Carnegie Mellon University

Seminars

The Department of Electrical and Computer Engineering invites prestigious colleagues to speak during weekly Graduate Seminars. All talks take place from 4:30 p.m. - 5:30 p.m. Please see below for venue details.

 For questions, please contact the committee chair, Giulia Fanti.


 

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.

Assistant Professor
Department of Electrical and Computer Engineering
Carnegie Mellon University

Lab-on-CMOS Bioelectronic Interfaces

Abstract

Bioelectronic interfaces have traditionally been used to probe microcellular environments in order to yield key insights about physiological and biochemical processes, thereby allowing specific therapeutic targets to be discovered. In the recent past, however, new developments in bioelectronic interfaces have opened up a wide range of new possibilities in medicine. For instance, bioelectronic interfaces are becoming increasingly important in bioelectronic medicine where they can be used to read and modulate neural electrical activity in order to trigger specific and disease-mitigating biomolecular responses.

In this talk, I will review exciting new developments in the state-of-the-art in bioelectronic interface engineering. Particularly, I will discuss the lab-on-CMOS approach to creating highly integrated bioelectronic interfaces. A lab-on-CMOS platform is a device that includes a CMOS integrated circuit packaged in a microsystems configuration such that the circuit may be interfaced directly with biological specimens. I will discuss key scientific inquiries that may be enabled by the lab-on-CMOS approach as well as its broader impact on bioelectronic medicine.

Bio

Marc Dandin is an Assistant Professor in the Electrical and Computer Engineering department at Carnegie Mellon University. Prior to joining Carnegie Mellon, he was an Adjunct Professor of Electrical Engineering at the George Washington University in Washington, DC, where he taught graduate courses in analog and radio-frequency integrated circuit design. He is the founder and was the CEO of Kiskeya Microsystems LLC, a company developing point-of-care diagnostics technologies for resource-limited settings. He also worked in industry as a specialist in intellectual property matters for several leading law firms in the Washington, DC area. He holds the PhD degree in Bioengineering, the M.S. and B.S. degrees in Electrical Engineering, all from the University of Maryland, College Park. He is the recipient of the Fischell Fellowship in Biomedical Engineering, and the inaugural Jimmy H.C. Lin Award for Entrepreneurship. Furthermore, he is a Senior Member of the IEEE. At CMU, his research focuses on integrated circuit design and microsystems development for biomedical applications.

Professor
School of Electrical and Computer Engineering
Georgia Institute of Technology

Improving Reliability of Near-Term Quantum Computers via Software-Based Error Mitigation

abstract

Quantum computing promises exponential speedups for an important class of problems. While quantum computers with few tens of qubits have already been demonstrated and machines with 100+ qubits are expected soon, these machines face significant reliability challenges – including gate error rates in the range of 1-2%, and measurement error rates in the range of 5-10%. As these machines do not have sufficient capacity to do error correction (which can incur 20x-50x physical qubits to form one fault-tolerant qubit), these machines are operated in the Noisy Intermediate Scale Quantum (NISQ) mode of computing. The computation on a NISQ machine can produce incorrect output. Therefore, the NISQ program is run thousands of times and the output log is analyzed to infer the correct output. However, the error rates are such that the likelihood of obtaining the right answer is still quite small for NISQ machines and this problem only becomes worse for programs with a large number of instructions.

In this talk, I will discuss some of our recent work that aims to improve the reliability of near term quantum computers by developing software techniques to mitigate the hardware errors. Our first work (ASPLOS 2019) exploits the variability in the error rates of qubits to steer more operations towards qubits with lower error rates and avoid qubits that are error-prone. Our second work (MICRO 2019) looks at executing different versions of the programs tuned to cause diverse mistakes so that the machine is less vulnerable to correlated errors, thereby making it easier to infer the correct answer. Our third work (MICRO 2019) looks at exploiting the state-dependent bias in measurement errors (state 1 is more error prone than state 0) and dynamically flips the state of the qubit to perform the measurement in the stronger state. We perform our evaluations on real quantum machines from IBM and demonstrate significant improvement in the overall system reliability. If time permits, I will also briefly discuss the hardware aspect of designing quantum computers, including cryogenic processor and cryogenic memory system.

