Electrical Engineering and Computer Science Professor Qinru Qiu Recognized as IEEE Fellow

Qinru Qiu

Electrical engineering and computer science professor, Qinru Qiu, has been recognized as a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) for her impactful contributions to the modeling and optimization of energy-efficient computing systems.  

IEEE is a global organization that is committed to advancing technology for the betterment of humanity. With over 409,000 members in more than 160 countries, less than 0.1% of voting members are chosen for elevation to this member grade each year.  

Qiu’s research interest focuses on improving the energy efficiency of computing, from runtime power and thermal management of computer systems, and energy harvesting real-time embedded systems, to her recent works in brain-inspired hardware and software for neuromorphic computing.  The goal of her research is to provide machine intelligence to today’s computing platforms to achieve autonomous resource management with energy and thermal awareness and explore emerging computing paradigms.  

“Professor Qiu has been leading the research community to seek solutions for highly energy-efficient machine intelligence through adopting biologically inspired models and processing mechanisms,” says nominator Diana Marculescu. “Her ground-breaking research has enabled a completely new computing paradigm, which leverages the unique property of different types of spike coding to replace the numerical calculation with simple logic operations, resulting in significant energy reduction.” 

“I am excited and thankful for the recognition and truly grateful for all the support that I have received,” says Qiu. “I look forward to continuing my work in developing and promoting techniques to improve the energy efficiency of emerging computing systems.” 

Electrical Engineering and Computer Science Professor Bryan S. Kim Receives Funding for Rack Scale Computing Research

Bryan Kim

Bryan S. Kim, assistant professor in electrical engineering and computer science, has received funding from semiconductor company FADU to explore how CXL, a new open standard for connecting computer components, would transform data center applications.

With CXL, an entire rack of computers can be connected through the peripheral component interconnect express (PCIe) bus with shared memory coherency, rethinking how computers access and share data.

“CXL is still in its infancy with only limited publicly available hardware. After all, its specification is only a few years old” Kim explained. “Furthermore, how CXL and its hardware would affect software system design is completely unexplored.”

Kim’s collaborative project will investigate the fundamental technologies for building a software system with CXL memory, the designs for resilient and reliable CXL fabric, and the transformation of data center applications due to CXL.

“While there is a large research community interest in CXL, there are only a handful of research groups who have published in this area,” Kim said. “I am grateful to be at the leading front and continuing the success of this project.”

Dean J. Cole Smith Recognized as a Fellow by the Institute for Operations Research and the Management Sciences

The Institute for Operations Research and the Management Sciences (INFORMS) announced that College of Engineering and Computer Science Dean J. Cole Smith will be part of the INFORMS Fellows Class of 2023.

INFORMS is the largest professional association for the decision and data sciences. It brings together academic and industry experts in operations research, analytics, management science, economics, behavioral science, statistics, artificial intelligence, data science, applied mathematics and other fields.

Smith was recognized for his “sustained leadership and service to INFORMS and the profession; impactful research in integer programming, network interdiction, and multilevel optimization; and for distinguished leadership in academia.”

“This is such a humbling honor to receive from an organization like INFORMS. The award recognizes students who’ve worked alongside me, mentors who guided my career, and leaders who provided me so many opportunities in research and administration,” said Smith.

“As we launch our own Master’s degree in Operations Research and System Analytics (OR/SA) at Syracuse, I encourage people to learn more about the INFORMS organization and the breadth of career opportunities afforded with an OR/SA degree.”

The new INFORMS Fellows will be honored during the organization’s annual meeting in Phoenix, Arizona from October 15th to 18th 2023.

Researchers’ Artificial Intelligence-Based Speech Sound Therapy Software Wins $2.5M NIH Grant

Three Syracuse University researchers, supported by a recent $2.5 million grant from the National Institutes of Health, are working to refine a clinically intuitive automated system that may improve treatment for speech sound disorders while alleviating the impact of a worldwide shortage of speech-language clinicians.

The project, “Intensive Speech Motor Chaining Treatment and Artificial Intelligence Integration for Residual Speech Sound Disorders,” is funded for five years. Jonathan Preston, associate professor of communication sciences and disorders, is principal investigator. Preston is the inventor of Speech Motor Chaining, a treatment approach for individuals with speech sound disorders. Co-principal investigators are Asif Salekin, assistant professor of electrical engineering and computer science, whose expertise is creating interpretable and fair human-centric artificial intelligence-based systems, and Nina Benway, a recent graduate of the communication sciences and disorders/speech-language pathology doctoral program.

Their system uses the evidence-based Speech Motor Chaining software, an extensive library of speech sounds and artificial intelligence to “think” and “hear” the way a speech-language clinician does.

The project focuses on the most effective scheduling of Speech Motor Chaining sessions for children with speech sound disorders and also examines whether artificial intelligence can enhance Speech Motor Chaining—a topic Benway explored in her dissertation. The work is a collaboration between Salekin’s Laboratory for Ubiquitous and Intelligent Sensing in the College of Engineering and Computer Science and Preston’s Speech Production Lab in the College of Arts and Sciences.

Clinical Need

In speech therapy, learners usually meet with a clinician one-on-one to practice speech sounds and receive feedback. If the artificial intelligence version of Speech Motor Chaining (“ChainingAI”) accurately replicates a clinician’s judgment, it could help learners get high-quality practice on their own between clinician sessions. That could help them achieve the intensity of practice that best helps overcome a speech disorder.

The software is meant to supplement, not replace, the work of speech clinicians. “We know that speech therapy works, but there’s a larger issue about whether learners are getting the intensity of services that best supports speech learning,” Benway says. “This project looks at whether AI-assisted speech therapy can increase the intensity of services through at-home practice between sessions with a human clinician. The speech clinician is still in charge, providing oversight, critical assessment and training the software on which sounds to say are correct or not; the software is simply a tool in the overall arc of clinician-led treatment.”

170,000 Sounds

A library of 170,000 correctly and incorrectly pronounced “r” sounds was used to train the system. The recorded sounds were made by 400-plus children over 10 years, collected by researchers at Syracuse, Montclair and New York Universities, and filed at the Speech Production Lab.

Benway wrote ChainingAI’s patent-pending speech analysis and machine learning operating code, which converts audio from speech sounds into recognizable numeric patterns. The system was taught to predict which patterns represent “correct” or “incorrect” speech. Predictions can be customized to individuals’ speech patterns.

During speech practice, the code works in real time with Preston’s Speech Motor Chaining website to sense, sort and interpret patterns in speech audio to “hear” whether a sound is made correctly. The software provides audio feedback (announcing “correct” or “not quite”), offers tongue-position reminders and tongue-shape animations to reinforce proper pronunciation, then selects the next practice word based on whether or not the child is ready to increase word difficulty.

Early Promise

The system shows greater potential than prior systems that have been developed to detect speech sound errors, according to the researchers.

Until now, Preston says, automated systems have not been accurate enough to provide much clinical value. This study overcomes issues that hindered previous efforts: Its example residual speech sound disorder audio dataset is larger; it more accurately recognizes incorrect sounds; and clinical trials are assessing therapeutic benefit.

“There has not been a clinical therapy system that has explicitly used AI machine learning to recognize correct and distorted “r” sounds for learners with residual speech sound disorders,” Preston says. “The data collected so far shows this system is performing well in relation to what a human clinician would say in the same circumstances and that learners are improving speech sounds after using ChainingAI.”

