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List of Past Events

Carlos H. Diaz at the Wilson Lecture Series

Nanoelectronics and VLSI

The quest for sustainable growth in computing performance and expanded functional capabilities of information technology and communication (ICT) products requires significant improvements in the energy-efficiency from the underlying technologies to the systems, architectures, algorithms, software layers. Fundamental changes in the information representation and processing may also be necessary to enable higher cognitive capabilities in artificial intelligence with the necessary energy-efficiency for widespread use. In this talk I will discuss challenges and research opportunities pertaining emerging transistors, memories, and 3D interconnect fabrics while also addressing the necessary completeness of the metrics to be met in order for the results of those research to become viable alternatives to state-of-the art technologies and their projected evolutionary paths. I will also briefly talk about hyper-dimensional information representation and processing for AI applications.

About the speaker

Carlos H. Diaz earned his bachelor’s degrees in electrical engineering and physics and his master’s degree in electrical engineering from Universidad de Los Andes, Bogota, Colombia. He holds a Ph.D. in electrical engineering from University of Illinois at Urbana-Champaign. He is Senior Director in Research and Development, Taiwan Semiconductor Manufacturing Company. He has published over 100 technical papers, holds over 200 US patents, and has published one book. Dr. Diaz was elected an IEEE Fellow in 2008 for his contributions to deep-submicron foundry technology. In 2011, he was co-recipient of the Annual Innovation Breakthrough Award, Ministry of Economic Affairs, Taiwan R.O.C., conferred to TSMC's 28nm logic technology. He was the recipient of the 2016 IEEE Andrew Grove Award for sustained contributions to and leadership in foundry advanced CMOS logic transistor technology. He received the Distinguished Alumni Award in Electrical and Computer Engineering, University of Illinois at Urbana-Champaign in 2018.

Neuromodulation information session

This informational event is open to all who would like to learn more about the field of neuromodulation. There will be a short presentation, followed by an interactive device showcase to display the therapeutic technology and treatment methodology. Attendees will also get a chance to explore the VR (virtually reality) room where they can explore brain anatomy. Please RSVP using the link and invite anyone who is interested. Join us on Monday, April 18th from 5:30-6:30 pm CT at the Medical Device Center in Moos Tower!

Prof. Charles Bouman at the Wilson Lecture Series

Plug-and-Play: A Framework for Integrating Physics and Machine Learning in CT Imaging

This talk presents emerging methods for the integration of physics-based and machine learning (ML) models with novel acquisition methods to push CT technology well beyond traditional limits. For example, while ML methods such as deep neural networks offer unprecedented ability to model complex behavior, they typically lack the flexibility and accuracy of traditional physics-based methods for modeling imaging sensors. In order to address this dilemma, we present plug-and-play methods as a general framework for getting the ``best of both worlds’’ by integrating traditional physics-based models based on probability distributions with action-based ML models. Throughout the talk, we present state-of-the-art examples using imaging modalities including computed tomography (CT), transmission electron microscopy (STEM), synchrotron beam imaging, optical sensing, scanning electron microscopy (SEM), and ultrasound imaging.

About the speaker 

Charles A. Bouman is the Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering at Purdue University. His research is in the area of Computational Imaging, with applications in medical, scientific, and commercial imaging. He received his B.S.E.E. degree from the University of Pennsylvania, M.S. degree from the University of California at Berkeley, and Ph.D. from Princeton University in 1989. He is a member of the National Academy of Inventors, a Fellow of the IEEE, AIMBE, IS&T, and SPIE. He is the recipient of the 2021 IEEE Signal Processing Society, Claude Shannon-Harry Nyquist Technical Achievement Award, the 2014 Electronic Imaging Scientist of the Year award, and the IS&T’s Raymond C. Bowman Award; and in 2020, his paper on Plug-and-Play Priors won the SIAM Imaging Science Best Paper Prize.

 

The State of Science and the Need for STEM Advocacy

Rising to the Moment lecture series

Register now for the second installment in the College of Science and Engineering’s new public lecture series, "Rising to the Moment," featuring Dr. Jayshree Seth, a 3M corporate scientist and the company’s first chief science advocate. Dr. Seth will discuss her unique role at 3M and the importance of diversity and equity in STEM.

Event details and registration

Event Cancelled: Memorial for Professor Jim Holte

A memorial for Professor James (Jim) Holte will be held at the University of Minnesota Campus Club located on the 4th floor of Coffman Memorial Union. 
(300 Washington Ave. SE, 55455)

Professor Holte's obituary in the Star Tribune

LinkedIn Lab with Judy Park: Use LinkedIn to Expand Your Network & Connect to Opportunities

LinkedIn is a professional social media website that can be helpful in building your professional network, researching potential employers, and learning of internship and job opportunities. Through this 1.5 hour virtual workshop we hope to help students learn how to successfully use LinkedIn to support their career development.

