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

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Dr. Babak Hassibi
Mose and Lillian S. Bohn Professor
ECE Department
California Institute of Technology

October 11, 2018
Scott Hall 6142
Reception following in Scott Hall Atrium at 5:30 p.m.
Abstract and bio

Watch the seminar here.

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Dr. Rajiv Laroia
Chief Technology Officer, Co-Founder
Light

Is Computational Imaging the Future of Photography?

October 18, 2018
Scaife Hall 125
12:00 p.m. - 1:30 p.m.
Refreshments will be served at 12:00 p.m.
Abstract and bio

Watch the seminar here.

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Dr. Eric Fossum
John H. Krehbiel Sr. Professor for Emerging Technologies
Director, Ph.D. Innovation Program
Associate Provost, Office of Entrepreneurship and Technology Transfer
Dartmouth University

November 1, 2018
Scott Hall 6142
Reception following in Scott Hall Atrium at 5:30 p.m.
Abstract and bio

Watch the seminar here.

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Dr. Alyosha Molnar
Associate Professor
ECE Department
Cornell University

December 6, 2018
Scott Hall 6142
Reception following in Scott Hall Atrium at 5:30 p.m.
Abstract and bio

Graduate Seminars

All seminars will take place in Scaife Hall 125. Refreshments are served at 4:00 p.m.

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

*The December 13th seminar featuring Dr. Yilin Mo will take place in Hamerschlag Hall D210 at 12:00 p.m.

Assistant Professor
The Robotics Institute
Carnegie Mellon University

Towards Imaging Systems That Make Sense of Multi-Path Light

Watch the seminar here.

Abstract

Light travels in many ways through a scene, following multiple paths and bouncing several times on surfaces and inside materials. By injecting light into the scene, measuring what is returned, and reasoning about the complex propagation that takes place in-between, we should be able to recover rich information about the scene. During the past two decades, the exponential growth of processing power and the accelerating advances in generalized optics have enabled the development of computational imaging systems that can extract more and more information from such multi-path light propagation effects, to perform inference tasks previously thought impossible.

To demonstrate this, in this talk, I will discuss advances we have made towards three broad research directions along the above lines. First, I will describe how to build cameras that can effectively capture video at frame rates of quadrillion frames per second, allowing for the visualization of light propagation. Second, I will explain how measurements from such cameras can be combined with physics-based optimization, to create systems that can see inside very turbid volumes at micron-scale resolutions. Third, I will show how similar imaging and computation can be used to develop systems that can measure 3D shape of objects that are not directly visible to a camera, for example because they are occluded by a wall. Finally, I will give an overview of ongoing research towards enabling such systems to operate in more general and uncontrolled environments, which is critical for enabling applications in material science, medical imaging, and industrial quality control.

Bio

I am an Assistant Professor at the Robotics Institute, Carnegie Mellon University, where I have been since February 2017. Before that, I was a PhD student and postdoctoral scholar at Harvard University, and even before that an undergraduate student at the National Technical University of Athens, Greece. I do computational imaging, which can be broadly described as coming up with systems that combine imaging (optics, sensors, illumination) and computation (physics-based modeling and rendering, inverse algorithms, learning) in innovative, unexpected, and meaningful ways. Particular problems I am interested in include imaging around walls or through skin, lightweight depth sensing, material acquisition, adaptive imaging, efficient rendering, and the integration of physics-based simulation, learning, and cameras. 

Assistant Professor
ECE Department
Carnegie Mellon University

Optimization in the Federated Setting

Watch the seminar here.

Abstract

The nascent field of federated learning explores training statistical models over massive networks of distributed devices. This task poses novel challenges in distributed optimization, including issues related to high communication, stragglers, and fault tolerance. By marrying systems-level constraints and optimization techniques, we provide robust methods and order-of-magnitude speedups for solving machine learning problems in this burgeoning setting. We corroborate empirical results with theoretical guarantees that expose systems parameters to give further insight into empirical performance.

