ESE Colloquia & Events

Spring 2018

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 ESEevents@seas.upenn.edu.

 

Wednesday, January 31st / 12pm / 3401 Building, 452C
Na (Lina) Li, Harvard University
Assistant Professor, Electrical Engineering
"Distributed Decision Making in Network Systems: Algorithms, Fundamental Limits, and Applications"

Read the Abstract and Bio

Abstract: Recent radical evolution in distributed sensing, computation, communication, and actuation has fostered the emergence of cyber-physical network systems. Examples cut across a broad spectrum of engineering and societal fields such as power grids, swarm robotics, air/ground transportation systems, green buildings, and other societal networks. Regardless of the specific application, one central goal is to shape the network collective behavior through the design of admissible local decision-making algorithms. This is nontrivial especially due to the challenges placed by the local connectivity, imperfect communication, time-varying uncertainty, and the complex intertwined physics and human interactions. In this talk, I will present our recent progress in formally advancing the systematic design in distributed coordination in network systems. We investigate the fundamental performance limit placed by the various challenges, design fast, efficient, and scalable algorithms to achieve (or approximate) these performance limits, and test and implement the algorithms on real-world applications including data centers and societal networks.

Bio: Na Li is an assistant professor in Electrical Engineering and Applied Mathematics of the School of Engineering and Applied Sciences in Harvard University since 2014. She received her Bachelor degree in Mathematics in Zhejiang University in 2007 and PhD degree in Control and Dynamical systems from California Institute of Technology in 2013. She was a postdoctoral associate of the Laboratory for Information and Decision Systems at Massachusetts Institute of Technology 2013-2014. Her research lies in distributed optimization and control of cyber-physical networked systems. She received NSF career award (2016) and AFSOR Young Investigator Award (2017), the Best Student Paper Award finalist in the 2011 IEEE Conference on Decision and Control.



 

Thursday, February 22nd
Jiantao Jiao, Stanford University
Ph.D. Candidate, Electrical Engineering
"Statistical Inference of Properties of Distributions: Theory, Algorithms, and Applications"

Read the Abstract and Bio

Abstract: Modern data science applications—ranging from graphical model learning to image registration to inference of gene regulatory networks—frequently involve pipelines of exploratory analysis requiring accurate inference of a property of the distribution governing the data rather than the distribution itself. Notable examples of properties include Shannon entropy, mutual information, Kullback-Leibler divergence, and total variation distance, among others.

This talk will focus on recent progress in the performance, structure, and deployment of near-minimax-optimal estimators for a large variety of properties in high-dimensional and nonparametric settings. We present general methods for constructing information theoretically near-optimal estimators, and identify the corresponding limits in terms of the parameter dimension, the mixing rate (for processes with memory), and smoothness of the underlying density (in the nonparametric setting). We employ our schemes on the Google 1 Billion Word Dataset to estimate the fundamental limit of perplexity in language modeling, and to improve graphical model and classification tree learning. The estimators are efficiently computable and exhibit a "sample size boosting” phenomenon, i.e., they attain with n samples what prior methods would have needed n log(n) samples to achieve.

Bio: Jiantao Jiao is a Ph.D. student in the Department of Electrical Engineering at Stanford University. He received the B.Eng. degree in Electronic Engineering from Tsinghua University, Beijing, China in 2012, and the M.Eng. degree in Electrical Engineering from Stanford University in 2014. He is a recipient of the Presidential Award of Tsinghua University and the Stanford Graduate Fellowship. He was a semi-plenary speaker at ISIT 2015 and a co-recipient of the ISITA 2016 Student Paper Award. He co-designed and co-taught the graduate course EE378A (Statistical Signal Processing) at Stanford University in 2016 and 2017, with his advisor Tsachy Weissman. His research interests are in statistical machine learning, high-dimensional and nonparametric statistics, information theory, and their applications in medical imaging, genomics, and natural language processing. He is a co-founder of Qingfan (www.qingfan.com), an online platform that democratizes technical training and job opportunities for anyone with access to the internet.


