Find Academic Supervisors

Chinwendu EnyiohaView profile

Assistant Professor

• Distributed Optimization
• Cyber-Physical Systems (CPS)
• Networked Control Systems
• +5 more

Chinwendu Enyioha is an Assistant Professor at the University of Central Florida (UCF), jointly appointed in the Department of Electrical and Computer Engineering and the Department of Computer Science. He holds a Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2014), with postdoctoral research at Harvard University and Tufts University. His research focuses on distributed optimization, control of networked systems, and safety in Cyber-Physical Systems (CPS), emphasizing resource-aware computation and adversarial resilience. He has received prestigious awards including the Ford Foundation Fellowship and the William Fontaine Scholar distinction. Education: Ph.D., Electrical and Systems Engineering, University of Pennsylvania, 2014 B.S., Mathematics (Summa Cum Laude), Gardner-Webb University, 2008 Research Interests: His work addresses challenges in distributed optimization, decision-making under communication constraints, and security in CPS. Applications include multi-robot systems, energy grids, and epidemic control. Current projects include information-efficient distributed sensing and reinforcement learning for autonomous systems. Awards and Activities: Fellow, Ford Foundation (administered by the NRC of the National Academies) William Fontaine Scholar, University of Pennsylvania Patterson Award, MAA Southeast Section Session Organizer, IEEE American Control Conference (ACC) 2016–2018 Technical Reviewer for IEEE/ACM journals and conferences Teaching and Mentorship: He teaches graduate courses in Linear Systems Theory, Nonlinear Control, and Digital Control Systems at UCF. His group includes PhD and MS students working on distributed algorithms, CPS security, and AI-driven control. He advises on over $20M in grants from NSF, NASA, and industry partners like L3Harris. Labs and Collaborations: His research integrates theoretical foundations with practical applications in robotics, energy systems, and network security, leveraging UCF’s interdisciplinary environment and partnerships in the Central Florida Research Park.

• Machine Learning
• Communication Theory
• Data Storage
• +3 more

Elnaz Banan Sadeghian is an Assistant Professor at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science and the Department of Electrical and Computer Engineering. She holds a PhD in Electrical and Computer Engineering from Georgia Institute of Technology (2017), alongside MS degrees from Georgia Tech (2015) and AmirKabir University (2008), and a BS from Shahid Beheshti University (2005). Her research focuses on Communication Theory, Machine Learning, Data Storage (particularly Magnetic Recording), Assistive Technology, and Brain-Computer Interfaces (BCI). Recent work includes neural network-based approaches for multitrack detection in magnetic recording and low-latency BCI systems leveraging time-series prediction and self-attention mechanisms. She also explores subject-independent BCI frameworks using supervised autoencoders. Dr. Sadeghian has received notable grants including an NSF CAREER Award and NSF CRII funding for machine learning applications in magnetic storage. Her contributions span over 20 peer-reviewed publications since 2007, addressing topics like asynchronous track detection algorithms, timing error correction in read channels, and fractal analysis of EEG signals for BCI. She is an ASTC Fellow and active member of IEEE and EMBS societies. Currently, she advises PhD candidates with expertise in machine learning and communication theory.

• Neuroscience
• Network Theory
• Biophysics
• +3 more

Jeremie Fish is a Research Assistant Professor in the Department of Electrical & Computer Engineering at Clarkson University, affiliated with the Coulter School of Engineering & Applied Sciences. Their research focuses on interdisciplinary applications of network theory, nonlinear dynamics, and data analysis to neuroscience, biophysics, and complex systems. Key areas include functional brain networks, stochastic self-assembly mechanisms, and synchronization phenomena in networks. Fish’s work often combines mathematical modeling with computational methods such as kernel density estimation and entropy-based inference. Recent publications highlight contributions to understanding fractal basins in neural systems, fragility in network structures, and entropic regression for neurological data. Their research also extends to environmental applications like forecasting harmful algal bloom impacts. Fish holds a position at Clarkson’s CAMP Building and can be reached at jafish@clarkson.edu. No scientific awards or grants are explicitly mentioned in the provided text. Advising records for students are not listed here. Research interests span computational neuroscience, network robustness, and systems biology, with a focus on developing novel analytical frameworks for complex systems.

