Satchidanadnan wins best student paper award at COMSNETS 2017

Bharadwaj Satchidanadnan, a graduate student in the Department of Electrical and Computer Engineering at Texas A&M University, won the best student paper award in the 9th International Conference on Communication Systems and Networks (COMSNETS 2017) in Bangalore, India.
Satchidanadnan co-wrote the paper “On Minimal Tests of Sensor Veracity for Dynamic Watermarking-Based Defense of Cyber-Physical Systems,” with his Ph.D. adviser, Dr. P.R. Kumar from the Computer Engineering and Systems Group.

Their paper addresses the problem of secure control of networked cyber-physical systems. More specifically, they consider the problem of controlling a physical plant with multiple inputs and multiple outputs, where the sensors measuring some of the outputs may be malicious. The malicious sensors can collude and report false measurements, fabricated possibly strategically, in order to achieve any objective that they may have, such as destabilizing the closed-loop system or increasing its running cost. In his paper, Satchidanadnan proposes a general technique termed Dynamic Watermarking, which allows the controller to detect such malicious sensors in the system and prevent them from causing performance degradation.

Satchidanandan earned his master’s degree from the Indian Institute of Technology, Madras, India, where he worked on wireless communications. Between May 2015 and August 2015, he interned at Intel Labs in Santa Clara, California, where he worked on interference cancellation algorithms for next generation wireless networks. His research interests include cyberphysical systems, power systems, security, database privacy, communications, control and signal processing.

Li receives NSF/SRC grant to find revolutionary solutions for more energy-efficient computing

Dr. Peng Li, professor in the Department of Electrical and Computer Engineering at Texas A&M University, has received a grant from the National Science Foundation (NSF) and Semiconductor Research Corporation (SRC) to help find revolutionary solutions that will enable more energy-efficient computing.

Li’s work is aimed at attaining brain-like learning performance by imitating how the brain represents, processes and learns from information. More specifically, by developing models of computation based on the third-generation spiking neural networks and efficient adaptive processor architectures.

“Drawing inspiration from the human brain offers rich insights and opportunities for developing disruptive computing solutions desperately needed in today’s machine learning and big data applications,” Li said. “It will enable the development of novel information processing paradigms by mimicking key computational characteristics of the brain.”

Li’s project will develop spike-dependent learning mechanisms to allow training of complex recurrent spiking neural networks. Self-adaptive processor architectures with integrated on-chip learning, light-weight runtime learning performance prediction and energy management will be developed to maximize the energy efficiency of the proposed neural processors while providing a guarantee for learning performance.

Li received nearly $500,000 for his project that was one of nine three-year projects awarded a total of $21.6 million by the NSF and SRC. The topics to be studied range from advanced brain-inspired computer architectures like Li’s to hybrid digital-analog designs. The goal of the program is to create a new type of computer that can proactively interpret and learn from data, solve unfamiliar problems using what it has learned and operate closer to the efficiency of the human brain.

“Only disruptive breakthroughs can enable computers to perform as the human brain does, in terms of problem-solving capability and lower power, which, for the human brain, is less than a light bulb’s worth of consumption,” said Dimitris Pavlidis, NSF’s Directorate for Engineering program director for the Energy-Efficient Computing: from Devices to Architectures (E2CDA) initiative.

Computing capabilities in the United States rely on the continuous research and development of new computing systems with rapidly increasing performance. However, improvements in computing performance are severely limited by the amount of energy needed.

According to Pavlidis, the three-year projects consider simultaneously novel approaches — including developing nanoscale devices and materials and integrating them into three-dimensional systems — while inventing new computer architectures to process, store and communicate data.

The initiative to create new types of high efficiency computing systems was detailed by the White House when it announced the Nanotechnology-Inspired Grand Challenge for Future Computing. This effort also aligns with one of the NSF’s big ideas for future investment as well as the National Strategic Computing Initiative to advance the “Work at the Human-Technology Frontier: Shaping the Future.”
To learn more about the projects visit the NSF’s website.

Duffield receives NSF grant to explore network traffic classification

Dr. Nick Duffield, a professor in the Department of Electrical and Computer Engineering at Texas A&M University, was awarded a grant from the National Science Foundation (NSF) that will allow him to research network traffic classification.

Duffield, in collaboration with Dr. Minlan Yu from Yale University, received the grant, which is titled “Distributed Approximate Packet Classification.” It is funded from 2016 to 2019 with a budget of $350,000.

Network traffic classification — assigning incoming packets to classes for processing based on pattern-matching rules — is critical for many network management tasks, including performance monitoring and fault diagnosis. However, as the number of classification tasks grows, the resources required to store and apply the rules, switch memory in particular, can become scarce. Duffield’s project takes an end-to-end view of traffic classification, observing that in addition to the memory usage at switches, other cheaper resources are involved in packet processing, specifically bandwidth to transfer selected packets to the receivers and downstream receivers that run applications. Trading off resources and even classification accuracy amongst these resources can lead to a better overall performance once the needs of downstream applications are factored in.

“The big research challenge now is how to realize these benefits in large and complex communications networks, such as in data centers, which can encompass millions of servers connected by hundreds of thousands of switches,” Duffield said.

