sparse dataflow accelerators. SAM defines a novel streaming representation and abstract dataflow interfaces that serve as an abstraction to decouple sparse accelerator implementations from their programs, similar to a stable ISA for dataflow. The second system, Mosaic, introduces modular and portable compilation solutions across different sparse accelerators and high-performance systems. These systems are a first step towards usable and programmable heterogeneous hardware acceleration for all. I will conclude by discussing the next steps to reach this goal, which include programming systems for accelerators in other domains and interoperation between accelerators across domains.

Olivia Hsu

Bio:Olivia Hsu is a final-year Computer Science PhD student at Stanford University, advised by Professors Kunle Olukotun and Fredrik Kjolstad. She received her BS in Electrical Engineering and Computer Science (EECS) at UC Berkeley. Her broad research interests include computer architecture, computer and programming systems, compilers, programming languages, and digital circuits/VLSI. Olivia is a 2024 Rising Star in EECS and an NSF Graduate Research Fellow, and her research won a distinguished paper award at PLDI 2023. To learn more about her work, please visit her website at https://cs.stanford.edu/~owhsu.

ECE RISE SEMINAR SERIES: Clean Energy Technologies - Dr. Nishil Talati
2025-02-25 16:00 - 17:00 | 2255 Engineering Hall, 1415 Engineering Drive, Madison, 53711
2255 Engineering Hall

Systems-Architecture Co-Design for AI and Large-Scale Data Analytics

AbstractModern computing systems are increasingly bottlenecked by the demands of data-intensive applications such as AI, data analytics, and scientific computing. These challenges stem from both the massive scale of data and inefficiencies caused by irregular access patterns. In this talk, I will present my research on innovative co-design approaches to systems and architectures that tackle these pressing issues. First, I will introduce Vortex, a system that accelerates large-scale data analytics on GPUs by orchestrating efficient data transfers across multi-GPU systems, proposing a concept called GPU resource disaggregation. Then, I will discuss Prodigy, a hardware-software co-design framework that minimizes memory stalls in irregular workloads on CPUs through advanced workload analysis and prefetching techniques. Together, these advances highlight how domain-specific co-design can drive transformative improvements, addressing fundamental bottlenecks in modern computing. Finally, I will share my vision for tackling future challenges in scaling AI capabilities and the research directions I am eager to pursue to address them.

Nishil Talati

BioNishil Talati is an Assistant Research Scientist in the Computer Science and Engineering department at the University of Michigan, where he also earned his PhD. His research focuses on computer architecture and systems software design to enhance the efficiency of modern data-driven applications. Nishil’s work has been featured in top-tier venues such as ISCA, MICRO, HPCA, ASPLOS, and VLDB, and has made a significant impact through industry tech-transfers, inspiring follow-up research, and earning several accolades. These include the 2021 HPCA Best Paper Award, honorable mentions at DATE 2023 and IISWC 2023, recognition as a 2023 ProQuest Distinguished Dissertation Award finalist, the Best Faculty Research Pitch Award at MIDAS event in 2023, and finalist status in the 2022 CSE Honors Competition.

ISyE - HFES Gen AI 101 with Python
2025-02-25 17:15 - 18:30 | 3210 Mechanical Engineering, 1513 University Avenue, Madison, 53706
Explore the basics of Generative AI with Python in this beginner-friendly workshop with the Human Factors & Ergonomics Society Student Chapter in UW-Madison. Learn concepts, practical skills, and get hands-on experience with Generative AI technologies and their APIs. No prior coding or AI experience needed, but it will be appreciated!

We are going to use Google Colab, thus no prior installation of any Python IDE is required.

Open to all, sign up here!

ECE RISE SEMINAR SERIES: Clean Energy Technologies - Dr. Noman Bashir
2025-02-27 12:00 - 13:00 | 4610 Engineering Hall, 1415 Engineering Drive, Madison, 53711
4610 Engineering Hall

Designing the Next Generation of Computer Systems for Sustainable AI

Abstract:Abstract: The key technology trend for the next decade is the transformative potential of artificial intelligence (AI), alongside its rapidly growing energy consumption and associated environmental impacts. However, our longstanding focus on energy efficiency alone will not solve the problem, and current computer systems powering AI applications are not designed to optimize carbon efficiency — that is, the amount of work performed per unit of carbon emitted. In this talk, I will present my work on rethinking how we design and operate computer systems for AI to enable carbon-aware optimizations. I will present my work on virtualizing the energy system to provide both visibility into, and software-defined control over, energy systems for computer applications. I will also describe how the proposed abstractions allow a wide range of AI applications to manage the variability of clean energy in software — accommodating diverse user and application requirements, goals, strategies, and tolerances for reducing carbon and energy. I will share my work on uncertainty- aware modeling of lifecycle carbon footprint, which helps stakeholders in computing and AI assess the discernibility of their decisions. Finally, I will outline my vision for enabling sustainable AI capabilities in a rapidly evolving landscape of AI hardware and software, and I will illustrate how these advances can drive broader societal decarbonization.

