Degrees:

Sc.D., Aalto University, 2015

Research Interests:

• Theoretical and Applied Electromagnetics
• RF and Microwave Engineering
• Antennas and Propagation
• Electromagnetics of Artificial Materials and Surfaces  

Current Research:

His research is mainly focused on engineering fields and waves, with emphasis on tailoring electromagnetic wave-matter interactions. In this context, he has made several scientific contributions on a broad range of topics in theoretical and applied electromagnetics and optics, including engineered RF/microwave materials, antennas and propagation, functional metasurfaces, plasmonics, and nanophotonics. Working with three leading research groups in the field of engineering light-matter interaction, he has successfully put forward fundamentally new concepts and ideas to go beyond the limitations of conventional designs and have investigated theoretically, numerically, and experimentally innovative aspects of wave interaction with engineered structures.

Degrees:

Ph. D., Physics, Texas A&M University

Masters of Science (5-Year Integrated), Physics, Indian Institute of Technology, Kanpur (IITK)

Areas of Expertise:

• Quantum information science
• Quantum sensing and metrology
• Quantum nano- and meta-photonics
• Bio-inspired materials
• Bio-nano ​interfaces
• Machine learning

Jha’s research focuses on developing quantum hardware using two-dimensional materials and heterostructures, III-V semiconductors, nanostructures, soft-materials, metamaterials, and hybrid combination of these materials. His research seeks to understand fundamental characteristics of these systems through combined experimental, theoretical, and computational studies and use those findings to gain control and induce novel optical, electrical, thermal, and mechanical responses in them. These responses, in turn, are leveraged to develop transformative devices and technologies for quantum information science, quantum sensing and metrology, nanophotonics, optoelectronics, and space exploration applications. Thus, his interdisciplinary research crosses the conventional scientific boundaries to merge applied physics with electrical engineering, materials science, and mechanical engineering.

Honors and Awards:

• Tingye Li Innovation Prize for Early Career Professionals (Finalist): 2016.
• American Physical Society, Travel Grant: 2011.
• Herman F. Heep and Minnie Belle Heep Foundation Graduate Fellowship: 2010.
• Robert A. Welch Foundation Graduate Fellowship: 2009-2012.

Selected Publications:

• P. K. Jha^*, H. Akbari^*, Y. Kim^*, S. Biswas, and H. A. Atwater, “Nanoscale axial position and orientation measurement of hexagonal boron nitride quantum emitters using a tunable nanophotonic environment,” Nanotechnology 33, 015001 (2022).
• L. Kim^*, S. Kim^*, P. K. Jha, V. W. Brar, and H. A. Atwater, “Mid-Infrared radiative emission from bright hot plasmons in graphene,” Nat. Mater. 20, 805 (2021).
• H. Ramezani, P. K. Jha, Y. Wang, and X. Zhang, “Nonreciprocal Localization of Photons,” Phys. Rev. Lett. 120, 043901(2018).
• P. K. Jha, M. Mrejen, J. Kim, C. Wu, Y. Wang, Y. V. Rostovtsev, and X. Zhang, “Coherence-Driven Topological Transition in Quantum Metamaterials,” Phys. Rev. Lett. 116, 165502 (2016).
• P. K. Jha^*, X. Ni^*, C. Wu, Y. Wang, and X. Zhang, “Metasurface-Enabled Remote Quantum Interference,” Phys. Rev. Lett. 115, 025501 (2015).
• K. E. Dorfman, P. K. Jha, D. V. Voronine, P. Genevet, F. Capasso, and M. O. Scully, “Quantum-Coherence- Enhanced Surface Plasmon Amplification by Stimulated Emission of Radiation,” Phys. Rev. Lett. 111, 043601 (2013).

Areas of Expertise:

• Stochastic modeling, control, and optimization
• Data science: predictive and prescriptive analytics
• Logistics and scheduling
• Energy management
• Queues and networks

Dr. Gautam’s research is on efficiently-operating systems with dynamics and uncertainty. He uses data-driven methods complemented by stochastic models for optimal design, performance analysis, and control of such systems. He has applied this work in computer systems, data centers, wireless and wireline networks, microgrids with renewable energy sources, smart manufacturing, and transportation. His methodologies are grounded in applied probability, data science, and optimization.

Honors and Awards:

• Amazon Scholar, Amazon Corporate LLC, May 2019-present.
• Fellow, IISE (Institute of Industrial and Systems Engineers, formerly IIE), May 2017.
• Outstanding Young Industrial Engineer Award (education category), by IIE, May 2006.

Selected Publications:

• Xu, J., Hou, I.-H. and Gautam, N. (2022) Age of information for single buffer systems with vacation server, IEEE-Trans. on Network Science and Engineering, Vol. 9, No. 3, 1198 – 1214.
• Xu, J. and Gautam, N. (2021) Peak Age of Information in Priority Queueing Systems, IEEE Transactions on Information Theory, Vol. 67, No. 1, 373-390.
• Ejaz, I., Alvarado, M., Gautam, N., Gebraeel, N. and Lawley, M. (2019) Condition-Based Maintenance for Queues with Degrading Servers, IEEE Transactions on Automation Science and Engineering, Vol. 16, No. 4.
• Kwon, S., Ntaimo, L. and Gautam, N. (2019) Demand Response in Data Centers: Integration of Server Provisioning and Power Procurement, IEEE Transactions on Smart Grid, Vol. 51, No. 4.
• Xu, J., Tran, H., Gautam, N. and Bukkapatnam, S. (2019) Joint Production and Maintenance Operations in Smart Custom-Manufacturing Systems, IISE Transactions, Vol. 51, No. 4, 406-421.
• Kwon, S., Ntaimo, L. and Gautam, N. (2017) Optimal Day-Ahead Power Procurement with Renewable Energy and Demand Response, IEEE Transactions on Power Systems, Vol. 32, No. 5, 3924-3933.
• Hsu, Y., Abedini, N., Gautam, N., Sprintson, A. and Shakkottai, S.  (2015) Opportunities for Network Coding: To Wait or Not to Wait, IEEE Transactions on Networking, Vol. 23, No. 6, 1876 – 1889.
• Mohapatra, A., Gautam, N., Sprintson, A. and Shakkottai, S. (2014) Optimal Network Coding Decisions in Delay-sensitive Wireless Transmission, IEEE Transactions on Communications, Vol. 62, No. 8, 2965-2976.
• Ko, Y.-M. and Gautam, N. (2013) Critically loaded multi-server queues with abandonments, retrials, and time-varying parameters, INFORMS Journal on Computing, Vol. 25, No. 2, 285-301.
• Gautam, N. (2012) Analysis of Queues: Methods and Applications, 802 pages, CRC Press (Taylor and Francis), Boca Raton, FL.

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

Degree:

• Ph.D. in Computer Science, Syracuse University

Research Interests:

• Text Mining
• Social Networks
• Social Media Mining
• Information Retrieval
• Multi-Agent Systems

Current Research:

Current research involves the development of algorithms for measuring the emotional contents in tweets, contained not only in words, but also in emoticons, punctuation marks and hashtags; for event prediction based on social media data; and for extending sentiment analysis to multiple languages. Current research also involves continuing work on building autonomous agent-based information gathering system for decision support.

Courses Taught:

• Social Media Mining
• Software Specification and Design
• Software Implementation
• Principles of Software Engineering
• Web Systems

Recent Publications:

A. Panasyuk, E.S. Yu & K. Merhrotra, “Controversial Topic Discovery on Members of Congress with Twitter,” Complex Adaptive Systems, 2014.

M. Rahman, Qinyun Zhu, & E.S Yu, “TRECT: A Hashtag Recommendation System for Twitter,” 2nd International Workshop on Recommender Systems meet Big Data & Semantic Technologies, 2013.

E.S. Yu, “Social Media Marketing and Mining,” Social Media Strategies Seminar, Panama City, Panama, January 17, 2013.

E.S Yu, “From Knowledge Exchange to Knowledge Discovery,” Capstone Conference, Chicago, July 14, 2010.

