ESE Colloquia & Events

Spring 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.

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Tuesday, January 31st
Seungbum Lim
Research Laboratory of Electronics, MIT
Towards Miniaturized High-Performance Power Electronics

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Abstract: Power electronics is important to various types of electrical and electronic equipment, and is a key factor determining the size, weight, energy consumption, and performance of a system. Demand for power electronic systems that are smaller, lighter, more efficient, and less costly is soaring in order to meet the needs of consumer, industrial, and commercial applications. My research focuses primarily on the design of miniaturized high performance power electronics and their applications in areas such as renewable energy, transportation, medical devices, robotics, data centers, and telecommunications.
A key route to achieving miniaturized high-performance converter is the development of power electronics that operates efficiently at substantially higher switching frequencies than are presently available. There are opportunities for notable advances in power electronics that take advantages of rapid changes in power semiconductors, magnetics, capacitors, sophisticated control methods with analog/mixed-signal ICs, and new materials and fabrication techniques. New design approaches that can leverage these changes promise unprecedented high performance, greatly improved functionality, and order-of-magnitude improvements in power density. These greatly improved power electronics can in turn revolutionize what is possible in many applications in terms of system size, energy efficiency, and functionality.
In pursuit of this vision, I will present the miniaturized high performance power electronics developed for ac-dc applications, including an LED driver and laptop charger. A novel grid interface ac-dc power converter demonstrates high frequency operation and significant size reduction of the passive component in power converters, and realizes high power density, high efficiency, and long life-time. Finally, I will discuss how architectural innovations in power electronics can benefit performance in many power processing systems, and further enlighten the path to much more sophisticated power electronics for a wide range of applications.

Bio: Seungbum Lim received the B.S. degree from Seoul National University in 2010 and the S.M. and Ph.D degree from the MIT in 2012 and 2016, respectively. He is currently a postdoctoral research associate in the MIT Research Laboratory of Electronics. His research interests lie in the design of high performance power electronics and their applications in numerous fields including electric vehicle, renewable energy, robotics, micro-grids, medical devices, telecommunications, transportations, and aerospace. He is the recipient of the Will Portnoy First Prize Award from IEEE Industry Applications Society.

Thursday, February 2nd
Gireeja Ranade
Microsoft Research, Redmond
An Informational Perspective on Uncertainty in Control

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Abstract: Developing high-performance cyber-physical systems requires a deep understanding of how uncertainty and unpredictability impair performance. In this talk, I discuss some theoretical perspectives to understand uncertainty in systems as well as practical protocols to mitigate it.

I will first introduce a notion of "control capacity," which parallels the notion of Shannon communication capacity, and provides a fundamental limit on the ability to stabilize a system with random time-varying parameters (modeled as multiplicative noise). Further, it can be used to quantify the value of side-information in control.

We contrast systems with noisy actuation (e.g., when motors on a drone cannot precisely execute control actions) to noisy sensing (e.g., miscalibrated cameras). In the first case, we show that linear control strategies are optimal, while in the second, we show that non-linear strategies can outperform them. Further, we use techniques from information-theory and probability-theory to bound the improvement that non-linear strategies can bring.

Finally, I will shift from quantifying the effect of uncertainty to methods for reducing uncertainty. With the aim of enabling industrial automation, I will discuss the development of highly-reliable low-latency wireless communication protocols for machine-to-machine communication.

The talk will include joint work with Jian Ding, Yuval Peres, Govind Ramnarayan, Anant Sahai, Sahaana Suri, Vasuki Narasimha Swamy, and Alex Zhai.

Bio: Gireeja Ranade is a postdoctoral researcher at Microsoft Research, Redmond. Before this she was a lecturer in EECS at UC Berkeley working on designing and teaching the pilot version of novel lower-division EECS classes (16AB). She received an MS and PhD in EECS from UC Berkeley and an SB in EECS from MIT. She has worked on topics in brain-machine interfaces, information theory, control theory, wireless communications and crowdsourcing.

Tuesday, February 7th
Zheng Zhang
Research Laboratory of Electronics, MIT
High-Dimensional and Stochastic Methods for Uncertainty-Aware Engineering Design Verification: Nanoscale Systems and Beyond

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Abstract: Uncertainties can cause significant performance degradations and functional failures in numerous engineering systems. Examples include (but are not limited to) nanoscale devices and systems with fabrication process variations, robot control without full knowledge of design or environmental parameters, energy systems with weather-dependent renewable energy sources, and magnetic resonance imaging (MRI) with incomplete and noisy scanning data. Modeling, controlling and optimizing these problems are generally data-intensive: one has to generate and analyze a huge amount of costly data in a parameter space. This often leads to the notorious curse of dimensionality: the complexity grows extremely fast with the number of uncertainty or/and design parameters.

This talk presents some fast non-Monte-Carlo techniques to verify the performance of uncertain engineering systems. These techniques can accelerate a lot of uncertainty-aware optimization, control and data inference tasks (e.g., yield optimization of silicon chips, robust control of robots and power systems, electrical property tomography using MRI data). The first part will present some fast algorithms to simulate nonlinear dynamic systems influenced by a small number of uncertain parameters. The second part will present some high-dimensional algorithms to predict the performance uncertainties of an engineering system influenced by many random parameters. The main application is variability analysis of nanoscale IC, MEMS and integrated photonics. Other application examples (e.g., energy systems and MRI) will also be demonstrated. On these benchmarks, our approaches are significantly faster than Monte Carlo (by 100X to 1000X) and recent spectral methods (by dozens of times).

Bio: Zheng Zhang is a postdoc associate with MIT, where he received his Ph.D. degree in Electrical Engineering and Computer Science in 2015. He is interested in high-dimensional uncertainty analysis and data inference for diverse engineering problems, including nanoscale devices and systems, hybrid systems (e.g., power systems and robots) and MRI. Dr. Zhang received the 2016 ACM Outstanding PhD Dissertation Award in Electronic Design Automation, the 2015 Doctoral Dissertation Seminar Award from the Microsystems Technology Laboratory of MIT, and the 2014 Best Paper Award from IEEE Transactions on CAD of Integrated Circuits and Systems. He is a TPC member of Design Automation Conference (DAC) and International Conference on Computer-Aided Design (ICCAD).

Thursday, February 9th
Rashmi Vinayak
University of California, Berkeley
Smart Redundancy for Big-Data Systems: Theory and Practice

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Abstract: Large-scale distributed storage and caching systems form the foundation of big-data systems. A key scalability challenge in distributed storage systems is achieving fault tolerance in a resource-efficient manner. Towards addressing this challenge, erasure codes provide a storage-efficient alternative to the traditional approach of data replication. However, classical erasure codes come with critical drawbacks: while optimal in utilizing storage space, they significantly increase the usage of other important cluster resources such as network and I/O. In the first part of the talk, I present new erasure codes and theoretical optimality guarantees. The proposed codes reduce the network and I/O usage by 35-70% for typical parameters while retaining the storage efficiency of classical codes. I then present an erasure-coded storage system that employs the proposed codes, and demonstrate significant benefits over the state-of-the-art in evaluations under real-world settings. Our codes have been incorporated into Apache Hadoop 3.0. The second part of the talk focuses on achieving high performance in distributed caching systems. These systems routinely face the challenges of skew in data popularity, background traffic imbalance, and server failures, which result in load imbalance across servers and degradation in read latencies. I present EC-Cache, a cluster cache that employs erasure coding to achieve a 3-5x improvement as compared to the state-of-the-art.

Bio: Rashmi K. Vinayak is a postdoctoral researcher in the EECS department at UC Berkeley, where she received her PhD in 2016. Her dissertation received the Eli Jury Award 2016 from the EECS department at UC Berkeley for outstanding achievement in the area of systems, communications, control, or signal processing. Rashmi is also a recipient of the Facebook Fellowship 2012-13, the Microsoft Research PhD Fellowship 2013-15, and the Google Anita Borg Memorial Scholarship 2015-16. She is also the recipient of the IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012. Her research interests lie in the theoretical and system challenges that arise in storage and analysis of big data.

Friday, February 10th
GRASP/ESE Joint Seminar: Sonja Glavaski
Program Director, ARPA-E
Grid of the Future: Controlling the Edge
*11am in Wu and Chen Auditorium*

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Abstract: The evolution of the grid faces significant challenges if it is to integrate and accept more energy from renewable generation and other Distributed Energy Resources (DERs). To maintain grid's reliability and turn intermittent power sources into major contributors to the U.S. energy mix, we have to think about the grid differently and design it to be smarter and more flexible.

ARPA-E is interested in disruptive technologies that enable increased integration of DERs by real-time adaptation while maintaining grid reliability and reducing cost for customers with smart technologies. The potential impact is significant, with projected annual energy savings of more than 3 quadrillion BTU and annual CO2 emissions reductions of more than 250 million metric tons.

