ESE Colloquia & Events

Spring 2019

ESE colloquia are held from 11-12:00pm in Towne 337, unless otherwise noted. For all Penn Engineering events, visit the Penn Calendar.

To be added to the ESE Events mailing list (which sends notifications regarding all departmental colloquia, seminars, and events) please email us at

Thursday, January 17th
Walid Saad, Virginia Tech
Associate Professor, Electrical and Computer Engineering
"Towards a Seamless Integration of Drones in Smart Cities: Communications and Security"

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Abstract: The use of unmanned aerial vehicles (UAVs), popularly known as drones, will be an integral component of emerging smart city applications ranging from delivery of goods to flying taxis. However, a seamless deployment of such drone-based applications requires addressing technical challenges across communications, security, autonomy, and control. In this talk, we focus on the wireless communications and security challenges of drone-based systems. From a communications perspective, UAVs can assume two roles: aerial base stations that enhance the coverage and capacity of wireless networks and flying users that require wireless cellular connectivity for enabling applications such as real-time streaming and item delivery. With this in mind, we introduce a foundational framework for designing three-dimensional (3D) wireless cellular networks that incorporate both drone base stations and cellular-connected drone users. For this novel 3D model, we study a number of key problems including drone deployment, network planning, and cell association. Then, we turn our attention to the cyber-physical security challenges brought forward by the deployment of drones. In this area, we present a holistic framework, with foundations in behavioral game theory, for addressing fundamental cyber-physical security problems pertaining to drone-based systems. In particular, we show how notions of risk, bounded rationality, and uncertainty can influence the security of drone-based systems and we develop new game-theoretic solutions that explicitly account for such factors in security analyses. We conclude by an overview on our ongoing research activities that cut across the areas of cyber-physical systems, wireless networks, game theory, machine learning, security, and control.

Biography: Walid Saad (S’07, M’10, SM’15, F’19) received his Ph.D degree from the University of Oslo in 2010. He is currently an Associate Professor at the Department of Electrical and Computer Engineering at Virginia Tech, where he leads the Network Science, Wireless, and Security laboratory. His research interests include cyber-physical systems, wireless networks, machine learning, game theory, security, unmanned aerial vehicles, and network science. Dr. Saad is a Fellow of the IEEE and an IEEE Distinguished Lecturer. He is also the recipient of the NSF CAREER award in 2013, the AFOSR summer faculty fellowship in 2014, and the Young Investigator Award from the Office of Naval Research (ONR) in 2015. He was the author/co-author of seven conference best paper awards at WiOpt in 2009, ICIMP in 2010, IEEE WCNC in 2012, IEEE PIMRC in 2015, IEEE SmartGridComm in 2015, EuCNC in 2017, and IEEE GLOBECOM in 2018. He is the recipient of the 2015 Fred W. Ellersick Prize from the IEEE Communications Society, of the 2017 IEEE ComSoc Best Young Professional in Academia award, and of the 2018 IEEE ComSoc Radio Communications Committee Early Achievement Award. From 2015-2017, Dr. Saad was named the Stephen O. Lane Junior Faculty Fellow at Virginia Tech and, in 2017, he was named College of Engineering Faculty Fellow. He currently serves as an editor for the IEEE Transactions on Wireless Communications, IEEE Transactions on Communications, IEEE Transactions on Mobile Computing, and IEEE Transactions on Information Forensics and Security.

Tuesday, January 22nd
Fengnian Xia, Yale
Associate Professor, School of Engineering and Applied Science
"Efficient Mid-Infrared Photodetection Using Graphene Plasmons at Room Temperature"

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Abstract: In the history of materials development, many classic materials (Si, III-Vs, organics, etc.) which can be produced reliably at large scale eventually have found critical applications after decades of intensive research, leveraging their distinctive properties. For example, silicon dominates the field-effect-transistor technology because perfect dielectric-silicon interface can be readily realized. Organic materials are currently widely used in flat-panel displays, because of their great light emitting properties and the availability of cost-effective production techniques. In this talk, I will first discuss the unique properties of graphene, the first two-dimensional material isolated about 15 years ago, including ultralow heat capacity, high mobility, and weak electron-phonon coupling strength. Leveraging these unique properties, I will then present an efficient mid-infrared photodetector based on graphene plasmons operational at room temperature. Since high-quality wafer-scale graphene can already be produced routinely, such efficient mid-infrared photodetectors may find applications in high-speed thermal imaging and free-space communications.

Biography: Fengnian Xia received the B.E. degree with the highest honor in electronics engineering from Tsinghua University, Beijing, China and Ph.D. degrees in electrical engineering from Princeton University, Princeton, NJ, USA. He held postdoc, engineer and research staff positions in IBM Thomas J. Watson research center in Yorktown Heights, NY, USA from 2005 to 2013. He joined Yale University in 2013 and he is currently the Barton L. Weller associate professor in engineering and science at Department of Electrical Engineering. He explores the light-matter interaction and quantum transport in low-dimensional materials and identifies their potential applications in computing, flexible electronics, imaging, optical communications, and energy harvesting.
Professor Xia’s honors include the National Science Foundation CAREER award (2016), the Office of Naval Research Young Investigator Award (2015), the IBM Pat Goldberg Memorial Best Paper Award (2014), the TR35 Award, MIT Technology Review’s Top Young Innovators under 35 (2011), the IBM Corporate Award, that corporation’s highest technical honor (2012), and the designation of the Weller Junior Professorship in Engineering and Science by Yale President in October 2015.

Thursday, January 24th
The Jack Keil Wolf Lecture in Electrical and Systems Engineering
Shuji Nakamura, University of California Santa Barbara
Professor, Materials and Electrical and Computer Engineering
Director, Solid State Lighting and Energy Center of UCSB
"The Invention of High Efficient Blue LEDs and Future Solid State Lighting"
3:00pm, Singh Center Glandt Forum. Reception to follow.

