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

Micro/nano engineering for medical diagnostics

Watch the seminar

Abstract

Micro/nano engineering can provide new nanomaterials and micro/nano devices for new capabilities in medical diagnosis. Detection of rare cells and molecules in complex real-world samples is challenging for requirements in both sensitivity and specificity. Nanomaterials and their device integration can boost device performance and enable new functions, while microdevice platform can provide portability, automation and precise spatiotemporal control. In this talk, I will present our recent efforts including developing technologies for detecting rare cells and cancer biomarkers in blood samples and discovering viruses from environmental samples. I will further discuss some of our current efforts and future directions.

Bio

Dr. Siyang Zheng is currently an associate professor of Biomedical Engineering, and Electrical and Computer Engineering at Carnegie Mellon University (CMU). Before moving to CMU this summer, he was an associate professor at The Pennsylvania State University (Penn State). Dr. Zheng earned his M.S. and Ph.D. in Electrical Engineering from Penn State in 2000 and California Institute of technology in 2007, respectively. He received his B.S. in Biological Sciences and Biotechnology from Tsinghua University in China. His main research theme is to develop micro/nano technologies for precision healthcare, at the interface of material, device and biomedicine. His recent work includes developing liquid biopsy technologies for non-invasive cancer diagnosis using nanomaterials and microdevices, innovative nanomaterials and biomaterials for cancer therapeutics, nanomaterial-integrated microdevices for virus discovery and diagnosis, new methods for protein/peptide enrichment and proteomics, and engineering innovations in microdevices and nanomaterials. He has published over 55 peer-reviewed journal papers, including on journals such as Nature Biomedical Engineering, Science Advances, and Journal of the American Chemical Society. He also holds 16 patents and patent applications. Among other honors, he is the recipient of the NIH Director's New Innovator Award and the American Cancer Society’s Research Scholar Award.

Assistant Professor
Department of Electrical and Computer Engineering
Carnegie Mellon University

Atom-thick 2D Materials and Electronic Devices

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Abstract

In the past fifty years, micro- and nano-devices have been mostly based on materials of three-dimensional (3D) nature, such as silicon and compound semiconductors. The success in creating atomically thin two-dimensional (2D) crystals is pushing electronics into a new era. The excellent mechanical flexibility, transparency and the design freedom provided by the transfer techniques of 2D crystals make them ideal candidates for enabling electronic devices with features that would be impossible to achieve using transitionally 3D rigid and wafer-based technologies. One critical distinction between 2D crystals and 3D crystals is that 2D crystals are all-surface materials. Therefore, it is essential to understand how 2D materials interact with their environments and how this surface interaction can be utilized to engineer the structure and properties of 2D materials. In the first part of my talk, I will introduce a plasma-based surface functionalization approach which allows fine engineering of the interaction between 2D materials and functionalizing dopants. A combined spectroscopic technique reveals novel features of functionalized 2D materials. In the second part, I will show you how engineered 2D materials can transform traditionally rigid electronic building blocks into soft ones, from a system-level perspective. A special focus will be given to the flexible RF energy harvesting solution enabled by phase-engineered 2D materials, which exemplifies how the unique properties of 2D materials can translate into novel device architecture and extend the boundary of high-speed soft electronics.

Bio

Dr. Xu Zhang joined the Department of Electrical and Computer Engineering at Carnegie Mellon University as a tenure-track assistant professor in September 2019. He received his PhD and Master’s degrees in electrical engineering from Massachusetts Institute of Technology (MIT) and a bachelor’s degree in physics from University of Science and Technology of China (USTC). His research interests are to enable advanced nano-electronic devices and their system-level integration, by leveraging the unique properties of emerging nanomaterials and nanotechnologies. He serves as reviewer for scientific journals including Nature Electronics, Nature Nanotechnology, Physical Review B, IEEE Electron Device Letters, Advanced Optical Materials, ACS Applied Materials & Interfaces, physica status solidi (b), Applied Physics A, etc. Xu Zhang is a recipient of the MIT Presidential Fellow (2010), MIT Global Fellow (2014) and the Enrico Fermi Fellow (2018).

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

Safe, secure, and private intelligence: From the edge to the fog and cloud

Abstract

The fourth industrial revolution shaped by Machine Learning (ML) algorithms is underway. However, the widescale adoption of the emerging intelligent learning methodologies is hindered by security, privacy and safety considerations in sensitive scenarios such as smart transportation, health-care, warfare, and financial systems.

In this talk, I discuss our recent progress in devising automated end-to-end algorithms, hardware, and software co-design and acceleration of assured machine learning systems. The first part of the talk is focused on the latest advances in deep learning on encrypted data. I show how co-optimization of learning transformations, logic synthesis, hardware design, and secure computing introduce a paradigm shift in achieving scalable performance for privacy preserving computing at an unprecedented scale. Our results demonstrate a significant performance leap over the best prior art in private machine learning, and enable evaluation of the largest benchmarks ever reported in this field. The latter part of the presentation briefly outlines our recent results in Deep Learning IP protection, attestation, and thwarting of the adversarial attacks. Our suggested solutions are systematically customized and accelerated on various computing platforms using our holistic automated co-design methodology. I summarize by outlining the challenges and opportunities ahead.