This talk will identify opportunities in developing next generation control technologies and grid operation paradigms that address these challenges and enable secure, stable, and reliable transmission and distribution of electrical power. Innovative approaches to coordinated management of bulk generation, DERs, flexible loads, and storage assets with multiple roles, and revenue streams will be discussed. Summary of ARPA-E NODES (Network Optimized Distributed Energy Systems) Program funding development of these technologies will be presented.

Bio: Dr. Sonja Glavaski is a Program Director at the Advanced Research Projects Agency-Energy (ARPA-E). Her technical focus area is data analytics, and distributed control and optimization in complex, cyber-physical, and networked systems with applications to control, monitoring, and security of energy systems. During her 20-plus-year career, Dr. Glavaski has contributed significantly to technical advancements in numerous product areas, including energy systems, hybrid vehicles, energy efficient building HVAC/R systems, and aircraft systems.

Prior to joining ARPA-E, Dr. Glavaski served as Control Systems Group Leader at United Technologies Research Center advancing knowledge and technology in the area of control & intelligent systems. Before being at UTRC, Dr. Glavaski led key programs at Eaton Innovation Center and Honeywell Labs. Dr. Glavaski received Ph.D. and MS in Electrical Engineering from California Institute of Technology, and Dipl. Ing and MS in Electrical Engineering from University of Belgrade.

Tuesday, February 14th
Nihar Shah
University of California, Berkeley
Learning from People

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Abstract: Learning from people represents a new and expanding frontier for data science. Two critical challenges in this domain are of developing algorithms for robust learning and designing incentive mechanisms for eliciting high-quality data. In this talk, I describe progress on these challenges in the context of two canonical settings, namely those of ranking and classification. In addressing the first challenge, I introduce a class of "permutation-based" models that are considerably richer than classical models, and present algorithms for estimation that are both rate-optimal and significantly more robust than prior state-of-the-art methods. I also discuss how these estimators automatically adapt and are simultaneously also rate-optimal over the classical models, thereby enjoying a surprising a win-win in the bias-variance tradeoff. As for the second challenge, I present a class of "multiplicative" incentive mechanisms, and show that they are the unique mechanisms that can guarantee honest responses. Extensive experiments on a popular crowdsourcing platform reveal that the theoretical guarantees of robustness and efficiency indeed translate to practice, yielding several-fold improvements over prior art.

Bio: Nihar B. Shah is a PhD candidate at the University of California, Berkeley. He is the recipient of the Microsoft Research PhD Fellowship 2014-16, the Berkeley Fellowship 2011-13, the IEEE Data Storage Best Paper and Best Student Paper awards for the years 2011/2012, and the SVC Aiya Medal from the Indian Institute of Science for the best masters thesis in ECE. His research interests include statistics and machine learning, with a current focus on applications to learning from people.

Friday, February 17th
Jean Anne C. Incorvia
Stanford University
Next-Generation Computing using Spin-Based and 2D Materials

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Abstract: We are at a time where the electronics industry is feeling pressure from two sides: on the small scale we are facing the fundamental physical limits of silicon, and on the large scale we are facing new big-data applications, such as for the internet of things. The future of computing will require both more energy-efficient electronics and more big-data-driven, application-specific designs.

Magnetic devices are a promising candidate for future electronics, due to their low voltage operation, nonvolatility, and low thermal budget, which can open up new energy-efficient, normally-off, memory-in-computing, 3D monolithic architectures. Magnetic materials are one of the few materials systems that can be more energy efficient than silicon transistors for memory and logic, and have been shown to be more energy efficient and faster than other emerging resistive memories.

Additionally, the emerging class of 2D materials, such as graphene and transition metal dichalcogenides (TMDs), have little to no surface roughness with monolayer thickness, and thus 2D transistors can be scaled without sacrificing mobility. They have the benefits of flexibility and low thermal budget, and new physics we can utilize such as the valley Hall effect. Thus, spin-based and 2D materials are very important classes of materials to explore for beyond-CMOS devices and systems.

I will present experimental results using three-terminal spin switches to build practical magnetic logic devices and circuits, and show they satisfy the requirements for beyond-CMOS devices. We show a single device can act as an inverter, and we are able to propagate bits between the spin switches to build up circuits. I will also show our work on voltage control of the spin and valley Hall effect in TMD materials, which could be used for future 2D-magnetic hybrid devices. I will discuss the future directions of this work, including building energy-efficient 3D monolithic systems of these emerging technologies, and looking further to quantum computing.

Bio: Jean Anne C. Incorvia is a postdoctoral research fellow at Stanford University in electrical engineering and a visiting scholar at UC Berkeley electrical engineering and computer science. She received her Ph.D. in physics from Harvard University in 2015, cross-registered at MIT, where she was a Department of Energy Graduate Student Fellow. She received her bachelor’s in physics from UC Berkeley in 2008. Her research focuses on emerging materials and devices for nanoelectronics.

Tuesday, February 21st
Roozbeh Tabrizian
University of Florida
Phononics Paradigm for Integrated Information Acquisition and Signal Processing at the Nanoscale

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Abstract: (1) The dispersion signature of acoustic phonons excited and trapped in nanoscale waveguides carries extensive information on thermomechanical properties and morphological contents of constituent materials. (2) Phononic dispersion of waveguides can be geometrically engineered to accurately tailor the existence, polarization and distribution of acoustic phonons at desired frequencies. This talk overviews the application of these two constituents of the phononics paradigm for realization of integrated material spectroscopy nanosytems, and linear and nonlinear phononic signal processors. I will present our recent accomplishments on phononic detection of morphological phase transitions in atomic-layered synthesized films. I will demonstrate the application of phononic dispersion engineering for realization of temperature stable frequency references, wideband filters, phononic mixers, frequency combs, etc. Finally, I will present our recent findings on elastic anharmonicity engineering in semiconductors and phonon-photon anharmonic exchange process in piezoelectric transducers that suggest the potential of integrated nonlinear phononics for realization of chip-scale millimeter-wave signal processors.

Bio: Roozbeh Tabrizian received the B.S. in Electrical Engineering from Sharif University of Technology, Tehran, Iran, in 2007, and the Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology, 2013. In September 2014, he joined the Department of Electrical Engineering, University of Michigan as a Postdoctoral research fellow. In August 2015, he joined the Department of Electrical and Computer Engineering, University of Florida as an Assistant Professor. His research at the University of Florida involves novel linear and nonlinear phononic devices, mixed-domain nanosystems for sensing, time keeping / transfer and memory applications, and RF MEMS. Dr. Tabrizian’s research has resulted in more than 30 journal and conference papers, 2 book chapters, 3 published patents and 5 patent applications. He is the recipient of outstanding paper awards at the 27th IEEE International Conference on Micro Electro Mechanical Systems (MEMS 2014) and the 16th International Conference on Solid-State Sensors, Actuators, and Microsystems (Transducers 2011).

Tuesday, February 21st
Marisel Villafane-Delgado
Michigan State University
Functional Connectivity in the Human Brain and Integrating Research and Teaching in Engineering Education
*2pm in Towne 337*

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Abstract: Cognition relies in the functional integration of multiple segregated specialized regions in the brain. Current functional connectivity measures present certain limitations in the quantification of global integration and characterization of network structure. During the first part of this talk, a measure of multivariate phase synchrony based on hyperdimensional geometry for quantifying the common integration among multiple nonlinear oscillators will be presented. This method provides insights of cognitive control mechanisms not quantified by bivariate measures. The second part of this talk will focus on teaching methods and their role in electrical engineering courses. Such methods include teaching as research (TAR), design of teaching modules following backward design and teaching for understanding, alignment of assessments, active and cooperative learning and project-based learning. The role of some of these methods will be described from various teaching experiences, including an ongoing TAR project aiming to identify student’s mathematical misconceptions and deficiencies relevant for signal processing courses, a module for teaching convolution inspired on project-based learning and flipped classroom, and from tutoring and designing modules and assessments aligned with teaching in the Electrical Engineering Analysis course.

Bio: Marisel’s doctoral research focuses on the development of signal processing techniques and algorithms for the assessment of complex and dynamic networks, with applications to functional connectivity networks in the human brain. Her research interests include signal processing for complex networks, data science and cognitive dynamic systems. Her teaching interests include effective teaching methods in the classroom, massive online teaching, evidence-based STEM teaching, teaching as research, and lifelong-learning challenges. She is a recipient of the National Science Foundation Graduate Research Fellowship, the University of Maryland Clark School of Engineering Distinguished Graduate Fellowship, and the Michigan State University Future Academic Scholars in Teaching Fellowship.

Thursday, February 23rd
Zhaoran Wang
Princeton University
Taming Nonconvexity with Data

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Abstract: Nonconvex optimization is becoming one of the most powerful workhorses of data science and artificial intelligence. Compared with convex optimization, it enjoys superior statistical accuracy, computational efficiency, and modeling flexibility in numerous modern settings. However, the empirical success of nonconvex optimization largely eludes the reach of the classical statistical and optimization frameworks, which prohibits us from designing more efficient algorithms in a principled manner.