Read the Abstract and Bio

Bio: Shuji Nakamura received a Ph.D. in Electrical Engineering from the University of Tokushima, Japan and worked at Nichia Chemical Industries from 1979 to 2000, after which he became a professor at UCSB. During his time at Nichia, he independently researched and ultimately demonstrated group-III nitride materials as possible blue light emitters.

Instrumental to his success was the development of a novel two-flow MOCVD, in 1990. This tool permitted him to obtain and explain p-type GaN while demonstrating the first high-quality InGaN layers, in 1992. This material was integrated into novel device structures, providing the bright violet, blue, or green LEDs and lasers that are known today. High-efficiency white light sources using these blue LEDs became available in 1996 and have since become the most efficient white light source known to man, changing the world forever. This achievement led him to be awarded the Nobel Prize in Physics in 2014.

Dr. Nakamura has received numerous awards for his work, including the Nishina Memorial Award (1996), the Materials Research Society Medal Award (1997), the Institute of Electrical and Electronics Engineers Jack A. Morton Award, the British Rank Prize (1998), the Benjamin Franklin Medal Award (2002), the Millennium Technology Prize (2006), the Czochralski Award (2007), the Prince of Asturias Award for Technical Scientific Research (2008), The Harvey Award (2009), and the Technology & Engineering Emmy Award (2012) awarded by The National Academy of Television Arts & Sciences (NATAS). He was elected a fellow of the U.S. National Academy of Engineering in 2003. He is the 2014 Nobel Laureate in Physics for the invention of efficient blue light-emitting diodes which has enabled bright and energy-saving white light sources. Dr. Nakamura received the 2014 Order of Culture Award in Japan. In 2015, he was inducted into the National Inventors Hall of Fame and received the 2015 Charles Stark Draper Prize for Engineering and the 2015 Global Energy Prize in Russia. He is also the recipient of The Mountbatten Medal Achievement Award in England (2017) and the 2018 Zayed Future Energy Prize Lifetime Achievement in the United Arab Emirates.

Since 2000, Dr. Nakamura has been a professor of Materials and Electrical and Computer Engineering at the University of California, Santa Barbara. He holds more than 200 U.S. patents and over 300 Japanese patents. He has published over 550 papers in his field. Dr. Nakamura is the Research Director of the Solid State Lighting and Energy Electronics Center and The Cree Chair in Solid State Lighting and Displays. In 2008, he co-founded Soraa, Inc., which operates vertically integrated fabrication facilities in California’s Silicon Valley and Santa Barbara.

Friday, January 25th
Lisa Wu, UC Berkeley
Postdoctoral Researcher, Electrical Engineering and Computer Science
"Hardware Acceleration in the World of Emerging Applications"
11:00am, Berger Auditorium
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Abstract: Semiconductor technology scaling coming to a screeching halt coupled with the explosion of data in almost every facet of our lives makes processing large volumes of data efficiently a critical problem to solve. In this talk, I will highlight three main challenges in designing accelerators and demonstrate that domain-specific hardware acceleration and specialization can provide orders of magnitude in compute efficiency for emerging applications. I will introduce the concept of datatype acceleration, where hardware primitives are designed to directly operate on already-defined software data structures and data containers, and show that specializing both the compute and memory subsystem provides orders of magnitude improvements in performance and energy efficiencies. Creating specialized encapsulated data accesses and datapaths allows us to mitigate unnecessary data movement, take advantage of traditional optimization techniques such as data and pipeline parallelism, and consequently provide substantial energy savings while obtaining significant performance gains. As case studies for three emerging application domains, I will briefly touch on accelerating database and graph analytics while offering in-depth examples in accelerating genomic analytics on the AWS EC2 F1 instances. As a vision for future hardware acceleration research, I will demonstrate how to create an ecosystem that makes designing, deploying, and using custom hardware almost as easy as writing and using software.

Bio: Lisa Wu is a postdoctoral researcher at University of California, Berkeley. Prior to joining UC Berkeley, she was a research scientist at Intel Labs. Her research interests include computer architecture and microarchitecture, accelerators, hardware-software co-design, energy-efficient computing, and emerging applications related to big data such as database and graph analytics, and healthcare such as genomics analytics for precision medicine. Lisa has a PhD in computer science from Columbia University, a MS in computer science and engineering from University of Michigan Ann Arbor, and a BS in electrical and computer engineering from University of Illinois Urbana-Champaign. Prior to pursuing her doctorate, she was a computer and performance architect at Intel for many years, architecting various Xeon and IPF server processors including leading the Xeon Phi Vector Processing Unit architecture.

Thursday, February 7th
Jing Li, University of Wisconsin-Madison
Assistant Professor, Electrical and Computer Engineering
"Liquid Silicon: A New Computing Paradigm Enabled by Monolithic 3D Cross-Point Memory"
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Abstract: Almost every subfield of electrical engineering and computer science are undergoing disruptive times. With Moore's Law coming to an end, an expanded roadmap for semiconductors beyond traditional CMOS scaling becomes unclear. At the other end, traditional application software development is being replaced by emerging machine learning techniques whose success will, in turn, rely on the availability of powerful, efficient and flexible computer systems. Due to these emerging applications, architecture is transitioning from mainstream CPU to heterogeneous and diverse options such as GPU, TPU, etc. The confluence of these key trends has created a wide efficiency gap, due to the mismatch between emerging application requirements and the relatively slow evolutionary improvements in existing CMOS-based computer hardware.
To close the gap, in this talk, I will present a reconfigurable memory-oriented computing fabric, namely Liquid Silicon (L-Si) by leveraging the monolithic 3D stacking capability of RRAM. L-Si addresses several key fundamental limitations of state-of-the-art reconfigurable architectures including FPGA, etc. in supporting emerging data-/search-intensive applications (e.g., machine learning and neural networks) through a series of innovations. It, for the first time, extends the configuration capabilities of existing reconfigurable architectures (FPGA, CGRA) from computation to the whole spectrum, from full memory to full computation, or intermediate states in between (partial memory and partial computation). Thus, it allows users more flexibility in customizing hardware to better match an application’s characteristics, for higher performance and energy efficiency. The talk will consist of four parts, technology, architecture, compiler tool, and algorithm, with a combined EE and CS flavor.