Bio

Moinuddin Qureshi is a Professor of Electrical and Computer Engineering at the Georgia Institute of Technology. His research interests include computer architecture, memory systems, hardware security, and quantum computing. Previously, he was a research staff member (2007-2011) at IBM T.J. Watson Research Center, where he developed the caching algorithms for Power-7 processors. He is a member of the Hall of Fame for ISCA, MICRO, and HPCA. His research has been recognized with the best paper award at MICRO 2018, best paper award at Computing Frontiers 2019, best paper award at HiPC 2014, and two selections (and three honorable mentions) at IEEE MICRO Top Picks. His ISCA 2009 paper on Phase Change Memory was recently awarded the 2019 Persistent Impact Prize in recognition of “exceptional impact on the fields of study related to nonvolatile memories”. He was the Program Chair of MICRO 2015 and Selection Committee Co-Chair of Top Picks 2017. He received his Ph.D. (2007) and M.S. (2003) from the University of Texas at Austin.

Assistant Professor
School of Electrical and Computer Engineering
Cornell University

Beyond Supervised Learning for Biomedical Imaging

Abstract

Today, many biomedical imaging tasks, such as 3D reconstruction, denoising, detection, registration, and segmentation, are solved with machine learning techniques. In this talk, I will present a flexible learning-based framework that has allowed us to derive efficient solutions for a variety of such problems, without relying on heavy supervision. I will primarily employ image registration as a concrete application and present the details of VoxelMorph, our unsupervised learning-based image registration tool. I will show empirical results obtained by co-registering thousands of brain MRI scans where VoxelMorph has yielded state-of-the-art accuracy with runtimes that are orders of magnitude faster than conventional tools. Finally, I will present some recent results where we used VoxelMorph to learn conditional deformable templates that can reveal population variation as a function of factors of interest, such as aging or genetics. Our code is freely available here.

Bio

Mert R. Sabuncu received a PhD degree in Electrical Engineering from Princeton University, where his dissertation dealt with the problem of establishing spatial correspondence across multiple images, such as MRI scans. Mert then moved to Massachusetts Institute of Technology for a post-doc at the Computer Science and Artificial Intelligence Lab, where he worked on various biomedical image analysis problems, including the segmentation of brain MRI scans. After his post-doc at MIT, Mert spent a few years at the A.A Martinos Center for Biomedical Imaging (Massachusetts General Hospital and Harvard Medical School) as a junior faculty member, where he built a research program on algorithmic tools for integrating genetics and medical imaging. Today, Mert is an Assistant Professor at Cornell’s School of Electrical and Computer Engineering, and Meinig School of Biomedical Engineering. His research group develops machine learning based computational tools for biomedical imaging applications.

 

Professor
Integrated Systems Engineering
The Ohio State University

Operational Equilibria of Electric and Natural Gas Systems with Limited Information Interchange

Abstract

Electric power and natural gas systems are typically operated independently. However, their operations are interrelated due to the proliferation of natural gas-fired generating units. We analyze the independent but interrelated day-ahead operation of the two systems. We use a direct approach to identify operational equilibria involving these two systems, in which the optimality conditions of both electric power and natural gas operational models are gathered and solved jointly. We characterize the equilibria that are obtained under different levels of temporal and spatial granularity in conveying information between the two system operators. Numerical results from the Belgian system are used to examine the impacts of different levels of information interchange on prices and operational cost and decisions in the two systems.

Bio

Antonio J. Conejo, professor at The Ohio State University, OH, received an M.S. from MIT, and a Ph.D. from the Royal Institute of Technology, Sweden. He has published over 200 papers in refereed journals, and is the author or coauthor of books published by Springer, John Wiley, McGraw-Hill and CRC. He has been the principal investigator of many research projects financed by public agencies and the power industry and has supervised 23 PhD theses. He is an INFORMS Fellow and IEEE Fellow and a former Editor-in-Chief of the IEEE Transactions on Power Systems, the flagship journal of the power engineering profession.

Associate Professor
Electrical and Computer Engineering & Biomedical Engineering
Carnegie Mellon University

Assistant Professor
Department of Electrical and Computer Engineering
Carnegie Mellon University

Thomas G. Myers Professor
Electrical Engineering, Bioengineering and Medical Engineering
California Institute of Technology

Wavefront shaping – the threading of light through scattering media

Abstract

Wavefront shaping has been an active research area over the past decade. Its ability to control light transmission through or into a scattering medium has significant biophotonics, computation, imaging, encryption and other applications. In this talk, I will discuss the various advancements we have made in wavefront shaping and talk about the use of wavefront shaping in biophotonics. I will also discuss two recent and surprising findings in our wavefront shaping work. The first is the intentional combination of wavefront shaping and a controlled scattering medium to create novel optical system – in effect, turning scattering from a ‘foe’ to a ‘friend’ for wavefront shaping. The second is the complete dropping of phase characterization in a new class of ‘wavefront shaping’ methods.