In this talk, I will illustrate how statistical thinking enables us to harness the power of nonconvex optimization. In specific, I will present an algorithmic framework for exploiting the latent geometry induced by the randomness of data. By integrating three new global exploration meta-algorithms — namely, homotopy continuation, tightening after relaxation, and noise regularization — with local search heuristics — such as the variants of gradient descent — this unified framework leads to new nonconvex optimization algorithms for a broad variety of challenging learning problems. In particular, these algorithms enjoy provably optimal statistical accuracy and computational efficiency, and moreover, lead to new scientific discoveries. Time permitting, I will discuss an interesting “more data, less computation” phenomenon, which arises from nonconvex optimization, but generalizes to even more algorithms.

Bio: Zhaoran is a graduate student at Princeton University, working at the interface of machine learning, statistics, and optimization. He is the recipient of the AISTATS (Artificial Intelligence and Statistics Conference) notable paper award, ASA (American Statistical Association) best student paper in statistical learning and data mining, INFORMS (Institute for Operations Research and the Management Sciences) best student paper finalist in data mining, and the Microsoft fellowship.

Friday, February 24th
Jun Yao
Harvard University
Integrating Nanowires for Computing, Bio-Sensing, and Brain Interface

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Abstract: The wide compositional, morphological, and surface properties achievable in synthesized nanowires afford unique performance advantages in devices ranging from computing to sensing elements. Post-synthetic control over the order and geometry of nanowires is central to device integration and realizing new functions. To this end, a novel ‘combing’ technique, by decoupling the force anchoring nanowires from force aligning them, has been developed to achieve the accurate control of both position and alignment in nanowires. This leads to the construction of complex computing architectures, exemplified by a nanocomputer featuring complexity beyond the start-of-art from bottom-up. Moreover, the ‘combing’ technique enables nanowire assembly in the vertical, off-substrate dimension, producing three-dimensional (3D) transistor structures capable of both mechanical and field-potential sensing, enabling the simultaneous electrical recording of action potential and mechanical contraction in cardiomyocytes. These assembled nanosensors are further integrated in porous and ultra-flexible electronic meshwork that can be injected through syringe into brain for engineering advanced neural interface.

Bio: Dr. Jun Yao holds a B.S. in Electrical Engineering (2003) and an M.S. in Physics (2006) from Fudan University in China. He received his Ph.D. in Applied Physics (2011) from Rice University with Prof. James M. Tour (and co-advisers Prof. Douglas Natelson and Prof. Lin Zhong). His Ph.D. research involved the discovery of the intrinsic memristive effect in silicon oxides and the implementation in application and commercialization. He then moved to Harvard University as a postdoctoral fellow working with Prof. Charles Lieber, focusing on the synthesis, assembly, fabrication and integration of nanomaterials for applications in electronics and bioelectronics.

Tuesday, February 28th
Shirin Saeedi Bidokhti
Stanford University
Centralized Processing and Caching: Architectures for Future Networks

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Abstract: Traditional communication networks will soon be incapable of supporting the high data rate and the massive number of connected devices envisioned in future network applications (e.g. the Internet of Things). Recent advances in cloud storage and processing has opened new doors to the design of future networks where devices are of limited computational capabilities, but have access to various local storage systems and their processing tasks can be transferred to cloud processors.

In this talk, I will discuss how centralizing information processing and distributing storage capabilities lead to new network architectures, coding opportunities, and communication criteria. In particular, I will focus on two key architectures for future communication networks: cloud radio access networks and cache-aided networks. I develop theoretical frameworks to capture the new challenges and opportunities in these networks, devise communication strategies that improve the performance by establishing cooperation among the distributed nodes, and prove performance guarantees. I will conclude by outlining interesting research questions that future networks will entail as the goal of communication becomes to learn certain information rather than to exactly reconstruct the entire data.

Bio: Shirin Saeedi Bidokhti is a postdoctoral scholar at Stanford University. Prior to that she was a postdoctoral scholar at the Technical University of Munich. She received her Ph.D. in Computer and Communication Sciences from the Swiss Federal Institute of Technology (EPFL) and is a recipient of the Prospective Researcher Fellowship (2012) and the Advanced Postdoc Mobility Fellowship (2014) from the Swiss National Science Foundation.

Thursday, March 2nd
Yanqi Zhou
Princeton University
Cloud-based Architecture

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Abstract: Businesses and Academics are increasingly turning to Infrastructure as a Service (IaaS) Clouds to fulfill their computing needs. Unfortunately, current IaaS systems provide a severely restricted pallet of rentable computing options which do not optimally fit the workloads that they are executing. Yanqi's research encompasses various aspects (Sharing Architecture [ASPLOS 2014], MITTS [ISCA 2016], CASH [ISCA 2016]) of computer architecture aimed at improving IaaS Cloud economic efficiency. The talk will focus on the design and evaluation of a manycore architecture (Sharing Architecture) and a memory bandwidth provisioning mechanism (MITTS). The Sharing Architecture is specifically optimized for IaaS systems by being reconfigurable on a sub-core basis. It enables better matching of workload to micro-architecture resources by replacing static cores with Virtual Cores which can be dynamically reconfigured to have different numbers of ALUs and amount of Cache. MITTS (Memory Inter-arrival Time Traffic Shaping) is a distributed hardware mechanism which limits memory traffic at the source (Core or LLC). MITTS shapes memory traffic based on memory request inter-arrival time using novel hardware, enabling fine-grain bandwidth allocation. In an IaaS system, MITTS enables Cloud customers to express their memory distribution needs and pay commensurately. In a general purpose multicore program, MITTS can be used to optimize for memory system throughput and fairness. MITTS has been implemented in the 32nm 25-core Princeton Piton Processor [HotChip 2016], as well as the open source OpenPiton [ASPLOS 2016] processor framework. The Sharing Architecture and MITTS provide fine-grain hardware configurability, which improves economic efficiency in IaaS Clouds.

Bio: Yanqi Zhou is the first graduate student of Prof. David Wentzlaff. Her research area is computer architecture, operating system, and parallel computing. She got her Bachelor’s degrees in Electrical Engineering, Computer Engineering, and Mathematics from University of Michigan and Shanghai Jiao Tong. As a research intern, she worked at Microsoft Research for two summers. Apart from research, she enjoys playing tennis, basketball, swimming, and yoga. As a music lover, she has been playing the violin for over ten years.

Tuesday, March 7th
Kaiyuan Yang
University of Michigan
Securing the Internet of Things: A Hardware Perspective

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Abstract: The Internet of Things (IoT) is expected to connect our physical world with billions of sensors and actuators, transforming the way we live and work, as well as creating enormous business markets. Security and privacy are seen as the most critical challenges to IoT growth in the future. Because of the severe energy and cost constraints as well as physical exposure of the IoT devices, software-only security cannot meet the performance demands and faces a variety of new threats targeting hardware, such as cache attacks, side-channel attacks, and semiconductor supply chain attacks. Therefore, to meet the unique challenges of IoT security, hardware designs are in strong demand to complement software defenses, specifically for providing efficient roots of trust, acceleration of secure communications, and protections against physical attacks. Security spans the complete stack of a system and can only be solved holistically through cross-layer co-optimizations.
In this talk, I will present hardware designs for IoT security, which cross analog and digital domains and incorporate system considerations. I will first present robust and portable true random number generators (TRNG) and physically unclonable functions (PUF) as roots of trusts for key generation and storage. Several of the designs employ commonly avoided higher order harmonics in multi-mode oscillators as entropy sources. Then, a compact and energy-efficient crypto accelerator for Advanced Encryption Standard (AES) will be shown as another type of fundamental block to support secure IoT system. On the other hand, finding potential security flaws is an indispensable part of enhancing system security. I will unveil one potential vulnerability of integrated circuits by presenting a hardware Trojan attack leveraging analog behaviors of processors, which represents the first fabrication-time hardware attack that is small, stealthy and controllable. Lastly, I will conclude the talk with my vision on future low-power and secure hardware platforms for various IoT applications, which requires strong security primitives, functional blocks (for sensing, power management and processing) fused with analog and digital security protections, and secure communication protocols co-optimized with hardware.

Bio: Kaiyuan Yang is a Ph.D. candidate in Electrical and Computer Engineering at the University of Michigan, Ann Arbor, where he received his M.S. degree in 2014. He received his Bachelor degree in Microelectronics from Tsinghua University, China, in 2012. His research interests include digital and mixed-signal circuit design for secure and low-power systems, hardware security, and circuit/system design using emerging technologies. He was the recipient of the 2016-2017 IEEE Solid-State Circuits Society (SSCS) Predoctoral Achievement Award, the Distinguished Paper Award at the 2016 IEEE International Symposium on Security and Privacy (S&P), the Best Student Paper Award (1st place) at the 2015 IEEE International Symposium on Circuits and Systems (ISCAS), the 2016 Pwnie Most Innovative Research Award Finalist, and the Best Undergraduate Thesis Award from Tsinghua University in 2012.