Bio


Virginia Smith is an assistant professor in Electrical and Computer Engineering at Carnegie Mellon University, and an affiliated faculty member in the Machine Learning Department. Her research interests are at the intersection of machine learning, optimization, and distributed systems. She has been the recipient of the NSF Graduate Research Fellowship, Google Anita Borg Memorial Scholarship, NDSEG Fellowship, and MLConf Industry Impact Award. Prior to CMU, Virginia received a Ph.D. from UC Berkeley and undergraduate degrees from the University of Virginia.

Assistant Professor
ECE Department
Iowa State University

Unsupervised Neural Network Learning from an Algorithmic Lens

Watch the seminar here.

Abstract

While a rigorous theoretical understanding for deep learning algorithms remains elusive, several recent breakthrough results (for provably learning shallow architectures) may point the way to formulating such a theory. However, the large majority of such results are applicable only for supervised learning problems.

In this talk, I will complement this line of work with a series of neural network learning results for the unsupervised setting. Our results explore special cases, where (1) the learned representations themselves obey conciseness assumptions (such as compositionality, sparsity, and/or democracy), and (2) the data obeys certain common generative assumptions (such as sparsity, factor models, or mixture of gaussians).

Our results can be viewed as formal evidence that (shallow) networks can be indeed used as unsupervised feature training mechanisms for a wide range of datasets, and may shed insights on how to practically train larger stacked architectures.

Bio

Chinmay Hegde is an assistant professor in Electrical and Computer Engineering at Iowa State University. Prior to this, he received his PhD at Rice University, and was a postdoctoral associate in CSAIL at MIT. His research focuses on developing fast and robust algorithms for machine learning and statistical signal processing, with applications to imaging problems.

He is the recipient of multiple awards, including best paper awards at SPARS, ICML and MMLS; the Budd Award for Best Engineering PhD Thesis in 2013; the Warren Boast Award for Undergraduate Teaching in 2016; the NSF CRII Award in 2016; the Black and Veatch Faculty Fellowship in 2017; and the NSF CAREER Award in 2018.

Professor
ECE Department
University of Texas - Dallas

Trusted 3rd-Party Module Acquisition through Proof-Carrying Hardware Intellectual Property (PCHIP)

Watch the seminar here.

abstract

The use of 3rd-party hardware Intellectual Property (IP), acquired in the form of code in a Hardware Description Language (HDL), enables fast development of new electronic systems and is prevalent in both commercial and defense applications. To alleviate the security and trustworthiness concerns arising by 3rd-party hardware IP in a globalized semiconductor industry, we adapted the well-known Proof Carrying Code (PCC) paradigm from the software community to enable formal yet computationally straightforward validation of security-related properties in hardware systems. These properties, agreed upon a priori by the IP vendor and consumer and codified in a temporal logic, outline the boundaries of trusted operation, without necessarily specifying the exact IP functionality. A formal proof is then crafted by the vendor and presented to the consumer, who can automatically validate compliance of the hardware IP to the agreed-upon security properties. In this presentation, I will first review our initial proof-of-concept framework for developing provably trustworthy hardware IP, along with its suitability for supporting information flow tracking (IFT). I will then describe how this framework gradually turned into an ecosystem comprising foundations, libraries, automation and examples on digital cryptographic circuits. Extensions enabling similar capabilities in the analog and mixed-signal domain will also be briefly discussed. I will conclude by emphasizing the need and pointing out the challenges involved in developing formal solutions in hardware security.