So Far, Just ‘R’

The experiment is currently focused on the “r” sound, the most common speech error persisting into adolescence and adulthood, and only on American English. Eventually, the researchers hope to expand software functionality to “s” and “z” sounds, different English dialects and other languages.

Ethical AI

The researchers have considered ethical aspects of AI throughout the initiative. “We’ve made sure that ethical oversight was built into this system to assure fairness in the assessments the software makes,” Salekin says. “In its learning process, the model has been taught to adjust for age and sex of clients to make sure it performs fairly regardless of those factors.” Future refinements will adjust for race and ethnicity.

The team is also assessing appropriate candidates for the therapy and whether different scheduling of therapy visits (such as a boot camp experience) might help learners progress more quickly than longer-term intermittent sessions.

Ultimately, the researchers hope the software provides sound-practice sessions that are effective, accessible and of sufficient intensity to allow ChainingAI to routinely supplement in-person clinician practice time. Once expanded to include “s” and “z” sounds, the system would address 90% of residual speech sound disorders and could benefit many thousands of the estimated six million Americans who are impacted by these disorders.

Written by Diane Stirling

Electrical Engineering and Computer Science Professor Senem Velipasalar Awarded Patent for Room Occupancy Detection Platform

Remembering to turn the lights off when leaving a room is easy, but letting the furnace that you’re headed out isn’t as simple. About 37% of all energy used by commercial buildings and 40% of energy used in residences go toward heating, ventilation, and air conditioning (HVAC). The costs related to heating and cooling unoccupied spaces in homes and office buildings have been a challenge for decades.

Current occupancy sensors only detect movement, so they can’t tell if someone is stationary. They also have trouble distinguishing between people and large pets, and often require an external power source and data processing. When a room is occupied, not being able to detect occupancy can cause user discomfort. On the other hand, not reliably knowing when a room is empty adds up to massive amounts of unnecessary heating and cooling costs for spaces without any people in them.

A collaboration between Electrical Engineering and Computer Science Professors Senem Velipasalar and Pramod Varshney, Mechanical and Aerospace Engineering Professor Ed Bogucz, Professor Tarek Rakha from Georgia Tech and SRI International, a nonprofit research institute, has developed a new sensor platform, MicroCam, which addresses many of the limitations that current systems face. Their project received funding from the U.S. Department of Energy’s Advanced Research Projects Agency – Energy (ARPA-E) and had to meet certain requirements. The platform had to be highly accurate, low-maintenance, affordable and easily self-commissioned for consumers while still providing more than 30% energy savings.

“It was important to us and ARPA-E that this platform be highly reliable, practical and inexpensive,” says Velipasalar. “This needed to be useful in real-world spaces, and it was designed to be battery-powered.”

The MicroCam is equipped with multi-modal sensors that can process motion, audio and video data. The camera can operate under daylight, low light or even no light conditions and it can be powered for more than a year on just three AA batteries – all the sensor processing is done inside one small unit.

“We do not use cloud computing, everything is captured and processed on this platform,” says Velipasalar. “You are not transferring or saving data, so it alleviates privacy concerns.”

While the MicroCam can detect occupancy, it does not share potentially private information.

“It senses your presence but only sends a 0 or 1 signal to the HVAC system,” says Velipasalar. “That binary occupancy result is the only data shared with the lead platform.”

Industrial and Interactive Design Professor Don Carr and his students worked with Velipasalar and Bogucz to design a prototype case for the MicroCam.

“Eventually we want a peel and stick and ideally you want to install one per room,” says Velipasalar. “If you have one of these in each room, you could monitor the entire space.”

Velipasalar was granted a patent in March 2023 titled “Low Power and Privacy Preserving Sensor Platform for Occupancy Detection.” It is the sixth patent she has been awarded over her career.

“This was a challenging project. We had to meet low cost and high accuracy requirements but it has incredible potential,” says Velipasalar.

The platform may have additional uses in the future including smart home integration and security monitoring. Velipasalar also sees possibilities for the MicroCam to provide activity monitoring and fall detection for families and nursing homes.

Reza Zafarani

Degree:

  • Ph.D., Arizona State University

Research Interests:

  • Big Data Analytics
  • Data Mining / Web Mining / Social Media Mining
  • Social Network Analysis / Social Computing
  • Large-Scale Information Networks
  • Behavior Analysis

Current Research:

My research lies in the intersection of data mining, machine learning, social sciences, and theory. A common pattern in my research is to collect and analyze large scale data to glean actionable patterns. I often employ theories from social sciences, psychology, or anthropology, in addition to developing and using advanced mathematical, statistical, and machine learning machinery to prove the validity of such patterns.

Courses Taught:

  • Data Mining
  • Social Media Mining

Selected Publications:

Reza Zafarani and Huan Liu, Evaluation without Ground Truth in Social Media Research, Communications of the ACM, June 2015

Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014

Pramod K. Varshney

Degree(s):

  • Ph. D. (Illinois) 1976

Lab/Center Affiliation(s):

  • Center of Advanced Systems and Engineering (CASE), Executive Director

Areas of Expertise:

  • Distributed sensor networks and data fusion
  • Statistical inference
  • Wireless communications
  • Signal processing
  • Machine learning
  • Human-machine teaming

My research addresses fundamental questions in statistics-based signal processing, data/information fusion, sensor data processing, data analytics, machine learning and AI.  My research has been generously funded for over four decades by Department of Defense, NSF, ARPA-E, EPA and many companies.  Starting in the early 1980s, I have pioneered the area of data/information fusion and inference in sensor networks. While a lot of my work has been inspired by Department of Defense applications, I have also applied my research results to a wide variety of non-defense applications including IoT and health-related applications. For example, I have worked on imaging for breast cancer detection, and methods for more accurate Alzheimer disease detection. My current research includes detection and tracking, secure inference in distributed sensing systems, human-machine teaming for inference, and information fusion. 

Honors and Awards:

  • ASEE Dow Outstanding Young Faculty Award, 1981
  • IEEE Fellow 1997
  • Third Millennium Medal IEEE 2000
  • President International Society of Information Fusion 2001.
  • Judith A. Resnik Award IEEE 2012
  • Doctor of Engineering honoris causa, Drexel University, 2014
  • Distinguished Alumni Award, ECE Department, Univ. of Illinois, 2015
  • Yaakov Bar-Shalom Award for a Lifetime of Excellence in Information Fusion, 2018
  • Claude Shannon-Harry Nyquist Technical Achievement Award, IEEE Signal Proc. Society, 2021
  • Pioneer Award, IEEE Aerospace and Electronic Society, 2021

Publications:

Books

  • P.K. Varshney, Distributed Detection and Data Fusion, Springer-Verlag, 1997.
  • G.L. Foresti, C. S. Regazzoni, and P. K. Varshney (eds.), Multisensor Surveillance Systems: The Fusion Perspective, Kluwer Academic Press, 2003.
  • K. Varshney and M. K. Arora (eds.), Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data, Springer Verlag, 2004.
  • A. Vempaty, B. Kailkhura and P. K. Varshney, Secure Networked Inference with Unreliable Data Sources,  Springer 2018