 

Monday, March 5th
Weina Wang, University of Illinois Urbana-Champaign & Arizona State University
Postdoctoral Researcher of Electrical, Computer, and Energy Engineering
"Delay Bounds and Asymptotics in Cloud Computing Systems"

Read the Abstract and Bio


Abstract: With the emergence of big-data technologies, cloud computing systems are growing rapidly in size and becoming more and more complex, making it costly to conduct experiments and simulations. Therefore, modeling computing systems and characterizing their performance analytically are more critical than ever in identifying bottlenecks, informing system design, and facilitating provisioning. In this talk, I will illustrate how we study the delay performance in cloud computing systems from different modeling perspectives. First, I will focus on the delay of jobs that consist of multiple tasks, where the tasks can be processed in parallel on different servers, and a job is completed only when all its tasks are completed. Such jobs with parallel tasks are prevalent in today’s cloud computing systems. While the delay of individual tasks has been extensively studied, job delay has not been well-understood, even though job delay is the most important metric of interest to end users. In our work, we establish a stochastic upper bound on job delay using properties of associated random variables, and show its tightness in an asymptotic regime where the number of servers in the system and the number of tasks in a job both become large. After this, I will also briefly summarize our results on delay characterization for data-processing tasks where the locality of data needs to be considered, and for data transfer in large-scale datacenter networks.

Bio: Weina Wang is a joint postdoctoral research associate in the Coordinated Science Lab at the University of Illinois at Urbana-Champaign, and in the School of ECEE at Arizona State University. She received her B.E. from Tsinghua University and her Ph.D. from Arizona State University, both in Electrical Engineering. Her research lies in the broad area of applied probability and stochastic systems, with applications in cloud computing, data centers, and privacy-preserving data analytics. Her dissertation received the Dean’s Dissertation Award in the Ira A. Fulton Schools of Engineering at Arizona State University in 2016. She received the Kenneth C. Sevcik Outstanding Student Paper Award at ACM SIGMETRICS 2016.

 

Thursday, March 8th
Pratik Chaudhari, University of California, Los Angeles
Ph.D. Candidate, Computer Science
"A Picture of the Energy Landscape of Deep Neural Networks"
**11am, Wu and Chen Auditorium**

Read the Abstract and Bio

Abstract: Deep networks are mysterious. These over-parametrized machine learning models, trained with rudimentary optimization algorithms on non-convex landscapes in millions of dimensions, have defied attempts to put a sound theoretical footing beneath their impressive performance.

This talk will shed light upon some of these mysteries. I will employ diverse ideas — from thermodynamics and optimal transportation to partial differential equations, control theory and Bayesian inference — and paint a picture of the training process of deep networks. Along the way, I will develop state-of-the-art algorithms for non-convex optimization.

The goal of machine perception is not just to classify objects in images but instead, enable intelligent agents that can seamlessly interact with our physical world. I will conclude with a vision of how advances in machine learning and robotics may come together to build such an Embodied Intelligence.

Bio: Pratik Chaudhari is a PhD candidate in Computer Science at UCLA where he works with Stefano Soatto. His research interests include deep learning, robotics and computer vision. He has worked on perception and control algorithms for safe autonomous urban navigation as a part of nuTonomy Inc. Pratik holds Master's and Engineer's degrees from MIT and a Bachelor’s degree from IIT Bombay in Aeronautics and Astronautics.
Website: pratikac.info


 

Tuesday, March 13th
Maria Gorlatova, Princeton University
Associate Research Scholar, Electrical Engineering
"Life on the Edge: Connecting Everyday Objects with Energy Harvesting and Fog Computing"

Read the Abstract and Bio

Abstract: Realizing the vision of the fully connected world — the Internet of Things (IoT) — requires advances in multiple areas. Energy harvesting and fog/edge computing can bring everyday objects to life in complementary ways: by using the environment to make the IoT nodes smaller and lighter, and by bringing advanced computing capabilities closer to the nodes to make them more adaptive and intelligent.

In this talk I will first describe our Columbia University work on designing and developing Energy Harvesting Active Networked Tags (EnHANTs), which we envisioned as small, flexible, energetically self-reliant tags that can be attached to objects that are traditionally not networked, such as clothing and produce. I will describe several steps that we took towards realizing this vision: our first-of-their-kind characterizations of the environmental light and motion energy availability for the EnHANTs and for other IoT devices, our energy harvesting adaptive resource allocation algorithms, and our EnHANT prototypes and a first-of-its kind energy-generating EnHANT testbed.

I will also describe our recent Princeton University work on making the IoT systems more intelligent with fog and edge computing. These emerging paradigms, which place advanced computing capabilities away from centralized datacenters and closer to the IoT nodes, are receiving increasing industry attention as the potential next multi-billion dollar tech markets. I will introduce our ongoing work in several related areas: restructuring computing for heterogeneous hierarchical fog architectures, using fog to enable the next generation of immersive augmented reality experiences, and contributing to the industry-wide OpenFog Consortium to inform emerging industry architectures.