Biography: Jing (Jane) Li is a Dugald C. Jackson Assistant Professor in the Department of Electrical and Computer Engineering at the University of Wisconsin – Madison. She is also affiliated with the Computer Science department. Her research interests include software/hardware co-design for both legacy and emerging applications, with a strong emphasis on real hardware demonstration through architecting, designing, fabricating and testing new hardware prototypes both at the chip level and system level. She is the recipient of NSF Career Award in 2018, DARPA's Young Faculty Award in 2016, IBM Research Division Outstanding Technical Achievement Award in 2012 for successfully achieving CEO milestone, multiple invention achievement awards and high-value patent application awards from IBM from 2010-2014, etc. She spent her early career at IBM T. J. Watson Research Center as a Research Staff Member after obtaining her Ph.D. degree from Purdue University in 2009.

Friday, February 8th
Gal Mishne, Yale University
Assistant Professor, Applied Math
"Local Geometric Spectral Data Analysis"
11:00am, Berger Auditorium

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Abstract: Modern technological developments have enabled the acquisition and storage of increasingly large-scale, high-resolution, and high-dimensional data in many fields. Yet in domains such as biomedical data, the complexity of these datasets and the unavailability of ground truth pose significant challenges for data analysis and modeling. In this talk, I present new unsupervised geometric approaches for extracting structure from large-scale high-dimensional data. By looking deep within the spectrum of the graph-Laplacian, we define a new robust measure, the Spectral Embedding Norm, to separate clusters from background, and demonstrate its application to both outlier detection and data visualization. This measure further motivates a new greedy clustering approach based on Local Spectral Viewpoints for identifying high-dimensional overlapping clusters while disregarding noisy clutter. We demonstrate our approach on two-photon calcium imaging data, successfully extracting hundreds of individual cells. Finally, to address the computational complexity of applying spectral approaches to large-scale data, we present a new randomized near-neighbor graph construction. Compared to the traditional k-nearest neighbor graph, using our near-neighbor graph for spectral clustering on datasets of a few million points is two orders of magnitude faster, while achieving similar clustering accuracy.

Joint work with Ronald Coifman, Jackie Schiller, Maria Lavzin, Xiuyuan Cheng, George Linderman, Ariel Jaffe, Yuval Kluger and Stefan Steinerberger.

Biography: Gal Mishne is a Gibbs Assistant Professor in the Applied Mathematics program at Yale University, working with Ronald Coifman. She received her Ph.D. in Electrical Engineering in 2017 from the Technion, advised by Israel Cohen. She holds B.Sc. degrees (summa cum laude) in Electrical Engineering and Physics from the Technion, and upon graduation worked as an image processing engineer for several years.

Friday, February 15th
Jeremy Munday, University of Maryland
Associate Professor, Electrical and Computer Engineering
Institute for Research in Electronics and Applied Physics
"Engineering the Quantum Vacuum"
9:30am, Wu and Chen Auditorium
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Abstract: The vacuum of space may seem empty and boring; however, this void is actually teeming with activity. According to the laws of quantum mechanics, fluctuations of electromagnetic fields are omnipresent even in empty space. These fluctuations can manifest themselves in a variety of ways, including the generation of nanoscale forces between objects—a phenomenon known as the Casimir effect. In this talk, I will discuss our development of novel measurement techniques to probe these interactions and how we can engineer and control such quantum effects for useful devices. I will demonstrate our ability to tailor the sign and magnitude of the force, as well as how we can induce rotations (i.e. a Casimir torque) between optically birefringent materials. Beyond interesting science, our ability to control these interactions will give us new opportunities for nanoscale devices and to modify chemistry and electronics in ways not previously possible. Finally, I will briefly outline a few additional research areas from our lab related to novel optical phenomena, materials, and devices.

Biography: Dr. Jeremy N. Munday is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Maryland, College Park. He received his PhD in Physics from Harvard University and was a postdoctoral scholar at Caltech prior to his appointment at Maryland. His research themes range from quantum electromechanical phenomena (such as the Casimir effect) to fundamental solar energy conversion processes with an emphasis on the optics, photonics, and thermodynamics of such systems. He is a recipient of the DARPA Young Faculty Award, the NSF CAREER Award, the ONR Young Investigator Program Award, the OSA Adolph Lomb Medal, the IEEE Photonics Society Young Investigator Award, the SPIE Early Career Achievement Award, and the NASA Early Career Faculty Space Technology Research Award.

Tuesday, February 19th
Mohammad Mirhosseini, Caltech
Kavli Postdoctoral Fellow, Kavli Nanoscience Institute
"Hybrid Quantum Networks: Interfacing Photons, Phonons, and Superconducting Qubits"
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Abstract: Quantum information science strives to utilize the fundamental laws of physics to achieve revolutionary improvement in computation, communication, and sensing. Existing quantum protocols rely on a wide variety of physical platforms for storing, transferring, and processing of quantum information. Optical photons are the ideal carriers of information because of their low loss, large bandwidth of transmission, and resilience to thermal noise. However, the task of processing quantum information is exceedingly difficult to achieve in the optical domain because of the weakness of optical
nonlinearities. Alternatively, superconducting quantum circuits provide a scalable means of storing and processing quantum information in the microwave regime, but lack a mechanism for long-range information transfer.

Hybrid quantum networks promise to combine such essential functionalities in a system where superconducting processing nodes are joined via optical communication links. An integral element in this architecture is a quantum interconnect capable of interfacing the electrical and optical components across an immense frequency gap. I provide a summary of my past and current research on optical and microwave quantum systems, and outline my future research directions, which aim to develop nano-engineered devices for entangling superconducting qubits with telecom-band optical photons and long-lived phonons.