Bio

Farinaz Koushanfar is a professor and Henry Booker Faculty Scholar in the Electrical and Computer Engineering (ECE) department at the University of California San Diego (UCSD), where she is the founding co-director of the UCSD Center for Machine-Integrated Computing & Security (MICS). Prof. Koushanfar received her Ph.D. in Electrical Engineering and Computer Science as well as her M.A. in Statistics/Machine learning from UC Berkeley. Her research addresses several aspects of efficient/secure computing, with a focus on safe and assured machine learning, hardware and embedded systems security, design automation, privacy-preserving computing, as well as real-time and explainable big data analytics under resource constraints.

Dr. Koushanfar is a fellow of IEEE, and a fellow of the Kavli Foundation Frontiers of the US National Academy of Engineering. She has received a number of awards and honors for her research, mentorship, teaching, and outreach activities including the Presidential Early Career Award for Scientists and Engineers (PECASE) from President Obama, the ACM SIGDA Outstanding New Faculty Award, Cisco IoT Security Grand Challenge Award, MIT Technology Review TR-35, Qualcomm Innovation Award, a number of Best Paper Awards, as well as Young Faculty/CAREER Awards from NSF, DARPA, ONR and ARO.

Assistant Professor
Department of Mechanical and Automation Engineering
Chinese University of Hong Kong – Shatin

Breaking the Resolution & Speed Limit – Next Generation Technology for Scalable Micro Additive Manufacturing

Abstract

Additive manufacturing printing, i.e., 3-D printing, is one of the most important technological innovations in the past few decades. Among the various techniques, two-photon polymerization (TPP) is the most precise 3-D printing process that has been used to create many complex structures for advanced photonic and nanoscale applications, e.g., microrobots, optical memories, metamaterials, photonic crystals, and bio-scaffolds etc. However, to date the technology still remains a laboratory tool due to its high operation cost and limited fabrication rate, i.e., serial laser scanning process. In this seminar, I will present our recent work on parallelization of the TPP process based on (1) temporal focusing and (2) binary hologram, where femtosecond light sheets or tens to hundreds of shaped laser beams are used to substantially improve the rate without sacrificing resolution. In addition, the engineered laser foci can improve the strength and structural integrity of the printed structures. Our experiments demonstrate arbitrarily complex structures can be fabricated at a record-breaking resolution and speed, i.e., lateral/axial resolution: 140 nm/175 nm at 10s mm3/min, which is 3-4 orders of magnitude higher than any existing fabrication methods. Our new methods provide an effective and low-cost solution to scale-up the fabrication of functional micro- and nano-structures (~$1.5/mm3). This means our technology may play a large role in fields such as healthcare, clean energy and water, computing, and telecommunications.

Bio

Prof. Shih-Chi Chen received his B.S. degree in Mechanical Engineering from the National Tsing Hua University, Taiwan, in 1999. He received his S.M. and Ph.D. degrees in Mechanical Engineering from the Massachusetts Institute of Technology, Cambridge, in 2003 and 2007, respectively. Following his graduate work, he entered a post-doctoral fellowship in the Wellman Center for Photomedicine, Harvard Medical School, where his research focused on biomedical optics and endomicroscopy. From 2009 to 2011, he was a Senior Scientist at Nano Terra, Inc., a start-up company founded by Prof. George Whitesides at Harvard University, to develop precision instruments for novel nanofabrication processes. Prof. Chen is presently an Associate Professor in the Department of Mechanical and Automation Engineering at the Chinese University of Hong Kong. His current research interests include ultrafast laser applications, biomedical optics, precision engineering, and nanomanufacturing. Prof. Chen is a member of the American Society for Precision Engineering (ASPE), American Society of Mechanical Engineers (ASME), SPIE, and the Optical Society (OSA). He received the prestigious R&D 100 Award in 2003 and 2018 for developing a six-axis nanopositioner and an ultrafast nanoscale 3-D printer respectively.

Associate Professor
Computer Science Department
University of Colorado - Boulder

Formal Synthesis of Controllers From Demonstrations

Abstract

Formal synthesis aims to construct provably correct controllers for physical plants specified by Ordinary Differential Equation (ODE) models for specifications such as safety, reachability, stability and robust path following. In this talk, we show how demonstrations from untrusted model predictive controllers (MPCs) can allow us to effectively learn controllers along with proofs of correctness under carefully stated assumptions. The approach proceeds by searching for proofs that a system can be controlled to satisfy a particular specification, and in turn, extracting implementations from the proofs. Theoretically, the proposed scheme can be made to converge in polynomial time using ideas from convex analysis. Finally, we demonstrate our approach using a series of simulation and laboratory experiments carried out on a 1/8-th scale model autonomous car.

Joint Work with Hadi Ravanbakhsh, Sina Aghli, Christoffer Heckman and Sanjit Seshia.

Bio

Sriram Sankaranarayanan is an associate professor of Computer Science at the University of Colorado, Boulder. His research interests include automatic techniques for reasoning about the behavior of computer and cyber-physical systems. Sriram obtained a PhD in 2005 from Stanford University where he was advised by Zohar Manna and Henny Sipma. Subsequently he worked as a research staff member at NEC research labs in Princeton, NJ. He has been on the faculty at CU Boulder since 2009. Sriram has been the recipient of awards including the President's Gold Medal from IIT Kharagpur (2000), Siebel Scholarship (2005), the CAREER award from NSF (2009), Dean's award for outstanding junior faculty (2012), outstanding teaching (2014), and the Provost's faculty achievement award (2014).

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