The work covered in this talk appeared in the IEEE Transactions on Mobile Computing, the IEEE Journal on Selected Areas in Communications, the IEEE Wireless Communications Magazine, and in the proceedings of ACM MobiCom, IEEE INFOCOM, and ACM SIGMETRICS, among others. It was highlighted in several media outlets, including the MIT Technology Review and the New Yorker Magazine.

Bio: Dr. Maria Gorlatova is an Associate Research Scholar at Princeton University Department of Electrical Engineering, and an Associate Director of the Princeton EDGE Lab. Dr. Gorlatova earned her Ph.D. in Electrical Engineering from Columbia University, and her M.Sc. and B.Sc. (Summa Cum Laude) degrees in Electrical Engineering from University of Ottawa, Canada. She has several years of industry experience, where she had been affiliated with Telcordia Technologies, IBM, and D. E. Shaw Research. Dr. Gorlatova is a recipient of the Google Anita Borg USA Fellowship, Canadian Graduate Scholar (CGS) NSERC Fellowships, and the Columbia University Presidential Fellowship. She is a co-recipient of the ACM SenSys Best Student Demonstration Award, the IEEE Communications Society Young Author Best Paper Award, and the IEEE Communications Society Award for Advances in Communications.




 

Wednesday, March 14th
Francesca Parise, Massachusetts Institute of Technology
Postdoctoral Fellow, Laboratory for Information and Decision Systems
"An Aggregative Game Framework for the Analysis of Socio-Technical Systems"
**11am, David Rittenhouse Laboratory Rm 3N1H**

Read the Abstract and Bio

Abstract: Many of today’s most promising technological systems involve large numbers of autonomous agents that influence each other and make strategic decisions within a given infrastructure. Examples include demand-response methods in energy markets, opinion dynamics and targeted marketing in social networks, routing decisions in transportation systems or economic exchange and international trade in financial networks. The analysis of agents behavior and equilibrium outcome in these large scale systems necessitates the development of new theoretical and algorithmic tools that combine ideas from game, network and control theory.

In this talk, I discuss how aggregative games can help us achieve such a goal by providing a systematic framework for the modeling and control of large scale socio-technical systems. Specifically, in the first part of the talk I will discuss how “average aggregative games” can be used to model systems where each agent is affected by the aggregated actions of the rest of the population and how iteratively broadcast information can be used to coordinate agents behavior, with application to the charging of electric vehicles. In the second part of the talk, I will consider systems where agents interactions are heterogeneous and can be described by a network. I will present a variational inequality framework for the analysis of such “network games” which allows us to extend previous literature results, gain a systematic understanding of how network interactions affect the equilibrium outcome and plan targeted interventions based on agents centrality measures in social and financial networks. I will conclude with a brief outlook on a new game theoretical framework that I am developing to model strategic interactions in very large scale networks by using the concept of “graphon games”.

Bio: Francesca Parise is a postdoctoral researcher at the Laboratory for Information and Decision Systems at MIT. She defended her PhD at the Automatic Control Laboratory, ETH Zurich, Switzerland in 2016 and she received the B.Sc. and M.Sc. degrees in Information and Automation Engineering in 2010 and 2012, respectively, from the University of Padova, Italy, where she simultaneously attended the Galilean School of Excellence.

Francesca’s main research interest is in control, network and game theory. She has worked on a broad set of topics, including systems biology, reachability analysis, distributed multi-agent systems, aggregative games and opinion dynamics.

Francesca was recognized as an EECS rising star in 2017 and is the recipient of the Guglielmo Marin Award from the “Instituto Veneto di Lettere ed Arti”, the SNSF Early Postdoc Fellowship, the SNSF Advanced Postdoc Fellowship and the ETH Medal for her doctoral work.



 

Thursday, March 15th
David Burghoff, Massachusetts Institute of Technology
Postdoctoral Fellow, Research Laboratory of Electronics
"Chip-Scale Platforms for Long-Wavelength Nanophotonics: Frequency Combs, Spectrometers, and Beyond"

Read the Abstract and Bio


Abstract: Optical sensors and systems have a unique ability to reshape the world, and long-
wavelength light in particular has enormous untapped potential. Mid-infrared and terahertz
photonics could address some of our most vexing challenges in medicine, energy, transportation, and security, but progress in this area has been hampered by a lack of compact sources. Although quantum cascade structures—semiconductor nanostructures whose optical properties are determined by design, not nature—have long been able to act as compact narrowband sources of light at mid-infrared and terahertz wavelengths, many practical applications require precise and scalable broadband operation.