Biography: Mohammad Mirhosseini is currently a Kavli Postdoctoral scholar at the California Institute of Technology working in Oskar Painter’s group. He received his PhD in 2016 from the Institute of Optics at the University of Rochester, where he was advised by Robert W. Boyd. His doctoral research studied quantum information with structured light and was recognized by the Carl E. Anderson award from the American Physical Society and the Emil Wolf prize from the Optical Society of America. His postdoctoral research aims at entangling distant superconducting qubits and developing integrated devices for interfacing qubits with traveling photons.

Thursday, February 21st
John Murray-Bruce, Boston University
Postdoctoral Research Associate, Computational Imaging
"Physics-driven Sensing and Processing: From Computational Periscopy to Particle Beam Microscopy"
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Abstract: In many areas of science and engineering, novel signal acquisition methods allow unprecedented access to physical measurements. From digital cameras to microscopes and nano-scale biosensors, the data generated are shaped by both the underlying physics of the phenomena and characteristics of the acquisition device. Meanwhile, in many practical scenarios, the useful signals are remarkably weak, the measurements sparse, or even the acquisition process itself may damage the observed sample. These realities therefore necessitate the development of techniques that combine signal processing with physics-driven modelling to transcend current capabilities and enable, for instance: imaging of hidden scenes (or computational periscopy), the algebraic inversion of physical fields, and the reduction of sample damage in particle beam microscopy.
This concept of combining physics with signal processing will be the main theme of my talk. First, I will show that computational periscopy with ordinary digital cameras can be made possible by judiciously exploiting the physics of light transport to analyze subtle shadows, in a photograph of a visible surface. Second, I will briefly present an algebraic inversion method for fields constrained by partial differential equations and highlight its application to load-balancing in processors. Finally, by developing a detailed model and analysis for particle beam microscopy, I will show how introducing time-resolution into the acquisition process can significantly reduce beam dose, and sample damage, without compromising on imaging quality.

Biography: Dr. Murray-Bruce is currently a postdoctoral researcher with the electrical and computer engineering department at Boston University. Prior to that, he was at Imperial College London, where he received the M.Eng. degree in 2012 and the Ph.D. degree supervised by Prof. Pier Luigi Dragotti in December 2016, both in electrical and electronic engineering. Whilst at Imperial, he was awarded the Maurice Hancock Prize in 2008, and the Institute of Engineering and Technology (IET) Prize for 'best all-round performance' in 2012.

Tuesday, February 26th
Maiken H. Mikkelsen, Duke University
Assistant Professor, Electrical and Computer Engineering and Physics
"New Designer Materials: Sculpting Electromagnetic Fields on the Atomic Scale"
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Abstract:New optical nanomaterials hold the potential for breakthroughs in a wide range of areas from ultrafast optoelectronics such as modulators, light sources and hyperspectral detectors, to efficient upconversion for energy applications, bio-sensing and quantum information science. An exciting opportunity to realize such new nanomaterials lies in controlling the local electromagnetic environment on the atomic- and molecular-scale (~1-10 nm), which enables extreme local field enhancements. We use creative nanofabrication techniques at the interface between chemistry and physics to realize this new regime together with ultrafast optical techniques to probe the emerging phenomena. Here, I will provide an overview of our recent research including high-speed thermal photodetectors, ultrafast spontaneous emission and metasurface-enhanced biosensors.

Biography: Maiken H. Mikkelsen is the James N. and Elizabeth H. Barton Associate Professor at Duke University in the Departments of Physics, Electrical & Computer Engineering, and, by courtesy, Mechanical Engineering & Materials Science. Currently, she is a Visiting Associate Professor at Stanford University in the Department of Materials Science & Engineering. She received her B.S. from the University of Copenhagen in 2004, her Ph.D. in Physics from the University of California, Santa Barbara in 2009 and was a postdoctoral fellow at the University of California, Berkeley. Her research focuses on nanophotonics and quantum materials to enable transformative breakthroughs for optoelectronics, the environment and human health. Her awards include the Maria Goeppert Mayer Award from the American Physical Society, the Early Career Achievement Award from SPIE, the NSF CAREER award, the Cottrell Scholar Award from the Research Corporation for Science Advancement and Young Investigator Program Awards from the ONR, ARO and AFOSR.

Thursday, February 28th
Negar Mehr, UC Berkeley
Research Assistant, Mechanical Engineering
"Towards Socially-Aware Autonomy for Mobility-Efficient Smart Cities"
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Abstract: As cities grow everywhere, and urban roadways become overburdened, efficient strategies are required for improving mobility. With the prevalence of smart sensing and Internet of Things (IoT) devices, such as smart phones and smart intersections, the physical infrastructures of our cities are being connected to the cyber world. As a result, cities are becoming smart. Moreover, with the emergence of new and inevitable technologies, such as autonomous and connected vehicles, mobility on demand systems, and electric vehicles, smart cities are rapidly evolving. As we experience the arrival of such technologies, there is an opportunity to reclaim urban mobility. However, a blind utilization of these technologies may deflect us from reaching this goal. In my research, I leverage the connectivity that is inherent in smart cities as well as the opportunities that new technologies such as autonomous and connected vehicles provide, to study the efficient operation of smart cities via management strategies that can guarantee overall societal benefits.

In this talk, I will focus on the societal-scale mobility implications of the increased deployment of autonomous and connected vehicles in mixed-autonomy traffic networks, where both human-driven and autonomous vehicles will coexist on the roads. I will first talk about the mobility implications of selfish autonomy, in which autonomous cars are not aware of their overall impact and simply attempt to optimize their own travel benefits. In this context, I will introduce conditions under which an increase in the fraction of autonomous vehicles on a traffic network, even when operating selfishly, results in increased societal mobility benefits. Conversely, I will show that if these conditions do not hold, overall network mobility may degrade as the fraction of autonomous vehicles increases. Having shown the negative consequences that the increased deployment of autonomous and connected vehicles may have on the operation of traffic networks, I will further discuss the use of traffic management strategies, such as pricing, which can guarantee the overall societal-scale efficiency of traffic networks with mixed vehicle autonomy.