Noman Bashir

Bio:Noman Bashir is a Computing & Climate Postdoctoral Impact Fellow at the MIT Computer Science & Artificial Intelligence Laboratory (CSAIL) and the MIT Climate & Sustainability Consortium (MCSC). His research rethinks the design and operation of large-scale computer systems to address emerging challenges, including rapidly rising computing demand, increasing energy constraints, and unintended socio- environmental implications. His work on improving resource utilization — deployed across all Google datacenters — demonstrates significant real-world impact. He earned his Ph.D. from the University of Massachusetts Amherst in 2022.

NEEP Seminar: Rabab Haider, University of Michigan
2025-02-27 12:00 - 13:00
Thursday, February 27 12:00 – 1:00pmERB 106 (Contact office@ep.wisc.edu for assistance with remote participation.)

Speaker: Rabab Haider, University of Michigan

Title: Fast algorithms and equitable frameworks for renewable-rich power systems

Abstract: Power grids are rapidly decarbonizing to address climate challenges and expanding to meet increasing demand from electrification and AI growth. However, integrating increasing quantities of intermittent renewable generation presents a challenge to how we operate power grids, reducing decision-making timescales from days to minutes. Digit twins layered with AI applications can provide much needed real-time visibility and control, such as network reconfiguration. Reconfiguration is vital for rapidly integrating more renewable generation while maintaining grid reliability, yet its large-scale, nonlinear nature is challenging for traditional algorithms. We will introduce the specialized use of physics-informed machine learning for fast and accurate reconfiguration decisions. In the second part of the talk, we will explore the implications of the changing power grid on electricity affordability – a tenet of energy equity. We propose a new framework based on locational marginal burden, which provides an interface between energy burden and the optimal power flow problem by leveraging tools from differentiable optimization.

Bio: Dr. Rabab Haider is an Assistant Professor of Civil and Environmental Engineering at the University of Michigan. Her research is centered on designing future energy systems that provide green, reliable, and affordable energy for All. Her group develops optimization and AI algorithms that advance energy system operations, planning, and market under deep decarbonization. Dr. Haider’s portfolio also includes engagement with multiple global organizations to enable widespread access to STEMM education, mentorship, and leadership training. She received her Ph.D. and S.M. degrees at MIT, and B.A.Sc in Engineering Science at the University of Toronto. She was previously named a MIT Energy Fellow and MathWorks-MIT Mechanical Engineering Fellow. Dr. Haider is also an Affiliate Faculty at the Georgia Institute of Technology and NSF AI Institute for Advances in Optimization (AI4OPT).

This seminar is presented by the Institute for Nuclear Energy Systems and the Nuclear Engineering & Engineering Physics Department.

MS&E Seminar Series: Dr. Kevin Gotrik
2025-02-27 13:00 - 14:00
UW-Madison Department of Materials Science and Engineering welcomes Dr. Kevin Gotrik. Their seminar on “Insights Into Innovation – The Surprising Versatility of Scotch Tape” will be on Thursday, Feb. 27 in MS&E 265 from 1 p.m. to 2 p.m.

Abstract

In this engaging talk, Kevin Gotrik will share his extensive experience working within innovation ecosystems, drawing from his impressive career in materials science and engineering, and touching upon technology ecosystem development encompassing World War II. He will delve into the practical aspects of fostering innovation ecosystems, offering insights on how to create environments that encourage interdisciplinary research and the free exchange of ideas. Kevin will discuss the importance of providing time and resources for exploratory projects, the role of collaboration across different fields, and the impact of a supportive organizational culture on driving innovation. This talk is geared towards graduate students, but all are welcome

Bio

Kevin Gotrik is the third graduate of the Engineering Physics undergraduate program at the University of Wisconsin-Madison. During his undergraduate studies, Kevin conducted research at the Leibniz Institute in Dresden, Germany, and the Fraunhofer Institute in Stuttgart, gaining valuable international experience early in his career.

