ESE Colloquia & Events

Spring 2018 (Full Schedule TBA)

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"

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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"

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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"

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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

Read the Abstract and Bio


Abstract: TBA

Bio: TBA



 

Tuesday, March 13th
Maria Gorlatova, Princeton University
Associate Research Scholar, Electrical Engineering

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Abstract: TBA

Bio: TBA



 

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"

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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.

Biography: 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, Microsystems Technology Laboratories
11am, Singh Center Glandt Forum

Read the Abstract and Bio

Abstract: TBA

Bio: TBA



 

Tuesday, March 20th
Negar Reiskarimian, Columbia University
Ph.D. Candidate, Electrical Engineering

Read the Abstract and Bio

Abstract: TBA

Bio: TBA



 

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

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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

Read the Abstract and Bio

Abstract: TBA

Bio: TBA



 

Thursday, March 29th
Marc Miskin, Cornell University
Postdoctoral Associate, Nanoscale Science

Read the Abstract and Bio

Abstract: TBA

Bio: TBA



 

Tuesday, April 3rd
Constantine Sideris, Caltech
Postdoctoral Scholar, Computing and Mathematical Sciences

Read the Abstract and Bio

Abstract: TBA

Bio: TBA



 

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

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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.

Biography: 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.