Duffield, who also has a courtesy appointment in the Department of Computer Science and Engineering and is director of the Texas A&M Engineering Big Data Initiative, received his bachelor’s degree in natural sciences in 1982 and a master’s in mathematics in 1983 from the University of Cambridge. He received his Ph.D. in mathematical physics from the University of London in 1987. His research focuses on data and network science, particularly applications of probability, statistics, algorithms and machine learning to the acquisition, management and analysis of large datasets in communications networks and beyond.

Before joining the department, Duffield worked at AT&T Labs-Research, Florham Park, New Jersey, where he held the position of distinguished member of technical staff and was an AT&T Fellow. He previously held post-doctoral and faculty positions in Dublin, Ireland, and Heidelberg, Germany.

Duffield, the author of over 150 refereed journal and conference papers and inventor of 50 U.S patents, is co-inventor of the smart sampling technologies that lie at the heart of AT&T’s scalable Traffic Analysis Service. He is specialty editor-in-chief for Big Data of the journal Frontiers in ICT and he was charter chair of the IETF working group on packet sampling. Duffield is an IEEE Fellow, an IET Fellow and serves on the board of directors of ACM SIGMETRICS. He is an associate member of the Oxford-Man Institute of Quantitative Finance. He is a Texas A&M principal investigator on the DARPA funded consortium DEDUCE: Distributed Enclave Defense Using Configurable Edges, and has received faculty research awards from Google and Intel.

Electrical and computer engineering former student named editor-in-chief of IET journal

Shiyan Hu, a former student from the Department of Electrical and Computer Engineering at Texas A&M University, was named editor-in-chief of the Institute of Technology’s (IET) newly launched journal, Cyber-Physical Systems: Theory & Application.

Cyber-Physical Systems (CPS) include smart washing machines, self-driving cars, medical devices and smart grid meters. As the digital world becomes more than handheld, researchers seek to get a better understanding of the interface between cyberspace and the tangible elements.

Hu is an expert in CPS and cybersecurity, and is director of Center for Cyber-Physical Systems at Michigan Tech Institute of Computer and Cybersystems. As founding editor, Hu will lead a team of associate editors who are leading experts worldwide, including several from Carnegie Mellon, Stanford, the University of Illinois, National Taiwan University and The University of Tokyo.

In the journal they will address the close interactions and feedback loop between cyber components (such as embedded sensing systems) and physical components (such as energy systems) in a system. The CPS research topics include smart energy systems, smart home/building/community/city, connected and autonomous vehicle systems and smart health.

Cyber-Physical Systems: Theory & Application is dedicated to all aspects of the fundamental and applied research in the design, implementation and operation of CPS systems, considering performance, energy, user experience, security, reliability, fault tolerance, flexibility and extensibility. Its scope also includes innovative big data analytics for cyber-physical systems such as large-scale analytical modeling, complex stochastic optimization, statistical machine learning, formal methods and verification and real-time intelligent control, which are all critical to the success of CPS developments.

As an elected Fellow of IET, Hu leads this journal and also chairs the IEEE Technical Committee on Cyber-Physical Systems (, an authoritative constituency overseeing all CPS related activities within IEEE. He has published more than 100 research papers (about 30 in the premier IEEE Transactions), received numerous awards recognizing his research impact to the field and served as associate editor or guest editor for seven IEEE/ACM Transactions.

IET is the largest engineering society in Europe with more than 180,000 members. Visit Cyber-Physical Systems: Theory & Application.

Gratz receives Association of Former Students Distinguished Achievement Award in Teaching

Dr. Paul V. Gratz, associate professor in the Department of Electrical and Computer Engineering at Texas A&M University, was awarded the 2016 Association of Former Students (AFS) Distinguished Achievement Award in Teaching — College Level. He is one of four faculty members in the college of engineering selected to receive the award.

Since 1982, the AFS teaching award has been presented to faculty members who are renowned for their expertise and exemplary dedication to the education of their students.

Dr. Miroslav Begovic, electrical and computer engineering department head, said Gratz deserves the award because he has been an early adopter of blended learning within the department and college, having restructured ECEN 350 as a blended learning class.

The restructured class features live, recorded lectures published online and online quizzes replacing traditional homework, among other enhancements. Those efforts have yielded two benefits — a two to three week increase in material covered during a semester as well as improvements in student retention from a traditionally high drop-rate class.

Gratz has also been a leader in the department’s efforts to develop a distance learning masters program. His ECEN 676 class during spring 2016 served as the pilot class for the distance learning masters program. Based on his experiences he is developing a set of distance learning training sessions for faculty.

Gratz is a member of the computer engineering and systems group. He received his Ph.D. in electrical and computer engineering from the University of Texas at Austin in 2008. His research interests include energy-efficiency, reliability and performance in processor microarchitectures, memory systems and on-chip interconnection networks.

He has received a Teaching Excellence Award from The Texas A&M University System and a Best Paper Award from the ASPLOS’09 conference.

The AFS teaching award will be formally presented to all recipients in spring 2017 at the annual college awards banquet.