Thursday, March 9th
Deep Jariwala
California Institute of Technology
Mixed-Dimensional Heterostructures for Nanoelectronics and Photovoltaics

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Abstract: The isolation of a growing number of two-dimensional (2D) materials has inspired worldwide efforts to integrate distinct 2D materials into van der Waals (vdW) heterostructures. While a tremendous amount of research activity has occurred in assembling disparate 2D materials into “all-2D” van der Waals heterostructures, this concept is not limited to 2D materials alone. Given that any passivated, dangling bond-free surface will interact with another via vdW forces, the vdW heterostructure concept can be extended to include the integration of 2D materials with non-2D materials that adhere primarily through noncovalent interactions. In the first part of this talk I will present our work on emerging mixed-dimensional (2D + nD, where n is 0, 1 or 3) heterostructure devices performed at Northwestern University. I will present two distinct examples of gate-tunable p-n heterojunctions. I will show that when a single layer n-type molybdenum disulfide (MoS2) (2D) is combined with p-type semiconducting single walled carbon nanotubes (1D), the resulting p-n junction is gate-tunable and shows a tunable diode behavior with rectification as a function of gate voltage and a unique anti-ambipolar transfer behavior. The same concept when extended to p-type organic small molecule semiconductor (pentacene) (0D) and n-type 2D MoS2 leads to a tunable p-n junction with a photovoltaic effect and an asymmetric anti-ambipolar transfer response. I will present the underlying charge transport and photocurrent responses in both the above systems using a variety of scanning probe microscopy techniques as well as computational methods. Finally, I will show that the anti-ambipolar field effect observed in the above systems can be generalized to other semiconducting heterojunction systems and extended over large areas with practical applications in wireless communication circuits.
The second part of talk will discuss my more recent work performed at Caltech on photovoltaic devices from 2D semiconductors such as transition metal dichalcogenides (TMDCs). High efficiency inorganic photovoltaic materials (e.g., Si, GaAs and GaInP) can achieve maximum above-bandgap absorption as well as carrier-selective charge collection at the cell operating point. But thin film photovoltaic absorbers have lacked the ability to maximize absorption and efficient carrier collection, concurrently often due to due to surface and interface recombination effects. In contrast, Van der Waals semiconductors have naturally passivated surfaces with electronically active edges that allows retention of high electronic quality down-to the atomically thin limit. I will show experimental demonstration of light confinement in ultrathin (< 15 nm) Van der Waals semiconductors (MoS2, WS2 and WSe2) leading to nearly perfect absorption. I will further present the fabrication and performance of our, broadband absorbing, heterostructure photovoltaic devices using sub-15 nm TMDCs as the active layers. I will conclude by presenting future prospects for TMDCs, other 2D materials and their heterostructures in photovoltaics, tunable emitters and switching device applications.

Bio: Deep Jariwala is a Resnick Prize Postdoctoral Fellow at the California Institute of Technology (Caltech) in Pasadena. Deep completed his undergraduate degree in Metallurgical Engineering from the Indian Institute of Technology in 2010. During his time as an undergraduate summer researcher, Deep contributed to the early pioneering works on chemical vapor deposition (CVD) of graphene, boron nitride and their heterostructures in Professor Pulickel Ajayan’s group at Rice University. Deep then went on to pursue his Ph.D. in Materials Science and Engineering (MSE) at Northwestern University under supervision of Professor Mark Hersam and Professor Tobin Marks, graduating in 2015. At Northwestern, Deep made contributions to the study of charge transport and electronic applications of two-dimensional (2D) semiconductors and pioneering the study of gate-tunable, mixed-dimensional, van der Waals heterostructures. Deep’s work during Ph.D. has earned him the highest graduate student level awards of multiple professional societies including the Russell and Sigurd Varian Award of the American Vacuum Society, the Graduate Student Award of the Materials Research Society, The Richard L. Greene Dissertation Award in Experimental Materials Physics of the American Physical Society as well as the highest department level honor in the form of Johannes E. and Julia R. Weertman Doctoral Fellowship of MSE, Northwestern University. Since September 2015 at Caltech, Deep is investigating strategies for enhancing light-matter interactions in 2D systems for efficient, ultra-thin, opto-electronic devices in Professor Harry Atwater’s group. His interests broadly lie at the intersection of new materials and solid state devices for opto-electronics and energy harvesting applications.

Tuesday, March 14th
Omid Abari
Massachusetts Institute of Technology
Software-Hardware Systems for the Internet of Things

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Abstract:​ ​Although interest in connected devices has surged in recent years, barriers still
remain to realizing the dream of the Internet of Things (IoT). There are two main challenges in delivering IoT systems. The first challenge is throughput. There will be billions of connected devices that need to send their data to the cloud, while our Wi-Fi and LTE networks are already congested. The second challenge is energy. Many IoT devices will be placed in inaccessible locations, and hence their lifetime will depend on how efficiently they use their batteries. Other IoT devices may be very small and must operate using a limited energy source. My work addresses both challenges by developing custom software-hardware systems for the Internet of Things. In this talk, I will present two examples of this research. The first example tackles the throughput challenge by developing new millimeter wave devices and protocols. My work addresses two main problems that prevent the adoption of millimeter wave frequencies in today’s networks: signal blockage and beam alignment. I show how my approach enables many new IoT applications, including untethered high-quality virtual reality. The second example tackles the energy challenge by introducing Caraoke, a smart city sensor that enables traffic management, speeding detection, and smart parking. The sensor is small, low-cost and low-power, and hence can be easily deployed on street lamps. Caraoke was deployed on Cambridge streets for 6 months and recently won the Boston smart city competition.

Biography: ​Omid Abari is a Ph.D. candidate in Electrical Engineering and Computer Science at MIT. He works on wireless networks and IoT systems. During his Ph.D., he designed, built, and deployed new software-hardware systems that deliver ubiquitous sensing, computing, and communications at scale. His research has been featured in Wired, Engadget, Techcrunch, and New Scientist. He was awarded the Merrill Lynch Fellowship in 2011 and Natural Sciences and Engineering Research Council of Canada (NSERC) Postgraduate Scholarships in 2011 and 2013. He won the ACM Student Research Competition (SRC) in 2014 and 2016. He received a Bachelor’s degree with high distinction in Communications Engineering from Carleton University in Canada, where he was awarded the Senate Medal for Outstanding Academic Achievement.

Thursday, March 16th
Hao Zhu
University of Illinois at Urbana-Champaign
Communication-Cognizant Distribution System Management

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Abstract: The operational paradigm of power distribution systems has witnessed significant transformations with increasing penetration of distributed energy resources (DERs). Voltage regulation, an important distribution operational task, has been greatly challenged by the intermittency and variability of DERs. Meanwhile, these DERs can be leveraged to regulate the grid voltage by quickly changing the reactive power outputs of their power-electronics interface. This talk will present a hybrid voltage control (HVC) strategy that can seamlessly integrate both the local and distributed control strategies. By designing a special voltage mismatch objective, we achieve the proposed HVC architecture using the partial primal-dual (PPD) algorithm that can allow for decentralized online implementations. The resultant HVC design improves over existing distributed methods by integrating with local voltage feedback. It can dynamically adapt to varying system operating conditions while being fully cognizant to the instantaneous availability of communication links. Under the worst-case scenarios of a total link failure, the proposed design naturally boils down to a surrogate local control implementations. Numerical tests on realistic feeder cases are presented to corroborate our analytical results and demonstrate the algorithmic performance.

Bio: Hao Zhu is an Assistant Professor of Electrical and Computer Engineering at UIUC. She received a BE degree from Tsinghua University in 2006, and MSc and PhD degrees from the University of Minnesota in 2009 and 2012, all in Electrical Engineering. She worked as a postdoc research associate on power grid modeling and validation at the UIUC Information Trust Institute before joining the ECE faculty in 2014. Her current research interests include power grid monitoring, distribution system operations and control, and energy data analytics. She received the NSF CAREER Award in 2017, the Siebel Energy Institute Seed Grant and the US AFRL Summer Faculty Fellowship in 2016. She is currently a member of the steering committee of the IEEE Smart Grid representing the IEEE Signal Processing Society.

Tuesday, March 21st
Vikrant Gokhale
Researcher, National Institute of Standards and Technology (NIST)
New Materials & Device Physics for the Next Generation of Phononic Microsystems

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Abstract: Phononic microdevices are critical components of today’s technology ecosystem, serving as sensors, transducers, clocks, and signal processing systems. This talk will present recent advances in phononic technology, encompassing a combination of novel material systems, device physics, designs, nanofabrication methods, and high-precision metrology techniques.

