THE GEORGE WASHINGTON UNIVERSITY Ph.D. Dissertations Department of
ELECTRICAL & COMPUTER ENGINEERING
Spring 2018 Newsletter
Innovating the technologies of the future 1
TABLE OF CONTENTS Message From the Chair
Department at a Glance
Faculty Member ProďŹ les
Research Highlights Lab Spotlight
Recent Research Awards Active Research Grants
Awards & Honors
29 Ph.D. Dissertations
Distinguished Lecture Series
AWARDS & HONORS
Message From the Chair Dear Friends of the ECE Department, The ECE Department at the George Washington University is engaged in cutting-edge technological research in multiple areas within ECE and cross-cutting disciplines. We are also proud of nurturing and shaping our students to become innovators and leaders in industry and academia. I’m excited to welcome Prof. Payman Dehghanian, a tenuretrack Assistant Professor in the area of electrical power and energy, to our faculty. You’ll see a detailed proﬁle of him and his research in this newsletter. We will miss the presence and guiding voice of Prof. Nicholas Kyriakopoulos, who will retire from the faculty at the end of May 2018, after having been on the faculty for 50 years! I wish him the best in his retirement. Our faculty continue to be greatly successful in their research endeavors, winning several grants and awards. The department boasts seven IEEE Fellows and one APS Fellow. The department provides opportunities for graduate and undergraduate students to work together on research problems along with faculty mentors. The ECE Distinguished Lecture Series provides an invaluable opportunity for students and faculty to listen to and interact with leaders in the ﬁeld. The department is a hotbed of innovation located in the heart of the nation’s capital. Why don’t you pay us a visit to take a look at who we are and what we do? Suresh Subramaniam Professor and Department Chair IEEE Fellow
Department at a Glance
Enrollment (Full-Time) Academic Year 2017-2018 Bachelors Masters Ph.D. Total
92 250 59 403
Degrees Granted Academic Year 2016-2017 Bachelor of Science Master of Science Ph.D. Total
13 80 11 104
Faculty Academic Year 2017-2018 Professors Associate Professors Assistant Professors Teaching Professors Research Professors Total
12 5 1 1 2 21
Bachelor of Science (ABET-accredited) Computer Engineering Electrical Engineering Master of Science Computer Engineering Electrical Engineering Telecommunications Engineering Combined Five-Year Programs BS / MS Computer Engineering BS / MS Electrical Engineering BS Computer Engineering/MS Electrical Engineering BS Electrical Engineering/ MS Computer Engineering Doctor of Philosophy Computer Engineering Electrical Engineering Minors & CertiďŹ cates Minor in Computer Engineering Minor in Electrical Engineering Graduate CertiďŹ cate in High-Performance Computing
Department at a Glance RESEARCH AREAS
Applied Electromagnetics Laboratory
Dr. Lawrence H. Bennett (Research Professor)
Dr. Roger H. Lang
Dr. Edward Della Torre Dr. Can E. Korman
Electronics, Circuits, MEMS/NEMS and Sensors Research
Dr. Roger H. Lang
Laboratory Dr. Shahrokh Ahmadi
Communications and Networks
Dr. Can E. Korman
Dr. Miloš Doroslovački
Dr. Mona Zaghloul
Dr. Hermann J. Helgert Dr. Tian Lan Dr. Suresh Subramaniam Computer Architecture and High-Performance Computing Dr. Tarek A. El-Ghazawi
GW-Intel Parallel Computing Center Dr. Tarek El-Ghazawi High Performance Computing Architectures and Technologies Laboratory Dr. Ahmed Louri
Dr. Howie Huang Dr. Ahmed Louri Dr. Guru Prasadh Venkataramani Electrical Power and Energy Dr. Payman Dehghanian Dr. Robert Harrington MEMS, Electronics, and Photonics Dr. Shahrokh Ahmadi Dr. Can E. Korman Dr. David J. Nagel (Research Professor) Dr. Volker Sorger Dr. Mona Zaghloul Signal and Image Processing, Systems and Controls Dr. Robert L. Carroll Dr. Miloš Doroslovački Dr. Kie-Bum Eom
High-Performance Computing Laboratory Dr. Tarek El-Ghazawi Institute for Massively Parallel Applications and Computing Technology Dr. Tarek El-Ghazawi Lab for Intelligent Networking and Computing Dr. Suresh Subramaniam Dr. Miloš Doroslovački Dr. Tian Lan Dr. Guru Prasadh Venkataramani LENR Energy and Spectroscopy Laboratory Dr. David Nagel Magnetic Material Testing Laboratory Dr. Edward Della Torre Magnetic Refrigeration Research Laboratory Dr. Edward Della Torre
Dr. Nicholas Kyriakopoulos Magneto-Optics Laboratory Dr. Edward Della Torre Orthogonal Physics Enabled Nanophotonics Dr. Volker Sorger
IEEE Fellows: 7 Recent NSF CAREER Awards: 2 Humboldt Awards: 1 AFOSR-YIP Awards: 1
MEET OUR FACULTY
ECE Welcomes Dr. Payman Dehghanian The Department of Electrical and Computer Engineering at George Washington University welcomes Dr. Payman Dehghanian. He ofﬁcially joined the ECE in January 2018 as an Assistant Professor. Dr. Dehghanian earned his Ph.D. degree in electrical engineering from Texas A&M University in 2017. He conducts research on power systems engineering, and his speciﬁc areas of interest are electric power system reliability and resilience assessments; weather-driven modeling and analysis of the electricity grid, decision support tools and advanced data analytics for smart electricity grid applications; asset management and maintenance in energy systems, and the synchrophasor technology and applications for power system protection and control. Our nation’s electricity grid, which is one of the most complex man-made systems ever to date, is constantly exposed to potential hazards ranging from weather-driven natural disasters to the looming threat of malicious cybersecurity attacks. Due to numerous factors such as rapid deployment of intermittent renewable generation, growing demand to ensure higher quality electricity to end customers, and intensiﬁed public focus and regulatory oversights, there is an urgent need to enrich the power delivery infrastructure resilience while reducing and mitigating such threatening risks. Dr. Dehghanian’s research interests conjoin fundamental backgrounds in electric power systems engineering, mathematics and statistical disciplines, as well as advanced decision theory and data science to come up with transformational solutions that can characterize and timely predict such high-impact low-probability threats, facilitate an efﬁcient and proactive real-time decision making for power grid operators, and mitigate the disastrous consequences to every aspect of our society that can potentially compromise our national security. Dr. Dehghanian was named the 2016 Best Engineering Graduate Student in the State of Texas (awarded by Texas Engineering Foundation Board of Trustees), and he received the 2015 IEEE-HKN Outstanding Young Electrical Engineer Award (awarded by the Institute of Electrical and Electronics Engineering’s Educational Activities Board). He was the recipient of the 2014 and 2015 IEEE Region 5 Outstanding Professional Achievement awards. In both 2016 and 2017, the Global Power and Energy Academic and Professional Community also selected him as a “World Top 20 Young Scholar for Next Generation of Research in Electric Power Systems.” 7
Ph.D. 1994, University of Maryland at College Park
Ph.D. 2017, Texas A&M University
Microelectronics; VLSI Systems
Smart Electric Power and Energy Systems
LAWRENCE BENNETT Research Professor Ph.D. 1958, Rutgers University Magneto-optics; Magnetic Refrigeration
EDWARD DELLA TORRE Professor Fellow of IEEE and APS D.E.Sc. 1964, Columbia University Magnetic Devices
ROBERT L. CARROLL Professor Ph.D. 1973, University of Connecticut
MILOŠ DOROSLOVAČKI Associate Professor and Associate Chair Ph.D. 1994, University of Cincinnati
Robotics and Controls Signal Processing for Communications, Adaptive Signal Processing, Detection and Estimation
TAREK A. EL-GHAZAWI Professor Fellow of IEEE Ph.D. 1988, New Mexico State University
HERMANN J. HELGERT Professor Ph.D. 1966, SUNY-Buffalo Satellite and Cellular Networks
HPC, Computer Architecture, Big Data
Ph.D. 1986, Purdue University
Ph.D. 2008, University of Virginia
Multimedia Processing, Image Processing
ROBERT HARRINGTON Professor Fellow of IEEE and IET Ph.D. 1967, University of Liverpool Electric Power and Energy
HPC, Big Data, Cloud
CAN KORMAN Professor Ph.D. 1990, University of Maryland at College Park Electromagnetics, Magnetism & Hysteresis, Device Electronics
NICHOLAS KYRIAKOPOULOS Professor D.Sc. 1968, The George Washington University Signal Processing, Controls and Systems Theory, Application of Technology to Arms Control
AHMED LOURI David and Marilyn Karlgaard Endowed Professor Fellow of IEEE Ph.D. 1988, University of Southern California Computer Architecture, NoCs, HPC, Machine Learning, Datacenters
DAVID NAGEL Associate Professor
Ph.D. 2010, Princeton University
Ph.D. 1977, University of Maryland at College Park
Cloud/Fog Computing, IoT, Cyber Security, Datacenter Networks
Low Energy Nuclear Reactions
ROGER LANG L. Stanley Crane Professor Fellow of IEEE Ph.D. 1968, Polytechnic Institute of Brooklyn
VOLKER J. SORGER Associate Professor Ph.D. 2011, The University of California, Berkeley Photonics, Optical Computing
SURESH SUBRAMANIAM Professor and Chair Fellow of IEEE Ph.D. 1997, University of Washington Optical Networking, Cloud/ Fog Computing, IoT, Data Center Networks
GURU PRASADH VENKATARAMANI Associate Professor Ph.D. 2009, Georgia Institute of Technology Computer Architecture, Hardware Security
MONA ZAGHLOUL Professor Fellow of IEEE Ph.D. 1975, University of Waterloo MEMS devices and sensors including Bio and Chemical sensors , and CMOS electronics
ECE Honors Dr. Nicholas Kyriakopoulos' Career
My Life at GW I came to GW as student and leave as professor. The path has been a challenging but highly enriching experience. In the Gymnasium in Greece, I was fascinated by nuclear engineering, but ended up studying electrical engineering because GW didnâ€™t have a nuclear engineering program. In the decade of the 60s, when space exploration presented exciting technological challenges, I started my career as an aerospace engineer working to improve the reliability of semiconductor devices in space applications. I was happy following that trajectory while working on my doctoral dissertation, until Nelson Grisamore, Associate Dean for Research in the GW School of Engineering called me, offered me an instructorship and gave me forty eight hours to respond. A group of young recruits undertook the challenge of transforming a part-time evening engineering program into full-time undergraduate and graduate programs. We developed new curricula and organized and equipped laboratories to support the new curricula. The original power laboratories were supplemented with modern computer, communications and control laboratories. At the same time, the School of Engineering embarked on an extensive continuing engineering education program that spanned the globe. Hermann Helgert and I were some of the most active members in organizing and lecturing in that program.