In 2013, Kevin earned his PhD in Materials Science and Engineering from the Massachusetts Institute of Technology (MIT), where he focused on the topics of self-assembly and nanolithography applications in semiconductor manufacturing. Kevin’s professional journey led him to 3M, where he has specialized in vacuum thin film applications. At 3M, he has thrived in an innovative environment that encourages scientific exploration beyond one’s primary area of expertise, known as “15% time.” This unique culture has allowed Kevin to pursue diverse scientific interests, resulting in over 150 international patent applications.

Kevin Gotrik enjoys sharing his experience on the power of interdisciplinary research and how to foster a culture of innovation.

ME 903 Seminar: Chris Tabor
2025-02-27 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. Chris Tabor is a Team Lead for Polymer and Responsive Materials at the Air Force Research Lab (ARL).

Midwest Mechanics Seminar Series: Professor Ken Kamrin
2025-02-28 12:05 - 12:55 | 3M Auditorium, rm 1106 Mechanical Engineering Building, 1513 University Ave, Madison, 53711
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 Ken Kamrin is a professor at UC Berkeley.

ISyE Engineering Robust & Scalable AI for Healthcare Systems
2025-03-03 11:30 - 12:30 | 1153 Mechanical Engineering, 1513 University Ave, Madison, WI, 53706, United States
Artificial intelligence (AI) is increasingly used in healthcare to enhance clinical decision-making,optimize operations, and improve patient outcomes. However, real-world deployment of AIsystems presents fundamental engineering challenges, including dataset shifts, physician-AIteam dynamics, and the need for continuous model validation and updating. These challengesthreaten the reliability and scalability of AI tools, limiting their ability to provide consistent valuein clinical environments.

In this talk, I will present engineering solutions that address these core challenges and enablethe development of AI systems that are both scalable and safe. First, I will discuss techniquesfor integrating longitudinal patient data into predictive models, improving their performanceover time. Second, I will introduce methods to detect and mitigate dataset shifts, ensuringmodels maintain accuracy when transitioning from development to real-world use. Finally, I willdescribe a novel rank-based compatibility measure and optimization framework that improvesmodel updating while preserving physician trust and workflow stability.

By developing these foundational methods, my work moves healthcare AI from an artisanal,model-by-model approach to a scalable engineering discipline. I will conclude by discussingfuture research directions, including AI personalization for individual physicians and thedevelopment of interactive AI validation systems that continuously adapt based on clinicianfeedback.

Bio: Erkin Ötleş, MD, PhD, is an engineer with deep medical expertise, specializing in developingmethods to create scalable and robust artificial intelligence systems for healthcare. Hisresearch addresses the core engineering challenges of AI deployment in real-world clinicalsettings—detecting and mitigating dataset shifts, designing AI systems that integrateseamlessly into physician workflows, and creating novel methods for continuous validation andmodel updating.

Dr. Ötleş has developed innovative machine learning techniques to model longitudinal patienttrajectories, optimize human-AI collaboration in clinical decision-making, and enhance thesafety and interpretability of AI tools in high-stakes environments. His work has been publishedin leading medical (JAMA Internal Medicine, The BMJ) and engineering (Machine Learning forHealthcare, Journal of the American Medical Informatics Association) venues, demonstratinghis ability to bridge theoretical advancements with practical implementation. His research hasalso been widely covered by the lay press, including NPR, WIRED, and STAT News,underscoring its broad societal impact.

As an engineer and a physician, Dr. Ötleş brings a unique systems perspective to AI inhealthcare. His work aims to transition healthcare AI development, evaluation, andimplementation from an artisanal, model-by-model process to a scalable engineering discipline—ensuring that AI tools used in medicine are not only powerful and plentiful but also safe,reliable, and adaptable to dynamic clinical environments.

BME Seminar Series: Jacob Witten, PhD
2025-03-03 12:00 - 13:00 | 1003 (Tong Auditorium) Engineering Centers Building, 1550 Engineering Drive, Madison, WI, 53706, United States
Artificial intelligence for state-of-the-art gene therapy

Jacob Witten, PhDPostdoctoral FellowAnderson LabMassachusetts Institute of Technology

Abstract:Lipid nanoparticles (LNPs) for RNA delivery have exploded onto the biomedical research scene with the success of mRNA vaccines for COVID-19. In addition to their promise as mRNA vaccines for infectious disease and cancer, LNPs have the potential to treat or cure patients with deadly lung diseases such as cystic fibrosis. However, gene therapy in the lung is a notoriously difficult challenge that has frustrated decades of researchers. Here, I take two approaches to identifying LNPs capable of addressing this challenge. First, I developed an in vitro primary cell platform to screen LNPs for lung mRNA delivery and identify two state-of-the-art LNPs for respiratory tract delivery to mice. Second, I developed Lipid Optimization using Neural networks (LiON), a deep learning strategy for LNP design. Using LiON I evaluated 1.6 million possible LNPs and identified two, FO-32 and FO-35, with mRNA delivery matching that of LNPs in ongoing clinical trials. Overall, this work shows the potential of deep learning to bring gene therapy to patients suffering from genetic disease.