Selected Recent Papers

  • Li, Q., Kailkhura, B., Goldhahn, R., Ray, P., and Varshney, P. K., “Robust Decentralized Learning Using ADMM With Unreliable Agents”, IEEE Trans. Signal Process, pp. 2743 – 2757, June, 2022
  • Trezza, A., Bucci, D. J., and Varshney, P. K., “Multi-Sensor Joint Adaptive Birth Sampler for Labeled Random Finite Set Tracking”, IEEE Trans. Signal Process, pp. 1010 – 1025, Feb, 2022
  • Yuan, Y., Yi, W., and Varshney, P. K., “Exponential Mixture Density based Approximation to Posterior Cramér-Rao Lower Bound for Distributed Target Tracking”, IEEE Trans. Signal Process, pp. 862 – 877, Feb, 2022
  • Chen, Q., Geng, B., Han, Y., and Varshney, P. K., “Enhanced Audit Bit Based Distributed Bayesian Detection in the Presence of Strategic Attacks”, IEEE Trans. on Signal and Information Process. over Networks, pp. 49 – 62, Jan, 2022
  • Bulusu, S., Khanduri, P., Kafle, S., Sharma, P., and Varshney, P. K., “Byzantine Resilient Non-Convex SCSG With Distributed Batch Gradient Computations”, IEEE Trans. on Signal and Information Process. over Networks ., pp. 754 – 766, Nov, 2021
  • Cheng, X., Khanduri, P., Chen, B., and Varshney, P. K., “Joint Collaboration and Compression Design for Distributed Sequential Estimation in a Wireless Sensor Network”, IEEE Trans. Signal Process, pp. 5448 – 5462, Sept, 2021
  • Geng, B., Cheng, X., Brahma, S., Kellen, D., and Varshney, P. K., “Collaborative Human Decision Making with Heterogeneous Agents”, IEEE Trans. on Computational Social Systems., pp. 469 – 479, Jul, 2021
  • Li, C., Li, G., and Varshney, P. K., “Communication-Efficient Federated Learning Based on Compressed Sensing”, IEEE Internet of Things Journal., pp. 15531 – 15541, Apr, 2021
  • Geng, B., Li, Q., and Varshney, P. K., “Utility Theory Based Optimal Resource Consumption For Inference In IoT Systems”, IEEE Internet of Things Journal., pp. 12279 – 12288, Mar, 2021
  • Ciuonzo, D., Rossi, P.S., and Varshney, P. K., “Distributed Detection in Wireless Sensor Networks Under Multiplicative Fading via Generalized Score Tests”, IEEE Internet of Things Journal., pp. 9059 – 9071, Feb, 2021
  • Joseph, G., Nettasinghe , B., Krishnamurthy, V., and Varshney, P. K., “Controllability of Network Opinion in Erdos-Renyi Graphs Using Sparse Control Inputs”, SIAM Journal on Control and Optimization., pp. 2321-2345, Jan, 2021
  • Joseph, G. and Varshney, P. K., “Measurement Bounds for Compressed Sensing in Sensor Networks With Missing Data”, IEEE Trans. Signal Process., pp. 905-916, Jan, 2021

Senem Velipasalar

Degrees:

  • Ph. D., Electrical Engineering, Princeton University, Princeton, NJ, 2007
  • M.A., Electrical Engineering, Princeton University, Princeton, NJ, 2004
  • M.S., Electrical Sciences and Computer Engineering, Brown University, Providence, RI, 2001
  • B.S., Electrical and Electronics Engineering, Bogazici University, Istanbul, Turkey, 1999

Lab/ Center/ Institute affiliations:

Director of the Smart Vision Systems Laboratory (http://www.vision.syr.edu/)

Faculty Affiliate, Aging Studies Institute

Areas of Expertise:

  • Machine Learning
  • Computer Vision
  • Wireless Smart Camera Networks
  • Mobile camera applications
  • Signal Processing

Prof. Velipasalar’s primary areas of research are machine learning and computer vision. More specifically, her research has focused on human activity classification and fall detection from egocentric cameras, and applications of machine learning to (i) thermal anomaly detection from UAV-mounted infrared cameras, (ii) driver behavior analysis from in-vehicle mounted cameras, (iii) 3D object detection, (iv) person detection from video data, (v) analysis of functional near infrared spectroscopy (fNIRS) data, (vi) dynamic multi-channel access, and (vii) defense against adversarial jamming attacks.

Honors/Awards:

  • NSF CAREER Award, 2011.
  • 2021 IEEE Region 1 Technological Innovation (Academic) Award.
  • 2021 IAAI Deployed Application Award for our paper titled “Preclinical Stage Alzheimer’s Disease Detection Using Magnetic Resonance Image Scans”.
  • Top 25 most downloaded IEEE Sensors Journal paper in the months of January-September 2017, and June 2018.
  • Graduate School All-University Doctoral Prize, received by my former Ph.D. student Burak Kakillioglu, 2022.
  • Graduate School All-University Doctoral Prize, received by my former Ph.D. student Natalie Sommer, 2021.
  • Graduate School All-University Doctoral Prize, received by my former Ph.D. student Yantao Lu, 2020.
  • 2017 IEEE Green Communications & Computing Technical Committee Best Journal Paper Award for our paper titled “Analysis of Energy Efficiency in Fading Channels under QoS Constraints”.
  • 2nd place Poster Award at the 17th Annual SyracuseCoE Symposium Student Poster Competition for our work titled “Heat Mapping Drones”, October 2017.
  • 2014 Excellence in Graduate Education Faculty Recognition Award.
  • Graduate School All University Doctoral Prize, received by my former Ph.D. student Akhan Almagambetov, 2014.
  • Nunan Research Day Poster Competition EECS Departmental Winner Award, received by Danushka Bandara (co-advised by Dr.Hirshfield), 2014.
  • Intelligent Transportation Society (ITS) of NY Best ITS Student Essay Award, received by my former Ph.D. student Akhan Almagambetov, based on our research on vehicle taillight tracking and alert signal detection, May 2013.
  • The college-wide award for “Applicability of Research to Business and Industry”, received by my former Ph.D. student Akhan Almagambetov, Nunan Lecture and Research Day, April 2013.
  • Third place paper award at the ACM/IEEE International Conference on Distributed Smart Cameras for the paper titled “Energy-efficient Feedback Tracking on Embedded Smart Cameras by Hardware-level Optimization“, 2011
  • EPSCoR First Award, 2009
  • Layman Award as PI, 2007
  • Layman Award as Co-PI, 2009
  • Best Student Paper Award at the IEEE International Conference on Multimedia & Expo (ICME) for the paper titled “Design and Verification of Communication Protocols for Peer-to-Peer Multimedia Systems,” 2006
  • IBM Patent Application Award, 2005
  • Travel Grant, Office of Graduate Affairs, Princeton University, 2005
  • Graduate Fellowship, Princeton University, 2002
  • Graduate Fellowship, Brown University, 1999

Selected Publications:

(Please visit https://ecs.syr.edu/faculty/velipasalar/ for a complete list)