Biography: Negar Mehr is a PhD candidate in the Department of Mechanical Engineering at UC Berkeley. She received her B.Sc. in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, in 2013. Her research interests lie in the intersection of control theory, game theory, and intelligent transportation systems. Specifically, she works on developing reliable and efficient solutions that can ensure efficient operation of societal-scale infrastructures such as transportation systems. Negar was the co-recipient of the first prize for the best student paper award at the International Conference on Intelligent Transportation Systems, 2016. She was also the graduate winner of the 2017 WTS-OC (Women Transportation Seminars-Orange County Chapter) scholarship. She is the recipient of several departmental fellowships including the Chang-Lin Tien graduate fellowship, the Oakley & Barratt Family graduate fellowship, the Graduate Division Block Grant award, and the Eltoukhy East-West Gateway fellowship. Negar was recognized as a rising star in EECS, Aeronautics & Astronautics, and Civil and Environmental Engineering.

Friday, March 1st
Shuo Sun, Stanford University
Postdoctoral Research Fellow, Electrical Engineering
"Quantum Nanophotonics: Engineering Atom-Photon Interactions on a Chip"
11:00am, Berger Auditorium
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Abstract:The ability to engineer controllable atom-photon interactions is at the heart of quantum optics and quantum information processing. In this talk, I will introduce a nanophotonic platform for engineering strong atom-photon interactions on a semiconductor chip. I will first discuss an experimental demonstration of a spin-photon quantum transistor [1], a fundamental building block for quantum repeaters and quantum networks. The device allows a single spin trapped inside a semiconductor quantum dot to switch a single photon, and vice versa, a single photon to flip the spin. I will discuss how the spin-photon quantum transistor realizes optical nonlinearity at the fundamental single quantum level, where a single photon could switch the transmission of multiple subsequent photons [2]. I will next discuss the promise of realizing photon-mediated many-body interactions in an alternative solid-state platform based on a more homogeneous quantum emitter, silicon-vacancy (SiV) color centers in diamond. I will introduce our efforts in creating strong light-matter interactions through photonic crystal cavities fabricated in diamond [3], and the use of cavity-stimulated Raman emission to overcome the remaining frequency inhomogeneity of the emitters [4]. Finally, I will outline the exciting prospects of applying inverse designed nanophotonic structures into quantum optics, and their potential applications in engineering photon-mediated atom-atom interactions.

[1] S. Sun et al., Nature Nanotech. 11, 539–544 (2016).
[2] S. Sun et al., Science 361, 57-60 (2018).
[3] J. L. Zhang* and S. Sun* et al., Nano Lett. 18, 1360–1365 (2018).
[4] S. Sun et al., Phys. Rev. Lett. 121, 083601 (2018)

Biography: Shuo Sun obtained his PhD in 2016 from the University of Maryland, College Park while working with Professor Edo Waks in the Department of Electrical and Computer Engineering and the Joint Quantum Institute (JQI). His was awarded for the Maiman grand prize from the Optical Society of America and the distinguished dissertation award from the ECE department of the University of Maryland for his pioneering work on experimental demonstration of a nanophotonic spin-photon quantum transistor. He was also nominated as 1 out of the 4 finalists for the Carl E. Anderson Dissertation Award from the American Physics Society. In 2017, he joined Stanford University as a postdoctoral research scholar, working with Professor Jelena Vuckovic in the Ginzton Laboratory.

Thursday, March 7th
Clarice Aiello, Stanford University
Life Sciences Research Foundation/Moore Foundation Postdoctoral Research Fellow, Bioengineering Department
"From Nanotech to Living Sensors: Unraveling the Spin Physics of Biosensing at the Nanoscale"
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Abstract: I am a quantum engineer interested in how quantum physics informs biology at the nanoscale.

As a physicist, I have developed high-performance nanosensors that essentially worked due to room-temperature quantum effects in noisy environments. Currently, I am focusing on “living sensors” -- organisms and cells that respond to minute stimuli, routinely outperforming technological probes in awe-inspiring ways. Unveiling and controlling the underlying physical mechanisms employed by “living sensors” impact: the engineering of ultrasensitive, bio-inspired electromagnetic probes; the elucidation of mesmerizing natural feats such as animal navigation; and the advancement of therapeutics for metabolic-related diseases.

Substantial in vitro and physiological experimental results are consistent with the fact that similar spin physics might underlie biosensing modalities as varied as organismal magnetic field detection and metabolic regulation of oxidative stress in cells.

Can spin physics be established -- or refuted! -- to account for physiologically relevant biosensing phenomena, and be manipulated to technological and therapeutical advantage? This is the broad, exciting question that I wish to address in my scientific career.

Biography: Clarice D. Aiello is a quantum engineer born and raised in Brazil. She trained as an experimental physicist in Europe, having earned a Diplome d’Ingenieur de l’Ecole Polytechnique in France, and an M.Phil. from the University of Cambridge, Trinity College, in England.

Research brought Clarice to the American shore. She completed her Ph.D. in Electrical Engineering at MIT with Prof. Paola Cappellaro. Her work has been funded by sources as diverse as the Fulbright Commission, the Schlumberger Foundation and UNESCO. Clarice is also a recipient of MIT’s School of Engineering’s “Graduate Student Award for Extraordinary Teaching and Mentoring”.

Clarice then undertook postdoctoral research with Prof. Naomi Ginsberg, in the Chemistry Department of the University of California at Berkeley. Currently, Clarice is a Life Sciences Research Foundation/Moore Foundation postdoctoral fellow with Prof. Manu Prakash, in Stanford University’s Bioengineering Department.