Degrees:

• BA of Electrical & Computer Engineering, Central South University, China
• MS of Computer Science, Louisiana Tech University

Research Interests:

• Intelligent Systems (Data Mining & Network Analysis)
• Big Data Analytics
• System Security

Courses Taught:

• CSE 581 Introduction to Database Management System
• CIS 342 Introduction to System Programming
• CIS 223 Statistical Reasoning & Practice
• CPS 196 Introduction to Programming
• CPS 181 Introduction to Computing

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

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.

Degree:

• Ph.D. Computer Science, Georgia Tech

Lab/ Center/ Institute affiliation:

Full Stack Security Lab (FSSL) at CST 4-294

Areas of Expertise:

• Blockchain and cryptocurrencies.
• Cyber-security, vulnerability discovery, attack detection and mitigation.
• Distributed systems and performance optimization.
• Software engineering.

Dr. Tang is broadly interested in cyber-security, systems, software engineering, and system measurement. His cyber-security research covers vulnerability discovery, attack detection, attack mitigation, and measurement of deployed systems.

His current research focuses on blockchain security, blockchain systems, blockchain applications, and blockchain education. He has worked on confidential computing, trusted execution environments, and cloud security.

Honors and Awards:

• The Ethereum Foundation academic award

• Two NSF SaTC grants and an NSF CNS grant

• The Best Paper award in IEEE Cloud 2012

• The Best Paper award in ACM/IEEE CCGrid 2015

• The AFRL visiting faculty research fellowship, 2017

Selected Publications:

• K. Li, Y. Wang, Yuzhe Tang. “DETER: Denial of Ethereum Txpool sERvices”, ACM CCS 2021, Acceptance rate=22%

• K. Li, J. Chen, X. Liu, Yuzhe Tang, X. Wang, X. Luo. “As Strong As Its Weakest Link: How to Break (and Fix) Blockchain DApps at RPC Service”, ISOC NDSS 2021, Acceptance rate=15.2%

• K. Li, Yuzhe Tang, J. Chen, Y. Wang, X. Liu. “TopoShot: Uncovering Ethereum’s Network Topology Leveraging Replacement Transactions”, ACM IMC 2021, Acceptance rate=28% 

• Y. Wang, Q. Zhang, K. Li, Yuzhe Tang, J. Chen, X. Luo, T. Chen. “iBatch: Saving Ethereum Fees via Secure and Cost-Effective Batching of Smart-Contract Invocations” ESEC/FSE 2021, Acceptance rate=24.5%

• C. Zhang, C. Xu, J. Xu, Yuzhe Tang, B. Choi. “GEM^2-Tree: A Gas-Efficient Structure for Authenticated Range Queries in Blockchain”, IEEE ICDE 2019, Full Paper, Acceptance rate=26.8%

Degree:

• Ph.D. in Computer Science, University of Virginia
• Master of Computer Science, University of Virginia
• B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology

Lab Affiliation:

Ubiquitous and Intelligent Sensing (UIS Lab)

Areas of Expertise:

• Pervasive and Ubiquitous Computing
• Machine Learning
• Internet of Things (IoT)
• Human Centric Computing and Sensing
• Wireless, Connected, and Mobile Health.

I am directing the Laboratory for Ubiquitous and Intelligent Sensing (UIS Lab) at Syracuse University. My research takes a multi-disciplinary approach to develop novel and practical human event sensing technologies that capture observable low-level physical signals from human bodies and surrounding environments and employ new machine learning, signal processing, and natural language processing techniques to rectify the existing sensing technologies. My research exquisition goes beyond the conventional learning or sensing approaches and addresses the research challenges, such as the uncertainties in physical world sensing, interpretability of ML inference, human factors such as the user-context and mobility, limitation of current technologies (i.e., IoT, CPS), and resource constraints of the sensing data and computation platform. A core focus of my research program is to integrate passive sensing and interpretable AI to advance human health assessment, identify latent markers, and automate health monitoring and interventions. Major ongoing funded research projects are (1) NSF SCH (Medium): Psychophysiological Sensing to Enhance Mindfulness-Based Interventions for Self-Regulation of Opioid Cravings, (2) NIH R021 and NIH R-01: Understanding speech, speech-motor-control, and emotional process in early childhood stuttering, (3) NSF CPS (Small): Developing a Socio-Psychological CPS for the Health and Wellness of Dairy Cows. 

Honors and Awards:

• IAAI Deployed Application Award, The Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21)

• Graduate Student Award for Outstanding Research, Department of Computer Science, UVA, 2018

• Nominated for the best paper award (AsthmaGuide), Wireless health 2016

Selected Publications:

• Harshit Sharma, Yi Xiao, Victoria Tumanova, Asif Salekin, “Psychophysiological Arousal in Young Children Who Stutter: An Interpretable AI Approach”, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2022. (and Ubicomp 2022)

• Jingyu Xin, Vir V. Phoha, Asif Salekin, “Combating False Data Injection Attacks on Human-Centric Sensing Applications”, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2022. (and Ubicomp 2022)

• Cramer et al., “Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States”, The Proceedings of the National Academy of Sciences USA (PNAS), 2022.

• Fatih Altay, Guillermo Ramón Sánchez, Yanli James, Stephen V. Faraone, Senem Velipasalar, Asif Salekin. Preclinical Stage Alzheimer’s Disease Detection Using Magnetic Resonance Image Scans, The Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21).

• Tianjia He, Lin Zhang, Fanxin Kong, and Asif Salekin. Exploring Inherent Sensor Redundancy for Automotive Anomaly Detection, The 57th Design Automation Conference (DAC), 2020.

• Salekin, Jeremy W. Eberle, Jeffrey J. Glenn, Bethany A. Teachman, and John A. Stankovic. 2018. A Weakly Supervised Learning Framework for Detecting Social Anxiety and Depression, ACM Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT), Vol. 2, No. 2, Article 81 (June 2018), 26 pages. (and Ubicomp 2018)

• Salekin, Z. Chen, M. Ahmed, J. Lach, D. Metz, K. de la Haye, B. Bell, and J. Stankovic, Distance Emotion Recognition, ACM Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT), Vol. 1, Issue 3, Sept. 2017, 96:1-96:24 (Ubicomp 2017)

Degrees:

• Ph.D., Computer Science, Marquette University, Wisconsin, USA (2013)
• M.S., Computer Science, Marquette University, Wisconsin, USA (2010)
• B.S., Computer Science and Engineering, Bangladesh University of Engineering and Technology (BUET), Bangladesh (2008)

Research interests:

• Mobile and pervasive health technologies
• Internet-of-Things
• Computer science education
• Impact of active learning pedagogy in CS courses
• Broadening participation of women and underrepresented students in CS

Current research:

Her research spans the domains of mobile healthcare, healthcare data analytics, and pervasive health technologies. Broadly, her research focuses on integrating mobile and pervasive technologies in health and wellness environments to improve users’ quality of life, mental and physical wellbeing. Her research also expands in the direction of mobile security, information and communication technology for development (ICT4D), Computer Science education, broadening participation in computing, best practices in undergraduate research, and how different pedagogical practices can increase diversity in CS. She is also interested in finding why and how people from diverse backgrounds are learning programming in 21^stcentury and how the development of new kind of scalable programming environments or platform can support all kind of learners.

