ESE Colloquia & Events

Fall 2017

ESE colloquia are held on Tuesdays 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.

Tuesday, September 12th
Liang Feng, University of Pennsylvania
Assistant Professor of Materials Science and Engineering
"Quantum Inspired Photonics"

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Abstract: Quantum mechanics, dealing with atoms and electrons on a single-particle level, is the basis of modern technologies in computing and communication. While photonics is considered a completely different research subject, its intrinsic capability of creating non-Hermitian Hamiltonians by optical gain and loss makes it an ideal platform to explore various quantum symmetry paradigms that were deemed impossible in the past. In this seminar, I will present our recent efforts on the quantum inspired photonics where quantum symmetries (such as parity-time symmetry) guide the design of integrated photonics for unprecedented optical properties. Although it was widely believed that optical loss is detrimental in nanophotonics, I will start from an opposite viewpoint and develop a new paradigm of positively manipulating optical losses, demonstrating unidirectional light-matter interaction tailored at a “quantum” exceptional point in the complex dielectric permittivity domain. Moreover, I will discuss harnessing optical losses to enable unique microlaser functionality. In particular, I will focus on an orbital angular momentum (OAM) microlaser that structures and twists the lasing radiation at the microscale, which is expected to address the growing demand for information capacity. These quantum explorations not only provide positive impacts on fundamental quantum physics but also facilitate technological breakthroughs in photonic devices. Hence, researches in quantum inspired photonics of such two-fold benefits are advancing both fields simultaneously.

Bio: Liang Feng is an Assistant Professor of Materials Science and Engineering at the University of Pennsylvania. He received his Ph.D. in Electrical Engineering from UCSD in 2010, and was subsequently a postdoc researcher in the Department of Electrical Engineering at California Institute of Technology and the NSF Nanoscale Science and Technology Center at UC Berkeley. Prior to joining Penn, he was an assistant professor of SUNY Buffalo from 2014 to 2017. He has authored and coauthored 54 papers in a variety of journals including Science, Nature Materials, Nature Photonics and PRL. His current research is supported by National Science Foundation, Army Research Office, and King Abdullah University of Science and Technology. He is a recipient of the U.S. Army Research Office Young Investigator Program (YIP) Award in 2016.



 

Tuesday, September 19th
Negar Kiyavash, University of Illinois at Urbana-Champaign
Associate Professor of Industrial and Enterprise Engineering (IE) and Electrical and Computer Engineering (ECE)
"Causal Inference in Complex Networks"

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Abstract: One of the paramount challenges of this century is that of understanding complex, dynamic, large-scale networks. Such high-dimensional networks, including social, financial, and biological networks, cover the planet and dominate modern life. In this talk, we propose novel approaches to inference in such networks, for both active (interventional) and passive (observational) learning scenarios. We highlight how timing could be utilized as a degree of freedom that provides rich information about the dynamics. This information allows resolving direction of causation even when only a subset of the nodes is observed (latent setting). In the presence of large data, we propose algorithms that identify optimal or near-optimal approximations to the topology of the network.

Bio: Negar Kiyavash is Willett Faculty Scholar at the University of Illinois and a joint Associate Professor of Industrial and Enterprise Engineering (IE) and Electrical and Computer Engineering (ECE). She is the director of Advance Data Analytics Program in IE and is further affiliated with the Coordinated Science Laboratory (CSL) and the Information Trust Institute. She received her Ph.D. degree in ECE from the University of Illinois at Urbana-Champaign in 2006. Her research interests are in design and analysis of algorithms for network inference and security. She is a recipient of NSF CAREER and AFOSR YIP awards and the Illinois College of Engineering Dean's Award for Excellence in Research.