Bio

Professor Yang's research efforts are in the areas of novel microscopy development and time-reversal optical wavefront control. In the field of microscopy, his group’s contributions include the invention of the ePetri technology and Fourier Ptychography. His group was the first to demonstrate that tissue scattering is phase conjugatable and that helped usher wavefront shaping into biophotonics. His group invented the digital optical phase conjugation technology- one of the primary wavefront shaping methods in use today. He is the Thomas G. Myers professor in the areas of Electrical Engineering, Bioengineering and Medical Engineering at the California Institute of Technology. He has received the NSF Career Award, the Coulter Foundation Early Career Phase I and II Awards, and the NIH Director's New Innovator Award. He is a fellow of the Coulter foundation, AIMBE, OSA and SPIE.

Professor and Henry Booker Faculty Scholar
ECE Department
University of California, San Diego

Associate Professor
Computer Science Department
University of Colorado - Boulder

Associate Professor
Statistics Department
Columbia University

Associate Professor
Electrical Engineering and Computer Science
University of California, Berkeley

Enabling the SmartGrid with IoT Sensors and Edge-Cloud Analytics

Abstract

Wireless sensors and edge-cloud analytics have the potential to gather and process vast amounts of data about the physical world, offering radical new insights about everything from critical infrastructure to interpersonal interactions. But designing, deploying, and operating geographically-distributed systems consisting a hierarchy of sensing, storage, compute, and communication elements raises interesting new challenges across the system stack. In this talk, we will discuss our experiences designing new IoT systems to address several power and power grid monitoring problems. In particular, this talk will focus on three systems—PowerBlade, Triumvi, and GridWatch—and their motivation, design, and deployment. PowerBlade explores how to cost-effectively characterize, capture, and classify widespread plug-load energy usage—representing the fastest growing and least understood segment of end-use energy consumption—across hundreds of homes and offices representing tens of thousands of sensors. Triumvi explores how to make circuit level energy metering, useful for a variety of facilities trending, energy savings, and fault detection & diagnostics applications, more efficient and scalable. Finally, GridWatch explores how to scalably and cost-effectively detect and respond to the power outages that stymie residential and business activity in under-developed power grids using mobile and fixed sensors, data analytics, and reporting systems in Sub-Saharan Africa, finding that conventional approaches to outage detection systems vastly underreport customer experiences. These systems all share a similar architecture, require new sensor devices and edge-cloud data processing, and wrestle with power management and networking. But they ultimately demonstrate both the tremendous potential and the significant challenges of this nascent computing class.

Bio

Prabal Dutta is an Associate Professor of Electrical Engineering and Computer Sciences at the University of California at Berkeley, where he co-directors the CONIX Research Center. Previously, he was a Morris Wellman Faculty Development Associate Professor of Electrical Engineering and Computer Science at the University of Michigan. His interests span circuits, systems, and software, with a focus on mobile, wireless, embedded, networked, and sensing systems with applications to health, energy, and the environment. His work has yielded dozens of hardware and software systems, has won five Top Pick/Best Paper Awards, two Best Paper Nominations, and a Potential Test of Time 2025 Award, as well as several demo, design, and industry competitions. His work has been directly commercialized by a dozen companies and indirectly by many dozens more, has been utilized by thousands of researchers and practitioners worldwide, and is on display at Silicon Valley’s Computer History Museum. His research has been recognized with an Alfred P. Sloan Research Fellowship, an NSF CAREER Award, a Popular Science Brilliant Ten Award, an Intel Early Career Faculty Fellowship, and as a Microsoft Research Faculty Fellowship Finalist. He has served as chair or co-chair of MobiSys’18, BuildSys’17, IPSN’17, ESWEEK’17 IoT Day, HotMobile’16, SenSys’14, and HotPower’11, and on the DARPA ISAT Study Group from 2012-2016, where he co-chaired numerous studies. He holds a Ph.D. in Computer Science from UC Berkeley (2009), where NSF and Microsoft Research Graduate Fellowships supported his research. He received an M.S. in Electrical Engineering (2004) and a B.S. Electrical and Computer Engineering (1997), both from The Ohio State University.

Fellow of  (NIST) and Professor Adjoint
Department of Physics
University of Colorado