As the school evolved from a part-time operation to full-time teaching and research, the department expanded into the areas of biomedical engineering and computer science. When the computer science faculty voted to form a separate department, I was elected Interim Chairman of the Electrical Engineering Department to organize the new department and coordinate the division of resources between the two departments. In subsequent years, I continued as the Curriculum Coordinator for the Electrical Engineering program, undergraduate student advisor, and author of Self-Study Reports for ABET. All along, my services to the University included participation in numerous Department, School and University Committees. Since the early eighties, I have been using my engineering knowledge to help society control and ultimately eliminate weapons of mass destruction. In support of the Nuclear NonProliferation Treaty, I speciﬁed and tested prototype remote tracking and data collection systems for monitoring spent nuclear fuel pools and tracking the transport of nuclear materials by ship and airplane. As a member of the U.S. Delegation to the Committee on Disarmament during the negotiations for the Chemical Weapons Convention, I contributed to the speciﬁcation and design of the veriﬁcation system that has become an integral part of the treaty. Under the auspices of the Pugwash Conferences on Science and World Affairs I initiated a project that resulted in the publication of “Veriﬁcation of Dual-use Chemicals under the Chemical Weapons Convention: The Case of Thiodiglycol” by the Swedish Peace Research Institute. Subsequently, as a member of the Veriﬁcation Working Group of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty I contributed to the formulation of the design speciﬁcations for the Global Communications Infrastructure of the treaty. Under the auspices of the Veriﬁcation Technologies Working Group of the European Safeguards Research and Development Association, I organized a project and co-edited the book “Verifying Treaty Compliance: Limiting Weapons of Mass Destruction and Monitoring Kyoto Protocol Provisions”. As a member of the International Group on Global Security I have contributed to a number of monographs on issues related to the implementation of arms control treaties. The combined experience in the various facets of treaty monitoring and veriﬁcation is reﬂected in my current research on modeling treaties with monitoring and veriﬁcation provisions as feedback control systems.
Research Highlight: Building Frameworks for Future Cybersecurity
Professor Tian Lan By Lujain Al-Khawi
Electrical engineering professor and researcher Tian Lan’s motto is to bridge the gap between theory and practice in computer security. For the last three years, he has worked with existing cyber systems and protocols to make them more intelligent, personalized, and user-friendly. What makes his solution signiﬁcant is its novelty. His research team’s designed framework is capable of solving a widespectrum of security problems leveraging an integrated engine that mimics both human reasoning and reﬂexive thinking, prompting modern-day computers to run more efﬁciently and more securely. Since 2010, he has worked with the GW team working with cyber security. Working especially with optimization, he has built prototypes for the Pentagon and Hawaii’s naval command center. Both are currently in tech transition with the military. Lan’s research entails four different key components of cyber security: reasoning, optimization, customization, and delivery. In working with reasoning and optimization, he and his collaborators developed a mission-oriented, resilient cloud for the Defense Advanced Research Projects Agency (DAPRA), which is able to self-optimize and ﬁght through cyber/physical attacks. As cyber systems are increasing and becoming more complex, Dr. Lan utilizes machine learning together with model-based approach to speed up the discovery of software bugs and vulnerabilities by several orders of magnitude in his recent project Symbiotic and Integrated Reasoning Engine (SIREN) for Autonomic CyberSystems. When you buy a phone, computer, or IoT device, the manufacturer is most likely to ship a standard software, one with superﬂuous code and program features that are never needed by most customers but are susceptible to cyber attacks. This standardized, one-size-ﬁts-all approach, termed feature bloating, has become the cause of many security problems we face today, and this is where Dr. Lan’s research in customization comes into play. Collaborating with Professor Guru Venkataramani and the Ofﬁce of Naval Research (ONR), Dr. Lan’s research team is working to develop a somewhat maverick solution and to deliver highly individualized, secure frameworks to reduce unnecessary code that often leads to bugs, backdoors, and unauthorized access to cyber systems, as indicated by his recent project “DIALECT: Communication Protocols Customization via Feature Diagnosis.” Lastly, Dr. Lan is able to bridge theory and practice through providing Security-as-a-Service. Working alongside professors Suresh Subramaniam and Howie Huang in a National Science Foundation (NSF) project focusing on new security delivery models, they are designing a pay-per-usage mechanism that offers security as an on-demand utility to customers. Instead of having to pay for a standardized service that could be either too expensive or inadequate, Dr. Lan’s collaboration aims to enable a pay-per-use business model for security services. Therefore, the customer of a computer system would pay in proportion to the service he or she is receiving, allowing for more customization for both the user and provider. Before reaching his career in communication and networks, Lan completed his undergraduate degree at Tsinghua University in China. After graduation, he brought his passion to study his Ph.D. at Princeton University, working with network optimization. As soon as he successfully defended his Ph.D., he hopped on his car and rode all the way to Washington, D.C. to join the GW faculty. Outside of research, he, his wife, and three-year-old son enjoy travelling around the world, especially visiting museums and science centers.
Research Highlight: MEMS Sensors Devices in Engineering: Innovating Biological/Chemical Sensors to Detect Diseases
Professor Mona Zaghloul By Lujain Al-Khawi
Professor Mona Zaghloul, the director of GW SEAS’ Institute for Micro-Electro-Mechanical Systems and Nano –ElectroMechanical Systems (MEMS/NEMS) and VLSI Technology, and Yangyang Zhao, an electrical engineering ECE Ph.D. candidate, are developing a handheld gas sensor to detect and monitor diseases like lung cancer and diabetes for at-home use. “We are working mostly on chemical gas sensors, and we are producing novel sensors which nobody has done before,” said Zaghloul. Zaghloul and Bhaven Mehta, a former graduate student who is now working with GlobalFoundries, previously IBM's chip -manufacturing unit, ﬁrst collaborated on the project with researchers at the National Institute of Standards and Technology (NIST), a Gaithersburg, MD-based government agency under the Department of Commerce. Today, Zaghloul and her team have produced several patents on many sensors. “We have sensors that detect chemical gases or bio sensors that are a marker for cancer or other diseases...but it was not commercialized because it needs more work to make it portable,” said Zaghloul. Since sensing systems are often bulky and need to be maintained in a lab setting, they are not compatible for at-home use. Therefore, the challenge of her research is to innovate a sensor which is small, yet selective and sensitive. To accomplish this goal, Dr. Zaghloul is focusing on using nanotechnology and plasmonics to develop very small sensors, and very sensitive and selective sensors. So far, she and her collaborators have constructed sensors that demonstrate sensitivity in the parts-per-billion range, and they proposed a selectivity mechanism “without hampering the sensitivity,” a novel development since research labs who have tried to improve sensors’ sensitivity have encountered a decrease in selectivity. These sensors can also be used in environmental and agricultural monitoring to study toxic gases in the atmosphere and ﬂuctuations in dispersing plumes and evaporation, which can prove to be very valuable for people with asthma and other outdoor-affected conditions. “Because we know how to design very small things at the Nano-scale, this [sensor] would be very small and have the electronics to communicate and to send signals. It would be very useful for those who have any allergies. In fact, the development of these sensors will impact several ﬁelds from public health, agriculture, and the food industry.