Xiao QinView profile

Professor

• Artificial Intelligence
• Machine Learning
• Database Systems
• +7 more

Xiao Qin is an Alumni Professor and Director of the Computer Science and Software Engineering Graduate Programs at Auburn University's College of Engineering. He holds a Ph.D. in Computer Science from the University of Nebraska-Lincoln, and M.S. and B.S. degrees from Huazhong University of Science and Technology. His research focuses on artificial intelligence, machine learning, database systems, cybersecurity, and healthcare informatics with a particular emphasis on NL2SQL systems, key-value store optimization, graph neural networks, and edge-cloud healthcare solutions. Dr. Qin leads interdisciplinary projects in smart healthcare systems, high-performance storage architectures, and AI-driven data management. His recent work includes advancements in semantic table discovery (DiscoverGPT), intelligent cache allocation (iCache), and distributed graph neural network training. He is affiliated with the Center for Artificial Intelligence and Cybersecurity Engineering and has contributed to initiatives like the Alabama Center for Paper and Bioresource Engineering. His academic contributions span 15+ recent publications (2023-2025) addressing challenges in database optimization, machine learning algorithms, and cloud infrastructure efficiency. His advising includes Corey McDaniels, a master’s student who won the 2024 Eisenhower Transportation Fellowship.

Mengchu ZhouView profile

Professor

• Systems Engineering
• Automation
• Discrete Event Systems
• +2 more

Dr. Mengchu Zhou is a Distinguished Professor in the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology. His research spans discrete event systems, industrial IoT, optimization algorithms, and intelligent manufacturing. With over 30 years of experience, he has made significant contributions to Petri net theory and its industrial applications. Research interests include intelligent automation, manufacturing systems, optimization algorithms, and quantum computing applications. His work frequently addresses scheduling problems, robotic systems, and IIoT implementations using advanced computational methods. Publication trends demonstrate consistent focus on scheduling optimization, manufacturing automation, and emerging computing paradigms. Recent works increasingly explore quantum computing applications and security aspects of industrial systems. Honors include election as National Academy of Inventors Fellow and the IEEE Norbert Wiener Award. His research has been recognized through multiple patents and technology transfer implementations. Dr. Zhou maintains strong industry connections and has advised numerous graduate students through his laboratory focused on automation and intelligent systems.

Annu PrabhakarView profile

Professor

• Human-Computer Interaction Design
• Health Informatics
• Diversity, Equity, and Inclusion (DEI)
• +1 more

Dr. Annu Prabhakar is a Professor at the University of Cincinnati's School of Information Technology, specializing in Human-Computer Interaction (HCI), Health Informatics, and inclusive technology design. She holds leadership roles including ACM-SIGCHI Global Inclusion co-chair and ABET accreditation commissioner. Her research bridges technology with social impact, focusing on maternal health, cultural inclusivity, and equitable access. Education: Ph.D. in Informatics, Indiana University Bloomington M.S. in Informatics, Indiana University Bloomington M.S. in Computer Science, Mississippi State University B.Tech. in Electrical and Electronics Engineering, College of Engineering Trivandrum Research Focus: Dr. Prabhakar's work explores culturally responsive HCI solutions for vulnerable populations, emphasizing maternal health support, DEI-centered AI, and global ICT4D initiatives. Her methodologies integrate participatory design, machine learning, and qualitative analysis to address disparities in healthcare access and technology adoption. Publication Trends: Recent articles (2016-2023) demonstrate a strong focus on maternal well-being technologies, social support systems, and equitable AI. Key themes include remote research methods for distributed populations, compassion-driven design frameworks, and cross-cultural technology adaptation, with recurring emphasis on low-income communities and ethical AI. Awards: George Barbour Award (2013) Golden Apple Teaching Award (2013) Best Paper Award at Pervasive Health (2017) ACM SIGCHI Honorable Mention (2017) Graduate Research Competition Winner (2019) Academic Service: Founded the Women in IT Student Group and teaches courses including HCI, Web Development, and Software Engineering. Leads interdisciplinary projects on stress-care technologies and STEM equity.