She has recently been chosen as a “Rising Star in Physics”, and intends to invest her interdisciplinary training to investigate how quantum physics informs biology at the nanoscale.

Friday, March 8th
Nima Fazeli, MIT
Graduate Research Assistant and PhD Candidate, Robotics and Manipulation
"Towards Robotic Manipulation – Understanding the World Through Contact"
11:00am, Berger Auditorium
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Abstract: Why is robotic manipulation so hard? As humans, we are unrivaled in our ability to dexterously manipulate objects and exhibit complex skills seemingly effortlessly. Recent research in cognitive science suggests that this ability is driven by our internal representations of the physical world, built over a life-time of experience. Our predictive ability is complemented by our senses of sight and touch, intuitive state-estimation, and tactile dexterity. Given the complexity of human reasoning, skill, and hardware, it is not surprising that we have yet to replicate our abilities in robots. In order to bridge this gap, we must take a holistic perspective on manipulation and build robotic systems that understand and interpret their physical world through contact.

In this talk, I will present two methodologies that strive to this end: First, a physics-based
methodology for the inference of contact forces and system parameters of rigid-bodies systems making and breaking contact. Second, how a robot can learn the physics of playing Jenga using a hierarchical-learning methodology purely from data. I will conclude the talk by touching upon data-augment contact models and providing perspectives on building robotic systems that embody intelligent manipulation.

Biography: Nima Fazeli is a PhD student with the Mechanical Engineering Department at MIT, working with Prof. Alberto Rodriguez. His research focuses on enabling intelligent and dexterous robotic manipulation by developing novel tools combining analytical methods, machine learning, and cognition/AI. During his PhD, Nima has developed inference algorithms for robotic systems undergoing frictional contact, performed empirical evaluations of contact models, demonstrated data-augmented contact models for manipulation, and developed a robotic system capable of learning the physics of playing Jenga using a hierarchical learning methodology. Nima received his masters from the University of Maryland at College Park where he spent most of his time developing analytical and data-driven models of the human (and, on occasion, swine) arterial tree together with novel inference algorithms to diagnoses cardiovascular diseases. His research has been supported by the Rohsenow Fellowship and featured in outlets such as CBS, CNN, and the BBC. He looks forward to robots playing and learning alongside his grandchildren.

Tuesday, March 12th
Mahsa Shoaran, Cornell University
Assistant Professor, Electrical and Computer Engineering
"Ultra-Low-Power Neural Interfaces: from Monitoring to Diagnosis and Therapy"
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Abstract: Implantable and wearable medical devices are increasingly being developed as alternative therapies for intractable diseases. In particular, undertreated neurological disorders such as epilepsy, migraine, and Alzheimer’s disease are of major public health concern around the world, driving the need to explore such new approaches. Despite significant advances in neural interface systems, the small number of recording channels in existing technology remains a barrier to their therapeutic potential. This is mainly due to the fact that simultaneous recording from a large number of electrodes imposes stringent energy and area constraints on the integrated circuits that interface with these electrodes. In this talk, I will first discuss an efficient compressive sensing framework for multichannel cortical implants. Next, I will present the design of our sub-microwatt per channel closed-loop seizure control device and both its in-vivo and offline performance. I will then discuss our latest work on the integration of machine learning algorithms for on-chip classification of neural data. Finally, I will give examples of how these results may be used towards designing new devices, to enhance the lives of millions of people suffering from disabling neurological conditions in future.

Biography: Mahsa Shoaran is currently an Assistant Professor in the School of Electrical and Computer Engineering at Cornell University. Prior to joining Cornell, she was a postdoctoral fellow in Electrical Engineering and Medical Engineering at the California Institute of Technology. She received her PhD from EPFL in 2015 and her B.Sc. and M.Sc. from Sharif University of Technology. Her research interests broadly include circuit, system, and algorithm design for diagnostic and therapeutic applications. Mahsa is a recipient of the 2019 Google Faculty Research Award, the Early and Advanced Swiss National Science Foundation Postdoctoral Fellowships, and the NSF Award for Young Professionals Contributing to Smart and Connected Health. She was named a Rising Star in EECS by MIT in 2015.

Thursday, March 14th
Christopher Torng, Cornell University
Ph.D. Candidate, Electrical and Computer Engineering
"Software, Architecture, and VLSI Co-Design for Efficient Task-Based Parallel Runtimes"
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Abstract: Fast-paced changes across the computing stack are creating opportunities for innovation by bridging software, architecture, and VLSI. Cross-cutting research is challenging, but it can expose key insights that would otherwise be hidden by abstractions. In this talk, I will demonstrate a cross-stack approach to improve the efficiency of task-based parallel runtimes, which are important because they underpin the parallelization of state-of-the-art graph analytics and machine learning frameworks. Shifting the focus downward, I will discuss a cross-stack approach that addresses key circuit-level challenges in integrated voltage regulation. To finish the talk, I will discuss my future plans to apply a cross-stack research approach to expand beyond the perceived limits of intelligence on the edge and also to decrease the challenges of complex ASIC design with hardware design techniques based on Lego-like tiling.

Biography: Christopher Torng is a Ph.D. student at Cornell University in the School of Electrical and Computer Engineering, where he also received his B.S. degree. He builds specialized architectures that tie together software with the underlying technology. His activities have resulted in a selection as a Rising Star in Computer Architecture (2018) by Georgia Tech and an IEEE MICRO Top Pick from Hot Chips (2018). He has also been involved with six research test chips that support his research, and he was the project or university lead for three of the chips. In his spare time, Chris enjoys figure skating on the ice.