Lab Spotlight: High Performance Computing Architectures and Technologies
HPCAT Research Team
Lab Mission We rely on computing in the design of systems for energy, transportation, ﬁnance, education, health, defense, entertainment, and overall wellness. However, today's computing systems are facing major challenges both at the technology and application levels. At the technology level, traditional scaling of device sizes has slowed down and the reduction of cost per transistor is plateauing, making it increasingly difﬁcult to extract more computer performance by employing more transistors on-chip. Power limits and reduced semiconductor reliability are making device scaling more difﬁcult – if not impossible – to leverage for performance in the future and across all platforms, including mobile, embedded systems, laptops, servers, and datacenters. Simultaneously, at the application level, we are entering a new computing era that calls for a migration from an algorithm computing world to a learning-based, data-intensive computing paradigm in which human capabilities are scaled and magniﬁed. To meet the everincreasing computing needs and to overcome power density limitations, the computing industry has embraced parallelism (parallel computing) as the only method for improving computer performance. Today, computing systems are being designed with tens to hundreds of computing cores integrated into a single chip and hundreds to thousands of
computing servers based on these chips are connected in datacenters and supercomputers. However, power consumption remains a signiﬁcant design problem, and such highly parallel systems still face major challenges in terms of energy efﬁciency, performance, and reliability. Professor Louri and his team investigate novel parallel computer architectures and technologies which deliver high reliability, high performance, and energy-efﬁcient solutions to important application domains and societal needs. The research has far-reaching impacts on the computing industry and society at large. Current research topics include: (1) the use of machine learning techniques for designing energy-efﬁcient, reliable multicore architectures, (2) scalable accelerator-rich reconﬁgurable heterogeneous architectures, (3) emerging interconnect technologies (photonic, wireless, RF, hybrid) for network-on -chips (NoCs) & embedded systems, (4) future parallel computing models and architectures including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), near data computing, approximate computing, and (5) cloud and edge computing.
Research at HPCAT Machine Learning for High Performance, Energy-Efﬁcient and Reliable NoCs With continued aggressive technology scaling, Network-on- Chips (NoCs) architectures are facing three major challenges including minimizing power consumption, scaling performance and providing a reliable and robust communication limited by area, power, and cost constraints. Researchers have proposed various techniques individually tackling these challenges, while few efforts to date have simultaneously targeted improving power, performance and reliability together. Due to the complexity of the interactions among three competing objectives and explosion of design space, it is harder to manually design rules and strategies for interconnection system for optimizing power, reliability and performance. In our research, we use Machine Learning (ML) algorithms, which can work with highdimensional data and automatically infer complex decisions, to balance reliability, performance, and energy efﬁciency for NoCs. We ﬁrst use supervised ML algorithms to build predictive decision models, which can optimize competing goals of two of the three targets (e.g. reliability and performance, performance and power, etc.). We further use reinforcement learning (RL) to eschew the prediction step and automatically learn a decision policy that directly maps system-level states to optimal decisions which can yield maximum beneﬁts on reducing power, enhancing reliability, and improving performance simultaneously.
Energy-Efﬁcient Scalable Multicore Architectures Over the last decade, Moore’s Law has slowed, while Dennard Scaling has ended. The end of voltage scaling has made power dissipation the fundamental barrier to scaling computing performance across all platforms –from hand-held, embedded systems, to laptops, to servers, to datacenters. This challenge, often called the power wall, is seen across the board. To meet power challenges, recent research has proposed various low-power techniques. Power-gating, for example, is an effective technique that powers off the under-utilized components to reduce static power consumption. Dynamic voltage and frequency scaling (DVFS) is another technique that saves power by leveraging the application load to dynamically adjust voltage and frequency. The simultaneous use of various low-power techniques in one system can reduce more power consumption while creating several problems. For example, these low-power techniques can potentially conﬂict with each other if they are employed concurrently and make decisions at inappropriate times. These conﬂicts can even negatively affect performance and power savings. In our research, we combine various power saving techniques while avoiding their shortcomings. The combination of different techniques leads to an explosion of design space. We further explore the use of machine learning to optimize the combined system.
Research at HPCAT Neural Networks Accelerator and Applications Neural networks (NNs) have been successfully implemented in modern artiﬁcial intelligence (AI) applications ranging from image processing to speech recognition to natural language processing. While NNs deliver superior accuracy, they still suffer from the high cost of computational complexity and a signiﬁcant amount of data movement. For example, convolutional neural networks (CNNs) are the most representative class of NNs, which have been widely applied to image classiﬁcation. The convolution layers of CNNs will dominate the runtime since convolution layers occupy over 90% of the total operations. Although these operations could be executed in parallel, the overall system cannot reach potential performance improvement due to the insufﬁcient on-chip memory to store the intermediate processing results. In this case, the energy consumption will still remain high, since data movement will cost more than computation. Therefore, energy-efﬁcient accelerators for NNs are needed for power-constrained devices (i.e. phones, drones, robots, and self-driving cars). This kind of accelerators should make neural networks feasible to current-generation IoT devices with high accuracy and ultra-low power consumption. We are working on minimizing the computational complexity of NNs by reducing redundant operations and weights. We are also designing energy-efﬁcient accelerators (e.g. ASIC, FPGA) speciﬁcally for NNs without violating prediction accuracy. Moreover, we are exploring efﬁcient memory allocation methods based on our designs, which will help to reduce the data movement cost. Further, we are going to explore silicon photonic based neural network circuits.
Accelerator-rich Heterogeneous Architectures In the dark silicon era, only a fraction of transistors on a chip can be switched simultaneously due to constrained power budget. To improve energy-efﬁciency, general-purpose cores are augmented with specialized hardware or accelerators. These accelerators, which are optimized for speciﬁc applications (e.g., application-speciﬁc integrated circuits), can improve energy efﬁciency per operation by orders of magnitude over software running on general-purpose cores. The integration of general-purpose cores with accelerators on the same chip in a heterogeneous design environment, places several challenges on the NoC design. The NoC needs to be heterogeneous to support different cores and accelerators and different activated subsets of on-chip nodes. Besides, trafﬁc over the NoC can have dynamically varying patterns based on application demands. In this research project, we address the NoC design challenges, by fully exploring the trafﬁc models of diverse types of cores and accelerators, and then designing network architectures that can be conﬁgured to adapt to speciﬁc workloads. Additionally, we consider heterogeneous technologies for the implementation of these accelerator - rich architectures. Recent 3-D integration techniques have enabled multiple logic dies, and memory dies of different technologies (SRAM, DRAM, NVM etc.) to be integrated on the same chip. It complicates NoC design by incorporating new vertical channels and enabling more ﬂexible memory access schemes. We are exploring 3-D NoC design and heterogeneous technologies for fast and high- bandwidth inter-core communication and on-chip memory access. 20
Research at HPCAT Approximate Communication Recent research has shown that on-chip data movement consumes much more power than computation and this trend will continue in the future. Additionally, some algorithms and applications, such as machine learning, big data analysis, image/video processing and computer vision are tolerant to modest errors. These applications are inherently tolerant to some error in their output whether due to noisy input data, multiple correct answers or not requiring very accurate execution. Leveraging this error tolerance can lead to signiﬁcant energy savings and performance improvements. As error correction techniques in data transmission, such as error correction codes and retransmission, consume energy and increase network latency, reducing the amount of error correction can signiﬁcantly improve network performance. This project exploits the fact that not all applications require strong error correction and investigates approximate communication with dynamic error correction methods to trade-off absolute accurate data transmission for the power savings and transmission time.