Bio

Yiorgos is a professor of Electrical and Computer Engineering at The University of Texas at Dallas, where he leads the Trusted and RELiable Architectures (TRELA) Research Laboratory. Prior to joining UT Dallas in 2011, he spent 10.5 years as a faculty of Electrical Engineering and of Computer Science at Yale University. He holds a Ph.D. (2001) and an M.S. (1997) in Computer Engineering from the University of California, San Diego, and a Diploma of Computer Engineering and Informatics (1995) from the University of Patras, Greece. His main research interests are in the application of machine learning and statistical analysis in the design of trusted and reliable integrated circuits and systems, with particular emphasis in the analog/RF domain. He is also investigating hardware-based malware detection, forensics and reliability methods in modern microprocessors, as well as on-die learning and novel computational modalities using emerging technologies. His research activities have been supported by NSF, ARO, AFRL, SRC, DARPA, Boeing, IBM, LSI, Intel, Advantest, AMS and TI. Yiorgos served as the 2016-2017 general chair and the 2013-2014 program chair of the IEEE VLSI Test Symposium, as well as the 2010-2012 program chair of the Test Technology Educational Program (TTEP). He is as an associate editor of the IEEE Transactions on Information Forensics and Security, the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, the IEEE Design & Test periodical and the Springer Journal of Electronic Testing: Theory and Applications, and he has also served as a guest editor for the IEEE Transactions on Computers and the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, and as a topic coordinator and/or program committee member for several IEEE and ACM conferences. He is a Senior Member of the IEEE, a recipient of the 2006 Sheffield Distinguished Teaching Award, a recipient of Best Paper Awards from the 2013 Design Automation and Test in Europe (DATE'13) conference and the 2015 VLSI Test Symposium (VTS'15).

Assistant Professor
ECE Department
Rutgers University

Portable and Wearable Electro-Fluidic Micro/Nanotechnologies for Health and Environmental Monitoring

Watch the seminar here.

Abstract

Portable and wearable devices for measuring physical parameters such as ECG, pulse oximetry, and temperature are widely used for health monitoring. The ability to continuously monitor physiology at the biomolecular level can further provide significant information in understanding the state of health. In this talk, I will discuss my group's work on fabricating micro- and nanosensing platforms for biomolecular and biochemical detection. In the first part of my talk, I will discuss wearable platforms we are developing for detection of cells and also inflammatory proteins in blood and saliva. I will then discuss a novel scheme for barcoding microparticles nanoelectronically, for multiplexed detection of analytes. I will then discuss our development of a novel electrochemical sensor using reduced graphene oxide for detection of inflammatory markers in exhaled breath condenstate for management of chronic respiratory diseases. Finally, I will talk about my groups efforts in developing novel probes for detection of toxic compounds in our regional water sources.

Bio

Mehdi Javanmard joined Electrical and Computer Engineering Department at Rutgers University in Fall 2014 as Assistant Professor. Before that he was Senior Research Engineer at the Stanford Genome Technology Center (SGTC) in the School of Medicine at Stanford University. He received his BS (2002) from Georgia Institute of Technology and the MS in Electrical Engineering at Stanford University (2004) working at Stanford Linear Accelerator Center researching the use of photonic nanostructures for high energy physics. In 2008, he received his PhD in Electrical Engineering at Stanford University working on development of electronic microfluidic platforms for low cost genomic and proteomic biomarker detection. At SGTC, he worked as a postdoctoral scholar from 2008-2009, and then as a staff engineering research associate from 2009 till 2014. In 2017 he was recipient of the Translational Medicine and Therapeutics Award by the American Society for Clinical Pharmacology & Therapeutics for his group's work in point of care diagnostic tools for assessing patient response to cancer therapies. He has received various awards as Principal Investigator from the National Science Foundation, DARPA, and the PhRMA foundation to support his research. His interests lie in developing portable and wearable technologies for continuous health monitoring and understanding the effects of environment on health.

Assistant Professor
ECE Department
Carnegie Mellon University

Integrated Photonic Circuits for Classical and Quantum Information Processing

Watch the seminar here.