Teaching Interests:

• Introduction to Programming
• Object-Oriented Programming
• Data Structure
• Mobile Application Programming
• Mobile and Pervasive Computing
• Computer Architecture

Honors:

• Provost LA Initiative Award, Florida International University, Spring 2018-2019
• Best paper award, IEEE Conference on Networking Systems and Security (NSysS’ 16), 2016
• Systers Pass-It-On (PIO) Award, Anita Borg Institute, 2014
• Best paper award, IEEE International Conference on e-Health Networking, Applications and Services (Healthcom’ 12), 2012

Recent Publications:

1. Claire Fulk, Grant Hobar, Kevin Olsen, Samy El-Tawab, Puya Ghazizadeh, and Farzana Rahman. Cloud-based Low-cost Energy Monitoring System through the Internet of Things. In Proceedings of the IEEE International Workshop of Mobile and Pervasive Internet of Things (PerIoT 2019), in Conjunction with IEEE Percom ’19. Japan, March 2019.
2. Farzana Rahman and Samy El-Tawab. App Development for the Social Good: Teaching Socially Conscious Mobile App Development in an Upper-Level Computer Science Course. In Proceedings of the 2019 ASEE Annual Conference and Exposition (ASEE ’19), Orlando, FL, July 2019.
3. Farzana Rahman. Leveraging Visual Programming Language and Collaborative Learning to Broaden Participation in Computer Science. In Proceedings of the 19th Annual Conference on Information Technology Education (SIGITE ’18), Ft Lauderdale, FL, Oct 2018.
4. Saiyma Sarmin, Nafisa Anzum, Kazi Hasan Zubaer, Farzana Rahman, A. B. M. Alim Al Islam. Securing Highly-Sensitive Information in Smart Mobile Devices through Difficult-to-Mimic and Single-Time Usage Analytics. In Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems, Computing, Networking and Services (MobiQuitous ’18), Nov 2018.
5. Farzana Rahman. From App Inventor to Java: Introducing Object-oriented Programming to Middle School Students Through Experiential Learning. In Proceedings of the 2018 ASEE Annual Conference and Exposition (ASEE ’18), Salt Lake City, UT, July 2018.
6. Farzana Rahman, Healthy Hankerings: Motivating Adolescents to Combat Obesity with a Mobile Application. In Proceedings of the 20th International Conference on Human-Computer Interaction (HCI International ’18), NV, July, 2018.
7. Farzana Rahman, Perry Fizzano, Evan M. Peck, Shameem Ahmed, and Stu Thompson. How to Build a Student-Centered Research Culture for the Benefit of Undergraduate Students. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education (SIGCSE ’18), Maryland, Feb 2017.

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:

• 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. Belman, Vir 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 Jin. EarEcho: 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.

Degree(s):

• Ph. D., State Univ. of New York at Stony Brook
• B.Tech., Indian Institute of Technology, Kanpur

Lab/Center Affiliation(s) :

• Syracuse Evolutionary and Neural Systems Exploration (SENSE) Lab
• Smart Grid Lab

Research Interests:

• Artificial intelligence
• Evolutionary algorithms
• Data mining
• Social networks
• Bioinformatics

Current Research:

Recent work has involved the development of algorithms for:

the recognition of patterns in promoter regions of genome sequences,
unsupervised detection of anomalies in data (including time series), and
optimization problems in the design of multiband cognitive radio networks.
Other current work includes the investigation of robustness properties of social networks, as well as the use of network models in understanding the dynamics of evolutionary algorithms.

Teaching Interests:

• Smart grid
• Social networks
• Evolutionary algorithms
• Neural networks

Honors:

• Distinguished Scholar Award, International Society of Applied Intelligence, July 2011.

Selected Publications:

Linkage Sensitive Particle Swarm Optimization (D. Devicharan and C.K. Mohan), in Handbook of Swarm Intelligence – Concepts, Principles, and Applications (eds. B.K. Panigrahi, Y. Shi, and B. Lim), pp. 119-132, 2011.

Rank-Based Outlier Detection with (HuaMing Huang, Kishan Mehrotra Chilukuri K. Mohan), in Journal of Statistical Computation and Simulation, Oct. 2011.

Distributed In-Network Path Planning for Sensor Network Navigation in Dynamic Hazardous Environments (D. Chen, C.K. Mohan, K.G. Mehrotra, and P.K. Varshney), in Wireless Communications and Mobile Computing 12(8): 739-754, 2012.

SMAlign: Alignment of DNA Sequences with Gap Constraints (F. Alobaid, K. Mehrotra, C.K. Mohan, and R. Raina), in Proc. BICOB, Las Vegas, March 2012.

Reference Set Metrics for Multi-Objective Algorithms (C.K. Mohan and K. Mehrotra), in Proc. SEMCCO, pp.723-730, Dec. 2011.

Degrees: 

Ph.D. in Computer Science – University of Mississippi (2021)

Areas of Expertise:

• Programming Languages
• Software Engineering
• Augmented Reality
• Virtual Reality

My research is focused in using multi-paradigm programming to solve accuracy issues on User Interactive System, especially in Virtual and Augmented Reality.  This research uses dependency relationships between loosely coupled components. We use techniques of Functional Reactive Programming and Change Propagation to identify how a change may cause additional modification in other several components and force the systems to wrap all these modification in a single update cycle. This research resulted in a design pattern that was applied into User Interfaces, Virtual Reality and it can be applied on many other research fields. I am also currently interested in research about CS Education and Accessibility technologies especially focused on education and autonomy.

Honors and Awards:

Dissertation Fellowship Award, University of Mississippi, 2019.

Outstanding Doctoral Student Award, University of Mississippi, 2021.

Order of Engineer inductee, School of Engineering. University of Mississippi. 2021.

Pledge of Computing Professional inductee, School of Engineering. University of Mississippi. 2021.

Upsilon Pi Epsilon inductee, School of Engineering. University of Mississippi. 2021.

IEEE Computer Science Society Professional Member. IEEE. 2021.

IEEE Education Society Professional Member. IEEE. 2021.

ACM SIGSOFT (Special Interest Group – Software Engineering) Professional Member. ACM. 2021

ACM SIGCSE (Special Interest Group – Computer Science Education) Professional Member. ACM. 2021

ACM SIGCHI (Special Interest Group – Computer-Human Interaction) Professional Member. ACM. 2021

Brazilian Computing Society (SBC) Professional Honor Member. SBC. 2021.

Selected Publications:

Joao Paulo O. Marum, J. Adam Jones, and H. Conrad Cunningham (2019), Towards a reactive game engine, in Proceedings of the 50th IEEE SouthEastCon, IEEE, Huntsville, AL, USA.

Joao Paulo O. Marum, H. Conrad Cunningham, and J. Adam Jones (2020), Unified library for dependency graph reactivity on web and desktop user interfaces, in Proceedings of the ACM Southeast Conference (ACMSE 2020), ACM, Tampa, FL, USA

Joao Paulo O. Marum, J. Adam Jones, and H. Conrad Cunningham (2020), Dependency graph-based reactivity for virtual environments, in Proceedings of the IEEE VR 2020 Workshop on Software Engineering and Architectures for Interactive Systems (SEARIS), IEEE, Atlanta, GA, USA.

Mobile ad hoc networks, Satellite networks, Artificial intelligence, and 5G-and-beyond networks

Degree:

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

Research interests:

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

Current Research:

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).

Teaching Interests:

• Programming Languages
• Computer Security
• Formal Logic and Discrete Mathematics

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).

Degree(s):

• Ph.D, Massachusetts Institute of Technology 1985
• M.S., Massachusetts Institute of Technology 1981
• B.S., Seoul National University, Seoul, Korea 1976

Lab/Center Affiliation(s):

• Electromagnetics Laboratory

Areas of Expertise:

• Electromagnetic fields and waves
• Waves in complex and anisotropic media
• Antennas and propagation
• Microwave engineering
• Microwave remote sensing

Wave propagation and scattering involving complex media:
We use the dyadic Green’s functions to solve a variety of theoretical and practical problems involving complex (anisotropic) media. We have investigated and solved the problems of wave propagation, reflection & transmission, radiation and scattering of electromagnetic waves in the presence of such anisotropic media as well as bianisotropic media. Problems of antennas and microstrip circuits are also being treated.  Anisotropic media that we have investigated include: (i) uniaxially anisotropic medium, (ii) biaxially anisotropic medium, (iii) electrically gyrotropic (gyroelectric) medium, and (iv) magnetically gyrotropic (gyromagnetic) medium.