Emerging Interconnects Technologies for Network-on-Chips There is a shift from multi-core to many-core architectures containing hundreds to a thousand cores on a single chip. However, traditional on-chip metallic interconnects require excessive power as technology keeps scaling and will not be able to provide communication support for manycore architectures. Moreover, fundamental signaling limitations (reﬂections, crosstalk), electromagnetic interference (EMI), clock skew, and other problems associated with metallic interconnects will only exacerbate the power dissipation problem and thereby limit the performance of future manycore processors. Emerging interconnect technologies such as silicon-photonics and wireless interconnects are under serious consideration for meeting the manycore communication requirements. However, the use of a single interconnect technology is not sufﬁcient to provide satisfactory performance. Silicon photonic on-chip interconnects offer low latency, low power consumption, and high bandwidth for on-chip communication. But, they suffer from scalability difﬁculties and high optical power loss (insertion loss) when scaled to thousands of cores. On the other hand, on-chip wireless interconnects have the advantages of distance-independent energy consumption. But, limited frequency spectrum and higher energy/bit limit the use of wireless technology. In this project we are leveraging the advantages of both wireless and photonic technologies, and exploring a network-on- chip called OWN (Optical Wireless Network) architecture for many-core systems. We are designing silicon photonic links for communication among the neighboring cores in a cluster of tiles with relatively short distances, and reconﬁgurable wireless links for inter-cluster while for inter-cluster communications and long distances on the chip.
Please visit http://hpcat.seas.gwu.edu to learm more about HPCAT. 21
Recent Research Awards Micro-scale Ultra-high Efﬁciency CPV/Diffuse Hybrid Arrays Source: DOE
Dr. Matthew Lumb (research scientist) has received a two-year, $1.4 million grant from the U.S. Department of Energy for the project “Microscale Ultra-high Efficiency CPV/Diffuse Hybrid Arrays.” Through this project, Dr. Lumb and GW will lead a new research effort in collaboration with several government, university and industry partners, including the Naval Research Laboratory, Northwestern University, MIT, Veeco, and XCeleprint. They are working to develop a new, high performance concentrator photovoltaic module that is fully integrated with conventional flat plate photovoltaic technology, with the goal of producing an extremely high efficiency solar module capable of capturing both direct and diffused light. The enabling technology is microtransfer printing, which allows precise, highly-parallel assembly of micro-scale solar cells and the heterogeneous integration of materials produced on different substrates. The new hybrid photovoltaic modules have the potential to harvest more energy from the sun than ever demonstrated before and increase the commercial competitiveness of concentrator photovoltaic technologies in a broad range of applications.
Active Research Grants Miloš Doroslovački “Defending Against Hardware Covert Timing” National Science Foundation and Semiconductor Research Corporation, Co-PI Tarek El-Ghazawi “Dynamically Adaptive Hybrid Nanoplasmonic NoCs” Air Force Ofﬁce of Scientiﬁc Research Tarek El-Ghazawi "RAISE: The Reconﬁgurable Optical Computer (ROC)" National Science Foundatio Tarek El-Ghazawi "GW-IPCC: GW Intel Parallel Computing Center " Intel Howie Huang “SHF: Small: Accelerating Graph Traversal on GPUs” National Science Foundation Howie Huang “CAREER: Hardware Error Resilient Virtualization Infrastructure” National Science Foundation Howie Huang "CSR: Small: IO-Efﬁcient Computer System for Graph Analytics" National Science Foundation Tian Lan “NeTS: Small: Collaborative Research: Rethinking Erasure Codes for Cloud Storage: A Quantitative Framework for Latency, Reliability, and Cost Optimization” National Science Foundation Tian Lan "DIALECT: Communication Protocols Customization via Feature DIAgnosis, Lacing, Elimination, Cross-grafting, and Trimming " Ofﬁce of Naval Research Tian Lan “Symbiotic and Integrated Reasoning ENgine (SIREN) for Autonomic Cyber Systems” Ofﬁce of Naval Research, Co-PI Roger Lang “Multi-frequency Soil Moisture Measurements and Analysis” NASA-Goddard Roger Lang “L Band Seawater Dielectric Measurements in Cold Oceans and Coastal Regions” NASAGoddard Ahmed Louri “Collaborative Research: Power-Efﬁcient and Reliable 3D Stacked Reconﬁgurable Photonic Network-on-Chips for Scalable Multicores” National Science Foundation Ahmed Louri “A Holistic Design Methodology for Fault-Tolerant and Robust Network-on-Chips (NoCs) Architectures” National Science Foundation Ahmed Louri “Collaborative Research: SPARTA: A Stream-based Processor and Run-Time Architecture” National Science Foundation Ahmed Louri “SHF: Medium: Collaborative Research: Scaling On-chip Networks to 1000-core Systems using Heterogeneous Emerging Interconnect Technologies” National Science Foundation Matthew Lumb “Micro-scale Ultra-high Efﬁciency CPV/Diffuse Hybrid Arrays” Department of Energy Volker Sorger “E2CDA: Collaborative Proposal: Nanophotonic-enabled Optical Neuromorphic Computing on Silicon” National Science Foundation Volker Sorger “DMREF/Collaborative Research: Theory-Enabled Development of 2D Metal Dichalcogenides as Active Elements of On-Chip Silicon-Integrated Optical Communication” National Science Foundation Volker Sorger "RAISE: The Reconﬁgurable Optical Computer (ROC)" National Science Foundation, Co-PI Volker Sorger “Dynamically Adaptive Hybrid Nanoplasmonic NoCs” Air Force Ofﬁce of Scientiﬁc Research Volker Sorger “Material-Based Electro-Optic Modulation on a Silicon Platform” Department of the Army
Active Research Grants Volker Sorger “From Direct Optical to Field-Induced Modulation of Photonic Modes Enabled by Novel 2D Materials ” Air Force Ofﬁce of Scientiﬁc Research Volker Sorger "From Direct Optical to Field-Induced Modulation of Photonic Modes Enabled by Novel 2D Materials" Air Force Ofﬁce of Scientiﬁc Research Suresh Subramaniam “NeTS: JUNO: Cost-Effective and Scalable Architectures for Multi-Granular Optical Networks” National Science Foundation Suresh Subramaniam “NeTS:Small:Collaborative Research: Ultrascale WDM-based Datacenter Networks: Architecture Design and Control Algorithms” National Science Foundation Suresh Subramaniam "A Server-Network Cooperative Approach to Data Center Energy Optimization", National Science Foundation, Co-PI Guru Prasadh Venkataramani “Defending Against Hardware Covert Timing” National Science Foundation and Semiconductor Research Corporation Guru Prasadh Venkataramani “Symbiotic and Integrated Reasoning ENgine (SIREN) for Autonomic Cyber Systems” Ofﬁce of Naval Research Guru Prasadh Venkataramani “CAREER: An Introspective Architecture for Manycore Performance and Power Debugging” National Science Foundation Guru Prasadh Venkataramani “A Server-Network Cooperative Approach for Datacenter Energy Optimization” National Science Foundation Guru Prasadh Venkataramani "DIALECT: Communication Protocols Customization via Feature DIAgnosis, Lacing, Elimination, Cross-grafting, and Trimming " Ofﬁce of Naval Research, Co-PI Mona Zaghloul "Surface Acoustic Wave Enhancement of Photocathode Performance" Department of Energy Mona Zaghloul ”Ambulatory Sensor Arrays for Real-Life Monitoring of Pediatric Patients with Asthma” National Institutes of Health
Selected Recent Publications M. Asghari Gharakheili, M. Fotuhi-Firuzabad, P. Dehghanian, “A New Multi-Attribute Support Tool for Identifying Critical Components in Power Transmission Systems,” IEEE Systems Journal, vol. 12, no. 1, pp. 316327, March 2018. P. Dehghanian, Y. Guan, and M. Kezunovic, “Real-Time Life-Cycle Assessment of Circuit Breakers for Maintenance using Online Condition Monitoring Data,” Industrial and Commercial Power Systems (I&CPS) Annual Meeting, May 2018, Niagara Falls, Canada. P. Dehghanian, S. Aslan, and P. Dehghanian, “Quantifying Power System Resiliency Improvement using Network Reconﬁguration,” IEEE 60th International Midwest Symposium on Circuits and Systems, 6-9 Aug. 2017, Boston, MA, USA. H. Fang, S. S. Dayapule, F. Yao, M. Doroslovacki, and G. Venkataramani, "Prefetch-guard: Leveraging hardware prefetchers to defend against cache timing channels," IEEE International Symposium on Hardware Oriented Security and Trust (HOST), Washington, DC, May, 2018. F. Yao, M. Doroslovacki, and G. Venkataramani, "Are coherent protocol states vulnerable to information leakage?," 24th IEEE International Symposium on High-Performance Computer Architecture, Vienna, Austria, Feb. 24-28, 2018. A. Ambaw and M. Doroslovacki, "Feature based order recognition of continuous-phase FSK using principal component analysis," 51st Asilomar Conference on Signals, Systems & Computers, Pacic Grove, CA, Oct.Nov. 2017. M. Alagappan, J. Rajendran, M. Doroslovacki, and G. Venkataramani, "DSF covert channels on multicore platforms," IFIP/IEEE VLSI-SoC 2017, Abu Dhabi, UAE, Oct. 2017. Y. Kim, R.J. Harrington, “Chapter: 'Analysis of Various Energy Storage Systems for Variable Speed Wind Turbines',” ADVANCEMENTS IN ENERGY STORAGE TECHNOLOGIES (ISBN 978-953-51-5713-7), 2017. Y. Hu, P. Kumar, G. Swope, H. Huang, “TriX: Triangle Counting at Extreme Scale,” IEEE High Performance Extreme Computing Conference (HPEC '17), 2017. A. Uppal, G. Swope, H. Huang. “Scalable Stochastic Block Partition,” IEEE High Performance Extreme Computing Conference (HPEC '17), 2017. S. Alamro, M. Xu, T. Lan, S. Subramaniam, "Shed: Optimal Dynamic Cloning to Meet Application Deadlines in Cloud", IEEE ICC , May 2018. Y. Chen, T. Lan, G. Venkataramani, "DAMGate: Dynamic Adaptive Multi-feature Gating in Program Binaries", In proceedings of ACM CCS, FEAST workshop , October 2017. Y. Wang, Y. Li, T. Lan, "Capitalizing on the Promise of Ad Prefetching in Real-World Mobile Systems", In proceedings of IEEE Mobile Adhoc and Sensor Systems (MASS) , October 2017. J. Fan, X. Wei, T. Wang, T. Lan, S. Subramaniam, "Deadline-Aware Task Scheduling in a Tiered IoT Infrastructure", In Proceedings of IEEE GLOBECOM , Dec 2017.