Phononic devices and integrated systems involving phononics, electronics and photonics have recently been implemented using piezoelectric semiconductors such as GaN, GaAs, n-AlN, and 4H-SiC. As an example, I will present my work on GaN micromechanical resonators (with integrated CNT or plasmonic absorber coatings) as low-noise infrared detector arrays. I will discuss phonon-electron interactions in piezoelectric semiconductor devices, and the conditions for achieving selective and directional attenuation/amplification of phonons. The talk will present the first measurements of acoustoelectric amplification in GaN resonant cavities, and the design of mechanically amplified oscillators. I will discuss energy dissipation mechanisms that impose fundamental limits on the performance of phononic devices, and strategies to mitigate or eliminate them. I will present our recently developed Scanning Dynamic Strain Microscopy technique that has enabled the first direct measurements of high-frequency strain, making it possible to meaningfully quantify tether loss. I will highlight the successful use of phononic crystal (PnC) tethers for mitigating this tether loss, resulting in resonators operating at the intrinsic loss limit. Concluding remarks will focus on future directions for phononic devices and systems, including multifunctional sensor systems, non-reciprocal devices, and electrically-pumped coherent acoustic phonon (CAP) sources. CAP sources can enable compact phonon spectroscopy and on-chip phononics, key technologies for a new generation of MEMS operating in the underutilized GHz-THz spectrum.

Bio: Vikrant Gokhale is a postdoctoral researcher at the National Institute of Standards and
Technology (NIST) in Gaithersburg, MD. He received a Ph.D. (2014) and M.S. (2010) in
Electrical Engineering from the University of Michigan, and a B.Tech. (2007) in Electronics and Instrumentation from VIT Vellore in India. From 2007 – 2008, he was at Honeywell Sensing and Controls working on smart MEMS pressure sensors. At NIST, Vikrant’s projects include the use of optical metrology techniques for quantifying femtometer-scale dynamics and energy dissipation mechanisms in high-frequency MEMS. Other research interests include phononics, RF MEMS, mechanics of 2D materials, nanofabrication technology, and integrated sensor systems.

Tuesday, March 28th
Dinesh Bharadia
Computer Science and Artificial Intelligence Laboratory, MIT
Full Duplex Wireless: From Impossibility to Practice

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Abstract: A long held assumption in wireless is that radios cannot transmit and receive at the same time on the same frequency. This assumption has informed several aspects of wireless network design: from radio design to PHY, MAC and network layers. In this talk, I will describe my research on invalidating this fundamental assumption. Specifically, I will describe the evolution of my research on self-interference cancellation over the last six years. The prototype has now been commercialized by Kumu Networks and has undergone successful field trials with Tier 1 network providers worldwide.
My work on self-interference cancellation has enabled a whole host of new applications beyond full-duplex radios. I will describe briefly, how we built a variety of systems: a full duplex relay which extends both range and capacity, a Wireless Virtual reality headset, low power connectivity solutions for IoT and fine-grained human motion tracking. I will also highlight the cross-disciplinary nature of the research spanning algorithms, software-hardware design, RF circuits, signal processing, PHY and MAC layers, and discuss both the challenges and opportunities in attacking problems that span several disciplines.

Bio: Dinesh Bharadia received his PhD from Stanford University, where he was advised by Prof Sachin Katti in 2016 and is currently a Postdoctoral Associate at MIT working with Prof. Dina Katabi and Prof. Mohammad Alizadeh. His research interests include advancing the theory and design of modern wireless communication systems, wireless imaging, sensor networks and data-center networks. In recognition of his work, Dinesh was named a Marconi Young Scholar for outstanding wireless research and awarded the Michael Dukakis Leadership award. He was also named as one of the top 35 Innovators under 35 in the world by MIT Technology Review in 2016. Dinesh is also recipient of the Sarah and Thomas Kailath Stanford Graduate Fellowship.
From 2013 to 2015, he was a Principal Scientist for Kumu Networks, where he worked to commercialize his research on full-duplex radios, building a product that underwent successful field trials at Tier 1 network providers worldwide like Deutsche Telekom and SK Telecom. Dinesh received his bachelor's degree in Electrical Engineering from the Indian Institute of Technology, Kanpur in 2010, where he received the gold medal for graduating at the top of his class. His research has been published at top conferences such as SIGCOMM, NSDI, MobiCom and has been cited over 2000 times.

Google Scholar can be located at
Publication list at dblp:

Thursday, April 6th
Giovanni De Micheli
Institute of Electrical Engineering & The Integrated Systems Center, EPFL
Emerging Integrated Systems: Technology and Design
*2pm in Towne 337*

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Abstract: New electronic devices are game changers for VLSI Systems on Chips. Indeed, new geometries, materials and properties enable both enhanced functionality of devices as well as new opportunities to achieve high performance with low energy consumption. VLSI design systems, leveraging new CAD algorithms, methods and tools are quintessential for exploring the wide spectrum of nano-device configurations as well as choosing the best architectural design.

Bio: Giovanni De Micheli is Professor and Director of the Institute of Electrical Engineering and of the Integrated Systems Centre at EPF Lausanne, Switzerland. He is program leader of the program. Previously, he was Professor of Electrical Engineering at Stanford University. He holds a Nuclear Engineer degree (Politecnico di Milano, 1979), a M.S. and a Ph.D. degree in Electrical Engineering and Computer Science (University of California at Berkeley, 1980 and 1983).

Prof. De Micheli is a Fellow of ACM and IEEE and a member of the Academia Europaea. His research interests include several aspects of design technologies for integrated circuits and systems, such as synthesis for emerging technologies, networks on chips and 3D integration. He is also interested in heterogeneous platform design including electrical components and biosensors, as well as in data processing of biomedical information. He is author of: Synthesis and Optimization of Digital Circuits, McGraw-Hill, 1994, co-author and/or co-editor of eight other books and of over 750 technical articles. His citation h-index is 92 according to Google Scholar. He is member of the Scientific Advisory Board of IMEC (Leuven, B), CfAED (Dresden, D) and STMicroelectronics.

Prof. De Micheli is the recipient of the 2016 IEEE/CS Harry Goode award for seminal contributions to design and design tools of Networks on Chips, the 2016 EDAA Lifetime Achievement Award, the 2012 IEEE/CAS Mac Van Valkenburg award for contributions to theory, practice and experimentation in design methods and tools and the 2003 IEEE Emanuel Piore Award for contributions to computer-aided synthesis of digital systems. He received also the Golden Jubilee Medal for outstanding contributions to the IEEE CAS Society in 2000, the D. Pederson Award for the best paper on the IEEE Transactions on CAD/ICAS in 1987, and several Best Paper Awards, including DAC (1983 and 1993), DATE (2005), Nanoarch (2010 and 2012) and Mobihealth(2016).

He has been serving IEEE in several capacities, namely: Division 1 Director (2008-9), co-founder and President Elect of the IEEE Council on EDA (2005-7), President of the IEEE CAS Society (2003), Editor in Chief of the IEEE Transactions on CAD/ICAS (1997-2001). He has been Chair of several conferences, including Memocode (2014) DATE (2010), pHealth (2006), VLSI SOC (2006), DAC (2000) and ICCD (1989).

Friday, April 7th
Keren Bergman
Lightwave Research Laboratory, Columbia University
Optical Data Movement in Extreme Scale Computing
*10am in Towne 337*

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Abstract: Performance scalability of next generation computing systems is becoming increasingly constrained by limitations in memory access, power dissipation and chip packaging. The processor-memory communication bottleneck, a major challenge in current multicore processors due to limited pin-out and power budget, becomes a detrimental scaling barrier to data-intensive computing. These challenges have emerged as some of the key hardware barriers to realizing the required memory bandwidths and system wide data movement. Recent manufacturing advances in nanoscale silicon photonic interconnect and switching technologies are providing the infrastructure for developing energy-efficient high-bandwidth optical interconnection networks. Importantly, the insertion of photonics into next-generation computing systems is not a one-to-one replacement. This talk examines the design and potential impact of photonic-enabled architectures for creating new classes of future extreme scale computing.

Bio: Keren Bergman is the Charles Batchelor Professor and Chair of Electrical Engineering at Columbia University. She was a founding member and currently serves as the inaugural Scientific Director of the Columbia Nano Initiative launched in 2014. Prof. Bergman received the B.S. from Bucknell University in 1988, and the M.S. in 1991 and Ph.D. in 1994 from M.I.T. all in Electrical Engineering. At Columbia, Prof. Bergman leads multiple cross-disciplinary programs at the intersection of computing and photonics. Her research focuses on the architectural design exploration and implementation of photonic systems that incorporate the advantages of manipulating information in the optical domain for advanced computing and data centers. Prof. Bergman is a Fellow of the OSA and IEEE.

