Carnegie Mellon University

ECE seminars

The Department of Electrical and Computer Engineering hosts two different seminars; the Department Lecture Series, and weekly Graduate Seminars. All talks take place from 4:30 p.m. - 5:30 p.m.

Please see below for venue details.

Department Lecture Series

alex acero

Dr. Alex Acero
Senior Apple Siri Director
Apple Inc.
Cupertino, CA

The Deep Learning Revolution
View abstract and bio

January 31, 2019
4:30 p.m.
Wean Hall 5421
Reception following at 5:30 p.m.

klara nahrstedt

Dr. Klara Nahrstedt
Ralph and Catherine Fisher Full Professor
Computer Science Department

Director, Coordinated Science Laboratory
University of Illinois Urbana-Champaign

April 4, 2019
4:30 p.m.
Scott Hall 6142
Reception following in Scott Hall Atrium at 5:30 p.m.

karen strauss

Dr. Karin Strauss
Senior Researcher, Microsoft
Affiliate Professor, Department of Computer Science & Engineering
University of Washington

April 11, 2019
12:00 p.m.
Hamerschlag Hall 1107
Refreshments served at 11:30 a.m.

Graduate Seminars

All seminars will take place in Wean Hall 5421. Refreshments are served at 4:00 p.m.

View all previously recorded seminars here. Andrew ID and password are required to view recorded seminars.

Associate Professor
EECS Department
Case Western Reserve University

Emerging Semiconductor Nanoscale Devices and Systems for Classical and Quantum Information Processing


Emerging semiconductors, ranging from wide bandgap or ultrawide bandgap materials (such as SiC, Ga2O3, AlN) to atomic layer crystals (such as transition metal dichalcogenides or TMDCs, black phosphorus), along with their heterostructures, offer compelling new platforms for electronic, photonic devices and transducers, where the unconventional and unique properties of these crystals can be harnessed for engineering both classical and quantum signal processing schemes. In this presentation, I will describe some of my research group’s latest endeavors and results on advancing solid-state device physics and engineering, by employing some of these emerging semiconductors (mainly 2D semiconductors, SiC, and Ga2O3). In classical domain, we build atomically thin electronic, optoelectronic devices, and a new class of nanoscale transducers, 2D nanoelectromechanical systems (NEMS), all enabled by 2D semiconductors and their van der Waals heterostructures. We demonstrate how the unconventional properties of these structures and their internal strong coupling effects have led to remarkably broad dynamic range and electrical tunability, as well as discoveries of new phenomena, device functions, and sensing modalities. Toward quantum engineering, atomistic defects in SiC and emerging 2D crystals support quantum emitters promising for enabling qubits at room temperature. Built on our attainments in SiC photonics and 2D devices, we explore such platforms and heterogeneous integration, toward realizing quantum transduction and information processing in chip-scale integrated systems.


Philip Feng is currently the Theodore L. & Dana J. Schroeder Associate Professor in EECS at Case School of Engineering, Case Western Reserve University (CWRU). His group’s research is primarily focused on emerging semiconductor devices and integrated systems. He received his Ph.D. in Electrical Engineering from California Institute of Technology (Caltech) in 2007. He was an invited participant at the National Academy of Engineering (NAE) 2013 U.S. Frontier of Engineering (USFOE) Symposium. Subsequently, he was selected to receive the NAE Grainger Foundation Frontiers of Engineering (FOE) Award in 2014. His recent awards include the NSF CAREER Award, 4 Best Paper Awards (with his advisees, at IEEE and American Vacuum Society (AVS) conferences), a university-wide T. Keith Glennan Fellowship, the Case School of Engineering Graduate Teaching Award (2014) and the Case School of Engineering Research Award (2015). A Senior Member of IEEE, he has served on the Technical Program Committees (TPC) and as Track/Session Chairs for IEEE IEDM, IEEE MEMS, Transducers, IEEE IFCS, IEEE SENSORS, IEEE NANO, etc., and as the MEMS/NEMS Chair for AVS’ 61st to 63rd Int. Symposia. Lately, he is also serving as a co-organizer and technical program chair for SiC Materials & Devices Workshop.

Distinguished University Professor
Computational Biology
Professor of Mathematics
Adjunct Professor Neurobiology
University of Pittsburgh

If space turned out to be time: Resonances in the visual cortex


When subjects are exposed to full field flicker in certain frequencies, they perceive a variety of complex geometric patterns that are often called flicker hallucinations. On the other hand, when looking at high contrast geometric patterns like op art, shimmering and flickering is observed. In some people, flicker or such op art can induce seizures. In this talk, I describe a simple network model of excitatory and inhibitory neurons that comprise the visual area of the brain. I show that these phenomena are reproduced and then give an explanation based on symmetry breaking bifurcations and Floquet theory. Symmetric bifurcation theory also shows why one expects a different class of patterns at high frequencies from those at low frequencies. Next, I will describe the flip side of this coin and discuss a theory of uncomfortable images. Many people exhibit visual discomfort when looking at high contrast geometric patterns such as seen in op art. I'll discuss some recent results where we show that such patterns can induce global oscillations in a network similar to the one used in the flicker study.