Selected Recent Publications V. Aggarwal, Y. Xiang, T. Lan, Y. Chen, "Sprout: A Functional Caching Approach to Minimize Service Latency in Erasure-coded Storage", IEEE Transactions on Networking, vol. 25, no. 6, pp. 3683 - 3694, December 2017. S. Alamro, M. Xu, T. Lan, S. Subramaniam. "CRED: Cloud Right-sizing to Meet Execution Deadlines and Data Locality", IEEE Trans-actions on Parallel and Distributed Systems, vol. 28, no. 12, pp. 3389 - 3400, December 2017. Zhou, Y., R. Lang, E. Dinnatl , D. Le Vine, " L-Band Model Function of the Dielectric Constant of Seawater," IEEE Trans of Geosci. Remote Sens., Vol 55, pp. 6964-6974, Dec 2017. A. Sikder, A. Kodi, S. Kaya, D. Carbaugh, S. Laha, A. Louri, H. Xin and J. Wu, “Sustainability in Network-onChips by Exploring Heterogeneity in Emerging Technologies,” in IEEE Transactions on Sustainable Computing, April 2018. S. V. Winkle, A. Kodi, R. Bunescu, A. Louri, “Extending the Power-Efﬁciency and Performance of Photonic Interconnects for Heterogeneous Multicores with Machine Learning,” in Proceedings of the 24th IEEE International Symposium on High-Performance Computer Architecture (HPCA), Vienna, February 24-28, 2018. Y. Sharma, J. Wu, A. Kantemur, J. Tak, A. Kodi, S. Kaya, A. Louri, H. Xin, “Reconﬁgurable Intra-chip Antenna for Future Wireless Communications,” in Proceedings of the 2018 USNC-USRI National Radio Science Meeting, Boulder, CO, January 4-8, 2018. H. Zheng, A. Louri, “EZ-Pass: An Energy & Performance-Efﬁcient Power-gating Router Architecture for Scalable NoCs,” in IEEE Computer Architecture Letters, vol. 17, no. 1, pp. 88-91, Jan.-June 2018. J. Wu, A. K. Kodi, S. Kaya, A. Louri, H. Xin, “Monopoles Loaded With 3-D-Printed Dielectrics for Future Wireless Intrachip Communications,” in IEEE Transactions on Antennas and Propagation, vol. 65, no. 12, pp. 6838 -6846, Dec. 2017. D. Machovec, B. Khemka, N. Kumbhare, S. Pasricha, A. A. Maciejewski, H. J. Siegel, A. Akoglu, G. A. Koenig, M. Wright, M. Hilton, R. Rambharos, C. Blandin, S. Hariri, C. Tunc, A. Louri, N. Imam, “Utility-Based Resource Management in an Oversubscribed Energy-Constrained Heterogeneous Environment Executing Parallel Applications,” in Parallel Computing, pp. 25, Nov. 2017. T. F. Canan, S. Kaya, A. Kodi, H. Xin, A. Louri, “Ultra-compact sub-10nm logic circuits based on ambipolar SB-FinFETs,” in Proceedings of the 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, MA, August 6-9, 2017. R. Amin, Z. Ma, R. Maiti, S. Khan, J. B. Khurgin, H. Dalir, V. J. Sorger, “Attojoule-Efﬁcient Graphene Optical Modulators”, Applied Optics 57, 18, 1-11 (2018).
Selected Recent Publications J. Peng, S. Sun, V. Narayana, V.J. Sorger, T. El-Ghazawi, “Integrated Nanophotonics Arithmetic for Residue Number System Arithmetic”, Optics Letters, 49, 9, 1-4 (2018). Z. Ma, R. Hemnani, L. Bartels. R. Agarwal, V. J. Sorger, “2D Materials in Electro-optic Modulation: energy efﬁciency, electrostatics, mode overlap, material transfer and integration”, Applied Physics A, 124: 126 (2018). F. Serdar Gokhan, H. Goktas, V. J. Sorger, “Analytical approach of Brillouin ampliﬁcation over threshold”, Applied Optics, 57, 4, 607-611 (2018). V. J. Sorger, R. Amin, J. B. Khurgin, Z. Ma, S. Khan, “Scaling Vectors for Atto-Joule per Bit Modulators” Journal Optics 20, 014012 (2018). R. Wang, H. Dalir, F. M. Koushyar, X. Xu, Z. Pan, S. Sun, V. J. Sorger, R. T. Chen, “Atto-Joule, High-Speed and Compact Plasmonic Modulator based on Adiabatic Coupled Waveguides”, Nanophotonics, https:// doi.org/10.1515/nanoph-2017-0092 (2018). R. Amin, M. Tahersima, Z. Ma, C. Suer, K. Liu, H. Dalir, V. J. Sorger, “Low-loss Tunable 1-D ITO-slot Photonic Crystal Nanobeam Cavity”, Journal Optics, 1-9, doi.org/10.1088/2040-8986/aab8bf (2018). N. Li, V. J. Sorger, D. Sadana, “Nanoscale Light Sources for Optical Interconnects” Journal of Lasers, Optics & Photonics 4, 3 (2017). S. Sun, R. Zhang, J. Peng, V. K. Narayana, H. Dalir, T. El-Ghazawi, V. J. Sorger, “MODetector (MOD): A DualFunction Transceiver for Optical Communication On-Chip”, Optics Express (accepted 3-2018) arXiv: 1412.01364 (2017). S. Sun, V. K. Narayana, Ib. Sarpkaya, J. Crandall, R. A. Soref, T. El-Ghazawi, V. J. Sorger, “Hybrid PhotonicPlasmonic Non-blocking Broadband 5×5 Router for Optical Networks”, IEEE Photonics Journal, doi: 10.1109/JPHOT.2017.2766087 (2017). R. Amin, C. Suer, Z. Ma, J. Khurgin, R. Agarwal, V. J. Sorger, “Active Material, Optical Mode and Cavity Impact on electro-optic Modulation Performance”, Nanophotonics, doi:10.1515/nanoph-2017-0072 (2017). V. K. Narayana, S. Sun, A. Mehrabian, V. J. Sorger, T. El-Ghazawi, “HyPPI NoC: Bringing Hybrid Plasmonics to an Opto-Electronic Network-on-Chip,” 46th International Conference on Parallel Processing (ICPP), Bristol, pp.131-140 (2017). H. Nejadriahi, V. J. Sorger, "On-chip Integrated All-Optical Fast Fourier Transform: Design and Analysis" Frontiers in Optics, OSA, paper JW4A.46 (2017). M. H. Tahersima, et al. "Light Emission Enhancement of 2D Materials in Monomer vs. Dimer Nanoantennae" Frontiers in Optics, OSA, paper JTu3A.25 (2017). Z. Ma, M. H. Tahersima, R. Amin, S. Khan, V. J. Sorger, "Sub-wavelength Plasmonic Graphene-based Slot Electro-optic Modulator" Frontiers in Optics, OSA, paper FM2A.3 (2017). S. Khan, Z. Ma, J. Jeon, C. J. Lee, V. J. Sorger, "Sub 1-Volt Graphene-based Plasmonic Electroabsorption Modulator on Silicon" Frontiers in Optics, OSA, paper FM2A.4 (2017).