Tuesday, May 9th - CANCELLED
Gianluca Setti
University of Ferrara, Italy
Compressive Sensing: From Algorithms to Circuits

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Abstract: Compressive Sensing is an acquisition technique which relies on the sparsity of the underlying signals, to enable sampling below the classical Nyquist rate. To do so, the signals must be acquired in an incoherent way with respect to the sparsity basis, which is classically obtained in practice by acquiring the signal through projection on a random PAM signal with i.i.d. symbols.

We first show that advantages with respect to the above “classical” compressive sensing approach can be achieved by exploiting the fact that, while sparsity is not under a system designer’s control, incoherence is, and therefore flexibility and creativity in implementing compressed sensing systems rely on the strategic design and control of incoherence. To accomplish this, we then assume that the signals to be acquired are not only sparse, but also localized, e.g. for nearly all practical applications, the signals of interest preferentially occupy a given subspace (for instance they are all low-pass or high-pass in the frequency
domain). We show how, for localized signals, the acquisition sequences can be designed to maximize their “rakeness,” that is, to maximize their capability to collect the energy of the samples during the acquisition phase and increase by several dBs the average SNR achieved in signal reconstruction.

We will then describe the design in a 0.18um CMOS technology of the most popular architecture for implementing A/D converters based on compressive sensing, namely the Random Modulation Pre-Integration (RMPI). We will show that the direct circuit implementation of the classical acquisition scheme exploiting i.i.d sequences leads to a highly suboptimal solution, and one needs therefore to follow a synergetic design between algorithm-circuit- system. We will show how the use of rakeness-based CS acquisition sequences can reduce the complexity of the implemented A/D from 16 to 8 stages for
processing ECG signals and from 64 to 24 for EMG ones. Furthermore, rakeness-derived sequences also eliminate the necessity for pre- or post-acquisition filtering stages intended to suppress high frequency artifacts and 60-Hz power-line noise interference.

Finally, we will show how the use of CS guarantee some level of privacy in information transmission, which makes the CS signal acquisition paradigm even more suitable for applications in the area of Body Area Networks and Internet of Things.

Bio: Gianluca Setti received a Dr. Eng. degree in Electronic Engineering and a
Ph.D. degree in Electronic Engineering and Computer Science from the University of
Bologna, Bologna in 1992 and in 1997, respectively. Since 1997 he has been with the
School of Engineering at the University of Ferrara, Italy, where he is currently a
Professor. Since 2000 he has also been a Faculty member in the Advanced Research
Center on Electronic Systems (ARCES) at the University of Bologna.

Dr. Setti has held various visiting positions, most recently at the University of Washington, at IBM T. J. Watson Laboratories, and at EPFL (Lausanne).

He is the recipient of numerous awards, including the 2004 IEEE Circuits and Systems (CAS) Society Darlington Award, the 2013 IEEE CASS Guellemin-Cauer Award and the 2013 IEEE CASS Meritorious Service Award. He is a Distinguished Lecturer for the IEEE CAS. Dr. Setti has also served as Editor-in- Chief for both the IEEE Transactions on Circuits and Systems - Part I: Regular Papers (2008-2009) and the IEEE Transactions on Circuits and Systems - Part II: Express Briefs, as the Technical Program Co-Chair for IEEE ISCAS (2007 and 2008) and as the 2010 President of the CAS Society. In 2013-2014 he served as the Vice President for Publication Services and Products for the IEEE, the first scientist not from North America to
serve in this role. He is also a Fellow of the IEEE.

He has authored over 260 publications and has edited 3 books in the areas of nonlinear circuits, recurrent neural networks, implementation and application of chaotic circuits and systems, statistical signal processing, electromagnetic compatibility, biomedical circuits and systems.


Fall 2016

Tuesday, September 27th
Gad Allon
University of Pennsylvania, M&T Program Director
Managing Service Systems in the Presence of Social Networks

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Abstract: We study the optimal service differentiation policy for service organizations with the presence of social networks. In our framework, customers' beliefs of the service quality evolve over time according to their own experiences and the reported experiences from their friends in the network. We characterize the conditions under which such belief system converges and the corresponding optimal service differentiation policy. Our main results can be summarized as follows. First, contrary to the existing literature, we show that, when customers directly report their experiences, the importance of a customer only depends on his economic value and his friends' economic values. In other words, the optimal policy only needs first-order friendship. Second, we demonstrate that the value of knowing social network structures critically depends on the correlation between customers' economic and social values. The social network value is higher if the correlation is lower. Third, we use a novel data set with more than 15,000 customers to show empirically that for many service providers, specifically those targeting mid and high end customers there are negative correlations between the social values and economic values of their customers. We also provide an intuitive explanation, with empirical justifications, of the differences between firms’ correlations.

Bio: Gad Allon is the Jeffrey A. Keswin Professor and Professor of Operations, Information and Decisions. He received his PhD in Management Science from Columbia Business School in New York and holds a Bachelor and Master degree from the Israeli Institute of Technology.
His research interests include operations management in general, and service operations and operations strategy in particular. Professor Allon has been studying models of information sharing among firms and customers both in service and retail settings, as well as competition models in the service industry. His articles have appeared in leading journals, including Management Science, Manufacturing and Service Operations Management and Operations Research. Professor Allon won the 2011 "Wickham Skinner Early-Career Research Award" of the Production and Operations Management Society. He is the Operations Management Department Editor of Management Science and serves on the editorial board of several journals. Gad is an award-winning educator, teaching courses on scaling operations and operations strategy. He is the co-founder of ForClass, a platform that enables professors to drive higher student engagement and accountability in their classrooms.

Tuesday, October 4th
Matteo Rinaldi
Northeastern University
Paradigm Shift in MEMS toward Multi-Functional and Near-Zero Power Integrated Microsystems

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Abstract: Sensors are nowadays found in a wide variety of applications, such as smart mobile devices, automotive, healthcare and environmental monitoring. The recent advancements in terms of sensor miniaturization, low power consumption and low cost allow envisioning a new era for sensing in which the data collected from multiple individual smart sensor systems are combined to get information about the environment that is more accurate and reliable than the individual sensor data. By leveraging such sensor fusion it will be possible to acquire complete and accurate information about the context in which human beings live, which has huge potential for the development of the Internet of Things (IoT) in which physical and virtual objects are linked through the exploitation of sensing and communication capabilities with the intent of making life simpler and more efficient
for human beings.

This trend towards sensor fusion has dramatically increased the demand of new technology platforms, capable of delivering multiple sensing and wireless communication functionalities in a small foot print. In this context, Micro- and Nanoelectromechanical systems (MEMS/NEMS) technologies can have a tremendous impact since they can be used for the implementation of high performance sensors and wireless communication devices with reduced form factor and Integrated Circuit (IC) integration capability.

This talk presents a new class of MEMS/NEMS devices that can address some of the most important challenges in the areas of physical, chemical and biological detection and can be simultaneously used to synthesize high-Q reconfigurable and adaptive radio frequency (RF) resonant devices. By combining the unique physical, optical and electrical properties of advanced materials such as thin film piezoelectric materials, graphene, photonic metamaterials, phase change materials and magnetic materials, multiple and advanced sensing and RF communication functionalities are implemented in a small footprint. Furthermore, a new class of sensors that can remain dormant, with near zero power consumption, until awoken by an external trigger or stimulus are presented as a solution to fundamentally break the paradigm of using active power to sense infrequent events and
enable a nearly unlimited duration of operation for unattended ground sensors.

Bio: Matteo Rinaldi received his Ph.D. degree in Electrical and Systems Engineering from the
University of Pennsylvania in 2010. He joined the Electrical and Computer Engineering
department at Northeastern University as an Assistant Professor in January 2012.

Dr. Rinaldi’s research focuses on understanding and exploiting the fundamental properties of
micro/nanomechanical structures and advanced nanomaterials to engineer new classes of
micro and nanoelectromechanical systems (M/NEMS) with unique and enabling features
applied to the areas of chemical, physical and biological sensing and low power
reconfigurable radio communication systems. In particular, his group has been actively
working on experimental research topics and practical applications to ultra-low power
MEMS/NEMS sensors (infrared, magnetic, chemical and biological), plasmonic micro and
nano electromechanical devices, medical micro systems and implantable micro devices for intra-body networks, reconfigurable radio frequency devices and systems, phase change material switches, 2D material enabled micro and nano mechanical devices.

The research in Dr. Rinaldi’s group is supported by several Federal grants (including DARPA, NSF, DHS) and the Keck foundation.

Dr. Rinaldi has co-authored more than 70 publications in the aforementioned research areas and also holds 2 patents and more than 10 device patent applications in the field of MEMS/NEMS.