 

Monday, October 2nd
Amir Ali Ahmadi, Princeton University
Assistant Professor of Operations Research and Financial Engineering
"Polynomial Optimization and Dynamical Systems"

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Abstract: In recent years, there has been a surge of exciting research activity at the interface of optimization (in particular polynomial, semidefinite, and sum of squares optimization) and the theory of dynamical systems. In this talk, we focus on two of our current research directions that are at this interface. In part (i), we propose more scalable alternatives to sum of squares optimization and show how they impact verification problems in control and robotics. Our new algorithms do not rely on semidefinite programming, but instead use linear programming, or second-order cone programming, or are altogether free of optimization. In particular, we present the first Positivstellensatz that certifies infeasibility of a set of polynomial inequalities simply by multiplying certain fixed polynomials together and checking nonnegativity of the coefficients of the resulting product.
In part (ii), we introduce a new class of optimization problems whose constraints are imposed by trajectories of a dynamical system. As a concrete example, we consider the problem of optimizing a linear function over the set of initial conditions that forever remain inside a given polyhedron under the action of a linear, or a switched linear, dynamical system. We present a hierarchy of linear and semidefinite programs that respectively lower and upper bound the optimal value of such problems to arbitrary accuracy.


Bio: Amir Ali Ahmadi is an Assistant Professor at the Department of Operations Research and Financial Engineering at Princeton University and an Associated Faculty member of the Department of Computer Science and the Department of Mechanical and Aerospace Engineering. Amir Ali received his PhD in EECS from MIT and was a Goldstine Fellow at the IBM Watson Research Center prior to joining Princeton. His research interests are in optimization theory, computational aspects of dynamics and control, and algorithms and complexity. Amir Ali's distinctions include the Sloan Fellowship in Computer Science, the NSF CAREER Award, the AFOSR Young Investigator Award, the DARPA Faculty Award, the Google Faculty Award, the Goldstine Fellowship of IBM Research, and the Oberwolfach Fellowship of the NSF. His undergraduate course at Princeton (ORF 363, ``Computing and Optimization’’) has received the 2017 Excellence in Teaching of Operations Research Award of the Institute for Industrial and Systems Engineers and the 2017 Phi Beta Kappa Award for Excellence in Undergraduate Teaching at Princeton University. Amir Ali is also the recipient of a number of best-paper awards, including the INFORMS Computing Society Prize (for best series of papers at the interface of operations research and computer science), the Best Conference Paper Award of the IEEE International Conference on Robotics and Automation, and the prize for one of two most outstanding papers published in the SIAM Journal on Control and Optimization in 2013-2015.



 

Wednesday, October 4th
Giorgos Tsironis, Harvard University
Visiting Fellow, School of Engineering and Applied Sciences
"Coherence and Collective Nonlinear Effects in Superconducting Metamaterials"
2pm, Towne 337

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Abstract: Macroscopic quantum devices are becoming reality not only for computational purposes but also as sensors and for other general applications  In this talk we will focus on superconducting technology and analyze the emergence of coherence in coupled networks of meta-atoms made of units such as SQUIDS and Josephson junctions.  These networks may operate classically in a negative permeability regime[1], induce intrinsic nonlinear localized modes and partial coherence in the form of chimeras[2], tame disorder through hysteretic loops or transmit through nonlinear frequency bands. In the quantum regime, on the other hand, meta-atoms may interact through injected electromagnetic fields and form propagating “quantum breathers”, i.e. compound semi-classical propagating modes induced by the nonlinearity of the qubit-field interaction [3].   These coherent modes generate self-induced transparency in the medium and in certain cases may also induce super-radiance.

[1]  N. Lazarides and G. P. Tsironis, rf SQUID metamaterials,  Appl. Phys. Lett. 90, 163501 (2007).

[2] N. Lazarides, G. Neofotistos, and G. P. Tsironis, Chimeras in SQUID metamaterials, Physical Review B  91, 054303 (2015).

[3] Z. Ivic, N. Lazarides, and G. P. Tsironis, Qubit lattice coherence induced by electromagnetic pulses in superconducting metamaterials, Scientific Reports  6, 29374(2016).