Fortunately, the large optical nonlinearity of a quantum cascade structure enables a wide
range of nonlinear devices, including broadband frequency combs. Optical frequency combs are light sources whose spectra consist of a large number of evenly-spaced lines, and their
extraordinary properties make them attractive for a number of consumer applications, including high-performance dual comb spectrometers-on- a-chip and lidar. In this talk, I will discuss my development of the first terahertz laser frequency combs based on quantum cascade structures, terahertz sources that combine the compactness of semiconductor lasers with the coherence & broadband output of a mode-locked laser.

In addition, I will discuss my work showing how these devices can be used to coherently
measure high signal-to- noise ratio terahertz spectra in just a few microseconds, using only chip-scale components. With signal processing, sensible spectra can even be measured with combs that are far less stable than are traditionally considered viable for coherent spectroscopy, even lasers operated in pulsed mode. Because these systems require no stabilization and no extraneous optical elements, they will enable the next generation of optical sensors and even complete spectrometers on-a-chip.

Bio: David Burghoff is a postdoctoral fellow in the Research Laboratory of Electronics at the Massachusetts Institute of Technology. He received his bachelor’s in Electrical Engineering from the University of Illinois at Urbana-Champaign, his master’s from the Massachusetts Institute of Technology, and his Ph.D. in Electrical Engineering from the Massachusetts Institute of Technology. His awards include MIT's Jin Au Kong Outstanding Thesis Award and the Intelligence Community Postdoctoral Fellowship. His research focuses on devices and systems that blend quantum nanostructure engineering, ultrafast & nonlinear optoelectronics, and integrated long-wavelength photonics, with applications in sensors, healthcare, energy, security, and transportation. His broader ambition is to usher in an era of ubiquitous mid-infrared and terahertz photonics, and to use these technologies to address some of our most pressing challenges.

 

Monday, March 19th
Yuhao Zhang, Massachusetts Institute of Technology
Postdoctoral Associate, Electrical Engineering and Computer Science
"Nitride Devices: Redefining the Limits of Power Electronics"
**11am, Singh Center Glandt Forum**

Read the Abstract and Bio

Abstract: Devices based on wide-bandgap semiconductors, in particular Gallium Nitride (GaN), are expected to revolutionize power electronics. GaN devices promise to trim the losses in power conversion circuits. What’s more, thanks to the capability to handle far higher power densities than today’s devices, they could greatly trim the size, weight and cost of power systems. Overall, over 10% reduction in global electric power consumption could be possible.

The development of GaN power devices has focused on a lateral geometry for over two decades. Recently, however, there has been a fast growing interest in vertical architectures. Vertical GaN power devices, in merely three to five years, have demonstrated superior performance than their lateral cousins for high-voltage and high-current power applications. They are very exciting candidates for many medium and high power applications, such as electric vehicles, data centers, smart grids, etc.

This talk will present our work on developing a new generation of vertical GaN power devices, including the advanced power diodes combining the merits of conventional Schottky and pn diodes, the novel “fin” power transistors with the 1200-2000 V voltage capability and a record low on-resistance, as well as the low-cost vertical GaN power devices on silicon substrates. In addition, we will discuss prospects of these emerging power devices and the great potential of their system-level integration and applications.

Bio: Dr. Yuhao Zhang is currently a postdoctoral research associate working with Professor Tomás Palacios at Massachusetts Institute of Technology (MIT). He received his Ph. D. and S. M., both in electrical engineering from MIT, in 2017 and 2013, respectively. Prior to joining MIT, he received his B. S. in physics from Peking University in 2011 with the highest honor. His research interest is at the intersection of power electronics, micro/nano-scale devices and advanced semiconductor materials. His research work has been cited by the media globally over 30 times. He received the MIT Microsystems Technology Laboratories Best Doctoral Dissertation Award in Spring 2017.



 

Tuesday, March 20th
Negar Reiskarimian, Columbia University
Ph.D. Candidate, Electrical Engineering
"Breaking Lorentz Reciprocity: From New Physical Concepts to Applications"

Read the Abstract and Bio

Abstract: Lorentz reciprocity is a fundamental characteristic of the vast majority of electronic and photonic structures. However, breaking reciprocity enables the realization of non-reciprocal components, such as isolators and circulators, which are critical to electronic and optical communication systems, as well as new components and functionalities based on novel wave propagation modes. In this talk, I will present a novel approach to break Lorentz reciprocity based on linear periodically-time-varying (LPTV) circuits. We have demonstrated the world's first CMOS passive magnetic-free non-reciprocal circulator through spatio-temporal conductivity modulation. Since conductivity in semiconductors can be modulated over a much wider range than the more traditionally exploited permittivity, our structure is able to break reciprocity within a compact form factor with very low loss and high linearity. I will discuss fundamental limits of space-time modulated nonreciprocal structures, as well as new directions to build non-reciprocal components which can ideally be infinitesimal in size. Furthermore, I cover some of the applications of nonreciprocal components in wireless communication systems.