Nicola SantoroView profile

Research Professor

• Algorithms
• Communication Networks
• Data Structures
• +8 more

Nicola Santoro is a Distinguished Research Professor in the School of Computer Science at Carleton University, Ottawa, Canada. His academic career spans over four decades, with a focus on distributed computing, algorithms, and network systems. He holds a Ph.D. from the University of Waterloo (1979) and a D.Sc. from the University of Pisa (Italy). His research interests include algorithms, communication networks, distributed computing, robotics, graph theory, and wireless communication. He has contributed extensively to the theoretical foundations of distributed systems, mobile robotics, and fault-tolerant computing. Santoro's work emphasizes scalable and efficient solutions for dynamic networks and multi-agent systems. Key contributions include studies on black hole search in dynamic networks, distributed algorithms for autonomous robots, and network decontamination. His publications span over 400 articles in top-tier journals and conferences. He has also authored influential textbooks, such as Design and Analysis of Distributed Algorithms (Wiley, 2007) and Distributed Computing by Mobile Entities (Springer, 2019). Prof. Santoro has advised numerous graduate students and researchers, shaping the next generation of computer scientists. His work bridges theory and practice, with applications in robotics, sensor networks, and parallel computing.

Andrea GasparriView profile

Professor

• Robotics
• Sensor Networks
• Networked Multi-Agent Systems
• +4 more

Andrea Gasparri is a Full Professor at the Department of Civil, Computer Science, and Aeronautical Technologies Engineering, Roma Tre University, Rome, Italy. His research spans robotics, sensor networks, networked multi-agent systems, and precision agriculture. Coordinated Robotics and Networked Multiagent Systems Laboratory. Active in H2020 projects like CANOPIES and PANTHEON, focusing on precision farming. Recipient of the Young Author Award (Agricontrol Conference 2019) and ICRA2017 Best Paper Finalist. Research Interests: Robotics and autonomous systems Distributed control and estimation Multi-agent coordination in precision agriculture Swarm intelligence Networked cyber-physical systems Federated learning for agricultural robots Recent Publications show expertise in finite-time consensus, distributed optimization, and precision agriculture applications, with a focus on robustness and scalability in multi-robot systems. Scientific Awards: Young Author Award, Agricontrol Conference 2019 Best Multi-Robot Systems Paper Finalist, ICRA2017 He has contributed to projects involving UAV path planning, robotic spraying systems, and demand-side management in agricultural cyber-physical systems.

Christopher AmatoView profile

Associate Professor

• Artificial Intelligence
• Multi-Agent Systems
• Reinforcement Learning
• +3 more

Christopher Amato is Associate Professor in the Khoury College of Computer Sciences at Northeastern University. His research develops principled methods for multi-agent systems operating under uncertainty with limited communication, with applications in multi-robot coordination, autonomous systems, and artificial intelligence. He directs research on reinforcement learning approaches for decentralized control in complex environments. His work integrates techniques from reinforcement learning, game theory, and probabilistic reasoning to enable efficient coordination in applications including disaster response, surveillance, and networked systems. Recent projects address cooperative multi-agent reinforcement learning (MARL) under partial observability, asynchronous learning frameworks, and robust multi-robot coordination. He co-organized the COMARL symposium on challenges in multi-agent reinforcement learning. Dr. Amato earned his PhD from UMass Amherst under Shlomo Zilberstein, with postdoctoral research at MIT working with Leslie Kaelbling and Jonathan How. His publications include foundational work on decentralized partially observable Markov decision processes (Dec-POMDPs) and multi-agent reinforcement learning. He currently advises seven PhD students working on MARL theory and applications. Research Areas: Partially observable reinforcement learning Multi-agent/robot systems Decision-making under uncertainty Scalable coordination algorithms