Selected Recent Publications S. Sun, R. Zhang, J. Peng, V. Narayana, T. EI-Ghazawi, V. J. Sorger, "Hybrid Photonic-Plasmonic Directional Coupler Enabled Optical Transceiver" Frontiers in Optics, OSA, paper JW4A.55 (2017). R. Amin, C. Suer, Z. Ma, I. Sarpkaya, J. B. Khurgin, R. Agarwal, V. J. Sorger, "Implications of Active Material and Optical Mode on Nanoscale Electro-Optic Modulation" Frontiers in Optics, OSA, paper JW3A.96 (2017). S. Sun, V. Narayana, A. Mehrabian, R. Zhang, T. EI-Ghazawi, V. J. Sorger, "Holistic Performance-Cost Metric for Post Moore Era", Frontiers in Optics, OSA, paper JTu2A.24 (2017). M. Xu, C. Liu, and S. Subramaniam, "PODCA: A passive optical data center network architecture," IEEE/ OSA Journal of Optical Communications and Networking, vol. 10, no. 4, pp. 409-420, April 2018. F. Yao, M. Doroslovacki, G. Venkataramani, “Are Coherence Protocol States vulnerable to Information Leakage?”, 24th IEEE International Symposium on High-Performance Computer Architecture (HPCA), February, 2018, Vienna, Austria F. Yao, J. Wu, G. Venkataramani, S. Subramaniam, “TS-Bat: Leveraging Temporal-Spatial Batching for Data Center Energy Optimization,” IEEE Global Communications Conference (GLOBECOM), December, 2017, Singapore M. Alagappan, J. Rajendran, M. Doroslovacki, G. Venkataramani, “DFS Covert Channels on Multi-Core Platforms,” 25th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC), October 2017, Abu Dhabi, UA S. Pourabar, M. Zaghloul, "Design and Simulation of Nano Plasmonic Biosensors," 2017 IEEE 60 th International Midwest Symposium on Circuits and Systems (MWSCAS), August 6-9, 2017. S. Guo, A. Arab, S. Krylyuk, A. Davydov, M. Zaghloul, "Fabrication and Characterizaton of humidty sensors based on CVD MoS2 thin ﬁlm", IEEE 2017 IEEE International Conference on Nanotechnoogy ( IEEE-NANO), Page 164-267, July 2017.
Awards & Honors
AWARDS & HONORS
Awards & Honors
Faculty Awards & Honors The National Academy of Inventors Elects Dr. Zaghloul to the Rank of Fellow The National Academy of Inventors (NAI) has elected Dr. Mona Zaghloul to the rank of Fellow. The NAI announced its 2017 class of Fellows on December 12, 2017, noting that it named 155 renowned academic inventors to NAI Fellow status. The NAI Fellows Selection Committee chooses as Fellows those who have “demonstrated a highly proliﬁc spirit of innovation in cre-
Dr. Mona Zaghloul
ating or facilitating outstanding inventions that have made a tangible impact on quality of life, economic development, and the welfare of society.”
Dr. Zaghloul is the ﬁrst SEAS professor to achieve this rank. She is also a Life Fellow of the IEEE. This is a great recognition of Dr. Zaghloul as a truly proliﬁc academic inventor.
Dr. Lan is a Recipient of the SEAS Faculty Recognition Award Each department chair was asked to nominate one faculty member who had had an exceptional year in teaching, in research or in service, or some combination thereof. The nominations were reviewed by a committee of four persons. Professor Tian Lan had an outstanding year in research in cloud computing, cybersecurity, and IoT. He received $1.7M in funding from ONR and NSF, and published 17 peer-reviewed articles in his ﬁeld’s top conferences and journals, including INFOCOM, SIGMETDr. Tian Lan RICS, ICDCS, DSN, CLOUD, Globecom, Transactions on Networking, Transactions on Parallel and Distributed Systems, Transactions on Network and Service Management, and Transactions on Information Forensics and Security. Professor Lan is a co-winner of the 2017 Hegarty Innovation Award for his work on cybersecurity. He also provides outstanding student mentorship – he advises 4 doctoral students and has graduated one student who is placed at AT&T Labs.
Awards & Honors
Faculty Awards & Honors Dr. Subramaniam Selected as an IEEE Communications Society Distinguished Lecturer for 2018 and 2019 Dr. Suresh Subramaniam has been selected as an IEEE Communications Society Distinguished Lecturer for 2018 and 2019. As an IEEE Distinguished Lecturer, he is expected to deliver several lectures on tour on behalf the IEEE on topics of his research interest. His current interests are elastic optical networking, data center networking, and cloud computing.
Dr. Suresh Subramaniam
Dr. Louri Re-Appointed Vice-Chair of the IEEE Computer Society Fellow Evaluation Committee for 2018
Dr. Ahmed Louri
Dr. Ahmed Louri has been re-appointed as a vice-chair for the Institute of Electrical and Electronics Engineers (IEEE) Computer Society Fellow Evaluation Committee for 2018. The IEEE Grade of Fellow is one of the most prestigious honors and is conferred upon a person with an extraordinary record of accomplishments in any IEEE ﬁeld of interest. The vice-chair determines whether the work of each candidate for the grade of Fellow is recognized and considered outstanding in the Society's ﬁeld of interest, and he is responsible for making the ﬁnal decision as to the elevation to the grade of IEEE Fellow.
Awards & Honors
Faculty Awards & Honors Dr. El-Ghazawi Was Presented with the IEEE Outstanding Leadership Award
Dr. Tarek El-Ghazawi
Dr. Tarek El-Ghazawi has served as the General Chair for three colocated conferences: The 19th IEEE International Conference on High-Performance Computing and Communications (HPCC2017), The IEEE Third International Conference on Data Science and Systems (DSS2017), and The IEEE 15th International Conference on Smart City (SmartCity2017), December 18-20 2017. During the awards ceremony, Professor El-Ghazawi was presented with the IEEE Outstanding Leadership Award given by the IEEE Technical Committee on Scalable Computing.
IEEE Computer Society Selects Dr. El-Ghazawi as a Distinguished Lecturer The IEEE Computer Society has selected Dr. Tarek El-Ghazawi as a Distinguished Lecturer in the Society’s Distinguished Visitors Program for the three-year term 2018-2020. As an IEEE Distinguished Lecturer, he is expected to deliver lectures around the world on research topics of his interest. His current research interests include high-performance computing; post-Moore’s laws processor paradigms, the convergence of HPC, clouds, big data, and machine learning; and extreme scale computing.
Dr. El-Ghazawi Selected as Distinguished Visiting Fellow by U.K. Royal Academy of Engineering The U.K. Royal Academy of Engineering has selected Dr. Tarek El-Ghazawi as a Distinguished Visiting Fellow. In this role, he will visit a number of universities in the United Kingdom to deliver lectures and collaborate on research. Dr. El-Ghazawi's current research interests include high-performance computing; post-Moore’s laws processor paradigms, the convergence of HPC, clouds, big data, and machine learning; and extreme scale computing.
Awards & Honors
SEAS R&D Showcase The SEAS Research & Development Showcase was founded by SEAS alumnus Randolph “Randy” Graves in 2007. The showcase has grown year by year and helps foster the growth of research and entrepreneurship at SEAS. Dean Dolling, Shuai Sun and Jiaxin Peng
ECE Students Win at the SEAS 2018 R&D Showcase Nearly 100 undergraduate and graduate students presented research posters and competed for more than $30,000 in prize money on February 21 at the 2018 SEAS Student Research and Development Showcase. The Showcase was held February 21— during the national Engineers’ Week celebration—in the Science and Engineering Hall.