Honors and Awards:

• Magna Cum Laude Honor, Seoul National University, Korea, 1976.
• Eta Kappa Nu Outstanding Undergraduate Teacher Award, 1999.
• IEEE Third Millennium Medal, 2000
• The Technology Alliance of CNY – College Educator of the Year Award, 2002
• IEEE Region 1 Award, August 2002 and August 2017
• Who’s Who in Electromagnetics

Selected Publications:

• Arlon T. Adams and Jay K. Lee, Electromagnetics, University Readers, First Edition, 2012; Cognella, Revised First Edition, 2015. [Undergraduate Electromagnetics course textbook, 632 pages]  https://titles.cognella.com/electromagnetics-9781626615847.html

• Komlan Payne, Kevin Xu, Jun H. Choi, and Jay K. Lee, “Multiphysics analysis of plasma-based tunable absorber for high-power microwave applications,” IEEE Transactions on Antennas and Propagation, vol. 69, no. 11, pp. 7624-7636, November 2021. DOI: 10.1109/TAP.2021.3070088

• Matthew S. Davis, Wenqi Zhu, Jared Strait, Jay K. Lee, Henri J. Lezec, Steve Blair, and Amit Agrawal, “Chiroptical response of aluminum nanocrescents at ultraviolet wavelengths,” Nano Letters, April 2020; nl-2020-005864.   DOI: 10.1021/acs.nanolett.0c00586

• Michael D. Enders, Jun H. Choi, and Jay K. Lee, “Integrated full hemisphere space-to-frequency mapping antenna with CRLH stripline feed network,” IEEE Transactions on Microwave Theory and Techniques, vol. 66, No. 11, pp. 4765-4772, November 2018. DOI: 10.1109/TMTT.2018.2864614

• Kepei Sun, Jay Kyoon Lee and Jennifer W. Graham, “Fields and wave modes analysis of rectangular waveguide filled with uniaxial medium,” IEEE Trans. Microwave Theory and Techniques, vol. 64, no. 11, pp. 3429-3440, Nov. 2016.

• Mohsen Yazdani, Jay K. Lee, Joseph Mautz, Ercument Arvas, and Kepei Sun, “Ray tracing theory in a radially uniaxial sphere,” IEEE Trans. Antennas and Propagation, Vol. 63, No. 11, pp. 5165- 5169, November 2015.

• Brian J. Rautio, Vladimir I. Okhmatovski, Andreas C. Cangellaris, James C. Rautio, and Jay K. Lee, “The unified-FFT algorithm for fast electromagnetic analysis of planar integrated circuits printed on layered media inside a rectangular enclosure,” IEEE Trans. Microwave Theory and Techniques, vol. 62, No. 5, pp. 1112-1121, May 2014.

• Ying Huang and Jay K. Lee, “Radiation from an arbitrarily oriented Hertzian dipole in the presence of a gyroelectric slab,” IEEE Trans. Antennas and Propagation, Vol. 62, No. 4, pp. 1837- 1843, April 2014.

• Jennifer W. Graham and Jay K. Lee, “Reflection and transmission from biaxially anisotropic-isotropic interfaces,” Progress In Electromagnetics Research, Vol. 136, pp. 681-702, 2013.

• Chong Mei, Moamer Hasanovic, Jay K. Lee, and Ercument Arvas, “Comprehensive solution to scattering by bianisotropic objects of arbitrary shape,” Progress In Electromagnetics Research B, Vol. 42, pp. 335-362, 2012.

• Abdullah Eroglu, Yun Hee Lee, and Jay K. Lee, “Dyadic Green’s functions for multi-layered uniaxially anisotropic media with arbitrarily oriented optic axis,” IET (Institution of Engineering and Technology) Microwaves, Antennas & Propagation, Vol. 5, Iss.15, pp. 1779-1788, 2011.

• Abdullah Eroglu and Jay K. Lee, “Far field radiation from an arbitrarily oriented Hertzian dipole in an unbounded electrically gyrotropic medium,” Progress in Electromagnetics Research (PIER), vol. 89, pp. 291-310, 2009.

Degree(s):

Ph.D. (U. of Maryland, College Park, 1998); M.A. (U. of Maryland, College Park, 1996); M.A. (U. of Michigan, Ann Arbor, 1988); B.A. (U. of Hong Kong, 1987).

Research Interests:

• Discrete Mathematics
• Computability Theory
• Computational Learning
• Artificial Intelligence
• Logic and Formal Methods

Current Research:

I study the role of queries and the associated learning strategies in solving computational problems. For example, the strategies for playing many board games and on-line games often resemble a basic paradigm in machine learning, namely, to learn a target concept via queries. By studying the (winning) strategies in specific concrete games, we may gain insights to determine the right set of queries and related parameters that are of interest in machine learning applications. I am also interested in graph coloring and graph labeling type problems. Many arise quite naturally in the study of social structures and networks. To better understand them it often requires new algorithms and analysis.

As an educator, I am interested in engaging students to do research. In addition, I like to explore new pedagogies to improve students’ reasoning ability, develop their inquisitiveness and strengthen their problem solving skills. Along in this direction I like to cast some of the research problems listed above in elementary settings and make them accessible to undergraduates and beginning graduate students.

Courses Taught:

• Artificial intelligence
• Data structures
• Algorithms
• Automata and Computability
• Formal methods

Selected Publications:

Andrew C. Lee, Sin-Min Lee and Ho-Kun Ng, On the balance index set of bi-regular and tri-regular graphs, 78, Journal of Combinatorial Mathematics and Combinatorial Computing, August 2011, pp.169-186.

Man Kong, Andrew C. Lee and Sin-Min Lee, On the Balance Index Sets of Homeomorph of Regular Graphs, Congressus Numerantium, vol. 204, Dec. 2010, pp. 193-203.

Andrew C. Lee and Sin-Min Lee and Hsin-Hao Su, On the balanced indexed set of generalized friendship graphs, envelope graphs of cycles and cubic graphs, Congressus Numerantium, 196 (2009), pp. 3-22.

William I. Gasarch and Andrew C. Lee, Inferring answers to queries, Journal of Computing and Systems Sciences Volume 74, No 4, 2008, 490-512.

Degrees

• Ph.D. in Computer Science, McGill University
• M.S. in Computer Science, Northeastern University (CN)
• B.S. in Computer Science, Northeastern University (CN)

Areas of Expertise:

• Cyber-Physical Systems
• Security
• Real-time Systems
• Resource Management

Dr. Fanxin Kong’s current research interest centers around Cyber-Physical System (CPS) security. Compared to conventional IT systems, challenges in CPS security are distinct in terms of not only consequences in case of security breaches but also attack surfaces. His recent works address real-time sensor attack detection and real-time recovery from attacks. These works are motivated by the fact that the timing correctness of attack defense receives considerably less attention than its functional correctness, but untimely defense is just damaging. The proposed solutions cover formal methods and machine learning techniques. 

Recent Publications:

• Lin Zhang, ZifanWang, Mengyu Liu, and Fanxin Kong, “Adaptive Window-Based Sensor Attack Detection for Cyber-Physical Systems”, in the 59th Design Automation Conference (DAC), 2022.

• Lin Zhang, Pengyuan Lu, Fanxin Kong, Xin Chen, Oleg Sokolsky and Insup Lee, “Real-Time Attack-Recovery for Cyber-Physical Systems using Linear-Quadratic Regulator”, in the 21st ACM SIGBED International Conference on Embedded Software (EMSOFT), 2021.

• Francis Akowuah and Fanxin Kong, “Real-Time Adaptive Sensor Attack Detection in Autonomous Cyber-Physical Systems”, in 27th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), 2021.

• Lin Zhang, Xin Chen, Fanxin Kong, and Alvaro A. Cardenas, “Real-Time Recovery for Cyber-Physical Systems using Linear Approximations”, in 41st IEEE Real-Time Systems Symposium (RTSS), 2020.

• Tianjia He, Lin Zhang, Fanxin Kong, and Asif Salekin, “Exploring Inherent Sensor Redundancy for Automotive Anomaly Detection”, in 57th Design Automation Conference (DAC), 2020.

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

Research interests:

• Flash and non-volatile memory-based systems
• Data storage systems
• File systems and key-value stores

Current Research:

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

Degrees

• M.S. in Computer Science, Syracuse University
• Ph.D. in Computer Engineering, Syracuse University

Teaching Interests

• Data Structures and Algorithms
• Object-Oriented Design
• Mobile Application Development
• Software Engineering

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.

Degree(s):

• Ph.D. University of Florida, Gainesville, FL, 1985.
• M.S. Middle East Technical University, Ankara, Turkey, 1980.
• B.S. Middle East Technical University, Ankara, Turkey, 1978.