Abstract

In addition to being the most successful material for electronics, Silicon is also an excellent photonic material receiving widespread interest from both academia and industry. Following the tremendous success enjoyed by integrated electronics, integrated photonic circuits in Silicon hold the promise of device scaling, mass fabrication, and system-level integration, which could revolutionize many traditional photonic technologies and create a wealth of practical applications. In this talk, I will provide several such examples focused on classical and quantum information processing. In the quantum domain, I will discuss the development of photonic technologies for efficient and low-noise quantum frequency conversion, an essential building block for quantum information science to bridge the frequency gap of disparate quantum nodes. The developed frequency converter has been successfully combined with photon pair sources and InAs/GaAs quantum dot emitters, thus paving the way for highly integrated quantum photonic circuits/networks in the future. In the classical domain, I will discuss the design and demonstration of wideband frequency combs in microresonators, covering a wavelength span of 1 um to 2um with two harmonically linked dispersive waves. Such octave-spanning frequency combs have been used for optical frequency synthesis with high accuracy using integrated photonics.

Bio

Qing Li is an Assistant Professor in the ECE department of Carnegie Mellon University. He received a B.E. in Electronics Engineering from Tsinghua University, China and a Ph.D. in the Department of Electrical and Computer Engineering from Georgia Institute of Technology. His doctoral research focused on developing signal processing technologies in both silicon and silicon nitride platforms. His postdoctoral work at NIST developed techniques for chip-scale quantum frequency conversion, octave-spanning microresonator frequency combs for optical frequency synthesis, and photonic interfaces for interrogating rubidium atomic systems. For his work, Qing Li has received the Outstanding Graduate Student (Colonel Oscar P. Cleaver) Award and Sigma Xi Best Ph.D. Thesis Award from Georgia Institute of Technology. His current research interest is centered on developing cutting-edge photonic technologies by combining novel photonic materials with unique device engineering based on nanophotonics.

Postdoctoral Research Fellow
ECE Department
Stanford University

Novel Devices and Integration Approaches Powered by Emerging Materials

Abstract

We are entering a new era of nanoelectronics and information technology, as conventional scaling-based approaches to Moore’s Law are slowing down. In response to high demand for energy-efficient abundant data processing, new frontiers could be enabled by new materials. However, there is a knowledge gap between new materials and system-level advances, which must be bridged through research, from lab to fab. A new field was enabled with the emergence of graphene and other two-dimensional (2D) materials, which offer unique properties at truly atomic-scale dimensions. Importantly, these can be assembled into novel heterostructures to tailor their electrical, optical, and thermal properties.

This talk will provide insights for alternative applications of 2D materials and heterostructures in disruptive electronics and thermal management. These include novel device concepts such as vertical transport and tapered-channel graphene transistors. Furthermore, integration modules such as layer-by-layer transfer, doping, and contact resistance improvement schemes will be discussed. Lastly, I will show recent results on engineering 2D heterostructures to achieve extreme thermal performance metrics. Taken together, these advances are promising for new functionalities, and for back-end of the line (BEOL) or three-dimensional (3D) heterogeneous integration of computing, memory, and thermal management.

Bio

Sam Vaziri is a postdoctoral research fellow in the department of Electrical Engineering (EE) at Stanford University. At Stanford, he is investigating electrical and thermal transport in two-dimensional (2D) materials and van der Waals heterostructures for novel electron device applications. He holds a Ph.D. in Nanoelectronics from KTH Royal Institute of Technology, Stockholm, in 2016, and dual MSc degrees: one in Nanotechnology from KTH, Stockholm, and one in Solid State Physics from K. N. Toosi University of Technology, Tehran. His previous research activities encompass metal-insulator-metal tunnel diodes, and 2D materials-based electronic and optoelectronic devices, such as vertical graphene-base transistors, as well as the integration of 2D materials into CMOS processes. Dr. Vaziri is a member of IEEE and Electron Devices Society Young Professionals Committee. He is the recipient of several professional research awards and fellowships including The Wallenberg Postdoctoral Research Fellowship (2016), IEEE Electron Devices Society Ph.D. Student Fellowship (2014), and ESSDERC Best Young Scientist Paper Award (2015).

Professor
Department of Signal Theory & Communications
Polytechnic University of Catalonia
Barcelona, Spain

Radio Resource Management in large scale satellite networks: potential applications for signal processing on graphs

Watch the seminar here.

