Find Academic Supervisors

Peter J. HawrylakView profile

Associate Professor

• Radio Frequency Identification (RFID)
• Internet of Things (IoT) Security
• Cyber-Physical Systems
• +5 more

Peter J. Hawrylak is an Associate Professor at The University of Tulsa, with appointments in the College of Engineering & Natural Sciences (Department of Electrical & Computer Engineering), Tandy School of Computer Science , and School of Cyber Studies . His work bridges RFID technology, IoT security, embedded systems, and high-performance computing. Education: Ph.D. in Electrical Engineering (2006), M.S. in Electrical Engineering (2004), B.S. in Computer Engineering (2002) from the University of Pittsburgh. Research Interests focus on securing low-power wireless devices, RFID applications, and IoT systems. He explores attack graph modeling, compliance methods, and hybrid cybersecurity frameworks for critical infrastructure like nuclear reactors and smart grids. Recent Publications highlight IoT cyberattack detection using machine learning, large-scale attack graph analysis, and electromagnetic strategies for infrastructure sensing. His work emphasizes scalability, compliance, and integration of heterogeneous computing techniques. Scientific Awards: Dean’s Excellence Faculty Award (2024) Ted Williams Award (AIM, Inc., 2015) Zelimir Schmidt Award for Outstanding Researcher (2017) XSEDE Campus Champion Fellow (2018-2019) Hawrylak has held leadership roles such as Chair of the IEEE Tulsa Section and Editor-in-Chief of journals including International Journal of Radio Frequency Identification Technology and Applications . He holds 15 patents and has published over 65 papers.

Xingquan ZhuView profile

Professor

• Data Mining
• Machine Learning
• Graph Neural Networks
• +2 more

Xingquan Zhu is Professor in the Department of Electrical Engineering and Computer Science at Florida Atlantic University. His PhD in Computer Science is from Fudan University. Research specializes in data mining, machine learning, and bioinformatics with significant NSF funding. Current projects: KMELIN: Knowledge Mining for Dynamic Networks (NSF IIS-1763452) COVID-19 knowledge base construction (NSF IIS-2027339) AI Deep Learning Laboratory development (NSF CNS-1828181) Publications focus on graph neural networks, time series mining, and ML applications in healthcare fraud detection and cancer diagnostics. Recent work emphasizes physics-guided neural models and fairness in continual learning systems.

Hernan MakseView profile

Professor

• Complex Networks
• Artificial Intelligence
• Neuroscience
• +3 more

Hernan Makse is a Distinguished Professor of Physics at the City College of New York (CCNY), affiliated with the Levich Institute. His research focuses on complex networks, artificial intelligence applications in healthcare and biology, and interdisciplinary network science. He explores symmetry in biological systems, influencers in social and biological networks, and network-based approaches to epidemiology, finance, and neuroscience. Key research interests include network dynamics, symmetry-driven network analysis, AI-driven medical diagnostics, and the intersection of network science with brain connectivity. His work bridges theoretical physics, computer science, and life sciences, with notable contributions to understanding synchronization in neural networks and the role of influencers in disease spread, politics, and ecosystems. Recent projects analyze social media's role in political polarization, AI for breast cancer detection, and symmetry patterns in metabolic networks. His recent publications emphasize network-based solutions for real-world challenges across multiple disciplines. The Levich Institute affiliation highlights his engagement with applied physics and interdisciplinary research. Notable collaborations involve applying network theory to healthcare, urban systems, and ecological resilience. Makse's work frequently appears in top journals and conferences, demonstrating a strong focus on translating theoretical insights into practical applications.