Looking to the future, I am broadly interested in exploring novel fundamental physical concepts that have strong engineering applications. I wish to work in an interdisciplinary area between integrated circuit design and closely related fields such as applied physics, applied electromagnetics and nanophotonics, and to identify and investigate ideas and concepts that can best be implemented using the semiconductor platform. Finally, I will share with you some examples of the exciting research directions I would like to pursue with the aim of participating in building the next generation of technologies that augment human lives.

Bio: Negar Reiskarimian received the Bachelor’s and Master’s degrees in electrical engineering from Sharif University of Technology in Iran, and is currently a PhD candidate in Electrical Engineering at Columbia University. She has published in top-tier IEEE IC-related journals and conferences, as well as broader-interest high-impact journals in the Nature family. Her research has been widely covered in the press, and featured in IEEE Spectrum, Gizmodo and EE Times among others. She is the recipient of numerous awards and fellowships, including Forbes “30 under 30”, Paul Baran Young Scholar, Qualcomm Innovation Fellowship and multiple IEEE societies awards and fellowships.


 

Thursday, March 22nd
Rabia Yazicigil, Massachusetts Institute of Technology
Postdoctoral Associate, Electrical Engineering and Computer Science
"Innovating Secure IoT Solutions for Extreme Environments"

Read the Abstract and Bio

Abstract: The Internet of Things (IoT) is redefining how we interact with the world by supplying a global view based not only on human-provided data but also human-device connected data. For example, in Health Care, IoT will bring decreased costs, improved treatment results, and better disease management. However, the connectivity-in-everything model brings heightened security concerns. The projected growth of connected nodes not only increases security concerns, it also leads to a 1000-fold increase in wireless data traffic in the near future. This data storm results in a spectrum scarcity thereby driving the urgent need for shared spectrum access technologies. These security deficiencies and the wireless spectrum crunch require innovative system-level secure and scalable solutions.

This talk will introduce energy-efficient and application-driven system-level solutions for secure and spectrum-aware wireless communications. I will present a novel ultra-fast bit-level frequency-hopping scheme for physical-layer security. This scheme utilizes the frequency agility of devices in combination with novel radio frequency architectures and protocols to achieve secure wireless communications. To address the wireless spectrum crunch, future smart radio systems will evaluate the spectrum usage dynamically and opportunistically use the underutilized spectrum; this will require spectrum sensing for interferer avoidance. I will discuss a system-level approach using band-pass sparse signal processing for rapid interferer detection in a wideband spectrum to convert the abstract improvements promised by sparse signal processing theory, e.g., fewer measurements, to concrete improvements in time and energy efficiency.

The tightly-coupled system solutions derived at the intersection of electronics, security, signal processing, and communications extend in applications beyond the examples provided here, enabling innovative IoT solutions for extreme environments.

Bio: Rabia Yazicigil is currently a Postdoctoral Research Associate at MIT. She received her PhD degree in Electrical Engineering from Columbia University in 2016. She received the B.S. degree in Electronics Engineering from Sabanci University, Istanbul, Turkey in 2009, and the M.S. degree in Electrical and Electronics Engineering from École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland in 2011.

Her research interest lies at the interface of electronics, security, signal processing and communication to innovate system-level solutions for future energy-constrained applications in the context of the Internet of Things. She has been a recipient of a number of awards, including the “Electrical Engineering Collaborative Research Award” for her PhD research on Compressive Sampling Applications in Rapid RF Spectrum Sensing (2016), the second place at the Bell Labs Future X Days Student Research Competition (2015), Analog Devices Inc. outstanding student designer award (2015) and 2014 Millman Teaching Assistant Award of Columbia University. She was selected among the top 61 female graduate students and postdoctoral scholars invited to participate and present her research work in the 2015 MIT Rising Stars in Electrical Engineering Computer Science.



 

Tuesday, March 27th
Christos Thrampoulidis, Massachusetts Institute of Technology
Postdoctoral Associate
"Accurate Inference in High-Dimensions: Structure and Fundamental Limits"

Read the Abstract and Bio

Abstract:The increasing availability of data and of computational resources has shifted the boundaries of what is deemed possible, creating an expectation for high-dimensional information extraction from observations that are incomplete and indirect, as well as, corrupted by noise, outliers and interference. Importantly, extracting useful signals in such instances relies critically on exploiting available structural information in ways that are statistically accurate, computationally efficient, and robust.