  • J. Chen, B. Kakillioglu and S. Velipasalar, “Background-Aware 3D Point Cloud Segmentation with Dynamic Point Feature Aggregation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, April 2022.
  • F. Altay and S. Velipasalar, “The Use of Thermal Cameras for Pedestrian Detection,” IEEE Sensors Journal, vol. 22, issue:12, 11489 – 11498, May 2022.
  • Y. Chu, D. Mitra, K. Cetin, N. Lajnef, F. Altay, S. Velipasalar, “Development and Testing of a Performance Evaluation Methodology to Assess the Reliability of Occupancy Sensor Systems in Residential Buildings,” Energy and Buildings, vol. 268, pp. 112148, 2022.
  • J. Wang, T. Grant, S. Velipasalar, B. Geng and L. Hirshfield, “Taking a Deeper Look at the Brain: Predicting Visual Perceptual and Working Memory Load from High-Density fNIRS Data,” IEEE Journal of Biomedical and Health Informatics, vol. 26, issue:5, pp. 2308-2319, December 2021.
  • J. Wang, W. Chai, A. Venkatachalapathy, K. L. Tan, A. Haghighat, S. Velipasalar, Y. Adu-Gyamfi, A. Sharma, “A Survey on Driver Behavior Analysis from In-Vehicle Cameras,” accepted to appear in the IEEE Transactions on Intelligent Transportation Systems, early access version available, November 2021.
  • F. Wang; C. Zhong, M. Cenk Gursoy, S. Velipasalar, “Resilient Dynamic Channel Access via Robust Deep Reinforcement Learning,” IEEE Access Journal, vol. 9 , pp. 163188 – 163203, December 2021.
  • N. M. Sommer, B. Kakillioglu, T. Grant, S. Velipasalar and L. Hirshfield, “Classification of fNIRS Finger Tapping Data with Multi-Labeling and Deep Learning,” IEEE Sensors Journal, vol. 21, issue: 21, pp. 24558-24559, doi: 10.1109/JSEN.2021.3115405, Nov. 2021.
  • Y. Zheng, Y. Lu, and S. Velipasalar, “An Effective Adversarial Attack on Person Re-identification in Video Surveillance via Dispersion Reduction,” IEEE Access Journal, vol. 8, pp. 183891 – 183902, Sept. 2020.
  • N. Sommer, S. Velipasalar, L. Hirshfield, Y. Lu and B. Kakillioglu, “Simultaneous and Spatiotemporal Detection of Different Levels of Activity in Multidimensional Data,” IEEE Access Journal, vol. 8, pp. 118205 – 118218, June 2020.
  • D. Bandara, T. Grant, L. Hirshfield and S. Velipasalar, “Identification of Potential Task Shedding Events Using Brain Activity Data,” Augmented Human Research, 5. 10.1007/s41133-020-00034-y, 2020.
  • M. Cornacchia and S. Velipasalar, “Autonomous Selective Parts-Based Tracking,” IEEE Transactions on Image Processing, vol. 29, pp. 4349-4361, January 2020.
  • B. Kakillioglu, A. Ren, Y. Wang and S. Velipasalar, “3D Capsule Networks for Object Classification with Weight Pruning,” IEEE Access Journal, pp. 27393-27405, Febr. 2020.
  • C. Zhong, M. Cenk Gursoy and S. Velipasalar, “Deep Reinforcement Learning-Based Edge Cashing in Wireless Networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 6 , issue 1, pp. 48-61, March 2020.
  • Y. Hu, Y. Li, M. C. Gursoy, S. Velipasalar, and A. Schmeink, “Throughput Analysis of Low-Latency IoT Systems with QoS Constraints and Finite Blocklength Codes,” IEEE Transactions on Vehicular Technology, vol. 69, issue 3, pp. 3093-3104, March 2020.
  • C. Zhong, Z. Lu, M. Cenk Gursoy and S. Velipasalar, “A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access,” IEEE Transactions on Cognitive Communications and Networking, vol. 5, issue 4, pp. 1125-1139, Dec. 2019.
  • Y. Lu and S. Velipasalar, “Autonomous Human Activity Classification from Wearable Multi-Modal Sensors,” IEEE Sensors Journal, vol. 19, issue: 23, pp. 11403-11412, Dec. 2019.
  • D. Qiao, M. Cenk Gursoy and S. Velipasalar, “Throughput-Delay Tradeoffs with Finite Blocklength Coding over Multiple Coherence Blocks,” IEEE Transactions on Communications, pp. 5892 – 5904, volume: 67 , Issue: 8 , Aug. 2019.
  • D. Bandara, L. Hirshfield and S. Velipasalar, “Classification of Affect Using Deep Learning on Brain Blood Flow Data,” Journal of Near Infrared Spectroscopy, 27(3), pp. 206-219, doi: 10.1177/0967033519837986, April 2019.
  • C. Ye, M. Cenk Gursoy and S. Velipasalar, “Power Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning,” IEEE Transactions on Communications, pp. 5629 – 5644, volume: 67 , Issue: 8 , Aug. 2019.
  • M. Cornacchia, B. Kakillioglu, Y. Zheng and S. Velipasalar, “Deep Learning Based Obstacle Detection and Classification with Portable Uncalibrated Patterned Light,” IEEE Sensors Journal, vol. 18, issue: 20, pp. 8416-8425, Oct 2018.
  • Y. Lu and S. Velipasalar, “Autonomous Footstep Counting and Traveled Distance Calculation by Mobile Devices Incorporating Camera and Accelerometer Data,” IEEE Sensors Journal, vol. 17, issue: 21, pp. 7157-7166, Nov. 2017.
  • K. Ozcan, S. Velipasalar and P. Varshney, “Autonomous Fall Detection with Wearable Cameras by using Relative Entropy Distance Measure,” IEEE Transactions on Human-Machine Systems, vol. 47, issue: 1, pp. 31-39, Febr. 2017.
  • M. Cornacchia, K. Ozcan, Y. Zheng and S. Velipasalar, “A Survey on Activity Detection and Classification Using Wearable Sensors,” IEEE Sensors Journal, vol. 17, issue: 2, pp. 386-403, Jan. 2017. Top 25 most downloaded IEEE Sensors Journal paper for nine consecutive months in 2017, and in June 2018 .
  • F. Erden, S. Velipasalar, A. Z. Alkar, A. Enis Cetin, “Sensors in Assisted Living: A Survey of Signal and Image Processing Methods ,” IEEE Signal Processing Magazine, volume:33, issue:2, pp. 36-44, March 2016.
  • K. Ozcan and S. Velipasalar, “Wearable Camera- and Accelerometer-based Fall Detection on Portable Devices ,” IEEE Embedded Systems Letters, volume: 8, issue: 1, pp. 6-9, March 2016.

Sucheta Soundarajan

Degree:

  • PhD, Computer Science (2013, Cornell University)

Areas of Expertise:

  • Social network analysis
  • Complex systems
  • Algorithmic fairness
  • Algorithms

Current Research:

Dr. Soundarajan’s research focuses on designing algorithms for analyzing social and other complex networks, including algorithms for characterizing the hierarchical structure of networks and the evolution of social networks.  She is particularly interested in designing fair network analysis algorithms for tasks such as link prediction and community/cluster detection.  Her work also explores the structure of real-world complex systems, including the behavior of individual animals in herds of dairy cows, language evolution in social media ecosystems, and stratification in scientific co-authorship networks. 