• Federated Learning
• Machine Learning
• Cybersecurity
• +3 more

Yan Xiao is an Adjunct Professor at the University of Illinois. His research focuses on federated learning, cybersecurity, and distributed systems, with an emphasis on adversarial robustness, privacy-preserving techniques, and optimization in machine learning frameworks. He has contributed to foundational tools like FedML and explores challenges in federated learning security, energy efficiency, and causal mechanisms. Key research interests include federated learning architectures, adversarial defense mechanisms, and the integration of reinforcement learning for automated feature selection. His work spans theoretical advancements (e.g., invariant aggregators for backdoor attacks) and practical applications (e.g., thermal management in HPC systems). Recent publications highlight trends in federated learning security, distributed optimization, and causal discovery. Notable contributions address straggler-free coreset-based learning and Bayesian-optimized energy efficiency in federated setups. No scientific awards or grants are explicitly mentioned in the provided texts. Advising details are not listed, though collaborations with research groups in federated learning and HPC are implied through publication topics.

• Computational Structural Biology
• Statistical Machine Learning
• DNA Shape Prediction
• +4 more

Remo Rohs is a tenured Professor of Quantitative and Computational Biology, Chemistry, Physics and Astronomy, Computer Science, and Biomedical Engineering at the University of Southern California Dornsife College. He leads the Rohs Lab, focusing on integrating genomics and structural biology to uncover molecular mechanisms in gene regulation. Chair, Department of Quantitative and Computational Biology (2021–2024) Section Head, Quantitative and Computational Biology (2018–2020) Full Professor with Tenure at USC (2016–present) Research Interests center on: Computational structural biology DNA shape prediction Protein-DNA recognition mechanisms Epigenetic effects on binding specificity RNA structure modeling High-throughput genomic analysis His work combines artificial intelligence , molecular simulations , and experimental validation to study transcription factors and chromatin structure. Recent publications highlight deep learning methods for DNA/RNA structure prediction and database development for molecular interactions. Scientific Awards include: Sloan Research Fellowship (2013–2017) Multiple RECOMB/ISCB Top-Paper Awards (2012–2017) American Chemical Society OpenEye Outstanding Junior Faculty Award (2015–2016) USC Mentoring Award (2015–2016) He has supervised 13 PhD graduates and published 92+ peer-reviewed papers . His lab develops tools like DNAproDB and TFBSshape for structural analysis of nucleic acid complexes.

Sajjan G. ShivaView profile

Professor

• Software Systems
• Cybersecurity
• Game Theory Applications in Security
• +3 more

Sajjan G. Shiva is a Professor at the Department of Computer Science , University of Memphis. He joined the University in 2002 as Director of the Computer Science Division and led its transition to a full department in 2005, serving as Founding Chair until 2015. Faculty positions: University of Alabama in Huntsville, Alabama A&M University Industry roles: Teledyne Brown Engineering, Intergraph Corporation, U.S. Army Space and Strategic Defense Command Education : PhD in Electrical Engineering, Auburn University (1975) Research Interests : Dr. Shiva specializes in game-theoretic cybersecurity , secure software engineering , cloud computing security , and network defense mechanisms . His work integrates artificial intelligence , computer architecture , and expert systems into security frameworks. Publications : Recent work focuses on crowdsourcing applications , SQL injection mitigation , and attack graph modeling . His research trends emphasize stakeholder-oriented security , runtime monitoring , and game-inspired defense architectures . Scientific Awards : Fellow of IEEE Life Member of ACM First Horizon Foundation Distinguished Professor Advising and Funding : Dr. Shiva has advised over 35 PhD and Master’s students, including Abdullah Abu Hussein (2017), Vivek Shandilya (2015), and Ramya Dharam (2014). His research is funded by NSF , NASA , U.S. Department of Defense , and ONR . Labs and Teams : He leads the Game Theory and Cyber Security Laboratory (GTC Lab), focusing on game-theoretic models and secure software frameworks . His collaborations include institutions like NASA, U.S. Army, and industry partners.