Abstract

Flexibility in the radio resource management and in the scheduling of users is of outmost importance in the evolution of wireless communication systems. In this respect, and among other paradigms, the future smart networks leverage the potential of ubiquitous cell-free communications to overcome the inter-cell interference limitation in cellular networks and provide additional macro-diversity. We refer to this paradigm as large scale wireless networks and they can be implemented either in the terrestrial or in the satellite communication segment. This talk presents the cell-free or beam-free concept in the satellite segment, which is a large scale network that is less known than the terrestrial one. Then the potential of taking into account the geometric structure of the user requirements is shown with the application of graph clustering techniques. The talk also highlights the challenges of dealing with heterogeneous types of contents and discusses open problems for signal processing on graphs.

Bio

Ana Pérez-Neira is full professor at UPC (Technical University of Catalonia) in the Signal Theory and Communication department. Her research topic is signal processing for communications and currently she is working in multi-antenna and multicarrier signal processing, both, for satellite communications and wireless systems. She has been in the board of directors of ETSETB (Telecom Barcelona) from 2000-03 and Vicerector for Research at UPC (2010-13). She created UPC Doctoral School (2011). Currently, she is Scientific Coordinator at CTTC (Centre Tecnològic de Telecomunicacions de Catalunya), where she is fellow researcher. From 2008-2016 she has been member of EURASIP BoD (European Signal Processing Association), from 2010-2016 member of IEEE SPTM (Signal Processing Theory and Methods), from 2017-2018 she is IEEE SPS Regional Director-at-Large by election and from 2019-2020 she has been elected as IEEE SPS member of the BoG. She is the coordinator of the European project SANSA and of the Network of Excellence on satellite communications, financed by the European Space Agency: SatnexIV. She has been the leader of 20 projects and has participated in over 50 (10 for European Space Agency). She is author of 50 journal papers (20 related with Satcom) and more than 200 conference papers (20 invited). She is co-author of 4 books and 5 patents (one on satcom). She has been guest editor in 5 special issues and associate editor of the IEEE Transactions on Signal Processing and EURASIP Signal Processing and Advances in Signal Processing. Currently she is member of the IEEE SPCOM (Signal Processing for Communications) technical committee. She has been the general chairman of IWCLD’09, EUSIPC’11, EW’14, IWSCS’14 and ASMS/SPSC’16. She is recipient for the 2018 EURASIP Society Award. She is the general chair of IEEE ICASSP’20.

Associate Professor
Department of Automation Tsinghua University
Beijing, China

Secure Information Fusion in Cyber-Physical Systems

 

Abstract

The concept of Cyber-Physical System (CPS) refers to the embedding of sensing, communication, control and computation into the physical spaces. Today, CPSs can be found in areas as diverse as aerospace, automotive, chemical process control, civil infrastructure, energy, health-care, manufacturing and transportation, most of which are safety critical. Any successful attack to such kind of systems can cause major disruptions, leading to great economic losses and may even endanger human lives. The first-ever CPS malware (called Stuxnet) was found in July 2010 and has raised significant concerns about CPS security. In this talk we discuss how to design secure and efficient information fusion algorithms for CPS. We first consider the binary hypothesis testing problem with multiple sensors and design secure algorithm against an unknown set of Byzantine sensors. We further quantify the cost of adding security to the system and prove that our algorithm causes minimum impact on the performance in the absence of an attack. Next we consider the state estimation problem, and prove necessary and sufficient conditions, under which a convex optimization based estimator is secure against Byzantine attacks.

Bio

Yilin Mo is an Associate Professor in the Department of Automation, Tsinghua University. He received his Ph.D. In Electrical and Computer Engineering from Carnegie Mellon University in 2012 and his Bachelor of Engineering degree from Department of Automation, Tsinghua University in 2007. Prior to his current position, he was a postdoctoral scholar at Carnegie Mellon University in 2013 and California Institute of Technology from 2013 to 2015. He held an assistant professor position in the School of Electrical and Electronic Engineering at Nanyang Technological University from 2015 to 2018. His research interests include secure control systems and networked control systems, with applications in autonomous driving and sensor networks.