Valerie KingView profile

Professor

• Graph Algorithms
• Randomized Algorithms
• Probabilistic Analysis
• +3 more

Valerie King is a Professor in the Department of Computer Science at the University of Victoria, Canada. Her research focuses on graph algorithms, randomized algorithms, probabilistic analysis, and fault-tolerant distributed computing. She holds a PhD from UC Berkeley. Her work emphasizes scalable and secure distributed systems, with contributions to consensus protocols, network algorithms, and adversarial resilience. Dr. King's research bridges theoretical foundations and practical applications in computing systems. Education: PhD in Computer Science from UC Berkeley. Research Interests: Graph algorithms, randomized algorithms, fault-tolerant distributed computing, and algorithmic defense mechanisms against cyberattacks. Her work often addresses challenges in network security, consensus protocols, and efficient distributed algorithms. Key research trends in her articles include Byzantine agreement protocols, communication-efficient distributed algorithms, and sublinear-time graph algorithms. She explores geometric network models, secure multi-party computation, and energy-efficient communication strategies. Her contributions span algorithm design, complexity analysis, and practical implementations in adversarial environments. Advising and Grants: While specific student names or grant details are not listed here, her extensive publication record reflects sustained research activity in distributed systems and algorithms. She contributes to foundational research in theoretical computer science with applications to real-world distributed networks. Labs/Teams: Affiliated with the Computer Science department at UVic, collaborating on projects related to distributed computing and algorithmic security. Her work often involves interdisciplinary approaches to network resilience and computational efficiency.

• Big Data
• Probabilistic Databases
• Information Extraction
• +7 more

Prof. Martin Theobald is a Full Professor in Computer Science with a focus on Big Data at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), Department of Computer Science. He leads the Big Data research group and serves as the Study Program Director for the Bachelor in Computer Science (BiCS) program since 2022. Previously, he held professorial roles at the University of Ulm (Co-Director of the Institute for Databases and Information Systems) and the University of Antwerp (Advanced Database Research and Modeling group). His academic journey includes a joint doctoral degree from Saarland University and the Max Planck Institute for Informatics in 2006, followed by a postdoctoral research stint at Stanford University's Infolab from 2006 to 2008. Education: PhD in Computer Science (2006) from Saarland University and Max Planck Institute for Informatics, Saarbrücken Postdoctoral Researcher at Stanford University's Infolab (2006–2008) Research Interests: Martin's work centers on Big Data systems, probabilistic databases, distributed stream processing, and machine learning. He has extensively explored techniques such as asynchronous iterative routing (AIR), real-time neural network training from data streams (TensAIR), and large-scale information extraction leveraging statistical methods. His contributions span scalable architectures, optimization frameworks for deep learning, and knowledge graph construction with high precision and recall. Publications: His recent work reflects a focus on real-time systems, convergence analysis of distributed algorithms, and robust embedding techniques. Key themes include the development of efficient dataflow engines, probabilistic database models, and frameworks for handling uncertain and temporal data. Scientific Awards: None explicitly mentioned in the provided texts. Advising & Grants: Martin has advised numerous students (though names are not listed) and contributed to over 100 international conferences/workshops as a PC member, reviewer, and organizer. He has held editorial roles, including Area Editor for Elsevier’s 'Information Systems' (2013–2018) and PVLDB (2020–2021). His research has driven advancements in Jeopardy! clue-solving SPARQL engines, automotive navigation systems under uncertainty, and the design of open-domain information extraction architectures. Labs/Teams: Current leadership of the Big Data research group at the University of Luxembourg. Previously led the 'Ranking and Uncertain Data Management' group at the Max Planck Institute for Informatics (2008–2012). Key collaborations include the development of the AIR dataflow engine and the YAGO-QA knowledge base system.

Wei YuView profile

Professor

• Cybersecurity
• Privacy
• Cyber-Physical Systems
• +3 more

Dr. Wei Yu is a Full Professor in the Department of Computer and Information Sciences at Towson University, USA, and serves as the Ph.D. program director in Information Technology since 2023. He holds a Ph.D. in Computer Engineering from Texas A&M University (2008), with prior degrees from Chinese institutions. His research focuses on cybersecurity, privacy, cyber-physical systems (CPS), IoT, data science, and machine learning applications. Key contributions include foundational work on secured edge intelligence in CPS domains like smart energy, manufacturing, and transportation. He has pioneered federated learning frameworks for privacy-preserving data analytics, digital twin architectures, and reinforcement learning for industrial IoT control. He has received prestigious awards including the NSF CAREER Award (2014–2021), USM Regents’ Faculty Award (2015), and Wilson H. Elkins Professorship (2016). His professional service includes senior editorial roles at IEEE Transactions on Information Forensics and Security, IEEE Internet of Things Journal, and IEEE Access. Research spans CPS security, IoT forensics, and AI-driven CPS applications. Notable projects include adversarial mitigation in federated learning, CAPTCHA robustness, and edge computing for smart transportation systems.