Sungho ShinView profile

Assistant Professor

• Mathematical optimization
• Control theory
• Machine learning
• +3 more

Sungho Shin is an Assistant Professor in the Department of Chemical Engineering at the Massachusetts Institute of Technology (MIT) . He leads the Shin Group, focusing on mathematical optimization, control theory, machine learning, and high-performance computing for applications in process systems engineering and energy systems. Education : PhD in Chemical Engineering (University of Wisconsin-Madison), B.S. in Mathematics and Chemical Engineering (Seoul National University) Postdoc : Argonne National Laboratory (Mathematics and Computer Science Division) His research addresses scalable algorithms for decision-making in complex systems, GPU computing for nonlinear optimization, data-driven control methods, and decarbonization of energy systems through modeling, optimization, and software development. His group has produced award-winning publications, including the 2022 Outstanding Paper Award from Mathematical Programming Computation. Key software contributions include MadNLP.jl (GPU-accelerated nonlinear programming solver) and ExaModels.jl (graph-structured optimization modeling). His team maintains local computing resources for GPU development, including NVIDIA GV100 and AMD Radeon VII systems. Scientific Awards : W. David Smith, Jr. Graduate Publication Award AIChE CAST Directors’ Student Presentation Award IFAC ADCHEM Young Author Award IFAC NMPC Young Author Award Korea Presidential Science Fellowship Kwanjeong Fellowship Grainger Wisconsin Distinguished Graduate Fellowship

David LeakeView profile

Professor

• Artificial Intelligence
• Case-Based Reasoning
• Cognitive Science
• +5 more

David Leake is a Professor of Computer Science at Indiana University's Luddy School of Informatics, Computing and Engineering, where he served as Executive Associate Dean from 2012-2021. He holds a Ph.D. in Computer Science from Yale University (1990) and is Editor in Chief Emeritus of AI Magazine after 17 years of service. His research focuses on artificial intelligence and cognitive science, including case-based reasoning, explanation generation, neuro-symbolic AI, intelligent information systems, and multistrategy learning. He has authored over 200 publications with 9,000+ citations. Recent publications demonstrate strong focus on integrating case-based reasoning with deep learning and large language models, particularly for explainable AI. Research spans hybrid AI systems, neural network implementations, and human-centered explanation methods. Scientific Awards: AAAI Distinguished Service Award (2014) Five-time best paper winner at International Conference on Case-Based Reasoning He advises graduate students and leads research funded by NSF, NASA, and ONR. Directs projects including SWALE (creative explanation) and the Stamping Advisor system. Maintains the IUCBRF Java framework and pedagogical resources for case-based reasoning.

Yiming XuView profile

Assistant Professor

• Mathematics of Data Science
• Random Sampling and Approximation
• Statistical Ranking
• +3 more

Yiming Xu is an Assistant Professor in the Department of Mathematics at the University of Kentucky. He holds a Ph.D. from the University of Utah, completed a postdoc at the University of Waterloo, and worked as a Quantitative Analyst at Wells Fargo Bank. His research focuses on the mathematics of data science, including random sampling, statistical ranking, optimization-based uncertainty quantification, and multifidelity methods. Xu emphasizes problem-driven innovation and collaborates across disciplines. Education: Ph.D. in Mathematics, University of Utah, 2022 Postdoctoral Fellowship, University of Waterloo Quantitative Analyst, Wells Fargo Bank Research interests span computational statistics, numerical analysis, and machine learning applications. His work bridges theoretical foundations and practical data-driven problems, often integrating optimization and probabilistic techniques. Recent contributions include advancements in gradient descent for network analysis, multifidelity distribution estimation, and archetypal analysis using optimal transport. Publications reflect a trajectory in stochastic methods, statistical learning, and computational mathematics, with applications in PDE solvers and uncertainty quantification. Awards: None explicitly listed. Advising and grants: Actively seeks motivated students for collaborative projects. Teaching includes courses on matrix theory, data science, and numerical methods.