I am a Distinguished University Professor of Computational Biology and Professor of Math at Pitt where I have been since 1982.

I received my Ph.D. in Theoretical Biology at the University of Chicago and did a postdoc at NIH with John Rinzel.

Honors include: Sloan Fellow, SIAM Fellow, winner of the 2015 Math Neuroscience prize.

I am an avid, if feckless gardener, collect old fountain pens, and have an encyclopedic knowledge of Popeye.

Research Scientist Machine Learning
HRL Laboratories, LLC
Malibu, CA

Generalized Sliced-Wasserstein Distances and Their Applications in Generative Modeling and Transfer Learning


Emerging from the optimal transportation problem and due to their favorable geometric properties, Wasserstein distances have recently attracted ample attention from the machine learning and signal processing communities. Wasserstein distances have been used in supervised, semi-supervised, and unsupervised learning problems, as well as in domain adaptation and transfer learning. However, the application of Wasserstein distances to high-dimensional probability measures is often hindered by their expensive computational cost. Sliced-Wasserstein (SW) distances, on the other hand, have similar qualitative properties to the Wasserstein distances but are significantly simpler to compute. The simplicity of computation of this distance has motivated recent work to use SW as a substitute for the Wasserstein distances. In this presentation, I first review the mathematical concepts behind sliced Wasserstein distances. Then I introduce an entire class of new distances, denoted as Generalized Sliced-Wasserstein (GSW) distances, that extends the idea of linear slicing used in SW distances to general non-linear slicing of probability measures. Finally, I will review various applications of SW and GSW in deep generative modeling and transfer learning.


Soheil Kolouri is currently a Research Scientist at HRL Laboratories, LLC, Malibu, CA, focusing on various machine learning problems, including continual learning, domain adaptation, zero-few/shot learning, generative modeling, and transfer learning. He is the co-PI for DARPA’s Lifelong Learning Machines (L2M) program and he is also the PI on HRL’s internal R&D project on Learning with Less Labels. He received his Ph.D. in Biomedical Engineering from Carnegie Mellon University in 2015, where he was awarded the Bertucci Fellowship for outstanding graduate students from the College of Engineering in 2014, and the Outstanding Dissertation Award for his thesis titled: “Transport-Based Pattern Recognition and Image Modelling,” from the Biomedical Engineering Department in 2015.

Assistant Professor
ECE Department
Coordinated Science Laboratory
University of Illinois Urbana-Champaign

Steerable ePCA


In photon-limited imaging, the pixel intensities are affected by photon count noise. Many applications, such as 3-D reconstruction using correlation analysis in X-ray free electron laser (XFEL) single molecule imaging, require an accurate estimation of the covariance of the underlying 2-D clean images. Accurate estimation of the covariance from low-photon count images must take into account that pixel intensities are Poisson distributed, rendering the sub-optimality of the classical sample covariance estimator. Moreover, in single molecule imaging, including in-plane rotated copies of all images could further improve the accuracy of covariance estimation. In this talk, we introduce an efficient and accurate algorithm for covariance matrix estimation of count noise 2-D images, including their uniform planar rotations and possibly reflection. Our procedure, steerable ePCA, combines in a novel way two recently introduced innovations. The first is a methodology for principal component analysis (PCA) for Poisson distributions, and more generally, exponential family distributions, called ePCA. The second is steerable PCA, a fast and accurate procedure for including all planar rotations for PCA. We demonstrate the efficiency and accuracy of steerable ePCA in numerical experiments with simulated XFEL datasets.


Zhao is an assistant professor in the Department of Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign, with affiliation to the Coordinated Science Laboratory. Prior to joining ECE Illinois in 2016, she was a Courant Instructor at the Courant Institute of Mathematical Sciences, New York University. She received her Ph.D. in Physics from Princeton University in 2013 working with Amit Singer and graduated from Trinity College, Cambridge University, with bachelor's and master's degrees in physics in 2008.

Emeritus Professor
ECE Department
Carnegie Mellon University

Research in Africa: Opportunities and Challenges


Opportunities abound in Africa for fundamental and applied research in a number of critical sectors, including energy, communication, transportation, health care and agriculture. This talk will survey some of these research opportunities and the possibilities for developing research projects in Africa, particularly through CMU-Africa in Kigali, Rwanda. I will also share some personal observations about challenges to doing research in Africa and approaches that can lead to effective and productive collaborations with researchers based in African countries.


Bruce Krogh is professor emeritus of electrical and computer engineering at Carnegie Mellon University and founding director of Carnegie Mellon University-Africa, a branch of Carnegie Mellon University’s College of Engineering in Kigali, Rwanda. He is currently a researcher at the CMU Software Engineering Institute. Throughout his career, Professor Krogh’s research has focused on the integration of computation, communication and control in a number of areas, including robotics, manufacturing, and energy management systems.

Assistant Professor
Computer Science
University of Massachusetts Amherst

EECS Department
University of Michigan

Applied & Engineering Physics
Cornell University

Lawrence Livermore National Laboratory
San Francisco, CA

Professor, and William Dawson Scholar
School of Computer Science
ECE Department

McGill University