Dr. Rinaldi was the recipient of the IEEE Sensors Council Early Career Award in 2015, the NSF CAREER Award in 2014 and the DARPA Young Faculty Award class of 2012. He received the Best Student Paper Award at the 2009, 2011 and 2015 (with his student) IEEE International Frequency Control Symposiums and the Outstanding Paper Award at the 18 th International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2015 (with his student).

Tuesday, October 11th - CANCELLED
Tomas Palacios
Massachusetts Institute of Technology

Read the Abstract and Bio
Bio: Tomás Palacios is the Emmanuel E. Landsman Career Development Associate Professor of Electrical Engineering and Computer Science at the Masscahusetts Institute of Technology (MIT). He is affiliated with the Department of Electrical Engineering and Computer Science and with the Microsystems Technology Laboratory. He studied Telecommunication Engineer in the Polytechnic University of Madrid, and he received his MS and PhD degrees in Electrical Engineering from the University of California - Santa Barbara in 2004 and 2006, respectively.
Tomás´ research interests include the design, processing and characterization of new electronic devices based on wide bandgap semiconductors for power amplification and digital applications beyond 100 GHz. When not at MIT, Tomás enjoys reading, listening to classical music, hiking and attending plays and concerts.
He is also author or coauthor of more than 130 scientific papers in international journals and conferences, three book chapter and multiple invited talks and patents. Recently Tomás has been awarded the DARPA Young Faculty Award (March 2008), the Office of Naval Research’ Young Investigator Award (March 2009) and the National Science Foundation (NSF) CAREER Award (July 2009).

Thursday, October 20th
The Jack Keil Wolf Lecture in Electrical and Systems Engineering
Thomas Kailath
Stanford University
The Process of Making Breakthroughs in Engineering
3pm, Wu and Chen Auditorium. Reception to follow in Levine Lobby

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Abstract: This presumptuous title was first suggested as a challenge, followed by an offer that I could not refuse. So, while there is no magic formula for making breakthroughs in any field, it is possible to glean some useful pointers from past experiences. Several factors come into play, technology being only one of them. The talk will examine these via a review of several case histories.

Bio:Thomas Kailath received a B.E. (Telecom) degree in 1956 from the College of Engineering, Pune, India, and S.M. (1959) and Sc.D. (1961) degrees in electrical engineering from the Massachusetts Institute of Technology. He then worked at the Jet Propulsion Labs in Pasadena, CA, before being appointed to Stanford University as Associate Professor of Electrical Engineering in 1963. He was promoted to Professor in 1968, and appointed as the first holder of the Hitachi America Professorship in Engineering in1988. He assumed emeritus status in 2001, but remains active with his research and writing activities. He also held shorter-term appointments at several institutions around the world: UC Berkeley, Indian Statistical Institute, Bell Labs, Indian Institute of Science, Cambridge University, K. U. Leuven, T.U. Delft, Weizmann Institute, Imperial College, MIT, UCLA ,T. U. Munich. 

His research and teaching have ranged over several fields of engineering and mathematics: information theory, communications, linear systems, estimation and control, signal processing, semiconductor manufacturing, probability and statistics, and matrix and operator theory. He has also co-founded and served as a director of several high-technology companies. He has mentored an outstanding array of over a hundred doctoral and postdoctoral scholars. Their joint efforts have led to over 300 journal papers, a dozen patents and several books and monographs, including the major textbooks: Linear Systems (1980) and Linear Estimation (2000). 

He received the IEEE Medal of Honor in 2007 for "exceptional contributions to the development of powerful algorithms for communications, control, computing and signal processing." Among other major honors are the Shannon Award of the IEEE Information Theory Society; the IEEE Education Medal and the IEEE Signal Processing Medal; the 2009 BBVA Foundation Prize for Information and Communication Technologies; the Padma Bhushan, India’s third highest civilian award; election to the U.S. National Academy of Engineering, the U.S. National Academy of Sciences, and the American Academy of Arts and Sciences; foreign membership of the Royal Society of London, the Royal Spanish Academy of Engineering, the Indian National Academy of Engineering, the Indian National Science Academy, the Indian Academy of Sciences, and TWAS (The World Academy of Sciences). 

In November 2014, he received a US National Medal of Science from President Obama "for transformative contributions to the fields of information and system science, for distinctive and sustained mentoring of young scholars, and for translation of scientific ideas into entrepreneurial ventures that have had a significant impact on industry."

Tuesday, October 25th
Grace Hopper Lecture Series
Muriel Medard
Massachusetts Institute of Technology
Network Coding - A Personal Account of Combining Theory and Practice
3pm, Wu and Chen Auditorium

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Abstract: This talk seeks to illustrate the interplay between theoretical development and engineering implementation, with a personal slant. It centers on Network Coding (NC), a modern information theoretic development that leverages algebraic data manipulation during transport through a network to enhance resource usage. The addition of data manipulation to network modeling went beyond traditional graph theoretic considerations, allowing a significant relaxation of constraints that had original been treated as essential and, consequently, to the circumvention of impasses. The new model afforded opportunities for improved resource usage in existing networks through developments such as our Random Linear Network Coding (RLNC). While RLNC provided provably optimal throughput within standard theoretical frameworks, introducing it into the most common Internet transport protocol, Transmission Control Protocol (TCP), required an inventive reinterpretation of TCP’s control signals. Our recent theoretical results in Equivalence Theory show there is no benefit, in terms of throughput, in combining NC with the type of coding commonly used to palliate mistransmissions in error-prone media such as wireless links. These results confirm the sense behind current operational practice, but contradict long-standing folk-theorems regarding the benefit of joint coding. However, when other performance metrics such as energy consumption are taken into account, in practice we have shown that combining NC with coding for wireless links leads to marked, cumulative gains. We shall conclude the talk with open challenges and research directions driven by the coming convergence of data storage and networking. No background knowledge will be assumed.

Bio: Muriel Médard is the Cecil H. Green Professor in the Electrical Engineering and Computer Science Department at MIT and leads the Network Coding and Reliable Communications Group at the Research Laboratory for Electronics at MIT. She has co-founded two companies to commercialize network coding, CodeOn and Steinwurf. She has served as editor for many publications of the Institute of Electrical and Electronics Engineers (IEEE), of which she was elected Fellow, and she is currently Editor in Chief of the IEEE Journal on Selected Areas in Communications . She was President of the IEEE Information Theory Society in 2012, and served on its board of governors for eleven years.  She received the 2009 IEEE Communication Society and Information Theory Society Joint Paper Award, the 2009 William R. Bennett Prize in the Field of Communications Networking, the 2002 IEEE Leon K. Kirchmayer Prize Paper Award and several conference paper awards. She was co-winner of the MIT 2004 Harold E. Edgerton Faculty Achievement Award. In 2007 she was named a Gilbreth Lecturer by the U.S. National Academy of Engineering.

Tuesday, November 15th
Douglas Densmore
Boston University
Circuits in Cells, Bits in Bugs - How (Synthetic) Biology is a Computing Platform
*Joint ESE - BE Seminar*

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Abstract: Successful computing systems leverage their underlying technologies to solve problems humans simply cannot. Electronic systems harness the power of electrons and semiconductors. Mechanical systems use physical force and physical interactions. Biological systems represent a computing paradigm that can harness evolution/adaptation, redundancy/replication, chemistry/natural processing, and living material/organisms. Engineered, living biological systems which make decisions, process “data”, record events, adapt to specific inputs/outputs, and communicate to one another will deliver exciting new solutions in bio-therapeutics, bio-materials, bio-energy, and bio-remediation. This is engineering of biological systems has been dubbed “Synthetic Biology”. In this talk, I will outline my vision for “Bio-Design Automation” for synthetic biology. Specifically I will highlight my research’s efforts in the specification, design, assembly, verification, and data management involved in automating synthetic biology. These challenges are addressed by a suite of software tools which draw inspiration from Electronic Design Automation. I will discuss how to leverage traditional logic synthesis techniques to create genetic circuits for synthetic biology using a tool called “Cello”. I will also outline hybrid microfluidic bio-computation captured in a workflow called “Fluigi”. I will close by discussing community and commercial involvement mechanisms via the Bio-Design Automation Consortium, the Nona Research Foundation, and Lattice Automation, Inc.

Bio: Douglas Densmore is a Kern Faculty Fellow, a Hariri Institute for Computing and Computational Science and Engineering Junior Faculty Fellow, and Associate Professor in the Department of Electrical and Computer Engineering at Boston University. His research focuses on the development of tools for the specification, design, and assembly of synthetic biological systems, drawing upon his experience with embedded system level design and electronic design automation (EDA).

He is the director of the Cross-disciplinary Integration of Design Automation Research (CIDAR) group at Boston University, where his team of staff and postdoctoral researchers, undergraduate interns, and graduate students develop computational and experimental tools for synthetic biology. His research facilities include both a computational workspace in the Department of Electrical and Computer Engineering as well as experimental laboratory space in the Boston University Center of Synthetic Biology (CoSBI).