In this talk, I will describe a framework of analysis to accurately quantify the statistical performance and the robustness of non-smooth convex-relaxation methods that are used as powerful tools to extract low-dimensional signal structures in high-dimensions. In particular, the framework sheds light on the relative performance of different algorithms and allows the engineer to fine-tune the involved parameters to improve the system as a whole. Also, it applies to a wide range of measurement models under generic observations. To demonstrate the generality of the above framework, I will present its applications to massive MIMO detection, quantized compressed sensing, and phase-retrieval. At the core of the framework, lies a new theorem on Gaussian process comparisons, which I will also highlight.

Finally, I will briefly discuss new opportunities in contemporary imaging problems that call for exploiting structure beyond signal-structure. In particular, I will demonstrate an efficient computational imaging technique that exploits opportunistically the presence of occluding objects, enabling for the first time imaging of hidden scenes without reliance on ultrafast time-of-flight measurements.

Bio: Christos Thrampoulidis received his Diploma in electrical and computer engineering from the University of Patras, Greece in 2011. He received a M.Sc. and a Ph.D. degree in electrical engineering in 2012 and 2016, respectively, both from the California Institute of Technology (Caltech), with a minor in applied and computational mathematics. He is currently a Postdoctoral Research Associate at the Research Laboratory of Electronics (RLE) at the Massachusetts Institute of Technology (MIT). His research interests include convex optimization, high-dimensional probability and statistics, signal processing, and computational imaging. He is a recipient of the 2014 Qualcomm Innovation Fellowship, and, of the Andreas Mentzelopoulos Scholarship and the I. Milias awards (2011) from the University of Patras.



 

Thursday, March 29th
Marc Miskin, Cornell University
Postdoctoral Associate, Nanoscale Science
"Making Machines the Size of Cells"

Read the Abstract and Bio

Abstract: This talk outlines a new approach for fabricating cell-sized machines that can freely explore space, interact with their environment, be manufactured en masse, and carry the full power of modern information technology. We start by identifying origami inspired fabrication as a scalable approach to building 3D machines, and miniaturize to the ultimate limit of folding atomically thin sheets. To do so, we turn atomically thin materials, like graphene, into actuators capable of bending elastically to micron radii of curvature. By patterning rigid panels on top of these actuators, we can localize bending to produce folds, and scale down existing origami patterns to produce a wide range of machines. These machines change shape in fractions of a second in response to environmental changes, can carry a range of electronic, chemical, and photonics payloads, and perform useful functions on time and length scales comparable to microscale biological organisms. Beyond simple stimuli, we demonstrate how to fabricate voltage responsive actuators that can be powered with onboard photovoltaics. Finally, we demonstrate that these mechanical technologies can be combined with silicon-based electronics, moving towards a complete platform for autonomous robotics at the cellular scale.

 

Biography: Marc Miskin is a Kavli Institute Postdoctoral Fellow in Nanoscale Science at Cornell. His work centers on building cell-sized structures and machines by folding atomically thin sheets of paper. He has been at Cornell since receiving his PhD in physics from the University of Chicago in 2014. His work has won several awards including a Springer Thesis Award and the Grainger Fellowship for excellence in experimental physics, and has been featured in media outlets including Newsweek, Nova Magazine, and Cosmos. Outside of research, he is actively involved in public science education, frequently appearing as a presenter at the local children's science museum.



 

Tuesday, April 3rd
Constantine Sideris, Caltech
Postdoctoral Scholar, Computing and Mathematical Sciences
"From DC to Daylight: Harnessing Electromagnetic Fields for Bioelectronics, Wireless Communications, and Silicon Photonics"

Read the Abstract and Bio

Abstract: Maxwell’s equations are responsible for explaining the fundamental operating principles behind much of today’s technology. In this talk, we will explore how understanding and controlling electromagnetic fields can provide significant impact across a multitude of applications over a wide frequency range on the electromagnetic spectrum. Starting from the low-frequency end of the spectrum, I will present the design and implementation of a new integrated magnetic biosensor. The magnetic biosensor is fabricated in a standard CMOS foundry process without any post-fabrication processing and can perform in-vitro detection of DNA, proteins, and cells by utilizing magnetic nanoparticles as labels. We will discuss three different, improved sensor designs which address sensor gain uniformity, enable multiplex target detection, and compensate sensor electrical and thermal drift based on spatial and temporal manipulations of the magnetic fields. Next, we will look into the RF domain and develop maximal performance bounds for antennas. I will present a rapid simulation technique which, when coupled with heuristic optimization algorithms, can quickly and effectively produce new antenna structures de-novo with little or no manual intervention. The efficacy of these techniques will be shown in the context of a 3D printed coupling antenna for a dielectric waveguide communication link. Moving higher in frequency, we will explore the near-infrared (NIR) part of the spectrum in the context of silicon photonic device optimization. I will present on-going work in designing grating coupler and power splitting devices with arbitrary splitting ratios by using adjoint optimization and highly efficient integral equation techniques. We will also explore exciting future directions in these research areas, leveraging modern computation and efficient numerical algorithms as well as holistic co-design of circuits and electromagnetics.