Bio: Giorgos P. Tsironis is a professor at the Physics Department of the University of Crete and currently visiting fellow in the School of Engineering and Applied Sciences of Harvard University. He obtained his PhD in Theoretical Condensed Matter and Statistical Physics from the University of Rochester (USA) in 1987. He was a postdoctoral associate in the University of California San Diego (1987-89) and the Fermi National Accelerator Lab (1989-91), and assistant professor of Physics at the University of North Texas (1991-96). He joined the Department of Physics of the University of Crete in 1994 as associate professor and became professor in 2000. He was visiting professor in the University of Barcelona (2000-1 and 2006-7) and professor in Nazarbayev University in Kazakhstan (2014-15). His research focuses in nonlinear and statistical physics with emphasis in applications. His current interests include superconducting and quantum metamaterials. He has published over 150 papers in refereed journals.



 

Tuesday, October 10th
Alekh Agarwal, New York Lab of Microsoft Research
Researcher of Machine Learning, Statistics, and Optimization
"Sample-Efficient Reinforcement Learning with Rich Observations"

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Abstract: This talk considers a core question in reinforcement learning (RL): How can we tractably solve sequential decision making problems where the learning agent receives rich observations? We begin with a new model called Contextual Decision Processes (CDPs) for studying such problems, and show that it encompasses several prior setups to study RL such as MDPs and POMDPs. Several special cases of CDPs are, however, known to be provably intractable in their sample complexities. To overcome this challenge, we further propose a structural property of such processes, called the Bellman Rank. We find that the Bellman Rank of a CDP (and an associated class of functions) provides an intuitive measure of the hardness of a problem in terms of sample complexity and is small in several practical settings. In particular, we propose an algorithm, whose sample complexity scales with the Bellman Rank of the process, and is completely independent of the size of the observation space of the agent. We also show that our techniques are robust to our modeling assumptions, and make connections to several known results as well as highlight novel consequences of our results.

This talk is based on joint work with Nan Jiang, Akshay Krishnamurthy, John Langford and Rob Schapire.

Bio: Alekh Agarwal is a researcher in the New York lab of Microsoft Research, prior to which he obtained his PhD from UC Berkeley. Alekh’s research currently focuses on topics in interactive machine learning, including contextual bandits, reinforcement learning and online learning. Previously, he has worked on several topics in optimization including stochastic and distributed optimization. He has won several awards for his research including the NIPS 2015 best paper award.



 

Tuesday, October 17th
Viveck Cadambe, Pennsylvania State University
Assistant Professor of Electrical Engineering
"An Information Theoretic Perspective of Consistent Distributed Storage"

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Abstract: Distributed key-value stores implementations are an integral part of modern cloud computing infrastructure, and are used by various applications including transactions, reservation systems, multi-player gaming and multi-processor programming. The principles of modern key-value store design are closely tied to a problem called consistent shared memory emulation, which is studied in distributed computing theory. The goal of consistent shared memory emulation is to implement a read-write data object in a distributed storage system. In shared memory emulation, it is important to be resilient to server failures, to allow concurrent access to external clients, and to ensure the following property known as consistency: when the data is being constantly updated, a client that reads from the system should obtain the latest (consistent) version of the data. Motivated by technological trends where key-value stores are increasingly implemented in high speed memory, we will use develop and use information theory ideas to understand and minimize the memory footprint (storage overhead) of consistent shared memory emulation.

In this talk, we present an overview of three main ideas. First, we present an atomically consistent shared memory algorithm that uses classical erasure codes in its storage scheme. The algorithm exposes the salient challenges of using erasure coding in shared memory emulation. Second, we present a new, relatively simplified, information theory framework that enables us to study the memory footprint of consistent shared memory emulation. Our framework opens the door to the development and use of compression and coding techniques to minimize the memory footprint of shared memory emulation. Third, time-permitting, we will show connections between our simplified information-theoretic formulation and the full-fledged shared memory emulation model via an impossibility result.