Yuexiao DongView profile

Associate Professor

• Sufficient Dimension Reduction
• High-dimensional Data Analysis
• Machine Learning
• +3 more

Yuexiao Dong is an Associate Professor in the Department of Statistics, Operations, and Data Science at Temple University's Fox School of Business and Management. He holds a PhD from Pennsylvania State University (2009) and a Bachelor's in Mathematics from Tsinghua University. His research focuses on sufficient dimension reduction, high-dimensional data analysis, and machine learning. Dr. Dong has been funded by the NSF for his work on 'New Developments in Sufficient Dimension Reduction' and serves as a Gilliland Research Fellow. He is an Associate Editor for the Journal of Systems Science and Complexity since 2015. His research is published in top journals such as the Annals of Statistics and Biometrika . Key interests include developing methodologies for efficient data analysis, particularly in handling high-dimensional and non-Euclidean data. He teaches Quantitative Foundations for Data Science at the undergraduate level. Education: Bachelor’s in Mathematics, Tsinghua University PhD in Statistics, Pennsylvania State University Awards: Gilliland Research Fellow NSF Grant: New Developments in Sufficient Dimension Reduction Dr. Dong’s work bridges statistical theory and applications, emphasizing computational efficiency and real-world relevance. Recent projects include Bayesian model averaging, real-time dimension reduction, and expectile-based regression techniques.

Serdar BozdagView profile

Associate Professor

• Machine Learning
• Computational Biology
• Bioinformatics
• +5 more

Serdar Bozdag is an Associate Professor in the Department of Computer Science and Engineering at the University of North Texas, with a joint appointment in Mathematics. He serves as Director of the Center for Computational Life Sciences, focusing on interdisciplinary research at the intersection of computational methods and biological systems. His research expertise spans machine learning applications in bioinformatics, multi-omics data integration, and network biology. Key areas include developing algorithms for disease biomarker discovery, integrating genomic, transcriptomic, and clinical data through graph neural networks, and creating computational tools for precision medicine. Notable contributions include frameworks like CanMod2 for co-regulatory module inference and CTDPathSim for pathway-based similarity analysis. Bozdag’s work bridges computer science and life sciences, addressing challenges in Alzheimer’s disease diagnosis, cancer systems biology, and plant-microbe interactions. He has pioneered methods for analyzing dynamic biological networks and temporal data patterns, with applications in drug response modeling and electronic health record analysis. His lab develops open-source software tools widely used in computational biology research. Current projects emphasize AI-driven approaches for multi-modal healthcare data fusion, predictive modeling of disease progression, and scalable solutions for big biological data. His research has implications for improving diagnostic accuracy, personalized treatment strategies, and understanding complex biological systems.

Shengli FuView profile

Professor

• Unmanned Aerial Vehicles
• Airborne Computing
• Wireless Communications
• +2 more

Shengli Fu is a Professor and Chair of Electrical Engineering at the University of North Texas. His office is located at Discovery Park, B276 with contact available via phone (940-891-6942) and email. His research focuses on Unmanned Aerial Vehicles (UAVs) , Airborne Computing , Wireless Communications , and Networked Systems . Key innovations include developing platforms for UAV-based airborne computing, exploring millimeter wave communications for aerial networks, and creating educational tools for cyber-physical systems. Recent publications (2019-2024) demonstrate strong emphasis on: Advanced aerial communication systems using directional antennas and software-defined radio Networked airborne computing infrastructures and testbeds Distributed optimization algorithms for mobile networks UAV-enabled data collection and trajectory planning Coded computation methods for heterogeneous systems No scientific awards, students, or lab information were mentioned in the provided materials.