His research interests include Computer Architecture, Embedded Systems, Logic Synthesis, Digital Logic Design, System Level Design, and Synthetic Biology.

Thursday, November 17th
Alexandros Dimakis
University of Texas at Austin
Discovering Causality in Data Using Entropy
3-4pm, Towne 337

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Abstract: Causality has been studied under several frameworks in statistics and artificial intelligence. We will briefly survey Pearl’s Structural Equation model and explain how interventions can be used to discover causality. We will also present a novel information theoretic framework for discovering causal directions from observational data when interventions are not possible. The starting point is conditional independence in joint probability distributions and no prior knowledge on causal inference is required.

Bio: Alex Dimakis is an Associate Professor in the Electrical & Computer Engineering department at The University of Texas at Austin. Prof. Dimakis received his Ph.D. in 2008 and M.S. degree in 2005 in electrical engineering and computer sciences from UC Berkeley and the Diploma degree from the National Technical University of Athens in 2003. During 2009 he was a CMI postdoctoral scholar at Caltech. He received an NSF Career award in 2011, a Google faculty research award in 2012 and the Eli Jury dissertation award in 2008. He is the co-recipient of several best paper awards including the joint Information Theory and Communications Society Best Paper Award in 2012.

Tuesday, November 22nd
Volker Sorger
The George Washington University
Orthogonal Physics Enabled Nanophotonics (OPEN): Attojoule Optoelectronics, Analogue Optical Compute Engines, and Smart Contact Lens IoT System

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Abstract: In nanophotonics we create optical material-systems, which are structured at length-scales smaller than the wavelength of light. When light propagates inside such sub-diffraction limited waveguide modes or cavities numerous novel and exciting physical phenomena emerge including unity-high index modulation, strong Purcell enhancement, and selective exciton-polariton modifications. However, in order to make use of these opportunities for real-world applications, one has to have the ability to integrate nanophotonic structures into functional devices, synergistic links and circuits. In this talk, I present some of our recent theoretical and experimental progress in exploring thresholdless lasing, attojoule-per-bit efficient modulators, and plasmonic and Soliton-based switching. Furthermore, I will show fundamental scaling laws of nanophotonic devices and derive a Figure-of-merit for optical information flow. Using the bosonic character of photons we develop in-the-network information processing engines. Here we map the computational algorithm onto the photonic hardware to demonstrate optical analogue compute engines based on residue arithmetic and neuromorphic computing. Lastly, I discuss scalable multi-adaptive and reconfigurable IOT devices and systems such as a micron-scale THz antenna, and a smart contact lens head-up display for augmented reality.

Bio: Volker J. Sorger is an assistant professor in the Department of Electrical and Computer Engineering, and the director of the Orthogonal Physics Enabled Nanophotonics (OPEN) lab at the George Washington University. He received his PhD from the University of California Berkeley. His research areas include opto-electronic devices, plasmonics and nanophotonics, including novel materials. Amongst his breakthroughs are the first demonstration of a plasmon laser (Nature 2009), Semiconductor plasmon laser (Nature Mat.) sub-wavelength scale waveguides (Nature Photonics, 2008; Nature Communication, 2011) and Transparent Conductive Oxides electro-optic modulation (Nanophotonics, 2012; Laser Photonics Reviews, 2015). Dr. Sorger received multiple awards among are the Intel graduate award 2007, SPIE BACUS scholarship 2009, MRS Gold award 2011, AFOSR Young Investigator award 2014, Outstanding Young Researcher Award at GWU 2016, and the Hegarty Innovation Prize 2016. Dr. Sorger is the OSA executive co-chair for technical group development, and member of the Board-of-Meetings at OSA and SPIE. He is the editor-in-chief for the journal ‘Nanophotonics’, CTO of BitGrid LLC, and member of IEEE, OSA, SPIE, and MRS. He is the founder of the photonic-materials subcommittee at the Integrated Photonics Research conference, and served on a task force of the National Photonics Initiative (NPI).

Tuesday, November 29th
Andrea Alù
The University of Texas at Austin
From Cloaking to One-Way Propagation: the Fascinating Physics and Engineering of Metamaterials

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Abstract: Metamaterials are artificial materials with properties well beyond what offered by nature, providing unprecedented opportunities to tailor and enhance the interaction between waves with materials. In this talk, I discuss our recent research activity in electromagnetics, nano-optics, acoustics and mechanics, showing how suitably tailored meta-atoms and arrangements of them open exciting venues to manipulate and control waves in unprecedented ways. I will discuss our recent theoretical and experimental results, including metamaterials for scattering suppression, nanostructures and metasurfaces to control wave propagation and radiation, large nonreciprocity without magnetism, giant nonlinearities in properly tailored metamaterials, and parity-time symmetric meta-atoms and metasurfaces. Physical insights into these exotic phenomena, new devices based on these concepts, and their impact on technology will be discussed during the talk.

Bio: Andrea Alù is the Temple Foundation Endowed Professor #3 at the University of Texas at Austin. He received his Laurea (2001) and PhD (2007) from the University of Roma Tre, Italy, and, after a postdoc at the University of Pennsylvania, he joined the faculty of the University of Texas at Austin in 2009. His current research interests span over a broad range of areas, including metamaterials and plasmonics, electromagnetics, nano-optics, photonics and acoustics. Dr. Alù is a Fellow of IEEE, OSA, and APS, and has received several scientific awards, including the NSF Alan T. Waterman award (2015), the OSA Adolph Lomb Medal (2013), and the URSI Issac Koga Gold Medal (2011).

Thursday, December 1st
Todd Coleman
University of California, San Diego
The Interplay between Data Science, Technology, and Health
11am-12pm, Towne 337
*Joint ESE - BE Seminar*

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Abstract: Dr. Coleman will discuss his research group’s efforts in developing flexible multi-functional flexible electronics and scalable inference tools to provide vulnerability profiles and decision support tools for improved interpretation of health and promotion of decision-making. Recent work in advancing flexible physiologic sensors, antennas, and integrated circuits will be discussed, with an emphasis on approaches that are clinically viable, and are compatible with scalable industry-adopted fabrication methods. Dr. Coleman will also discuss novel applied probability methods of interpreting such acquired physiologic data for prediction, diagnosis, and improving health outcomes. An emphasis will be placed on engineering aggregate systems that address socioeconomic and scalability challenges. A few examples will be provided, that include: developing inexpensive and easy-to-deploy physiologic screening tools to predict delayed neurodevelopment in infants; and developing new approaches to measure and interpret electrical activity of the digestive system for disambiguating and identifying physiologic abnormalities underlying GI disorders. Throughout the talk, Dr. Coleman will emphasize the inter-disciplinary nature of this research, involving themes from applied mathematics, electrical engineering, bioengineering, and medicine.

Bio: Todd P. Coleman received B.S. degrees in electrical engineering (summa cum laude) and computer engineering (summa cum laude) from the University of Michigan. He received M.S. and Ph.D. degrees from MIT in electrical engineering, and did postdoctoral studies at Mass General vccvHospital in quantitative neuroscience. He is currently an Associate Professor in Bioengineering at UCSD, where he is the co-director of the Center for Perinatal Health within the Institute of Engineering in Medicine. His research has been featured on CNN, BBC, and the New York Times. In 2015, Dr. Coleman was recognized by the National Academy of Engineering as a Gilbreth Lecturer; by the Root as "one of 100 African-Americans most responsible for 2015's most significant moments, movements, and ideas"; and by TEDMED as an invited speaker.


Tuesday, December 6th
Alex Zettl
University of California, Berkeley
Exploring sp2-bonded materials: From graphene liquid cells to quantum craters and atomic collapse

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Abstract: I will discuss recent experiments on nanostructures based on sp2-bonded carbon and boron nitride, including determination of the detailed (dynamic) atomic structure in graphene and BN sheets using transmission electron microscopy, the imaging of foreign atoms and molecules within nanoscale environmental liquid cells, the creation of customized "quantum craters" for relativistic Dirac fermions, and the observation of atomic collapse long ago predicted for ultra-heavy nuclei.

Bio: Alex Zettl received his B.A. from UC Berkeley in 1978 and his Ph.D. from UCLA in 1983. He joined the Physics Department faculty at UC Berkeley in 1983. Currently he is Professor of Physics at UC Berkeley, Senior Scientist at LBNL, and Member of the Kavli Energy NanoSciences Institute at Berkeley. Awards and Honors include Presidential Young Investigator Award (1984-89), Sloan Foundation Fellowship (1984-86), IBM Faculty Development Award (1985-87), and Miller Professorship (1995), Lucent Technologies Faculty Award (1996), Fellow of the American Physical Society (1999), Lawrence Berkeley National Laboratory Outstanding Performance Award (1995 and 2004), James C. McGroddy Prize for New Materials (2006), Miller Professorship (2007), and R&D 100 Award (2004 & 2015).