Bio: Viveck Cadambe is an Assistant Professor in the Department of Electrical Engineering at Pennsylvania State University. Dr. Cadambe received his Ph.D from the University of California, Irvine in 2011. Between 2011 and 2014, he was a postdoctoral researcher, jointly with the Electrical and Computer Engineering (ECE) department at Boston University, and the Research Laboratory of Electronics (RLE) at the Massachusetts Institute of Technology (MIT). Dr. Cadambe is a recipient of the 2009 IEEE Information Theory Society Best Paper Award, the 2014 IEEE International Symposium on Network Computing and Applications (NCA) Best Paper Award, the 2016 NSF Career Award and a finalist for the 2016 Bell Labs Prize. His research involves understanding of data communication and storage using the tools of information theory and coding theory. His interests include applications to wireless communication networks, and cloud storage and computing systems.



 

Tuesday, October 24th
Weijie Su, University of Pennsylvania
Assistant Professor of Statistics
"HiGrad: Statistical Inference for Stochastic Gradient Descent in Online Machine Learning"

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Abstract: Stochastic gradient descent (SGD) is an immensely popular approach for optimization in settings where data arrives in a stream or data sizes are very large. Despite an ever-increasing volume of works on SGD, much less is known about statistical inferential properties of predictions based on SGD solutions. In this talk, we introduce a novel procedure termed HiGrad to conduct inference on predictions, without incurring additional computational cost compared with the vanilla SGD. HiGrad begins by performing SGD iterations for a while and then split the single thread into a few, and it hierarchically operates in this fashion along each thread. With predictions provided by multiple threads in place, a t-based confidence interval is constructed by decorrelating predictions using covariance structures given by the Ruppert– Polyak–Juditsky averaging scheme. Under certain regularity conditions, the HiGrad confidence interval is shown to attain asymptotically exact coverage probability. Finally, the performance of HiGrad is evaluated through extensive simulation studies and a real data example.

This is joint work with Yuancheng Zhu.

Bio: Weijie Su is an Assistant Professor of Statistics in the Department of Statistics at the Wharton School, University of Pennsylvania. Prior to joining Penn in Summer 2016, Su obtained his Ph.D. in Statistics from Stanford University in 2016, under the supervision of Emmanuel Candès. He received his bachelor's degree in Mathematics from Peking University in 2011. Su's research interests are in high-dimensional inference, multiple testing, first-order optimization algorithms, and privacy-preserving data analysis.



 

Wednesday, November 1st
The Jack Keil Wolf Lecture in Electrical and Systems Engineering
Mark Horowitz, Stanford University
Yahoo Founder's Professor of Electrical Engineering and Computer Science
"A Life of Breaking and Making: What Happens When You Mix a Driving Curiosity and Episodic Overconfidence"
3pm, Wu and Chen Auditorium. Reception to follow in Levine Lobby

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Abstract: This talk will describe how my deep and genuine desire to figure out how things work, coupled by periods of confidence (not always warranted) that I could, given time and effort, succeed in this task, allowed me to explore new areas that I had no business working in. This is not to say that each new adventure went smoothly: most contained times when I questioned my sanity and cursed my foolish confidence. These characteristics combined a general disinterest with long term planning has served me well. My only global strategy was to follow my mom’s advice and find work I really loved doing.

Not having long term plans allowed me to follow my nose and take advantage of a number of serendipitous events that crossed my path, which helped me discover what work I loved to do. I will end the talk describing some “lessons” I have learned that have helped me have fun and be successful.

Bio: Mark Horowitz received his BS and MS in Electrical Engineering from MIT in 1978, and his PhD from Stanford in 1984. Since 1984 he has been a professor at Stanford working in the area of digital integrated circuit design. While at Stanford he has led a number of processor designs including: MIPS-X, one of the first processors to include an on-chip instruction cache; Torch, a statically-scheduled, superscalar processor; Flash, a flexible DSM machine; and Smash, a reconfigurable polymorphic manycore processor. He has also worked in a number of other chip design areas including high-speed memory design, high-bandwidth interfaces, and fast floating point. In 1990 he took leave from Stanford to help start Rambus Inc, a company designing high-bandwidth memory interface technology.