Bio: Constantine Sideris received the B.S., M.S., and PhD degrees with honors from the California Institute of Technology in 2010, 2011, and 2016 respectively. He was a visiting scholar at UC Berkeley’s Wireless Research Center from 2013 to 2014. He was a lecturer in the Electrical Engineering department for Caltech’s popular machine learning project course in 2017. He is currently a postdoctoral scholar in the Electrical Engineering and Computational and Mathematical Sciences departments at Caltech. His research interests include RF and millimeter-wave integrated circuits and computational electromagnetics for biomedical applications, wireless communications, and silicon photonics. He was a recipient of an NSF graduate research fellowship in 2010, the Analog Devices Outstanding Student Designer Award in 2012, and the Caltech Leadership Award in 2017.


 

Wednesday, April 4th
Qiaomin Xie, Massachusetts Institute of Technology
Postdoctoral Scholar, Laboratory for Information and Decision Systems
"Resource Allocation in Datacenters"
**11am, David Rittenhouse Laboratory Rm 3N1H**

Read the Abstract and Bio

Abstract: With the growing dependence upon datacenters to deliver high-quality services, efficient resource allocation in datacenters becomes increasingly important. Exciting opportunities for system redesign, as well as algorithmic/theoretical challenges unique to datacenters, arise across all layers of the system. In this talk, I will present two complementary approaches for provably efficient resource allocation.

In the first part, I will consider resource allocation under the lens of stochastic network modeling and analysis. For scheduling data-intensive applications, we develop a novel class of algorithms that exploit data locality constraints. Our algorithms achieve heavy-traffic delay optimality with unknown arrival rates, solving an open problem in affinity scheduling. Experiments on Amazon EC2 show >10x acceleration over existing schedulers. At the network level, the controlled environment in datacenters makes centralized allocation scheme possible. We propose a centralized design for joint flow congestion control and packet scheduling, and establish that it achieves the “baseline performance”.

In the second part, I will present a new data-driven approach, which aims to discover allocation policies automatically for analytically intractable problems with complicated dynamics, complementing classical approaches that are driven by human ingenuity. We develop a reinforcement learning approach that is capable of learning the optimal policies directly from observed data. We show that our Nearest Neighbor Q-Learning algorithm enjoys finite-sample, polynomial-time guarantees in an online setting.

Bio: Qiaomin Xie is a postdoctoral researcher with the Laboratory for Information and Decision Systems at MIT. In Fall 2016, she was a research fellow at the Simons Institute for the Theory of Computing. Qiaomin received her Ph.D. in Electrical and Computing Engineering from University of Illinois Urbana Champaign in 2016, and graduated from Tsinghua University with a B.E. in Electronic Engineering. Her research interests lie in the broad area of computer and networked systems, with a recent focus on resource allocation in datacenters as well as learning-based networked systems. She is the recipient of UIUC CSL PhD Thesis Award (2017), the Yi-Min Wang and Pi-Yu Chung Research Award (2015), and the best paper award from IFIP Performance Conference (2011).


 

Thursday, April 5th
Manuel Monge, Neuralink Corp.
Engineer
"High-Precision Electronic Medicine: Localization, Stimulation, and Beyond"

Read the Abstract and Bio

Abstract: Over the past decades, remarkable advances toward miniaturized biomedical devices have been made and have enabled the development of new approaches to the diagnosis and treatment of human diseases. For instance, smart pills are being used to image the gastrointestinal tract, distributed sensors are being developed to map the function of the brain, and neural prostheses are being designed to help the visual, hearing, and motor impaired. However, most of today’s implantable devices present critical limitations regarding size, power consumption, and functionality. Furthermore, several medical conditions could be dramatically improved if even smaller bioelectronic devices were to exist.