Lab/Center Affiliation(s):

• Syracuse Center of Excellence
• CASE Center

Research Interests:

• Intelligent systems applications
• Indoor environment control
• Modeling and forecasting for financial systems
• Medical Instrumentation

Current Research:

Modeling complex systems for improved control, forecasting, and signal processing is at the heart of my current research. I use a combination of analytical and computational methods, such as neural nets, fuzzy logic and genetic algorithms, validated by experimental and simulated data. We have applied this approach to process control, indoor environmental control, medical instrumentation, diagnostics and prognostics, and computational finance.

Courses Taught:

• Controls

Honors:

• Eta Kappa Nu, Member
• Tau Beta Pi, Member
• Golden Key, Honorary Member
• Who is Who in Science and Engineering, 9th Edition, 2006
• Outstanding Undergraduate Teacher, Eta Kappa Nu Syracuse University Chapter, 1998
• K.S. Fu Award, North American Fuzzy Information Processing Society, 1997
• Outstanding Service Award, Syracuse University, College of ECS, 1997
• Who is Who in American Education, 4th Edition, 1994
• University of Florida, Presidential Recognition, 1983

Selected Publications:

Dhummi, V., D. W. Demetriou, H. Palanthandalam-Madapusi, H. E. Khalifa, C. Isik, “Robust Occupancy-based Distributed Demand Ventilation”, International J. of Ventilation, Volume 9, No 4, March 2011.

Colak, S., C. Işık, H. E. Khalifa, J. Dannenhoffer, J. Grunewald, “Heat and Moisture Transport Modeling for a Protective Garment with an Active Sweating Device”, in ASME Congress, Vancouver, Canada, 2010.

Demetriou, D., O. Ozdemir, H. E. Khalifa and C. Isik, “Distributed Demand Controlled Ventilation for Improving IAQ”, Proc. Indoor Air 2008, Copenhagen, Denmark, 2008.

Sazli, M.H., Isik, C., “Neural Network Implementation of the BCJR Algorithm”, Digital Signal Processing, Volume 17, January 2007, pp 353-359.

Ari, S., I. Cosden, H. E. Khalifa, J. Dannenhoffer, P. Wilcoxen, and C. Isik, “Individual Thermal Comfort and Energy Optimization”, Proc. Clima2007, Helsinki, Finland, 2007.

S. Zhang, C.K. Mohan, P. Varshney, C. Isik, K. Mehrotra, S. Wang, Z. Gao, and R. Rajagopalan, “Coupling of Airflow and Pollutant Dispersion Models with Evacuation Planning Algorithms for Building System Controls”, ASHRAE Transactions. Vol. 112. Part 1. 2006.

Isik, C., “Blood Pressure Measurement”, invited chapter, the Encyclopedia of Medical Devices and Instrumentation, 2nd Ed, Vol 1. John G. Webster (Ed), Wiley, 2006.

Degree:

• Ph.D., Computer Science, Purdue University, 2015
• M.S., Computer Science, Marquette University, 2010
• B.S., Computer Science and Engineering, Bangladesh University of Engineering and Technology, 2008

Research interests:

• Security of computer networks and systems
• IoT systems security
• Program analysis, software testing and verification
• Vulnerability detection

Current Research:

His research focuses on the security of computer networks and systems. The software of computer networks and systems continues to have exploitable vulnerabilities, which are lucrative targets for adversaries. Within this broad domain, his particular emphasis is on automated detection of vulnerabilities as well as creating resilient protocols and systems. His research primarily builds on and expands program analysis, software engineering, and formal verification. His interests span several domains of computing, including network communication protocols, operating systems, distributed systems, internet-of-things (IoT) systems and embedded devices.

Honors:

• Distinguished Paper Award at NDSS (Network and Distributed System Security Symposium) 2018
• Bilsland Dissertation Fellowship Award from the Graduate School at Purdue University, 2015
• Graduate Teaching Fellowship Award from Dept. of Computer Science at Purdue University, 2014

Recent Publications:

1. Yahyazadeh, P. Podder, E. Hoque, and O. Chowdhury. Expat: Expectation-based Policy Analysis and Enforcement for Appified Smart-Home Platforms. In the proceedings of the 24th ACM Symposium on Access Control Models and Technologies (SACMAT 2019), Toronto, ON, Canada, June 2019
2. Samuel Jero, Endadul Hoque, David Choffnes, Alan Mislove, and Cristina Nita-Rotaru. Automated Attack Discovery in TCP Congestion Control Using a Model-guided Approach. In the proceedings of Network and Distributed System Security Symposium (NDSS), San Diego, California, Feb 2018. [Distinguished Paper Award]
3. Endadul Hoque, Omar Chowdhury, Sze Yiu Chau, Cristina Nita-Rotaru, and Ninghui Li. Analyzing Operational Behavior of Stateful Protocol Implementations for Detecting Semantic Bugs. In the Proceedings of IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), Denver, CO, June 2017.
4. Sze Yiu Chau, Omar Chowdhury, Endadul Hoque, Huangyi Ge, Aniket Kate, Cristina Nita-Rotaru, and Ninghui Li. SymCerts: Practical Symbolic Execution For Exposing Noncompliance in X.509 Certificate Validation Implementations. In the Proceedings of IEEE Symposium on Security and Privacy (S&P), San Jose, CA. May 2017.

Degree(s):

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

Research Interests:

• Wireless communications
• Information theory
• Signal processing
• Cognitive radio systems
• Energy efficiency
• Quality of service provisioning in wireless systems
• Wireless information-Theoretic security

Current Research:

Our recent research concentrated on the energy efficiency in wireless systems, performance analysis in wireless multiuser networks operating under quality of service (QoS) constraints, and energy-security tradeoffs in multi-antenna wireless communications.

We are also currently exploring cooperative and cognitive operation in wireless systems and the impact of interference. In particular, we study the spectrum and energy efficiency in cooperative relay channels, conduct performance analysis in cognitive radio channels in the presence of channel and/or sensing uncertainty, and investigate the throughput in fading interference channels under queuing constraints.

Teaching Interests:

• Digital communications
• Wireless communications
• Information theory
• Signals and systems
• Probability theory and random processes

Honors:

• Gordon Wu Graduate Fellowship, Princeton University
• NSF CAREER Award, 2006
• 2008 EURASIP Journal of Wireless Communications and Networking Best Paper Award
• University of Nebraska-Lincoln (UNL) College Distinguished Teaching Award, 2009
• Holling Family Master Teacher Award (UNL University-wide Teaching Award), 2009
• Maude Hammond Fling Faculty Research Fellowship, 2011

Select Publications:

Journal Papers

D. Qiao, M. C. Gursoy and S. Velipasalar, “Energy Efficiency in Multiaccess Fading Channels under QoS Constraints,” EURASIP Journal on Wireless Communications and Networking: Special Issue on Green Radio, 2012:136, April 2012.

M. C. Gursoy, “Secure Communication in the Low-SNR Regime,” IEEE Transactions on Communications, vol. 60, no. 4, pp. 1114- 1123, April 2012.

D. Qiao, M. C. Gursoy, S. Velipasalar, “Transmission Strategies in Multiple Access Fading Channels with Statistical QoS Constraints,” IEEE Transactions on Information Theory, vol. 58, no. 3, pp. 1578-1593, March 2012.

S. Akin and M. C. Gursoy, “Performance Analysis of Cognitive Radio Systems under QoS Constraints and Channel Uncertainty,” IEEE Transactions on Wireless Communications, vol. 10, no. 9, pp. 2883-2895, Sept. 2011.

M. C. Gursoy, “MIMO Wireless Communications under Statistical Queueing Constraints,” IEEE Transactions on Information Theory, vol. 57, no. 9, pp. 5897-5917, Sept. 2011.

D. Qiao, M. C. Gursoy, and S. Velipasalar, “Secure Wireless Communication and Optimal Power Control under Statistical Queueing Constraints,” IEEE Transactions on Information Forensics and Security, vol. 6, no. 3, pp. 628-639, Sept. 2011.

D. Qiao, M. C. Gursoy, and S. Velipasalar “Energy Efficiency in the Low-SNR Regime under Queueing Constraints and Channel Uncertainty,” IEEE Transactions on Communications, vol. 59, no. 7, pp. 2006-2017, July 2011.