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ECE RISE SEMINAR SERIES: Trustworthy AI – Dr. Lydia Zakynthinou
2025-03-03 12:00 - 13:00 | 2534 Engineering Hall, 1415 Engineering Drive, Madison, Wisconsin, 53706, United States
2534 Engineering Hall

Algorithmic Stability for Trustworthy Machine Learning and Statistics

Abstract:Data-driven systems hold immense potential to positively impact society, but their reliability remains a challenge. Their outputs are often too brittle to changes in their training data, leaving them vulnerable to data poisoning attacks, prone to leaking sensitive information, or susceptible to overfitting. Establishing fundamental principles for designing algorithms that are both stable—to mitigate these risks—and efficient in their use of resources is essential for enabling trustworthy data-driven systems.In this talk, I will focus on statistical estimation under differential privacy—a rigorous framework that ensures data-driven system outputs do not reveal sensitive information about individuals in their input. I will present algorithmic techniques that take advantage of beneficial structure in the data to achieve optimal error for several multivariate tasks without requiring any prior information about the data, by building on robustness against data poisoning attacks. Lastly, I will highlight the deeper connection between differential privacy and robustness that underpins these results.

Bio:Lydia Zakynthinou is a FODSI postdoctoral research fellow in the Simons Institute for the Theory of Computing at UC Berkeley, hosted by Michael I. Jordan. She earned her Ph.D. in Computer Science from Northeastern University under the supervision of Jonathan Ullman and Huy Nguyen. Her research lies in trustworthy machine learning and statistics, with a focus on data privacy and generalization, and has been recognized with a Meta PhD fellowship and a Khoury PhD Research Award. She holds a diploma in Electrical and Computer Engineering from NTUA and a MSc in Logic, Algorithms, and Theory of Computation from NKUA in Greece.

ISyE - An evening with industry - GRAINGER x IISE
2025-03-03 18:00 - 19:00 | 1106 Engineering Hall, 1415 Engineering Drive, Madison, 53711
Grainger representatives are excited to share the various career paths and opportunities that they have to offer. They will also be sharing their experiences working for Grainger and would be happy to answer any questions. This evening is hosted by the Institute of Industrial and Systems Engineering (IISE) student chapter.

There will also be a case study to apply your engineering skills. Case studies are accounts of real engineering situations and projects that provide a context for you to practice your problem-solving skills.

ECE Discovery Panel: Semiconductor Engineering
2025-03-04 12:20 - 12:50 | 1413 Engineering Hall – Cheney Room, 1415 Engineering Drive, Madison, WI, 53711, United States
Engineering undergraduates! Join us in the Cheney Room (1413 Engineering Hall) as faculty members explore the technical area of Semiconductor Engineering! All undergraduate students are welcome as Assistant Professor Chirag Gupta, Professor Umit Yusuf Ogras, and Assistant Professor Eric Tervo talk about application ideas, advanced course electives in this area, and future job opportunities. It’s a great place to ask your questions about classes and career paths in this growing ECE field.

Come for the insights, stay for the pizza!

ECE RISE SEMINAR SERIES: Trustworthy AI - Dr. Mahdi Haghifam
2025-03-05 12:00 - 13:00 | 4610 Engineering Hall, 1415 Engineering Drive, Madison, 53711
4610 Engineering Hall

The Interplay of Generalization, Memorization, and Privacy in Trustworthy Machine Learning

Abstract:Machine learning is transforming numerous aspects of modern society, and its increasing use in high-stakes applications necessitates responsible development. In this talk, I will present my research on the foundations and methodologies for building trustworthy ML, focusing on three interconnected challenges: generalization, memorization, and privacy. First, I will explore generalization: how can we ensure that ML models reliably predict on unseen data? I will discuss my work on developing novel information-theoretic measures to characterize and reason about generalization. Next, I will examine data memorization, showing how it can coexist with generalization and may even be necessary for accurate learning. Finally, I will focus on differential privacy, a rigorous framework for mitigating data memorization, and present my work on designing differentially private optimization algorithms. I will conclude by discussing key open questions in the area of trustworthy ML.