Friday, March 15th
Harish Krishnaswamy, Columbia University
Associate Professor, Electrical Engineering
"Confluence of Electromagnetics, Circuits and Systems Enables The Third Wireless Revolution"
11:00am, Berger Auditorium
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Abstract: Integrated circuits have fueled several revolutions that have deeply impacted modern society, including the computing revolution, the internet and the first two wireless revolutions. We are at the dawn of the third wireless revolution, which I call the Wireless Mobile Reality revolution. Over the next fifteen years, new wireless paradigms spanning from radio frequencies to millimeter-waves and terahertz will change the way in which we interact with the real world, through applications such as mobile virtual and augmented reality, vision quality imaging, gesture recognition and bio- and materials-sensing.

However, at the same time, integrated circuits are starting to run out of steam - technology scaling is no longer yielding better transistors that are faster and lower power. Therefore, circuit design needs to be refreshed with new tools and techniques that draw inspiration from the layers below (electromagnetics and device physics) and the layers above (communication systems and networking).

In this talk, I will describe research along these lines from the CoSMIC lab at Columbia University. I will start by describing a new approach to breaking Lorentz reciprocity to engineer high-performance non-reciprocal components, such as gyrators, isolators and circulators. I will then talk about how these integrated non-reciprocal circulators enable practical integrated full-duplex wireless radios. Finally, I will talk about the FlexICoN project at Columbia which is taking a holistic and cross-layer view of full-duplex networks from the physical layer to the networking layer. I will also briefly touch upon other work from CoSMIC lab in the same vein related to high-power, high-efficiency millimeter-wave radios, MIMO radios, opto-electronic LIDARs and city-scale wireless testbeds.

Biography: Harish Krishnaswamy (S’03–M’09) received the B.Tech. degree in electrical engineering from IIT Madras, Chennai, India, in 2001, and the M.S. and Ph.D. degrees in electrical engineering from the University of Southern California (USC), Los Angeles, CA, USA, in 2003 and 2009, respectively. In 2009, he joined the Electrical Engineering Department, Columbia University, New York, NY, USA, where he is currently an Associate Professor and the Director of the Columbia High-Speed and Millimeter-Wave IC Laboratory (CoSMIC).

In 2017, he co-founded MixComm Inc., a venture-backed startup, to commercialize CoSMIC Laboratory’s advanced wireless research. His current research interests include integrated devices, circuits, and systems for a variety of RF, mmWave, and sub-mmWave applications.

Dr. Krishnaswamy was a recipient of the IEEE ISSCC Lewis Winner Award for Outstanding Paper in 2007, the Best Thesis in Experimental Research Award from the USC Viterbi School of Engineering in 2009, the DARPA Young Faculty Award in 2011, a 2014 IBM Faculty Award, the Best Demo Award at the 2017 IEEE ISSCC, Best Student Paper Awards (First Place) at the 2015 and 2018 IEEE Radio Frequency Integrated Circuits Symposia, and the 2019 IEEE MTT-S Outstanding Young Engineer Award . He has been a member of the technical program committee of several conferences, including the IEEE International Solid-State Circuits Conference since 2015 and the IEEE Radio Frequency Integrated Circuits Symposium since 2013. He currently serves as a Distinguished Lecturer for the IEEE Solid-State Circuits Society and as a member of the DARPA Microelectronics Exploratory Council.

Thursday, March 28th
Linxiao Zhu, University of Michigan
Postdoctoral Fellow, Mechanical Engineering
"Control of light and heat for new energy applications"
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Abstract: Light carries energy and heat, and plays a key role in many energy-conversion processes. The capabilities to tailor electromagnetic energy transfer at the nanoscale represent important opportunities for novel energy applications. In this talk I will present two sets of studies integrating experiments and theory. In the first part, I will discuss how to use near field electromagnetic energy transfer for energy conversion and photonic refrigeration. I will begin by showing an experiment achieving a 40-fold enhancement of thermophotovoltaic conversion rates, by reducing the distance between a thermal emitter and a photovoltaic cell to the nanoscale. This lays the foundation for exploring near-field thermophotovoltaics for waste heat recovery. I will then show a demonstration of active photonic refrigeration through control of the chemical potential of photons. This points to a fundamentally new, promising way for solid state refrigeration by combining nanoscale photonics and optoelectronics.

In the second part, I will discuss how to turn the cold outer space to a thermodynamic resource for passive cooling and energy efficiency. I will introduce our first demonstration of passive radiative cooling to below the ambient air temperature under direct sunlight. Next I will show an experiment achieving passive cooling to 42 °C below the ambient temperature, pointing to new regimes of applications such as food preservation in remote areas. I will also show results of lowering the temperature of a solar absorber by 13 °C while maintaining the sunlight absorption, pointing to significant efficiency improvement for solar cells. Finally, I will give an overview of my future research directions.

Biography: Dr. Linxiao Zhu received B.S. in Physics (2010) at the University of Science and Technology of China, and Ph.D. in Applied Physics (2016) at Stanford University. His doctoral research is on controlling electromagnetic heat transfer using photonic structures, supervised by Prof. Shanhui Fan in Department of Electrical Engineering at Stanford University. Dr. Zhu is currently a postdoctoral research fellow with Prof. Pramod Reddy and Prof. Edgar Meyhofer in Department of Mechanical Engineering at the University of Michigan, working on the experiments of near-field based energy conversion and refrigeration.

Friday, March 29th
Nikolai Matni, UC Berkeley
Postdoctoral Scholar, Electrical Engineering and Computer Science
Safety and Robustness Guarantees with Learning in the Loop
11:00am, Berger Auditorium
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Abstract: In this talk, we present recent progress towards developing learning-based control strategies for the design of safe and robust autonomous systems. Our approach is to recognize that machine learning algorithms produce inherently uncertain estimates or predictions, and that this uncertainty must be explicitly quantified (e.g., using non-asymptotic guarantees of contemporary high-dimensional statistics) and accounted for (e.g., using robust control/optimization) when designing safety critical systems. We focus on the safety constrained optimal control of unknown systems, and show that by integrating modern tools from high-dimensional statistics and robust control, we can provide, to the best of our knowledge, the first end-to-end finite data robustness, safety, and performance guarantees for learning and control. We further show how this approach can be incorporated into an adaptive polynomial-time algorithm with non-asymptotic convergence rate (regret bound) guarantees. As a whole, these results provide a rigorous and contemporary perspective on safe reinforcement learning as applied to continuous control. We conclude with our vision for a general theory of safe learning and control, with the ultimate goal being the design of robust and high performing data-driven autonomous systems.