Conference Papers

D. Qiao, M. C. Gursoy, and S. Velipasalar, “Throughput Regions for Fading Interference Channels under Statistical QoS Constraints,” Proc. of the 2012 IEEE Global Communications Conference (Globecom), Dec. 2012.

M. Ozmen and M. C. Gursoy, “Energy Efficiency in Fading Interference Channels under QoS Constraints,” Proc. of the 2012 International Symposium on Information Theory and its Applications (ISITA), Oct. 2012.

Q. Chen and M. C. Gursoy, “Spectrum and Energy Efficiency in Two-Way Multi-Relay Networks with Selective Relaying,” Proc of the 2012 Military Communications Conference (MILCOM), Oct. 2012.

M. Ozmen and M. C. Gursoy, “Impact of Channel and Source Variations on the Energy Efficiency under QoS Constraints,” Proc. of the 2012 IEEE International Symposium on Information Theory (ISIT), Boston, July 2012.

G. Ozcan and M. C. Gursoy, “Channel Sensing and Estimation in Cognitive Relay Networks,” Proc. of the 2012 the IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Turkey, June 2012.

Areas of Expertise:

Human Vision and Psychophysics

Neurophysiology

Human Factors

Software Engineering and Design

Multi-disciplinary approaches to problems like GUI design and visualizations. Biology inspired computing.

Degree(s):

• Ph. D. Pennsylvania State University

Research Interests:

• Smart grid
• Sensors and measurement
• High speed electronic devices and integrated circuits
• Power engineering
• Power electronics

Current Research:

I am actively developing several research projects in the area of Smart Grid systems, including Distributed Resources, EVs, Microgrid Design and Analysis. Other projects include the design and analysis of FinFET, as well as the development of Thin Film Sensors.

Courses Taught:

• Electronics devices
• Circuits
• Power engineering
• Diversity and ethics in the workplace

Selected Publications:

Nikkhah Mojdehi, P. Ghosh, and M. Fardad, “Energy and Cost Minimization of Bidirectional Frequency Regulation Service by EV following FERC Order 755,” IEEE Power and Energy Society General Meeting, 2015.

Mohammad Nikkhah Mojdehi and Prasanta Ghosh, “Minimization of Energy Usage and Cost for EV during Reactive Power Service”, Best Student Paper, IEEE International conference on Smart Energy Grid Engineering, 2015.

Chenrui Jin, Xiang Sheng and Prasanta Ghosh, “Optimized Electric Vehicle Charging with Intermittent Renewable Energy Sources”, IEEE Journal of Selected Topics in Signal Processing, Vol. 8, No. 6, pp 1063-1072, 2014.

Chenrui Jin, Jian Tang, Prasanta Ghosh, “Optimizing Electric Vehicle Charging with Energy Storage in the Electricity Market,” IEEE Transactions on Smart Grid, vol.4, no.1, pp311-320, 2013.

Feng and P. Ghosh, “Design Consideration in the Development of Multi-Fin FETs for RF Applications” World Journal of Nano Science and Engineering, 2012.

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:

• vqSGD: Vector Quantized Stochastic Gradient Descent, Venkata Gandikota, Daniel Kane, Raj K. Maity, Arya Mazumdar. IEEE Transactions on Information Theory, 2022.

• Support Recovery of Sparse Signals from a Mixture of Linear Measurements, Venkata Gandikota, Arya Mazumdar, Soumyabrata Pal. Advances in Neural Information Processing Systems (NeurIPS), 2021.

• Recovery of Sparse Linear Classifiers from Mixture of Responses, Venkata Gandikota, Arya Mazumdar, Soumyabrata Pal. 34th Conference on Neural Information Processing Systems (NeurIPS), 2020.

• Reliable Distributed Clustering with Redundant Data Assignment, Venkata Gandikota, Arya Mazumdar, Ankit Rawat. IEEE Symposium on Information Theory (ISIT), 2020.

• Nearly Optimal Sparse Group Testing, Venkata Gandikota, Elena Grigorescu, Sidharth Jaggi, Samson Zhou. IEEE Transactions on Information Theory, 2019.

Degree

• Ph.D., Computer Science, New Mexico State University & University of Udine

Areas of Expertise:

• Machine Learning
• Differential Privacy
• Optimization
• Algorithmic Fairness
• Power Systems

I work on Artificial Intelligence, Privacy, and Machine Learning (ML). My research group and I focus on two main directions: (1) We study the ethical and trustworthy aspects of ML systems, especially (differential) privacy, fairness, robustness, and their interaction, and (2) we are interested in the integration of operations research and learning to build novel neural optimization algorithms to solve complex decision problems.

Selected Honors & Awards

• Caspar Bowden PET Award for Outstanding Research in Privacy Enhancing Technologies, (2022).
• Early Career Spotlight (for research on ML and Optimization). At IJCAI 2022.
• Google Research Scholar Award (for research on Privacy), (2022).
• NSF CAREER Award, (2022)
• Best Paper Award, IEEE Transaction of Power System, (2022).
• Early Career Researcher Award (for research on the integration of ML and Optimization). By the Association for Constraint Programming, (2021).
• Mario Gerla Young Investigator Award for Research in Computer Science, ISSNAF, (2021).
• Outstanding Reviewer Award, At NeurIPS 2021.
• Best Paper Award, IEEE Transaction of Power System, (2021)
• Differentially Private Temporal Map Challenge Award, NIST (2020).
• Best AI Dissertation Award, AI*IA, (2018)
• Most Visionary Workshop Paper Award, At AAMAS 2017.
• Best Student Paper Award, At CMSB 2013.

Selected Publications:

Please see https://web.ecs.syr.edu/~ffiorett/publications.html for an updated list of my publications.

Degree(s):

• BSc in Electrical Engineering, Sharif University of Technology, Iran, 1998.
• MSc in Control Engineering, Iran University of Science and Technology, 2000
• PhD in Mechanical Engineering, University of California, Santa Barbara, 2006

Areas of Expertise:

• Convex optimization
• Complex networks
• Dynamical systems
• Control theory

Makan Fardad has expertise in convex optimization and its applications in the areas of distributed control, signal processing, and social networks.

Honors:

• Dean’s Award for Excellence in Engineering Education, 2015.
• Recipient of 3 National Science Foundation Awards, 2009, 2013, 2015.

Selected Publications:

Fardad, F. Lin, and M. R. Jovanovic, “Design of Optimal Sparse Interconnection Graphs for Synchronization of Oscillator Networks,” IEEE Transactions on Automatic Control, vol. 59, pp. 2457-2462, 2014.

Lin, M. Fardad, and M. R. Jovanovic, “Algorithms for Leader Selection in Stochastically Forced Consensus Networks,” IEEE Transactions on Automatic Control, vol. 59, pp. 1789-1802, 2014.

Lin, M. Fardad, and M. R. Jovanovic, “Design of Optimal Sparse Feedback Gains via the Alternating Direction Method of Multipliers,” IEEE Transactions on Automatic Control, vol. 58, pp. 2426-2431, 2013.

Lin, M. Fardad, and M. R. Jovanovic, “Optimal Control of Vehicular Formations with Nearest Neighbor Interactions,” IEEE Transactions on Automatic Control, vol. 57, pp. 2203-2218, 2012.

Fardad and M. R. Jovanovic, “Design of Optimal Controllers for Spatially Invariant Systems with Finite Communication Speed,” Automatica, vol. 47, pp. 880-889, 2011.

Degree(s):

• Ph.D., Electrical Engineering, Arizona State University, 2012
• MSc. , Electrical Engineering, West Virginia University, 2008
• BSc., Electrical Engineering, University of Tehran, 2006

Research Interests:

• Integration of renewable energy into power systems
• Power system stability and control
• Power system reliability and security
• Phasor Measurement Units (PMU) in smart grids

Current Research:

My research focuses on integration of renewable energy resources and power system stability with high penetration of renewables. I investigate how power systems are impacted when various renewables are integrated into systems. I also investigate how power system operation and planning needs to be modified to accommodate more renewables while achieving reliable power systems.