• Semantic Web
• Digital Libraries
• Metadata
• +7 more

Jason A. Clark is a Professor and Head of Research Optimization, Analytics, and Data Services (ROADS) at Montana State University (MSU) Library. He holds an MLS from the University of Wisconsin-Madison (2003), an MA from the University of Vermont (2002), and a BA from Marquette University (1996). His work focuses on semantic web development, metadata systems, and AI ethics in libraries. Clark has led initiatives such as the Open SESMO project and contributed to standards like RO-Crate. He is a recipient of the 2024 MUS Teaching Scholar and MSU Public Engagement Fellow awards. Education: M.L.S., University of Wisconsin-Madison, 2003 M.A., University of Vermont, 2002 B.A., Marquette University, 1996 Key Roles: Lead, Research Informatics (MSU Library, 2020–present) Head, Special Collections & Archival Informatics (2017–2020) Clark’s research explores algorithmic literacy, machine UX, and ethical AI in libraries. His recent work includes leveraging reinforcement learning for scholarship accessibility and developing frameworks for responsible AI in archives. He has authored over 50 publications and led grants funded by IMLS and the Council on Library and Information Resources (CLIR). Awards and Recognition: MUS Teaching Scholar (2024) MSU Public Engagement Research Fellow (2024) Leading Change Institute Fellow (2019) Library Journal Mover and Shaker (2015) Grants and Outreach: Clark oversees projects like the National Forum on Web Privacy and the RE:Search initiative. He chairs the DLF eResearch Network and contributes to open science initiatives such as the Barcelona Declaration on Open Research Information. Labs/Teams: Leads the ROADS team at MSU Library, focusing on data-driven solutions for research, analytics, and library services.

Zheng ZhangView profile

Professor

• Quantum Computing
• Compiler Design
• Parallel Computing
• +3 more

Zheng Zhang is a Professor in the Department of Computer Science at Rutgers University, School of Arts and Sciences. He holds academic expertise in compiler design, quantum computing, and parallel computing. Previously, he earned a Ph.D. in Computer Science from the College of William and Mary, an M.S. in Computational Operations Research from the same institution, and a B.S. in Electronic Engineering from Shanghai Jiaotong University. His research focuses on advancing compilation techniques for emerging architectures, including quantum computers, GPUs, and multi-core processors. Key projects include optimizing quantum circuit compilation for NISQ devices and enhancing qubit reuse through dynamic circuits. He has received NSF grants and contributed to the ISCA Distinguished Artifact Award (2024). Teaching: Currently instructs CS314 (Principles of Programming Languages). Research groups include Computer and Networked Systems and Programming Languages & Compilers. Recent activities include publications on VQE compilation and leadership in ASPLOS, PLDI, and MICRO conferences. Grants: NSF Grant, NSF XPS Grant Labs/Teams: Quantum Compilation Research Group Notable Achievement: Erdős Number 2

Minghui ChengView profile

Assistant Professor

• Digital Twin-enabled Smart Cities
• Life-cycle Management of Infrastructure
• Sustainability and Resilience
• +3 more

Minghui Cheng is an Assistant Professor at the University of Miami, jointly appointed in the School of Architecture and the College of Engineering's Department of Civil & Architectural Engineering, with an affiliation to the Climate Resilience Academy. His research focuses on digital twin technologies for smart cities, life-cycle management of infrastructure systems, and climate resilience strategies. Prior to this role, he was a postdoctoral associate at Cornell University's Systems Engineering department and earned his PhD in Structural Engineering from Lehigh University in 2021. Education: PhD in Structural Engineering, Lehigh University, 2021 BS in Civil Engineering, Hunan University, China, 2016 Visiting Student, Arizona State University, 2015 His work bridges engineering and data science, emphasizing probabilistic modeling, uncertainty quantification, and stakeholder decision-making frameworks. Recent projects include developing automated knowledge graphs (AutoGraCS) for bridge network management and Bayesian network-based digital twin frameworks for infrastructure systems. His research addresses urban resilience to climate change and integrates multi-source data for life-cycle risk assessment. Advising & Grants: While no advisees are listed, his projects likely involve graduate students in systems engineering and civil engineering. He has contributed to grants focused on infrastructure resilience and digital twin applications. Labs & Teams: Affiliated with the Climate Resilience Academy, collaborating on interdisciplinary projects to enhance urban infrastructure sustainability and disaster preparedness.