Mahdi Haghifam

Bio:Mahdi Haghifam is a Distinguished Postdoctoral Researcher at Khoury College of Computer Sciences, Northeastern University, hosted by Jonathan Ullman. He received his PhD from the University of Toronto and the Vector Institute, where he was advised by Daniel M. Roy. Mahdi’s research focuses on the foundations and algorithms of trustworthy machine learning, particularly in the areas of privacy, generalization, and memorization. During his PhD, he worked as a research intern at Google Brain and ServiceNow Research. His contributions have been recognized with a Best Paper Award at ICML 2024 and several fellowships from the University of Toronto.

ME Faculty Candidate Seminar
2025-03-05 12:00 - 13:00 | 2188 Mechanical Engineering Building, 1513 University Avenue, Madison, WI, 53706, United States
Join the Department of Mechanical Engineering for Faculty Candidate Seminars during the Spring 2025 semester.

These will take place on Mondays and Wednesdays of each week from 12-1pm in room 2188 ME Building.

BME Seminar Series: Arash Farhad, PhD
2025-03-06 12:00 - 13:00 | 2180 Mechanical Engineering, 1513 University Ave, Madison, WI, 53706, United States
Engineering multistate trackable cells for smart precision therapeutics

Arash Farhadi, PhDPostdoctoral ScholarVoigt LabMassachusetts Institute of Technology

Abstract:The engineering of immune cells and microbes into living therapeutics is emerging as a powerful approach for treating many diseases. However, two key challenges must be addressed to unlock the full potential of living therapeutics: intricately programming cells to perform diverse therapeutic tasks, and effectively monitoring them once administered to the body. My research provides the foundation to address these major challenges. In my presentation, I will outline how cells can be engineered to differentiate into multistate communities with distributed functions, using Synthetic Differentiation circuits. The biomolecular mechanism of Synthetic Differentiation circuits can be engineered to tune the community composition, expand the number of unique states, and replenish population imbalances in the community. I will highlight examples demonstrating the versatility of these circuits in applications spanning living therapeutics and agricultural biotechnology. Additionally, I will demonstrate noninvasive methods for tracking the location and function of cells deep in the body. Most methods to image cells rely on light, which has limited penetration depth. Conversely, ultrasound can image deep in tissue but lacks genetic reporters. I will introduce the first acoustic reporter genes (ARGs)—a ‘GFP’ for ultrasound—that enable imaging of cells and their gene expression inside the living, intact animal. Together, these technologies will enable next-generation living therapeutics capable of simultaneously targeting many disease hallmarks while providing real-time feedback to scientists and clinicians.

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NEEP Seminar: Dean Price, Idaho National Laboratory
2025-03-06 12:00 - 13:00
Thursday, March 612:00-1:00pmERB 106Remote Participation: Please contact office@ep.wisc.edu for the Zoom link.

Title: AI Founded on Physics

Abstract: The nuclear industry sits firmly between seeing the significant benefit of Artificial Intelligence (AI) technologies and guarding against the challenges these novel technologies could create. The path forward lies in the use of AI methods firmly grounded in the physical principles governing conventional physics-based simulations. This seminar will explore ways in which Physics-INformed (PIN) machine learning and AI models for the analysis of fission-based reactor systems can aid the rapidly growing industry in its pursuit of safe and economical reactor development. A special focus will be given to the formulation of some AI models under a PIN framework such that the discussed methods can be applied to a wide variety of scenarios. Practical applications of these models, such as optimizing reactor performance and dynamics characterization, will also be discussed.

Speaker: Dean Price, Idaho National Laboratory

Bio: Dean Price is a Russell L. Heath distinguished postdoctoral associate at Idaho National Laboratory in the Reactor Physics Methods and Analysis Group. He holds a PhD in Nuclear Engineering and Radiological Science from the University of Michigan where he was awarded fellowships from both the National Science Foundation as well as the Nuclear Energy University Program. He has published 20 articles in peer-reviewed journals along with numerous conference papers and technical reports. His research interests focus on the integration of high fidelity multiphysics simulations with data driven methods to support the development and deployment of advanced reactors.

This seminar is presented by the Institute for Nuclear Energy Systems and the Nuclear Engineering & Engineering Physics Department.