Organized in partnership with the UMN KSEA student organization, and UMN Career Services. All UMN community members are welcome. Please email Jane Sitter with any questions about this event:sitt0036@umn.edu

Prof. Iris Bahar at the Wilson Lecture Series

Scalable ML Architectures for Real-time Energy-efficient Computing

Despite the strengths of convolutional neural networks (CNNs) for object recognition, these discriminative techniques have several shortcomings that leave them vulnerable to exploitation from adversaries. Discriminative-generative approaches offers a promising avenue for robust perception by combining inference by deep learning with sampling and probabilistic inference models to achieve robust and adaptive understanding. Our focus is on implementing a scalable, computationally efficient generative inference stage that can achieve real-time results in an energy efficient manner. In this talk, I will present our work on a discriminative-generative approach for pose estimation that offers high accuracy especially in unstructured and adversarial environments. I will then describe our hardware implementation of this algorithm to obtain real-time performance with high energy-efficiency and its implications for future directions in designing scalable and efficient ML algorithms.

About the speaker

R. Iris Bahar received the B.S. and M.S. degrees in computer engineering from the University of Illinois, Urbana, and the Ph.D. degree in electrical and computer engineering from the University of Colorado, Boulder. She recently joined the faculty at the Colorado School of Mines in January 2022 and serves at Department Head of Computer Science. Before joining Mines, she was on the faculty at Brown University since 1996 and held dual appointments as Professor of Engineering and Professor of Computer Science. Her research interests focus on energy-efficient and reliable computing, from the system level to device level. Most recently, this includes the design of robotic systems. She is the 2019 recipient of the Marie R. Pistilli Women in Engineering Achievement Award and the Brown University School of Engineering Award for Excellence in Teaching in Engineering. She is a fellow of the IEEE and a ACM Distinguished Scientist.

Prof. Jiarong Hong at the Wilson Lecture Series

Innovative Digital Inline Holography: from Flow Measurements to Cancer Diagnostics

Particles (e.g., dust, cells, droplets) with size ranging from nm to mm are ubiquitous in our daily life. To determine their properties in situ is vital for many applications. Here I will share with you our recent advancement in both the instrumentation and computational algorithms of digital inline holography (DIH) as a low cost and compact tool for 3D imaging of particles suspended in the flow. Enabled by such advancement, we have implemented DIH not only to study fundamental problems in the field of fluid dynamics, but also characterize particle properties and behaviors in agriculture, meteorology, medical sciences, microbiology, material sciences, etc. A number of such examples including measurements of wall-bounded turbulent flows, characterization of risks of airborne disease transmission, and detection of cancer cells in a blood stream will be provided.

Prof. Jiarong Hong is with the Department of Mechanical Engineering at the University of Minnesota Twin Cities

Professional & Academic Email Writing Workshop for UMN International Students

Student English Language Support (SELS) and Career Services are collaborating to help international students successfully navigate professional & academic email writing. In this interactive workshop, you will learn about professional email etiquette, appropriate language to use in professional/academic emails, and how to concisely write all parts of a professional or academic email. This is a hybrid workshop - with an in-person and online option. Please register to confirm your option, after registering you will receive the link.

Prof. Derya Aksaray at the Wilson Lecture Series

Reinforcement Learning for Dynamical Systems with Temporal Logic Specifications

Dynamical systems such as drones, mobile robots, or autonomous cars are envisioned to achieve complex specifications which may include spatial (e.g., regions of interest), temporal (e.g., time bounds), and logical (e.g., priority, dependency, concurrency among tasks) requirements. As specifications get more complex, encoding them via algebraic equations gets harder. Alternatively, such specifications can be compactly expressed using temporal logics (TL). In this talk, I will address the problem of learning optimal control policies for satisfying TL specifications in the face of uncertainty. Standard reinforcement learning (RL) algorithms are not directly applicable when the objective is to satisfy a TL specification. To overcome this limitation, I will formulate an approximate problem that can be solved via RL and present the suboptimality bound of the proposed solution. Then, I will discuss the scalability of learning with TL objectives and present a more tractable RL formulation.

About the speaker

Derya Aksaray is currently an Assistant Professor in the Department of Aerospace Engineering and Mechanics at the University of Minnesota (UMN). Before joining UMN, she held postdoctoral research positions at the Massachusetts Institute of Technology from 2016-2017 and at Boston University from 2014-2016. She received her Ph.D. degree in Aerospace Engineering from the Georgia Institute of Technology in 2014. Her research interests lie primarily in the areas of control theory, formal methods, and machine learning with applications to autonomous systems and aerial robotics.