I also investigate the problems at the intersection of network science theory and power system analysis. This includes identification of critical contingencies and solutions to prevent cascading blackouts.

Courses taught:

• Introduction to Power Systems
• Power System Analysis
• Power Electronics

Selected Publications:

Eftekharnejad, G.T. Heydt, and V. Vittal., “Optimal Generation Dispatch with High Penetration of Photovoltaic Generation”, IEEE Transactions on Sustainable Energy, Vol 6, Issue 3, pages 1013-1020, July 2015.

Eftekharnejad, V. Vittal, G.T. Heydt, B. Keel, and J. Loehr, “Impact of Increased Penetration of Photovoltaic Generation on Power Systems”, IEEE Transactions on Power Systems, Vol. 28, Issue 2, pages 893 – 901, May 2013.

Eftekharnejad, V. Vittal, G.T. Heydt, B. Keel, and J. Loehr, “Small Signal Stability Assessment of Power Systems with Increased Penetration of Photovoltaic Generation: A Case Study”, IEEE Transactions on Sustainable Energy, Vol. 4, Issue 4, pages 960 – 967, October 2013.

Degree:

• Ph.D. Computer Engineering, Case Western Reserve University

Research Interests:

• Computer Architecture
• Embedded System Design
• System Verification
• VLSI Design Automation

Current Research:

Design automation of embedded processors with extension of super-scalar architectures to exploit inherent thread-level parallelism and instruction-level parallelism at run time within a single chip. Hardware-software interactions in such systems and their applicability to shared-memory systems, and their performance and functional verification.

Courses taught:

• ECS 101 Introduction to Engineering and Computer Science
• CSE 262 Digital System Design and Simulation with VHDL
• CSE 561 Digital Machine Design
• CSE 671 Embedded System Design
• CSE 661 Advanced Computer Architecture
• CSE 765 VLSI Testing and Verification

Selected Publications:

Improving Memory Space Utilization in Multi-core Embedded Systems using Task Recomputation. Koc H, Tosun S, Kandemir M, and Ercanli E, International Journal of Computer Science and Network, Volume 1, Issue 5, pp. 27-34, Oct 2012.

Exploiting Large On-Chip Memory Space Through Data Recomputation, Koc H, Kandemir M, Ercanli E. In Proceedings of the 23rd IEEE International SoC Conference (SOCC 2010), pp. 513-518, Las Vegas, NV, Sept 2010.

An ILP Formulation for Recomputation Based SPM Management for Embedded CMPs. Koc H, Ercanli E, Kandemir M, Ozturk O; In Proceedings of the 5th Workshop on Optimizations for DSP and Embedded Systems (ODES’07). San Jose, CA. Mar 2007.

Reducing Off-Chip Memory Access Costs Using Data Recomputation in Embedded Chip Multi-processors. Koc H, Kandemir M, Ercanli E, Ozturk O; In Proceedings of the 44th Design Automation Conference (DAC’07). San Diego, CA. June 2007. (Ranked #3 in Most Popular Papers Category from ACM Digital Library’s Refereed Journals and Conference Proceedings Downloaded in September 2007).

Compiler-Directed Temporary Array Elimination. Koc H, Ercanli E, Kandemir M, Son SW. The 4th Workshop on Optimizations for DSP and Embedded Systems. NY. Feb 2006.
Minimizing Energy Consumption of Banked Memories Using Data Recomputation. Koc H, Ozturk O, Kandemir M, Narayanan S, Ercanli E. In Proceedings of Intl Symposium on Low Power Electronics and Design (ISLPED’06). Tegernsee, Germany. Oct 2006.

Automated Code Generation For Database Applications. Ercanli E, Ozgencil N, Kahraman MG. The 14th Intl Conference on Intelligent and Adaptive Systems and Software Engineering (ISCA’05). Toronto, Canada, June 2005.

Degree:

• Ph. D. Syracuse University

Lab/Center Affiliation(s):

• Center for Information Systems Assurance and Trust
• Institute for National Security and Counter Terrorism

Research Interests:

• Computer security
• Systems assurance
• Formal verification

Current Research:

Shiu-Kai Chin’s research uses mathematical logic for the design and verification of trustworthy computer systems. Examples of computer systems that must be trustworthy are command and control systems, financial services, and distributed control of the power grid. His focus is on policy-based design and verification with an emphasis on using computer-assisted reasoning using higher-order logic theorem provers.

Shiu-Kai supports the Air Force’s research in trustworthy systems and hardware-based security. His work with JP Morgan Chase was used to reason about the security and integrity of credentials and entitlements in large-value commercial transactions.

Courses Taught:

• Hardware design
• Access control
• Secure computer architecture
• Certified security by design

Honors:

• Provost Faculty Fellow
• Laura J. and L. Douglas Meredith Professor for Teaching Excellence
• Chancellor’s Citation for Outstanding Contributions to the University’s Academic Programs
• 2005 Syracuse University Outstanding Teacher of the Year
• Crouse Hinds Award for Excellence in Education

Selected Publications:

Shiu-Kai Chin, “Teaching Undergraduates Certified Security by Design,” 19th Colloquium for Information Systems Security Education, Las Vegas, NV, June 15-17, 2015.

Glenn Benson, Shiu-Kai Chin, Sean Croston, Karthick Jayaraman, Susan Older, Banking on interoperability: Secure, interoperable credential management, Computer Networks, Volume 67, 2014, pp. 235-251.

Shiu-Kai Chin, Erich Devendorf, Sarah Muccio, Susan Older, and James Royer, “Formal Verification for Mission Assurance in Cyberspace: Education, Tools, and Results,” Proceedings of the 16th Colloquium for Information Systems Security Education, Lake Buena Vista, FL, June 11-13, 2012, pp. 75—82.

Shiu-Kai Chin and Susan Older, Access Control, Security, and Trust: A Logical Approach, CRC Press, 2011.

Shiu-Kai Chin, “Logic Design for Access Control, Security, and Trust,” (Invited Keynote) Engineering of Reconfigurable Systems and Algorithms (ERSA’11) Las Vegas, 18-21 July 2011

Shiu-Kai Chin, Sarah Muccio, Susan Older, and Thomas N. J. Vestal, “Policy-Based Design and Verification for Mission Assurance,” in Igor Kotenko and Victor Skormin (Eds.), Computer Network Security, 5th International Conference on Mathematical Methods, Models and Architectures for Computer Network Security, MMM-ACNS 2010, St. Petersburg, Russia, September 2010.

Glenn Benson, Shiu-Kai Chin, Sean Croston, Karthick Jayaraman, and Susan Older, “Credentials Management for High-Value Transactions,” in Igor Kotenko and Victor Skormin (Eds.), Computer Network Security, 5th International Conference on Mathematical Methods, Models and Architectures for Computer Network Security, MMM-ACNS 2010, St. Petersburg, Russia, September 2010.

Degree(s):

• Ph. D., University of Connecticut

Lab/Center Affiliation(s):

• Communication Laboratory

Research Interests:

• Statistical inference applied to decentralized systems
• Network information theory
• Signal processing for MIMO, multi-user, and multi-carrier communications

Current Research:

My current work focuses on decentralized inference and in particular, how inference functions can impact the value of information in a networked system. Specific topics of interests include the sufficiency principle in the decentralized setting that guides optimal data reduction, the role of feedback and interactive communication in networked inference, and architectural issues in data fusion systems.

Another area of current interest is in characterizing the performance limits of multi-user networks, and in particular the classical interference channel. On the applied side, we have been involved in developing MIMO communication systems for airborne platforms when MIMO channels exhibits rank deficiency due to the lack of scatterers and in developing robust, efficient, and secure software radio systems.

Courses Taught:

• ELE606: Probability
• ELE651: Digital Communications
• ELE751: Wireless Communications
• ELE757: Information Theory

Honors:

• NSF Career Award (2006)
• Finalist, DARPA Spectrum Challenge (2013-2014)
• IEEE Fellow (2015)

Selected Publications:

Akofor and B. Chen,”Interactive distributed detection: architecture and performance analysis,”IEEE Trans. Information Theory. vol. 60, pp.6456-6473, Oct. 2014.