In this talk, I will provide an overview of implantable medical devices and present our efforts for engineering microscale devices to enable high-precision electronic medicine. In the first part of the talk, I will describe a novel approach for locating microscale devices inside the body using concepts from magnetic resonance imaging (MRI). We have demonstrated a new microchip that mimics the behavior of nuclear spins and can be located in space by the application of magnetic field gradients. Using this technique, we can locate a device smaller than 1 mm3 with sub-millimeter resolution in vivo. Such miniature devices could reach currently inaccessible locations inside the body with high precision to perform diagnosis and treatment of localized disease. In the second part, I will focus on neural stimulation techniques for retinal prostheses, which are devices aiming to restore vision in patients suffering from advanced stages of retinal degeneration (e.g., retinitis pigmentosa). I will present a fully intraocular epiretinal implant that reduces area and power consumption, and increases the functionality and resolution of traditional implementations. Finally, I will discuss some exciting research directions and potential applications of the developed techniques.

Bio: Manuel Monge received the BS degree in Electrical Engineering from the Pontifical Catholic University of Peru in 2008 with honors, and the MS and PhD degrees in Electrical Engineering from the California Institute of Technology in 2010 and 2017, respectively. His research interests focus on the miniaturization of medical electronics by combining and integrating physical and biological principles into the design of microscale integrated circuits. He is currently working at Neuralink Corp., developing ultra-high-bandwidth brain-machine interfaces.


He is the recipient of the 2017 Charles Wilts Prize from the Department of Electrical Engineering at Caltech for outstanding independent research in electrical engineering leading to a PhD, and the 2017 Demetriades-Tsafka-Kokkalis Prize in Biotechnology from the Division of Engineering and Applied Science at Caltech for the best thesis in the field of biotechnology. He was also the co-recipient of the 2015 IEEE CICC Best Student Paper Award, 2nd Place, and the recipient of the Caltech Rosen Scholarship in 2014.



 

Monday, April 23rd (Joint MEAM-ESE Seminar)
Cunjiang Yu, University of Houston
Assistant Professor, Department of Mechanical Engineering
""Manufacturing, Materials, and Device Innovations for Soft and Curvy Electronics"
**11am, Towne 225 (Raisler Lounge)**

Read the Abstract and Bio

Abstract: Innovative manufacturing technologies and materials are critical in building next-generation electronics devices, especially when we are migrating from conventional electronics to emerging electronics with unique form factors, such as those flexible, stretchable and wearable and curvilinear electronics, which hold promise in a broad range of areas such as healthcare, robotics, human-machine interfaces, etc. In this talk, I will present some of our recent research progress on manufacturing, materials, and device innovation for stretchy and curvy electronics. Existing strategies to enable mechanical stretchability in soft electronics heavily rely on special mechanical architectures, which impose a heavy burden on sophisticated fabrication and associated cost. I will show our recent results on developing a completely new set of stretchable electronics, namely “fully rubbery electronics”. Fully rubbery electronics are constructed completely based on elastomeric electronic materials and therefore intrisically stretchable. The fully rubbery electronics in thin sheets mimics the format and functionalities of our elastic human skin. I will then show our recent progress on developing 3D curvilinear electronics, a class of overlooked electronics with 3D curvilinear form factors. A new manufacturing approach, namely conformal additive transfer printing, will be presented. Different type of 3D curvilinear devices such as smart contact lenses with integrated sensors and electronics for multi-functionalities will be demonstrated.

Bio: Dr. Cunjiang Yu is the Bill D. Cook Assistant Professor of Mechanical Engineering at the University of Houston, with joint appointments in Electrical and Computer Engineering, Materials Science and Engineering, and Biomedical Engineering. He got B.S. in Mechanical Engineering and M.S. Electrical Engineering in 2004 and 2007, respectively, from Southeast University, Nanjing, China. He then received his Ph.D. in Mechanical Engineering at Arizona State University in 2010. Following the completion of his PhD, he was trained as a postdoc at the University of Illinois at Urbana-Champaign before joining UH in Oct. 2013. His research focuses on fundamental and application aspects of soft, curvy electronics. His recent research outcomes have been reported or highlighted by many media outlets, such as Time, Discovery, BBC News, NBC News, Science News, USA Today, etc.

He is a recipient of NSF CAREER Award, ONR Young Investigator Award, MIT Technology Review 35 Top Innovators under the age of 35 - TR35 China, ACS Petroleum Research Fund Doctoral New Investigator Award, American Vacuum Society Young Investigator Award, 3M Non-Tenured Faculty Award, UH University level Award of Excellence in Research & Scholarship, UH College of Engineering Junior Faculty Research Excellence Award.