Graduate Student Theoretical Research:
Shuai Sun, “Breaking the Boundary between Optical Communication and Data Processing” mentored by Volker Sorger, 2nd Place tie (Prize $4,000)
Jiaxin Peng and Shuai Sun, “Residue Number System Arithmetic Based on Integrated Nanophotonics” mentored by Volker Sorger and Tarek El-Ghazawi, 2nd Place tie (Prize $4,000)
Awards & Honors
Graduate Student Experimental Research:
Fan Yao, Yongbo Li, Yurong Chen, and Hongfa Xue, Title: “StatSym: V ulnerable Path Discovery through Statistics-Guided Symbolic Execution” mentored by Suresh Subramaniam and Tian Lan, 2nd Place (Prize $4,000)
Sultan Alamro and Maotong Xu Title: “Shed: Optimal Dynamic Cloning to Meet Application Deadlines in Cloud” mentored by Suresh Subramaniam and Tian Lan, Runner-up tie ($1,000 each)
SEAS R&D Showcase The SEAS Research & Development Showcase was founded by SEAS alumnus Randolph “Randy” Graves in 2007. The showcase has grown year by year and helps foster the growth of research and entrepreneurship at SEAS.
MEET OUR STAFF
Staff MARK A. AGLIPAY Computer Applications Specialist BS, 2004, University Of Maryland Information Systems Management
DR. AMIR ASLANI Laboratory Manager Ph.D., 2016, The George Washington University Electrical Engineering
ROBERT J. BADEN
Department Operations Manager MA, 2009, Shepherd University College Student Development & Administration
BRENDA FLORES Department Operations Assistant BA, 2009, Georgetown University Classics & Psychology
Ph.D. DISSERTATIONS SPRING 2018
Ph.D. Dissertations Stabilization and Dynamic Performance Enhancement of DC Distributed Power Systems ABSTRACT This dissertation proposes methods to stabilize and enhance the dynamic performance of DC distributed power systems (DPSs). Various applications are based on the DPS conﬁguration such as electric vehicles, and DC microgrids. A DPS can be readily synthesized by connecting two switched-mode regulators in series. Switched-mode regulators are tightly regulated devices, so cascading two of them would adversely affect their relative stability margins. As a consequence, the voltage at the point of interfacing the converters might be unstable. The instability originates due to an impedance interaction between the output impedance of the source converter (Zo(s)) and the input impedance of the load converter (Zin(s)). Therefore, these impedances are crucial for distributing DC power stably. Many criteria were proposed in order to build a deterioration-free DPS. The criteria deﬁne permissible areas in the complex plane for the Nyquist plot of Zo(s)/Zin(s) in order to prevent any impedance interaction. Designing a DPS according to any of the criteria leads to a long manufacturing time. Therefore, passive damping methods (PDMs) were introduced in order to integrate regulators that are supplied by different vendors, so the manufacturing time is reduced. Yet, PDMs jeopardize the efﬁciency and power-density of DPSs due to the incurred power-loss, and the added weight and size. Active damping methods (ADMs) were introduced in order to improve the dynamic performance of DPSs using novel control techniques. Hence, the efﬁciency, size, and weight of DPSs are not affected. The proposed ADMs in the literature have limitations that can be classiﬁed as compatibility with a speciﬁc switched-mode regulator (such as buck converters), ability to stabilize a system without improving much the dynamic performance, and incapability of handling multi-converter load subsystems. This dissertation proposed three control techniques that address the drawbacks of the present ADMs in the literature. The ﬁrst controlling method aims to diminish the magnitude of the source converter output impedance such that the source converter will act as an ideal voltage source. As a consequence, the dynamic performance of the source and load converters will be intact, leading to ensuring the bus voltage stability. The second controller injects the bus-voltage oscillations into the control loop of the source converter using a high-pass ﬁlter. The ﬁlter suppresses the DC quantity of the bus voltage, while passing the high-frequency oscillations. Thus, the dynamic performance is highly improved. The third controller improves the dynamic performance of DPSs that are under AverageCurrent-Mode (ACM) control technique by adding an extra negative feedback to the original ACM controller. Hence, the impedance interaction between the source and load converters is eradicated. Each proposed controller was veriﬁed using small-signal analyses, time-based simulations and experiments. Their outcomes were in agreement of validating its veracity. AHMED ALDHAHERI B.A. Electrical Engineering, June 2009, The United Arab Emirates University M.S. Electrical Engineering, May 2014, The George Washington University DISSERTATION: Stabilization and Dynamic Performance Enhancement of DC Distributed Power Systems FIELD OF STUDY: Electrical Engineering ADMISSION TO DOCTOR OF PHILOSOPHY PROGRAM: Fall 2014 ADVISOR OF THE CANDIDATE’S RESEARCH: Professor Robert J Harrington
Ph.D. Dissertations Finite Control Set Model Predictive Control for Multiple Distributed Generators Microgrids ABSTRACT This dissertation proposes two control strategies for AC microgrids that consist of multiple distributed generators (DGs). The control strategies are valid for both grid-connected and islanded modes of operation. In general, microgrid can operate as a stand-alone system (i.e., islanded mode) or while it is connected to the utility grid (i.e., grid-connected mode). To enhance the performance of a micrgorid, a sophisticated control scheme should be employed. The control strategies of microgrids can be divided into primary and secondary controls. The primary control regulates the output active and reactive powers of each DG in grid-connected mode as well as the output voltage and frequency of each DG in islanded mode. The secondary control is responsible for regulating the microgrid voltage and frequency in the islanded mode. Moreover, it provides power sharing schemes among the DGs. In other words, the secondary control speciﬁes the set points (i.e. reference values) for the primary controllers. In this dissertation, Finite Control Set Model Predictive Control (FCS-MPC) was proposed for controlling microgrids. FCS-MPC was used as the primary controller to regulate the output power of each DG (in the grid-connected mode) or the voltage of the point of DG coupling (in the islanded mode of operation). In the grid-connected mode, Direct Power Model Predictive Control (DPMPC) was implemented to manage the power ﬂow between each DG and the utility grid. In the islanded mode, Voltage Model Predictive Control (VMPC), as the primary control, and droop control, as the secondary control, were employed to control the output voltage of each DG and system frequency. The controller was equipped with a supplementary current limiting technique in order to limit the output current of each DG in abnormal incidents. The control approach also enabled smooth transition between the two modes. The performance of the control strategy was investigated and veriﬁed using PSCAD/EMTDC software platform. This dissertation also proposes a control and power sharing strategy for microgrids in both grid-connected and islanded modes based on centralized FCS-MPC. In grid-connected mode, the controller was capable of managing the output power of each DG and enabling ﬂexible power regulation between the microgrid and the utility grid. In islanded mode, the controller regulated the microgrid voltage and frequency, and provided a precise power sharing scheme among the DGs. In addition, the power sharing can be adjusted ﬂexibly by changing the sharing ratio. The proposed control also enabled plug-and-play operation. Moreover, a smooth transition between the two modes of operation was achieved without any disturbance in the system. Case studies were carried out in order to validate the proposed control strategy with the PSCAD/EMTDA software package.