 

Tuesday, November 7th
Bruno Sinopoli, Carnegie Mellon University
Professor of Electrical and Computer Engineering

Read the Abstract and Bio

Abstract: TBA

Bio: TBA



 

Tuesday, November 14th
Roy H. Olsson III, Defense Advanced Research Projects Agency (DARPA)
Program Manager of the Microsystems Technology Office (MTO)
"Untethered: Overcoming Energy and Bandwidth Limits of Wireless Sensor Networks"

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Abstract: Wireless communications and miniature sensing technologies have developed significantly over the past decade with the advent of the smart phone. However, the communications and sensing technologies developed around the cellular handset market are largely incompatible in energy consumption, frequency band, and data rate with biomedical, environmental, and infrastructure sensing, referred to here as the untethered internet-of-things (IoT). While communications for consumer wireless sensors have primarily developed in the 2.4 GHz ISM band, the radio frequency (RF) propagation losses at this frequency are extremely high in the body, on the ground, or through structural materials and foliage. In contrast to cellular phones that can recharge relatively large batteries daily, untethered IoT sensors must operate for many years without plugging in to recharge the battery or without a battery at all. The low carrier frequencies and energy expense of RF transmissions necessitates sensing and processing technologies for detecting, classifying and compressing the large amount of sensor data into useful information, while the amount of energy available is orders of magnitude lower than a smart phone.

This seminar will present microtechnologies for communications and sensing that are compatible with the stringent energy consumption, data rates and operating frequencies needed for the untethered IoT. First, neural recording sensors and electronics for monitoring hundreds of neurons within the power and bandwidth limitations imposed by both the body and an inductively coupled wireless telemetry link will be presented. Next, microelectromechanical resonators for providing high performance RF components in the 100 kHz to 400 MHz bands required in biomedical, ground emplaced and structural sensors will be discussed. A new class of intelligent nanowatt wakeup sensors and radio receivers will be presented that can detect and classify sensor and RF signatures while consuming less power than the leakage rate of a small battery. Finally, I will point to new research areas to fulfil the vision of the untethered IoT such as miniature antennas, efficient energy harvesters, and techniques for bandwidth and energy constrained communications.

Bio: Roy (Troy) H. Olsson III is a program manager in the Microsystems Technology Office (MTO) at the Defense Advanced Research Projects Agency (DARPA). His research interests include materials, devices, and architectures for low-power processing of wireless and sensor signals, vanishing materials, miniature antennas, and phased array antennas. Prior to joining DARPA, Troy was a Principal Electronics Engineer in the MEMS Technologies Department at Sandia National Laboratories in Albuquerque, NM. At Sandia, Troy led research programs developing aluminum nitride and lithium niobate piezoelectric micro-devices for processing of RF, inertial and optical signals. He received B.S. degrees (Summa Cum Laude) in electrical engineering and in computer engineering from West Virginia University in 1999 and the M.S. and Ph.D. degrees in electrical engineering from the University of Michigan, Ann Arbor in 2001 and 2004. His graduate research was in the area of low power electronics and sensor arrays for interfacing with the central nervous system.

Troy has authored more than 100 technical journal and conference papers and holds 25 patents in the area of microelectronics and microelectromechanical systems (MEMS). He served on the organizing committee of the 2011 Phononics Conference and was a Member of the Technical Program Committee for the IEEE Ultrasonics Symposium (IUS) from 2010-2016. He is a Senior Member of the IEEE and a Member of the IEEE Solid State Circuits Society; the IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society; Eta Kappa Nu; and Tau Beta Pi. Together with the Sandia Microresonator Research Team, he was awarded an R&D100 award in 2011 for his work on Microresonator Filters and Frequency References.



 

Tuesday, November 28th
Tamer Basar, University of Illinois at Urbana-Champaign
Swanlund Endowed Chair & CAS Professor of Electrical and Computer Engineering

Read the Abstract and Bio

Abstract: TBA

Bio: TBA



 

Tuesday, December 5th
Na (Lina) Li, Harvard University
Assistant Professor of Electrical Engineering

Read the Abstract and Bio

Abstract: TBA

Bio: TBA