Xu, S. Zhu and B. Chen,”Decentralized data reduction with quantization constraints,”IEEE Trans. Signal Processing, vol. 62, pp. 1775-1784, April 2014.

Chen, B. Chen, and P.K. Varshney,”A new framework for distributed detection with conditionally dependent observations,”IEEE Trans. Signal Processing, vol. 60, pp. 1409-1419, March 2012.

Shang, G. Kramer, and B. Chen,”A new outer bound and the noisy-interference sum-rate capacity for Gaussian interference channels,”IEEE Transactions on Information Theory, vol. 55, pp. 689-699, February 2009.

Chen, R. Jiang, T. Kasetkasem, and P.K. Varshney,”Channel aware decision fusion for wireless sensor networks,”IEEE Trans. Signal Processing, vol. 52, pp. 3454-3458, Dec. 2004.

Degree(s):

• Ph. D., University of Illinois at Urbana-Champaign, 1987

Research Interests:

• VLSI timing analysis and simulation
• Transistor/circuit level power leakage reduction
• Software debugging and verification
• Distributed data sharing and collaboration

Current Research:

A current work that a doctoral student and I are working on is to develop techniques to reduce leakage power of circuits during idle times. Two specific techniques are developed: (1) Leakage power behavior is examined for reordering serially connected transistor blocks. Based on that, we can then determine a primary input vector to a circuit to reduce its leakage power during idle mode. (2) Effect of body bias is studied for nano-scale transistor. A hybrid technique (mixing reverse body bias and forward body bias) is developed to reduce power leakage during idle mode. Another current work that a doctoral student and I are working on is to develop a tool for software debugging and verification. Traditional IDE allows setting of break points, but provides minimum supports in reasoning and bug locating. The goal of this research work is to allow programmers to query various properties of programs and help locating the causes of property violations. Another current work that a doctoral student and I are working on is to design a transistor level circuit simulator, which gives an accuracy near that of SPICE, and can handles much larger circuits in much less run time. Other research work involves distributed data sharing and collaboration, design of platform and protocol for emergency response systems, etc.

Teaching Interests:

• VLSI timing analysis
• VLSI computer-aided design
• Transistor level leakage power reduction
• Multimedia information systems
• Modeling and performance evaluation of computer/communication systems
• Object-oriented databases
• Computer networks
• Parallel/distributed processing
• Computer architecture

Select Publications:

Don P. McGarry, C.Y. Roger Chen.; “IC.NET — Incident Command “Net”: A system using EDXL-DE for intelligent message routing,” 2010 IEEE International Conference on Technologies for Homeland Security (HST), pp. 197 – 203, Nov. 2010.

Jae Woong Chun and C. Y. Roger Chen, A Novel Leakage Power Reduction Technique for CMOS Circuit Design, IEEE International SoC Design Conference (ISOCC), Nov. 1010.

Veerapaneni Nagbhushan, C. Y. Roger Chen: Modeling and reduction of complex timing constraints in high performance digital circuits. IEEE International Conference on Computer Design (ICCD) 2009

Ting-Wei Chiang, C Y Roger Chen and Wei-Yu Chen , “A Technique for Selecting CMOS Transistor Orders,” IEEE International Conference on Computer Design (ICCD), Oct. 2007.

Ting-Wei Chiang, C Y Roger Chen and Wei-Yu Chen, “An Efficient Gate Delay Model for VLSI Design,” IEEE International Conference on Computer Design (ICCD), Oct. 2007.

Degree(s):

• Ph.D. in Electrical and Computer Engineering

Lab/Center Affiliation(s):

Smart Grid Lab

Smart Grid and Power Engineering Lab at Syracuse University has been developed to support teaching and research in contemporary Power Engineering.

• Power Engineering Lab section has been designed for basic and advanced teaching and research in Power Electronics, Sensors & Measurements, Electromechanical Devices, including control systems, communication, and signal processing aspects. The advanced power electronics boards, supported by the real-time interfaces, are equipped for modern undergraduate and graduate level contemporary experimentation in power electronics and electromechanical devices.
• Smart Grid Lab section enables continued development of a multidisciplinary research concept that includes advances in communication and control systems, cyber-security, privacy, and economics. This section consists of the setups for electrical transmission and distribution system, solar and wind energy integration, and synchrophasor measurement technology implementation.
• Smart Home Lab section has been designed for building wired and wireless communication infrastructure and algorithm development for future smart home ideas. The lab is equipped with smart metering infrastructure, providing state-of-the-art environment to our researchers.
• Distant learning concept implementation in Smart Grid Lab has been developed to enable the on-line lectures and student presentations, as well as the distant lab experiment demonstrations.

Research Interests:

• Distributed generation integration and control in electric power microgrid
• Dynamic optimization of microgrid operations and islanding capabilities
• Microgrid secure communication and cybersecurity infrastructure
• Singularity detection and classification and signal segmentation
• Segmentation and motion estimation in time-varying images

Current Research:

• Methodology for the development of sustainable microgrid infrastructure with distributed generation including difficult‑to‑predict renewable energy sources (survey of existing assets, economic feasibility study, technical feasibility study, microgrid design and pre‑deployment parameter validation, on‑site commissioning, post‑deployment metrics validation)
• Microgrid stability and protection challenges (synchrophasor technology implementation for real time monitoring and control, protection schemes coordination with distributed generation, merging challenges of monitoring and protection infrastructure)
• Sustainable microgrid operations by real time load scheduling optimization, using nonlinear and heuristic approaches
• Microgrid cyber secure, distributed, and resilient communication architecture
• Smart metering implementation for demand priority optimization and consumer’s cost reduction with energy availability and signal disaggregation constraints
• Singularity detection and classification and signal segmentation in medical electroencephalography signals for real time epileptic seizure monitoring

Courses Taught:

Power engineering basic and advanced courses in:

• Power electronics
• Electromechanical devices
• Sensors & measurements
• Electric power systems
• Distributed generation integration in smart grid
• Advanced measurements in power engineering
• Microprocessor based power system protection
• Control of distributed generation

Signal processing and applied mathematics basic and advanced courses in:

• Signals & systems
• Digital signal processing
• Functional methods and linear analysis
• Spectral analysis and adaptive filtering
• Wavelets

Supporting teaching courses in:

• Introduction to Electrical Engineering
• Introduction to Smart Grid
• Smart grid: Security, Privacy, & Economy

Professional activities:

• American Society for Engineering Education, St Lawrence Section Conference Co‑Chair, Syracuse, April 17‑18, 2015
• American Society for Engineering Education, St Lawrence Section Chair, 2015

Skills:

• Six-sigma green belt, SUNY-ESF, Syracuse, NY, 2012
• Anti-explosion defense of electrical appliances on over-ground places imperiled from explosive blends, Institute for Nuclear Sciences “Vinca”, Belgrade, Serbia, Yugoslavia, 1995
• Specialist’s examination certificate (the specialist’s examination for engineers dealing with construction projects), Chamber of the Economy of Serbia, Belgrade, Yugoslavia, 1993 (Serbian/Yugoslavian equivalent to Professional Engineer in the USA)

Publications & Presentations:

Arnav Kavadia et al., The Smart Grid: Operational, Privacy, Security & Economic Issues, American Society for Engineering Education, St Lawrence Section Conference, Syracuse, NY, 2015

Tomislav Bujanovic & Prasanta Ghosh, Laboratory Experiments for Enhanced Learning of Electromechanical Devices, American Society for Engineering Education, Zone 1 Conference, Bridgeport, CT, 2014

Tomislav Bujanovic & Ikhlas Abdel-Qader, On Wavelet Transform General Modulus Maxima Metric for Singularity Classification in Mammograms, Open Journal of Medical Imaging, 2013, 3, 17-30

Tomislav Bujanovic et al., Development of Undergraduate Power Engineering Teaching and Learning for future Smart Grid, American Society for Engineering Education, St. Lawrence Section Conference, Buffalo, NY, 2013

Liwen Sun et al., Wavelet Application to Detect Spikes in EEG Signals Due to Epileptic Seizure, IEEE Signal Processing in Medicine and Biology Symposium, New York City, NY, 2012