Selected Publications:

Sucheta Soundarajan and John Hopcroft. Use of Local Group Information to Identify Communities in Networks. ACM Transactions on Knowledge Discovery from Data (TKDD). 2015.

Sucheta Soundarajan, Tina Eliassi-Rad, and Brian Gallagher. A Guide to Selecting a Network Similarity Method. SIAM Conference on Data Mining (SDM). 2014.

Bruno Abrahao, Sucheta Soundarajan, John Hopcroft, and Robert Kleinberg. A Separability Framework for Analyzing Community Structure. ACM Transactions on Knowledge Discovery from Data (TKDD-CASIN). 2014.

Bruno Abrahao, Sucheta Soundarajan, John Hopcroft, and Robert Kleinberg. On the Separability of Structural Classes of Communities. 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). 2012.

Sucheta Soundarajan and John Hopcroft. Using Community Information to Improve the Precision of Link Prediction Methods. World Wide Web (WWW) 2012.

Qinru Qiu

Degree(s):

  • PhD

Lab/Center Affiliation(s):

  • AMPS (Advanced Microprocessor and Power-aware Systems)

Research Interests:

  • Dynamic power and thermal management for computer systems
  • Power and performance optimization of energy harvesting real-time embedded systems
  • Neuromorphic computing and high performance computing for cognitive applications

Current Research:

Excessive energy dissipation has become one of the limiting factors that prevents the sustained growth of computation power of IT facilities. High power consumption reduces system reliability, increases energy and cooling cost, and cuts the battery cycle time of mobile devices. Aiming at curbing the system energy dissipation, green computing has attracted substantial interests in recent years. Dr. Qiu’s primary research interest covers different areas in green computing, from runtime power and thermal management of computer systems to energy harvesting real-time embedded system. The goal of her research is to provide machine intelligence to today’s computing platforms to achieve autonomous resource management with energy and thermal awareness.

Her second research area is architecture design of neuromorphic computing. Neuromorphic computing refers to the emerging computation concept inspired by the principles of information processing in human neural system. It is widely accepted that human beings are much superior to machines in some areas such as image recognition. With the increase of our knowledge on brain function and our capability in realizing massive parallel computation and communication, it is time to investigate new algorithm and hardware architecture for signal processing and perception. Dr. Qiu’s research focuses on the software and hardware development for such computing systems.

Courses Taught:

  • VLSI Design
  • Computer architecture

Honors:

  • ACM SIGDA Distinguished Service Award (2011)
  • NSF Career Award (2009)
  • American Society for Engineering Education (ASEE) Summer Research Faculty Fellowship (2007)

Selected Publications:

Shen, Y. Tan, J. Lu, Q. Wu and Qinru Qiu, “Achieving Autonomous Power Management Using Reinforcement Learning,”ACM Transactions on Design Automation of Electronic Systems, Vol. 18, Iss. 2, pp. 24032, March 2013.

Ge, Qinru Qiu, and Q. Wu, “A Multi-Agent Framework for Thermal Aware Task Migration in Many-Core Systems,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Volume: 20 , Issue: 10, pp. 1758 – 1771, 2012.

Liu, J. Liu, Q. Wu and Qinru Qiu, “Harvesting-Aware Power Management for Real-Time Systems with Renewable Energy,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Volume: 20 , Issue: 8, pp. 1473 – 1486, 2012.

Qinru Qiu, Q. Wu, M. Bishop, R. Pino, and R. W. Linderman, “A Parallel Neuromorphic Text Recognition System and Its Implementation on a Heterogeneous High Performance Computing Cluster,” IEEE Transactions on Computers, Digital Object Identifier: 10.1109/TC.2012.50.

H. Lu, Qinru Qiu, A. R. Butt and K. W. Cameron, “End-to-End Energy Management,” Computer, 44 (11), November 2011.

Vir V. Phoha

Degree:

  • Ph.D. Texas Tech University

Research Interests:

  • Cyber Security – Cyber offense and defense
  • Machine Learning
  • Smart phones and tablets security
  • Biometrics — network based and standalone

Current Research:

My focus is to do original research that cuts across conventional rigorously defined disciplines and unifies basic and common concepts across disciplines. In particular, my research centers around security (malignant systems, active authentication, for example touch based authentication on mobile devices) and machine learning (decision trees, statistical, and evolutionary methods) with a focus on large time series data streams and static data sets, and computer networks (anomalies, optimization). I am also using these methods to build field realizable defensive and offensive Cyber-based systems.

Courses Taught:

  • Security and Machine learning; Biometrics
  • Applied Cryptography

Honors:

  • IEEE Fellow
  • AAAS Fellow (elected 2018);  NAI Fellow (elect 2020)
  • IEEE Region 1 Technological Innovation  Award, 2017
  • SDPS Fellow (elected 2010)
  • ACM Distinguished Scientist (elected 2008)
  • ACM Distinguished Speaker (2012-2015)

Selected Publications:

  • Amith K. BelmanVir V. Phoha. Discriminative Power of Typing Features on Desktops, Tablets, and Phones for User Identification.ACM Transactions on Privacy and Security. 23(1): 4:1-4:36 (2020)
  • Jin, Vir V. Phoha and R. Zafarani, “Graph-based Identification and Authentication: A Stochastic Kronecker Approach,” in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2020.3025989.
  • Li, W. Wang, Y. Gao, Vir V. Phoha and Z. Jin, “Wrist in Motion: A Seamless Context-Aware Continuous Authentication Framework Using Your Clickings and Typings,” in IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 2, no. 3, pp. 294-307, July 2020, doi: 10.1109/TBIOM.2020.2997004.
  • Yang Gao; Wei Wang; Vir V Phoha; Wei Sun; Zhanpeng JinEarEcho: Using Ear Canal Echo for Wearable Authentication.Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Vol. 3, No. 3, Article 81. Publication date: September 2019. Presented in The 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2019), London, UK, September 11-13, 2019.
  • Shukla and Vir V. Phoha, “Stealing Passwords by Observing Hands Movement,” in IEEE Transactions on Information Forensics and Security, vol. 14, no. 12, pp. 3086-3101, Dec. 2019, doi: 10.1109/TIFS.2019.2911171.

Kristopher Micinski

Degree:

  • Doctorate of Philosophy, Computer Science, University of Maryland at College Park
  • Bachelor of Science, Computer Engineering, Michigan State University

Areas of Expertise:

  • Programming Languages
  • Static Analysis
  • Formal Methods
  • Foundations of Computer Security and Privacy

My research lies at the intersection of the theory and application of program analyses. Program analyses are tools that examine programs and determine (prove) facts about them. For example, a program analysis might prove that a program can never crash due to a type error. In general, however, program analyses can be arbitrarily complex and infer subtle program invariants relating to myriad applications (such as computer security).

Because program analyses must always approximate program behavior (otherwise they could solve the halting problem), there is an inherent tradeoff between analysis precision and analysis performance. Currently, program analyses are often applied only in limited contexts, as gaining acceptable performance requires too many compromises in terms of analysis precision. My current work focuses on three concurrent threads: tackling fundamental issues relating to scaling static analysis (specifically, scaling analyses to run on supercomputers rather than a single machine as all current analyses do); engineering those analyses (to allow analysis reuse); and applying those analyses to computer security (e.g., to check properties such as information flow and to support complex reverse engineering tasks).