ABDULRAHMAN BABQI B.A. Electrical Engineering, May 2008, Umm Al-Qura University M.S. Electrical Engineering, Jan 2014, The George Washington University DISSERTATION: Stabilization and Dynamic Performance Enhancement of DC Distributed Power Systems FIELD OF STUDY: Electrical Engineering ADMISSION TO DOCTOR OF PHILOSOPHY PROGRAM: Spring 2014 ADVISOR OF THE CANDIDATE’S RESEARCH: Professor Robert J Harrington
Ph.D. Dissertations PUSHING THE ENVELOPE OF MOBILE COMPUTING: IMPROVING SECURITY, ENERGY, AND LATENCY BY BRIDGING THE GAP BETWEEN ANALYTICAL MODELING AND SYSTEM DESIGN ABSTRACT Recent research ﬁndings and reports have raised the concerns for mobile security vulnerability and energy inefﬁciency. Besides, the rapid emergence of new domains of mobile applications also impose new requirements for energy efﬁciency and service latency. These long-standing issues and new challenges call for novel approaches to further push the envelope of mobile computing, which needs to be secure, energy efﬁcient and fast. First, contemporary permission-based security mechanisms on mobile platforms are proven to be ineffective to protect users' private data. We present SARRE, a semantics-aware security rule recommendation and enforcement system, which employs statistical inference and collaborative ﬁltering techniques to automatically assign security rules to information ﬂows in the system, thus preventing information leakage. The SARRE system is prototyped on Android devices. Evaluations with 1473 popular apps and 213 real-world malware samples demonstrate SARRE is effective to provide the ﬁne-grained protection over private data with low performance overhead. Second, in cellular networks, tail states are designed for a tradeoff between energy efﬁciency and latency. However, the energy consumed during tail states becomes a huge energy drainer itself. Especially, trafﬁc patterns of ad modules are inefﬁcient for cellular networks. We reveal the reason for the inefﬁciency, and propose our design of the ﬁrst ad management framework that is fully compatible with existing ad ecosystem, aiming to improve the efﬁciency of contemporary ad libraries. The adfetching decisions are optimized based on a novel energy accounting method we proposed based on Shapley Value. Our fully -implemented system achieves up to 45% energy savings than existing policies, and is transparent to mobile apps. To further capitalize the promise of saving energy by ad prefetching in real-world mobile systems, considering several signiﬁcant runtime factors, we make a novel use of Markov Decision Process to model the energy minimization problem for ad prefetching (EMAP), and propose an algorithmic solution to the EMAP problem. Further, we implement the algorithmic solution with our ad prefetching system. By replaying real-world user traces on Android devices, we show our proposed solution consistently outperforms existing On-Demand policy on Android by up to 59% in saving ad-related energy, while a simple Fill-Up-Buffer policy can be even 2 times worse than the default On-Demand policy. Finally, due to the proliferation of the Internet of Things and interactive applications involving big data analytics and media processing, the gap between ever-increasing user expectations and limited mobile device resources has made mobile edge computing a promising technique. We propose a new optimization horizon, Quality-of-Result (QoR) in mobile edge computing, and present our systematic optimization framework, MobiQoR, to trade QoR for improved response time and energy saving. In our framework, mobile workload can be divided, ofﬂoaded and distributively processed by neighboring edge nodes. The optimization in our design aims to minimize the energy consumption and the service latency by jointly optimizing the task ofﬂoading decisions and the selections of all edge nodes' QoR levels. Evaluations show that MobiQoR outperforms existing strategies by up to 77.0% for face recognition and 189.3% for movie recommendation. Further, we consider real-world edge environment consisted of multiple edge clients and edge servers, we address two problems unique in heterogeneous edge computing: the cost accounting and workload assignment. To determine the cost of each unit of workload being concurrently processed on an edge-device, we propose to model the problem as a multi-choice game, and use Shapley Value for cost accounting. With the total cost decoupled, we are able to use the distributive Hungarian algorithm to solve the workload assignment problem in an efﬁcient manner. Results show that our policy of workload assignment guided by multi-choice Shapley value is able to consistently outperform the two other baselines. The advantage of our policy is further enlarged when the heterogeneity level of the network or computing resource in edge environment is increased. We also show interesting patterns of the joint effects of different resources' heterogeneity levels and the weighting factor between them, which provide useful inputs for edge resource optimization.
YONGBO LI B. S. Electrical Engineering, May 2012, Shaanxi University of Science and Technology, China DISSERTATION: Pushing the Envelope of Mobile Computing: Improving Security, Energy, and Latency by Bridging the Gap between Analytical Modeling and System Design FIELD OF STUDY: Mobile Computing ADMISSION TO DOCTOR OF PHILOSOPHY PROGRAM: Fall 2012 ADVISOR OF THE CANDIDATE’S RESEARCH: Professor Tian Lan 40
Ph.D. Dissertations Modulation of Magneto-Optical Properties of Metallic Nanostructures by External Stimuli ABSTRACT Over the last few decades, with the advancement of high tech fabrication of devices, many new phenomena are observed that were not possible in bulk materials. The nanostructures like thin ﬁlms are subject to extensive research with many applications in sight. One of such applications is non-volatile memory devices with high areal density and low power consumption. The irreversible tailoring of the mechanical, or electronic properties of nanostructures has been carried out previously, [KUM03], [VALDO], [TRIO!], however, the reversible and dynamic control of the intrinsic properties like the magnetic are shown recently [WEI07]. These modiﬁcations are however limited by the thickness of the thin ﬁlms used. The charge neutrality is disturbed to induce the reversible changes and is affected by the screening length. Here, in this research, we tried to explore the effects of thickness on the behaviour of critical parameters like coercivity, saturation magnetization, squareness etc. of magnetic nanostructures. These ﬁeldinduced variations are an alternative to the spin current-induced changes, which are currently employed for the reverse the magnetization in the memory devices. Secondly, the quantum effects arc signiﬁcant in the nanomaterials and require deeper understanding. To explore the quantum of behaviour of magnons conﬁned in the intermetallic nanostructures like CoPd, not too much has been done. Though, some theoretical aspects of magnons entanglement has been presented, [MOR05], yet experimental evidences arc yet to be realized. Although, the spin-photon entanglement is actively researched in many semiconducting systems like quantum dots (QDs), yet the magnon-photon entanglement in metallic systems is yet an area to be explored. We here discover the magnon-photon entanglement. Thus, in a nutshell, the purpose of this research is as below:
To explore the dynamic and reversible control of magnetic properties of metallic nanostructures like CoPd based on the thickness.
To explore the quantum entanglement of magnons in metallic thin ﬁlms under BEC temperatures.
ABID SIDDIQUE B.S. in Electrical Engineering, June 2007, University of Engineering & Technology, Peshawar, Pakistan M.S. in Electrical Engineering, May 2010, The George Washington University, USA DISSERTATION: Grid Integration of Hybrid Energy Storage Systems into Renewable Distributed Generations FIELD OF STUDY: Electromagnetics ADMISSION TO DOCTOR OF PHILOSOPHY PROGRAM: Fall ADVISOR OF THE CANDIDATE’S RESEARCH: Dr. Edward Della Torre, Co-Advisor Professor Larence H. Bennett
Ph.D. Dissertations Vector Magnetization Model ABSTRACT In hysteresis modeling, we predict magnetization as a function of the applied ﬁeld and magnetic state. Most hysteresis models are scalar. A scalar model assumes that only the magnitude of the applied ﬁeld changes and the direction of the magnetization is always along the direction of the applied ﬁled. In this dissertation, I assumed the magnitude of the applied ﬁeld is constant and we rotate the magnetization. I developed a vector model that can successfully predict the direction of the magnetization. The idea of decomposing magnetization into reversible and irreversible components is introduced in chapter 2 of the dissertation. In chapter 3, I developed the model for uniaxial media by using Stoner-Wohlfarth model for the direction of reversible component. This has been done in chapter 4 by assuming anisotropy ﬁeld is parallel with easy axis as another variation of the model. A comparison between the two variations has been made in chapter 4. In chapter 5, I generalized the idea to the cubic particles. I also showed and proved a graphical visualization of the Stoner-Wohlfarth model. Finally, in chapter 6, I showed how we can improve the model by taking averages over orientation of hysteron’s easy axes.
ALI JAMALI B.S. in Electrical Engineering, June 2003, Tehran University, Iran M.S. in Electrical Engineering, May 2013, The George Washington University, USA DISSERTATION: Grid Integration of Hybrid Energy Storage Systems into Renewable Distributed Generations FIELD OF STUDY: Electromagnetics ADMISSION TO DOCTOR OF PHILOSOPHY PROGRAM: Fall 2013 ADVISOR OF THE CANDIDATE’S RESEARCH: Professor Edward Della Torre
NASA Has Chosen Serena Auñón-Chancellor, MD as a Member of the International Space Station NASA is announcing an addition to the NASA lineup for upcoming launches, and making changes to some assignments for International Space Station missions in 2018. Dr. Serena Auñón-Chancellor, who previously was assigned to Expedition 58/59, has been reassigned to the Expedition 56/57 crew, launching in June. She is taking the place of astronaut Jeanette Epps who will return to NASA’s Johnson Space Center in Houston to assume duties in the Astronaut Ofﬁce and be considered for assignment to future missions. Dr. Serena M. Auñón-Chancellor began working with NASA as a Flight Surgeon in 2006. In 2009, she was selected as a NASA astronaut. During her NASA career, Dr. AuñónChancellor spent more than nine months in Russia supporting medical operations for International Space Station crew members in Star City. She also served as Deputy Crew Surgeon for STS-127. Board certiﬁed in both Internal and Aerospace Medicine, Dr. AuñónChancellor currently handles medical issues for both the Commercial Crew and International Space Station Operations branch. In addition to a bachelor’s degree in electrical engineering from The George Washington University, Auñón-Chancellor holds a doctorate in medicine from the University of Texas Health Science Center at Houston, is board certiﬁed in internal and aerospace medicine, and earned a master’s degree in public health from the University of Texas Medical Branch.
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