Yongge WangView profile

Professor

• Cryptography
• Blockchain
• Post-Quantum Cryptography
• +6 more

Yongge Wang is a Professor at the Department of Software and Information Systems within the College of Computing and Informatics at the University of North Carolina at Charlotte. His research spans cryptography, blockchain, post-quantum cryptography, deep learning, and secure communications. He has contributed to industry standards such as IEEE 1363.2 (SRP5) and IEEE 1363.3 (WANG-KE), as well as IETF RFC 4050 for ECDSA with XML-Signature syntax. Research Interests : Cryptography: Focused on post-quantum schemes (e.g., RLCE, polarRLCE), garbled computation, and secure authentication protocols. Blockchain: Led work on consensus protocols (XP for asynchronous networks), DeFi mechanisms, and censorship-resistant systems. Algorithmic Randomness: Pioneered LIL testing techniques for pseudorandom generators, analyzing vulnerabilities in Debian OpenSSL. Publications : His recent articles (2016-2024) address blockchain security, code-based cryptography, garbled circuits, and quantum-resistant algorithms, with trends emphasizing decentralized systems and secure cloud computation. Grants and Funding : Secured multiple NSF and UNC Charlotte grants for cybersecurity research, including projects on privacy-preserving databases and secure storage systems. Education : Holds a PhD in Mathematics from Heidelberg University (Germany), focusing on randomness and computational complexity. Teaching : Courses include Applied Cryptography, Cloud Data Storage, and Network Security, with a strong emphasis on academic integrity and hands-on projects. Software Tools : Developed the LIL statistical testing package for pseudorandomness, available as a Java application.

Martin SeifridView profile

Assistant Professor

• Organic Materials
• Self-Driving Labs
• Materials Informatics
• +7 more

Martin Seifrid is an Assistant Professor at NC State University's Department of Materials Science and Engineering (College of Engineering). His research focuses on designing organic materials with controlled structures for applications in energy storage, healthcare, and neuromorphic computing. He leads the Data-Driven Organic Materials Lab, which develops self-driving labs powered by machine learning to accelerate materials discovery. Key areas include organic mixed ionic-electronic conductors, materials informatics, and automation through tools like Chemspyd. His work integrates synthesis, characterization, and computational methods. Recent efforts emphasize automated experimentation frameworks and open-source robotics interfaces for chemical synthesis. Notable projects include optimizing organic photovoltaic materials and understanding thin film morphology in semiconductors. Publications highlight advancements in self-driving lab systems, machine learning for material design, and novel material characterization techniques. He collaborates widely, contributing to interdisciplinary initiatives in autonomous laboratory development. His lab is equipped with state-of-the-art equipment for automated chemistry and advanced material analysis. While currently no scientific awards are listed, his research has been featured in prominent journals like Science , Chemistry of Materials , and Journal of Materials Chemistry A . He actively mentors graduate students in materials science and engineering through collaborative projects at NC State.

• Statistics Education
• Data Science Education
• Technology-Enhanced Learning
• +7 more

Dr. Hollylynne S. Lee is the Distinguished University Professor of Mathematics and Statistics Education at North Carolina State University, with a focus on K-14 statistics and data science education. As Senior Faculty Fellow at the Friday Institute and Co-Director of the Hub for Innovation and Research in Statistics Education , she leads multi-million-dollar NSF and IES-funded projects that transform teacher preparation and develop open educational technology tools. With over 7 million in external funding, her work spans designing technology environments for statistical reasoning, preparing educators through online professional development, and advancing national data science initiatives. Key research areas include: Technology integration in statistics instruction Formative assessment systems for probabilistic reasoning Online learning models for teacher education Computational data analysis in science classrooms K-12 data literacy curriculum development Global mathematics teacher training frameworks Recent publications demonstrate her leadership in: Designing CODAP-based instructional modules Topic modeling for professional development motivation Diagnostic assessment systems for statistical misconceptions AP Statistics classroom practices analysis Data visualization toolkits for student discussions Personalized online learning for in-service teachers Major awards highlight her impact: Robert Foster Cherry Award (2022) William D Warde Statistics Education Award (2023) RTI University Scholar (2018) ASA Fellow (2020) She serves on national boards including the Mathematical Sciences Education Board and U.S. National Commission on Mathematics Instruction , and has shaped international statistics education conferences. Her collaborative work extends to institutions like Concord Consortium, RTI International, and University of Georgia.