Recent Publications:

  • Symbolic Path Tracing to Find Android Permission-Use Triggers. NDSS Workshop on Binary Analysis Research (BAR 2019).
  • User Comfort with Android Background Resource Accesses in Different Contexts Symposium on Usable Privacy and Security (SOUPS 2018).
  • User Interactions and Permission Use on Android (CHI 2017).

Bryan S. Kim

Degree:

  • Ph.D. in Computer Science and Engineering, Seoul National University
  • M.S. in Electrical Engineering and Computer Science, Seoul National University
  • B.S. in Electrical Engineering and Computer Science, University of California, Berkeley

Areas of Expertise:

  • Flash and non-volatile memory-based systems
  • Data storage systems
  • File systems and key-value stores
  • Next-generation storage architecture and hardware

I am broadly interested in computer systems and particularly focused on data storage systems. Current research directions include, but are not limited to, capacity-variant storage systems, self-learning systems, and next-generation key-value storage.

Recent Publications:

  • Hyeongyu Lee, Juwon Lee, Minwook Kim, Donghwa Shin, Sungjin Lee, Bryan S. Kim, Eunji Lee, and Sang Lyul Min. “SpartanSSD: a Reliable SSD under Capacitance Constraints,” in ACM/IEEE International Symposium on Low Power Electronics and Design, 2021
  • Jinhyung Koo, Junsu Im, Jooyoung Song, Juhyung Park, Eunji Lee, Bryan S. Kim, and Sungjin Lee. “Modernizing File System through In-Storage Indexing,” in USENIX Symposium on Operating Systems Design and Implementation, 2021
  • Jeseong Yeon, Leeju Kim, Youil Han, Hyeon Gyu Lee, Eunji Lee, and Bryan S. Kim. “JellyFish: A Fast Skip List with MVCC,” in ACM/IFIP International Middleware Conference, 2020
  • Youil Han, Bryan S. Kim, Jeseong Yeon, Sungjin Lee, and Eunji Lee. “TeksDB: Weaving Data Structures for a High-Performance Key-Value Stores,” in International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), 2019
  • Bryan S. Kim, Eunji Lee, Sungjin Lee, Sang Lyul Min. “CPR for SSDs,” in ACM SIGOPS Workshop on Hot Topics in Operating Systems (HotOS), 2019
  • Bryan S. Kim, Jongmoo Choi, and Sang Lyul Min. “Design Tradeoffs for SSD Reliability,” in USENIX Conference on File and Storage Technologies (FAST), 2019
  • Geonhee Lee, Hyeon Gyu Lee, Juwon Lee, Bryan S. Kim and Sang Lyul Min. “An Empirical Study on NVM-based Block I/O Caches,” in ACM SIGOPS Asia-Pacific Workshop on Systems (APSys), 2018
  • Bryan S. Kim, Hyun Suk Yang, and Sang Lyul Min. “AutoSSD: an Autonomic SSD Architecture,” in USENIX Annual Technical Conference (ATC), 2018
  • Bryan S. Kim. “Utilitarian Performance Isolation in Shared SSDs,” in USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage), 2018
  • Bryan S. Kim, Yonggun Lee, and Sang Lyul Min. “Framework for Efficient and Flexible Scheduling of Flash Memory Operations,” in IEEE Non-Volatile Memory Systems and Applications (NVMSA), 2017
  • Bryan S. Kim and Sang Lyul Min. “QoS-aware Flash Memory Controller,” in IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), 2017

Garrett Ethan Katz

Degrees:

  • B.A. Philosophy, Cornell University, 2007
  • M.A. Mathematics, City College of New York, 2011
  • Ph.D. Computer Science, University of Maryland, College Park, 2017

Areas of Expertise:

  • Automated Planning
  • Automated Program Induction and Synthesis
  • Robotic Manipulation
  • Neural Computation

Current Research:

My research focuses on “vertically integrated” artificial intelligence, ranging from low-level robotic motor control and synaptic learning rules to high-level planning and abstract reasoning.  My recent work has focused on neurocomputational systems for cognitive-level robotic imitation learning.

Honors and Awards:

  • Best Paper Award at the SAI Computing Conference, 2020
  • Larry S. Davis Doctoral Dissertation Award, UMD, 2018
  • Best Student Paper Award at the 9th International Conference on Artificial General Intelligence 2016

Selected Publications:

  • Katz GE, Tahir N.  Towards Automated Discovery of God-Like Folk Algorithms for Rubik’s Cube.  In 2022 AAAI Conference on Artificial Intelligence.  AAAI.
  • Katz GE, Akshay, Davis GP, Gentili RJ, Reggia JA. Tunable Neural Encoding of a Symbolic Robotic Manipulation Algorithm. Frontiers in Neurorobotics. 2021:167.
  • Tahir N, Katz GE. Numerical Exploration of Training Loss Level-Sets in Deep Neural Networks. In 2021 International Joint Conference on Neural Networks (IJCNN) 2021 (pp. 1-8). IEEE.
  • Katz GE, Gupta K, Reggia JA. Reinforcement-based Program Induction in a Neural Virtual Machine. In 2020 International Joint Conference on Neural Networks (IJCNN) 2020 (pp. 1-8). IEEE.

M. Cenk Gursoy

Degree(s):

  • Ph.D. , Princeton University, 2004.
  • B.S., Bogazici University, Istanbul, Turkey, 1999.

Lab/ Center/ Institute Affiliations:

Director, Wireless Communication and Networking Lab.

Senior Research Associate and Core Faculty Member, Autonomous Systems Policy Institute

Areas of Expertise:

Wireless Networking

Signal Processing

Communication/Information Theory

Machine Learning

Decision Making Theory

Optimization

Unmanned Systems

Dr. Gursoy has broad research expertise in the general areas of wireless communications and networking, signal processing, information theory, optimization, and machine learning. In particular, he has conducted research in detection and estimation, hypothesis testing, anomaly detection, optimal resource allocation, wireless performance evaluation, cognitive radio networks, dynamic spectrum access, energy efficiency analysis, multiple-antenna communication, millimeter wave communications, low-latency communications, physical-layer security, radio access networks, scheduling, edge computing, content caching, and 4G/5G/beyond-5G wireless network design. His expertise in information theory includes the analysis of wireless channel capacity and optimal signaling and coding schemes. He further has expertise in machine learning through the design, implementation and application of deep learning, reinforcement learning, and federated learning algorithms. Moreover, he has studied sequential optimization and decision-making in highly dynamic scenarios (involving autonomous and unmanned systems), and security and privacy in distributed learning.

Honors and Awards:

  • 2020 IEEE Region 1 Technological Innovation (Academic) Award
  • 2019 The 38th AIAA/IEEE Digital Avionics Systems Conference Best of Session    Award.
  • 2017 IEEE Green Communications & Computing Technical Committee Best Journal Paper Award.
  • 2017 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) Best Paper Award
  • 2008 EURASIP Journal of Wireless Communications and Networking Best Paper Award
  • NSF CAREER Award

Selected Publications:

Selected Publications:

  • X. Wang and M. C. Gursoy, “Resilient Path Planning for UAVs in Data Collection Under Adversarial Attacks,” in IEEE Transactions on Information Forensics and Security, vol. 18, pp. 2766-2779, 2023.
  • M. Guo and M. C. Gursoy, “Joint Activity Detection and Channel Estimation for Intelligent-Reflecting-Surface-Assisted Wireless IoT Networks,” in IEEE Internet of Things Journal, vol. 10, no. 12, pp. 10207-10221, June, 2023.
  • Y. Zhu, X. Yuan, Y. Hu, T. Wang, M. C. Gursoy and A. Schmeink, “Low-Latency Hybrid NOMA-TDMA: QoS-Driven Design Framework,” in IEEE Transactions on Wireless Communications, vol. 22, no. 5, pp. 3006-3021, May 2023.
  • Y. Zhu, Y. Hu, X. Yuan, M. C. Gursoy, H. V. Poor and A. Schmeink, “Joint Convexity of Error Probability in Blocklength and Transmit Power in the Finite Blocklength Regime,” in IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2409-2423, April 2023.
  • Y. Shi, Y. E. Sagduyu, T. Erpek and M. C. Gursoy, “How to Attack and Defend NextG Radio Access Network Slicing with Reinforcement Learning,” IEEE Open Journal of Vehicular Technology, vol. 4, pp. 181-192, 2023.
  • D. Deng, X. Li, S. Dang, M. C. Gursoy and A. Nallanathan, “Covert Communications in Intelligent Reflecting Surface-Assisted Two-Way Relaying Networks,” IEEE Transactions on Vehicular Technology, vol. 71, no. 11, pp. 12380-12385, Nov. 2022
  • X. Wang, M. C. Gursoy, T. Erpek, and Y. E. Sagduyu, “Learning-Based UAV Path Planning for Data Collection with Integrated Collision Avoidance,” IEEE Internet of Things Journal, vol. 9, no. 17, pp. 16663-16676, Sep. 2022.
  • X. Wang and M. C. Gursoy, “Learning-Based UAV Trajectory Optimization with Collision Avoidance and Connectivity Constraints,” IEEE Transactions on Wireless Communications, vol. 21, no. 6, pp. 4350-4363, Jun. 2022.
  • Z. Lu, C. Zhong, and M. C. Gursoy, “Dynamic Channel Access and Power Control in Wireless Interference Networks via Multi-Agent Deep Reinforcement Learning,” IEEE Transactions on Vehicular Technology, vol. 71, no. 2, pp. 1588-1601, Feb. 2022.
  • Z. Xu, J. Tang, C. Yin, Y. Wang, G. Xue, J. Wang, M. C. Gursoy, “ReCARL: Resource Allocation in Cloud RANs with Deep Reinforcement Learning,” EEE Transactions on Mobile Computing, vol. 21, no. 7, pp. 2533-2545, Jul. 2022
  • M. Guo and M. C. Gursoy, “Joint Activity Detection and Channel Estimation in Cell-Free Massive MIMO Networks with Massive Connectivity,” IEEE Transactions on Communications, vol. 70, no. 1, pp. 317-331, Jan. 2022.
  • H. Huang, D. Qiao and M. C. Gursoy, “Age-Energy Tradeoff Optimization for Packet Delivery in Fading Channels,” IEEE Transactions on Wireless Communications, vol. 21, no. 1, pp. 179-190, Jan. 2022.
  • F. Wang, C. Zhong, M. C. Gursoy, and S. Velipasalar, “Resilient Dynamic Channel Access via Robust Deep Reinforcement Learning,” IEEE Access, vol. 9, pp. 163188-163203, 2021.
  • P. Sinha, I. Guvenc, and M. C. Gursoy, “Fundamental Limits on Detection of UAVs by Existing Terrestrial RF Networks,” IEEE Open Journal of the Communications Society, vol. 2, pp. 2111-2130, 2021
  • X. Wang and M. C. Gursoy, “Uplink Coverage in Heterogeneous mmWave Cellular Networks with Clustered Users,” IEEE Access, vol. 9, 2021.
  • M. Guo and M. C. Gursoy, “Statistical Learning Based Joint Antenna Selection and User Scheduling for Single-Cell Massive MIMO Systems,” IEEE Transactions on Green Communications and Networking, vol. 5, no. 1, pp. 471-483, March 2021

Venkata S.S. Gandikota

Degrees:

  • Ph.D. Computer Science – Purdue University
  • MS Computer Science – Purdue University
  • MSc Mathematics – Birla Institute of Technology and Science, Goa, India
  • B.E. Computer Science – Birla Institute of Technology and Science, Goa, India

Areas of Expertise:

  • Coding Theory & Lattices
  • Sparse Recovery
  • Sublinear algorithms
  • Foundations of Machine Learning

Venkata’s research focuses on the algorithmic aspects of computing with structured data and their applications in various domains such as communication, data storage, health care, and collaborative learning. His research mainly aims to identify an underlying structure in the data that can be leveraged to design efficient algorithms for certain computational tasks. When the data is devoid of any favorable structure, we aim to embed one to facilitate computations. In the process, we use tools and develop techniques from several areas of mathematics and computer science like optimization, coding theory, high-dimensional geometry, the geometry of numbers, sublinear-time algorithms, and differential privacy.

Honors and Awards:

  • SOURCE 2022 — Research Assistant Grant to support undergraduate student involvement in research.
  • CUSE Seed Grant 2021.

Selected Publications:

Biao Chen

Degree(s):

  • Ph. D., University of Connecticut

Lab/Center Affiliation:

  • Communication Laboratory

Areas of Expertise:

  • Information Theory
  • Signal Processing
  • Statistical Learning Theory

Chen’s area of research interest mainly focuses on information theory, signal processing, and foundational theory to machine learning, with applications to wireless communications and sensor networks. On the applied side, he has worked extensively on software radio system design, including leading two student teams to compete as finalist in the DARPA Spectrum Challenge and DARPA Spectrum Collaboration Challenge. His most recent endeavors include the development of passive RF sensing theory and systems for a variety of indoor situational awareness missions.

Honors and Awards:

IEEE Fellow (2015)

NSF CAREER Award (2006)

Selected Publications:

  • Y. Liu, T. Wang, Y. Jiang and B. Chen, “Harvesting Ambient RF for Presence Detection Through Deep Learning” , IEEE Trans. Neural Networks and Learning Syst., vol. 33, no. 4, pp. 1571-1583, April 2022, doi: 10.1109/TNNLS.2020.3042908.
  • S. Zhu, B. Chen, Z. Chen and P. Yang, “Asymptotically Optimal One- and Two-Sample Testing With Kernels,” in IEEE Transactions on Information Theory, vol. 67, no. 4, pp. 2074-2092, April 2021, doi: 10.1109/TIT.2021.3059267.
  • G. Xu, W. Liu and B. Chen, “A Lossy Source Coding Interpretation of Wyner’s Common Information,” in IEEE Transactions on Information Theory, vol. 62, no. 2, pp. 754-768, Feb. 2016, doi: 10.1109/TIT.2015.2506560.
  • H. Chen, B. Chen and P. K. Varshney, “A New Framework for Distributed Detection With Conditionally Dependent Observations,” in IEEE Transactions on Signal Processing, vol. 60, no. 3, pp. 1409-1419, March 2012, doi: 10.1109/TSP.2011.2177975.
  • X. Shang, G. Kramer and B. Chen, “A New Outer Bound and the Noisy-Interference Sum–Rate Capacity for Gaussian Interference Channels,” in IEEE Transactions on Information Theory, vol. 55, no. 2, pp. 689-699, Feb. 2009, doi: 10.1109/TIT.2008.2009793.

Elizabeth Carter

Degrees:

  • Ph.D. Environmental Engineering, Cornell University
  • MSc. Environmental Information Systems, Cornell University
  • B.S. magna cum laude, University of Massachusetts, Amherst

Research Interests:

  • Disaster response and mitigation
  • Hydrometerology and hydroclimatology
  • Detection of water from space
  • Space/time statistics
  • Machine learning/artificial intelligence
  • High-performance computing
  • Algorithmic bias in water resources management and engineering ethics

Honors:

  • NASA-USGS postdoctoral fellow
  • USDA-AFRI predoctoral fellow
  • Cornell University Graduate Dean’s scholar (2013-2019)

Current research:

Dr. Carter’s research in applied computational hydroclimatology attempts to fuse tools from modern data science with risk assessment in water resources engineering to mitigate social, environmental, and economic impacts of hydroclimatic extremes. Our success in utilizing our water resources infrastructure to reduce damages associated with the variable hydroclimate depends on our ability to diagnose and predict this hydroclimate variability at timescales which are relevant for adaptive management. This task is hampered by spatial and temporal sparsity of observations of hydrologic and hydroclimatic flux, complex patterns of space/time covariability in observations, and extremely low signal-to-noise ratio in hydroclimatic systems at the local scale. My research seeks to combat these obstacles by 1) integrating new sources of observational data, mostly from space-based assets, into diagnostic/predictive frameworks of hydrologic/hydroclimatic flux; 2) grounding data-driven analysis in a physical understanding of the hydrologic system through feature engineering and model diagnostics; 3) developing and utilizing data science algorithms which are appropriate for multivariate space/time systems, and 4) quantifying bias, error, and uncertainty in space/time models. Applications include automatic flood detection from multispectral and synthetic aperture radar (SAR) imagery for disaster response (NASA/USGS/NGA), developing custom hydrometeorological forecasts for adaptive reservoir management, detecting drivers of hydroclimatic variability of the Great Lakes, and quantification of evapotranspiration and groundwater flux from space (NASA/USDA).

Recent publications:

Carter, E., Herrera, D. A., & Steinschneider, S. (2021). Feature engineering for subseasonal-to-seasonal warm-season precipitation forecasts in the Midwestern US: towards a unifying hypothesis of anomalous warm-season hydroclimatic circulation. Journal of Climate, 1-67.

Sleeter, R., Carter, E., Jones, J.W., Eggleston, J., Kroeker, S., Ganuza , J., Dobbs, K., Coltin, B., McMichael, S., Shastry, A., Longhenry, R., Ellis, B., Jiang, Z., Phillips, J., and Furlong, P. M. (2021). Satellite-Derived Training Data for Automated Flood Detection in the Continental U.S.: U.S. Geological Survey data release, https://doi.org/10.5066/P9C7HYRV.

Tonitto, Christina; Woodbury, Peter; Carter, Elizabeth. (2020). Predicting greenhouse gas benefits of improved nitrogen management in North American maize. Journal of Environmental Quality 49 (4), 882-895.

Knighton, James; Pleiss , Geoff; Steinschneider, Scott; Carter, Elizabeth; Lyon,Steven; Walter, M. Todd. (2019). Reproduction of regional precipitation and discharge extremes with meso-scale climate products via machine learning: an evaluation for the Eastern CONUS. Journal of Hydrometeorology.

Carter, Elizabeth; Melkonian, Jeffrey; Steinschneider, Scott; Riha, Susan. (2018). Yield response to climate, management, and genotype: a large-scale observational analysis to identify climate-adaptive crop management practices in high-input maize systems. Environmental Research Letters, 13-11.

Carter, Elizabeth; Steinschneider, Scott. (2018). Hydroclimatological Drivers of Extreme Floods on Lake Ontario. Water Resources Research. 54: 4461-4478.

Carter, Elizabeth; Hain, Christopher; Anderson, Martha; Steinschneider, Scott. (2018). A water balance based, spatiotemporal evaluation of terrestrial evapotranspiration products across the contiguous United States. Journal of Hydrometeorology. 19: 891-905.

Carter, Elizabeth; Melkonian, Jeffrey; Steinschneider, Scott; Riha, Susan. (2018). Spatial gradients in management impact analysis of crop yield response to climate at large spatial scales. Agricultural and Forest Meteorology. 256: 242-252.

Carter, Elizabeth; Melkonian, Jeff; Riha, Susan; Shaw, Stephen. (2016). Separating heat stress from moisture stress: analyzing yield response to high temperature in irrigated maize. Environmental Research Letters. 11-9.

J. Cole Smith

Degrees:

  • PhD, Industrial and Systems Engineering, Virginia Tech, 2000
  • BS, Mathematical Sciences, Clemson University, 1996

Areas of Expertise:

  • Integer programming and combinatorial optimization
  • Network flows and facility location
  • Computational optimization methods
  • Large-scale optimization due to uncertainty or robustness considerations

My research interests lie in the field of mathematical optimization, especially in mixed-integer programming and combinatorial optimization. Much of my research has recently focused on network interdiction and fortification, along with bilevel mixed-integer optimization problems. I am particularly interested in interdiction problems that involve uncertain data, and/or in which there is an asymmetry of information among the players. My research has applications in areas including logistics, national security, healthcare, production, ecology, and sports. This research has recently appeared in journals such as Operations Research, Mathematical Programming, IISE Transactions, Networks, and INFORMS Journal on Computing, and has been supported by agencies including the National Science Foundation, the Office of Naval Research, the Air Force Office of Scientific Research, the Defense Threat Reduction Agency, and the Defense Advanced Research Projects Agency.

Honors:

  • 2019 Member, Academy of Distinguished Alumni for the Grado Department of Industrial and Systems Engineering at Virginia Tech
  • 2018 Fellow, Institute of Industrial and Systems Engineers
  • 2014 Glover-Klingman Prize for Best Paper in Networks (Sullivan and Smith, 2014)
  • 2010 Hamed K. Eldin Outstanding Young Industrial Engineer in Education Award
  • 2009 IIE Operations Research Division Teaching Award
  • 2007 IIE Transactions Best Paper Award (Lim and Smith, 2007)

Selected Publications:

* Lozano, L., Bergman, D., and Smith, J.C., “On the Consistent Path Problem,” Operations Research 68(6), 1913-1931, 2020.

* Holzmann, T. and Smith, J.C., “The Shortest Path Interdiction Problem with Randomized Interdiction Strategies: Complexity and Algorithms,” Operations Research, 69(1), 82-99, 2021.

* Nguyen, D. and Smith, J.C., “Network Interdiction with Asymmetric Cost Uncertainty,” European Journal of Operational Research, 297(1), 239-251, 2022.

* Lozano, L. and Smith, J.C., “A Binary Decision Diagram Based Algorithm for Solving a Class of Integer Two-Stage Stochastic Programs,” Mathematical Programming, 191(1), 381-404, 2022.

* Curry, R.M. and Smith, J.C., “Minimum-cost Flow Problems Having Arc-activation Costs,” Naval Research Logistics, 69(2), 320-335, 2022.