Bio: Nikolai is a postdoctoral scholar in EECS at UC Berkeley working with Benjamin Recht. He received the B.A.Sc. and M.A.Sc. in Electrical Engineering from the University of British Columbia, and the Ph.D. in Control and Dynamical Systems from the California Institute of Technology in June 2016 under the advisement of John C. Doyle. His research interests broadly encompass the use of learning, optimization, and control in the design and analysis of safety-critical data-driven cyber-physical systems. He was awarded the IEEE CDC 2013 Best Student Paper Award, and the IEEE ACC 2017 Best Student Paper Award (as co-advisor).

Tuesday, April 23rd
Peter Kinget, Columbia University
Department Chair & Bernard J. Lechner Professor, Electrical Engineering
Connecting Bits to the Physical World
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Abstract: Analog, RF and power integrated circuits are the key connectors between the physical world and the digital or cyber world. In this talk I will give my perspective on broader research trends in analog integrated circuit design research and illustrate several of these trends with results from my research group. The analog circuit design discipline emerged in conjunction with electronics and as such has many decades of history. At the same time, electronics are constantly undergoing tremendous changes. In recent decades the key platform for integrated circuits has been CMOS. Under the impetus of “Moore’s Law,” CMOS transistors have scaled by orders of magnitude, which drove the necessity of a constant rejuvenation of analog design techniques. Innovations in analog design are an intricate interplay between novel devices, novel circuit paradigms and novel signal processing. Recently we have been experiencing a shift from traditional analog-to-digital conversion, to analog-to-information conversion (based on compressive sampling), and now to analog-to-feature conversion. This is an example of a top-down shift driven by changing application needs, in particular emerging machine-learning systems. Scaling transistors does not only allow for higher system integration, but also enables significant power reductions. Combining advanced transistors with novel circuit design paradigms encoding analog information in the time domain makes it now possible to design integrated circuits that require less than 1nanoW to operate. These innovations, in turn, create bottom-up opportunities for entirely new classes of systems, e.g., for the Internet of Things.

Bio: Peter R. Kinget received an engineering degree in electrical and mechanical engineering and the Ph.D. in electrical engineering from the Katholieke Universiteit Leuven, Belgium. He has worked in industrial research and development at Bell Laboratories, Broadcom, Celight and Multilink before joining the faculty of the Department of Electrical Engineering, Columbia University, NY in 2002, where he currently is the Department Chair and Bernard J. Lechner Professor in Electrical Engineering. He is also a consulting expert on patent litigation and a technical consultant to industry. His research interests are in analog, RF and power integrated circuits and the applications they enable in communications, sensing, and power management. Peter is widely published and received several awards. He is a Fellow of the IEEE. He is a "Distinguished Lecturer" for the IEEE Solid-State Circuits Society (SSCS), and has been an Associate Editor of the IEEE Journal of Solid State Circuits (2003-2007) and the IEEE Transactions on Circuits and Systems II (2008-2009). He has served on the program committees of many of the major solid-state circuits conferences and has been an elected member of the IEEE SSCS Adcom (2011-2013 and 2014-2016).

Thursday, May 9th
Vivienne Sze, MIT
Associate Professor, Electrical Engineering and Computer Science
Efficient Computing for AI and Robotics

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Abstract: Computing near the sensor is preferred over the cloud due to privacy and/or latency concerns for a wide range of applications including robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics. However, at the sensor there are often stringent constraints on energy consumption and cost in addition to the throughput and accuracy requirements of the application. In this talk, we will describe how joint algorithm and hardware design can be used to reduce energy consumption while delivering real-time and robust performance for applications including deep learning, computer vision, autonomous navigation/exploration and video/image processing. We will show how energy-efficient techniques that exploit correlation and sparsity to reduce compute, data movement and storage costs can be applied to various tasks including image classification, depth estimation, super-resolution, localization and mapping.

Bio: Vivienne Sze is an Associate Professor at MIT in the Electrical Engineering and Computer Science Department. Her research interests include energy-aware signal processing algorithms, and low-power circuit and system design for portable multimedia applications, including computer vision, deep learning, autonomous navigation, and video process/coding. Prior to joining MIT, she was a Member of Technical Staff in the R&D Center at TI, where she designed low-power algorithms and architectures for video coding. She also represented TI in the JCT-VC committee of ITU-T and ISO/IEC standards body during the development of High Efficiency Video Coding (HEVC), which received a Primetime Engineering Emmy Award. She is a co-editor of the book entitled "High Efficiency Video Coding (HEVC): Algorithms and Architectures" (Springer, 2014). Prof. Sze received the B.A.Sc. degree from the University of Toronto in 2004, and the S.M. and Ph.D. degree from MIT in 2006 and 2010, respectively. In 2011, she received the Jin-Au Kong Outstanding Doctoral Thesis Prize in Electrical Engineering at MIT. She is a recipient of the 2018 Facebook Faculty Award, the 2018 & 2017 Qualcomm Faculty Award, the 2018 & 2016 Google Faculty Research Award, the 2016 AFOSR Young Investigator Research Program (YIP) Award, the 2016 3M Non-Tenured Faculty Award, the 2014 DARPA Young Faculty Award, the 2007 DAC/ISSCC Student Design Contest Award, and a co-recipient of the 2017 CICC Outstanding Invited Paper Award, the 2016 IEEE Micro Top Picks Award and the 2008 A-SSCC Outstanding Design Award. For more information about research in the Energy-Efficient Multimedia Systems Group at MIT visit: