Find Academic Supervisors

Haibing LuView profile

Professor

• AI Fairness and Governance
• Data Security & Privacy
• Big Data Analytics
• +4 more

Haibing Lu is a Professor and Department Co-Chair of Information Systems & Analytics at Santa Clara University's Leavey School of Business. He holds a Ph.D. in Information Technology from Rutgers University (2011), and B.S./M.S. in Mathematics from Xi'an Jiaotong University (2002/2005). His research focuses on AI fairness, data privacy, cybersecurity, and big data analytics, addressing real-world challenges in healthcare, transportation, and cloud computing. Education: Ph.D. in Information Technology, Rutgers University, 2011 M.S. in Mathematics, Xi'an Jiaotong University, 2005 B.S. in Mathematics, Xi'an Jiaotong University, 2002 Research interests include: AI governance and ethical frameworks Privacy-preserving technologies (e.g., differential privacy) Cybersecurity for IoT and smart grids Applications of machine learning in healthcare and transportation His work has been featured in prestigious journals like IEEE Transactions on Dependable and Secure Computing, ACM Transactions on MIS, and M&SOM, alongside media outlets such as The New York Times and WIRED. Key publications include studies on AI fairness in LLMs, privacy in cloud computing, and secure authentication protocols. Awards include: AACSB Innovations That Inspire Award (2021) M&SOM Best Paper Award Repeated SCU Leavey School Excellence Awards for Teaching/Research/Service Grants and funding sources include NSF, IBM, PARC, and industry partnerships like AKTANA and Ultimate Software. He advises on interdisciplinary projects involving data science, cybersecurity, and sustainable energy systems. Labs/Initiatives: Leads research in Information Systems & Analytics at SCU, with collaborations on metaverse-driven data management and AI governance frameworks.

Maria C. A. LeiteView profile

Associate Professor

• Mathematical Modeling
• Dynamical Systems
• Nonlinear Networks
• +6 more

Maria C. A. Leite is an Associate Professor in the Department of Mathematics & Statistics at the University of South Florida St. Petersburg. She holds a Ph.D. in Mathematics from the University of Houston (2005), an M.S. in Mathematics from the University of Porto (2001), and additional degrees in Quality Compliance and Chemical Engineering. Her research focuses on mathematical modeling, dynamical systems, nonlinear networks, and optimal control, with applications to epidemiology, ecology, neurosciences, environmental sustainability, and molecular dynamics. Her work emphasizes understanding the relationship between network structure and dynamics, particularly in complex biosystems. Recent projects include computational molecular dynamics collaborations with the Basque Center for Applied Mathematics, ecological modeling of species viability under environmental stress, and Boolean framework studies on neural control of breathing. She has also explored interdisciplinary topics such as food sustainability through AI-driven approaches and daylight's physiological impacts. Leite has secured multiple grants and published extensively, with over 20 peer-reviewed articles since 2006. Her work spans theoretical advances in nonlinear networks to applied projects like optimizing tree harvesting in forests and analyzing the 2017 Nigerian meningitis outbreak. Her research has been presented at international conferences including the ISAAC Congress and IEEE Data Engineering Workshops. She actively mentors students and collaborates with researchers globally to bridge mathematical theory with real-world environmental, health, and sustainability challenges.

Sylvain SenéView profile

Professor

• Computer Science
• Theoretical Computer Science
• Discrete Mathematics
• +5 more

Sylvain Sené is a Professor of Computer Science at Aix-Marseille University (AMU), affiliated with the Department of Computer Science and Interactions (DII) and the Computer Science and Systems Laboratory (LIS). He leads the ANR-funded project FANs (Foundations of Automata Networks) and focuses on discrete mathematics, theoretical computer science, and computational properties of automaton networks. His research bridges abstract computational models with applications in biology, particularly gene regulatory networks and cellular reprogramming. Research Interests: Automaton networks, Boolean networks, and cellular automata Computational complexity and dynamical systems Discrete mathematics and theoretical computer science Applications in systems biology and genetic networks Key Projects: Principal investigator of the ANR project FANs (2019–2023), focusing on advancing automata network theory Collaborations with CNRS, Chilean universities, and international institutions Advising & Grants: Directed PhD students including Pacôme Perrotin and Martín Ríos Wilson Secured funding through ANR and other national/international grants Labs & Teams: LIS (Computer Science and Systems Laboratory), collaborating with interdisciplinary teams in biology and mathematics.

Jinwook LeeView profile

Assistant Professor

• Optimization
• Risk Modeling
• Stochastic Optimization
• +17 more

Jinwook Lee is an Assistant Professor in the Decision Sciences and MIS department at LeBow College of Business, Drexel University. His research focuses on optimization, risk modeling, and artificial intelligence systems, particularly applied to blockchain, supply chain, and financial decision-making. He is actively involved in funded research, including an NSF SBIR grant on blockchain-based supply chain systems. Research Interests: His expertise spans Stochastic Optimization , Mathematical Programming , Blockchain , Operations Research , and Data Privacy . He develops models for uncertainty quantification, risk assessment, and fairness in AI systems using advanced mathematical tools such as pseudo-Boolean functions and multivariate risk measures (e.g., MVaR, MCVaR). His work bridges theoretical rigor with practical applications in finance, supply chains, and forecasting. The recent articles reflect a strong trend toward integrating blockchain and AI with privacy-preserving forecasting , equity metrics , and secure supply chains . His publications span top journals in operations research and management science, indicating a consistent and impactful research trajectory in quantitative decision sciences. Scientific Awards: NSF SBIR Grant (Award #2112078), Co-PI, Mar 2022 Advising and Grants: While direct student names are not listed, his collaborative work with Matthew J. Schneider and András Prékopa suggests active mentorship and research collaboration. He leads a funded project on blockchain-based tracking and distribution with $255,786 from the National Science Foundation, indicating strong grant-writing capability and research leadership. Labs and Research Teams: Though no formal lab name is provided, his research activities suggest leadership in a group focused on blockchain, AI, and risk modeling, likely involving interdisciplinary collaboration across business analytics, computer science, and operations research.

• Formal Methods
• Concurrency Theory
• Distributed Systems
• +2 more

Wim Hesselink serves as a Professor within the Fundamental Computing group at the University of Groningen's Faculty of Science and Engineering. As an external employee ( Externe medewerker ), he maintains an active research profile with recent publications through 2025 and an ORCID identifier (0000-0002-1413-4320). His research centers on formal verification of concurrent systems, specializing in mutual exclusion protocols, linearizability of data structures, and temporal logic applications. Key contributions include theorem proving for shared memory systems, first-come-first-served scheduling principles, and wait-free algorithm design. His work directly addresses challenges in nondeterminism and atomicity within distributed computing environments. Analysis of his 2021-2025 publications reveals consistent focus on concurrency control mechanisms using formal methods. Major themes include lock algorithm verification (MCSH), temporal logic frameworks (UNITY), and linearizability proofs for arbitrary data objects. His methodology frequently employs proof assistants to establish correctness in shared-memory systems with nondeterministic behavior. Professor Hesselink has supervised 4 students and produced 125 research outputs including 94 articles, 13 book chapters, and 11 conference contributions. His international collaborations span multiple countries as visualized in the research network map, though specific grant details remain unreported in the source material.

Clemens DubslaffView profile

Assistant Professor

• Formal Methods
• Probabilistic Model Checking
• Symbolic Methods
• +4 more

Clemens Dubslaff is an Assistant Professor in the Formal System Analysis group at TU Eindhoven (The Netherlands). His research focuses on improving the reliability and explainability of computing systems through formal methods, particularly probabilistic model checking and symbolic techniques. He holds a Ph.D. from TU Dresden (Germany) and has affiliations with TU Dresden's Cluster of Excellence CeTI and Collaborative Research Center CPEC. His work addresses challenges in analyzing complex configurable systems and enhancing system transparency through explainable AI approaches. Education: B.Sc. in Mathematics and Computer Science (TU Dresden) M.Sc. in Computational Logic (NOVA University Lisbon) Ph.D. in Formal Methods (TU Dresden) Research Interests: Formal methods and model checking Symbolic analysis techniques Configurable and reconfigurable systems Explainability in AI and verification Probabilistic systems analysis Recent Achievements: NWO VENI Grant (2023) Launched OxiDD decision diagram framework (2024) Key contributions to feature-based software analysis Labs/Teams: Formal System Analysis group at TU/e, collaborating with Dresden's CeTI and CPEC initiatives.

Wilfred G. van der WielView profile

Professor

• Nanoelectronics
• Quantum Devices
• Brain-Inspired Computing
• +12 more

Wilfred G. van der Wiel is a Full Professor in the Nano Electronics department at the MESA+ Institute for Nanotechnology, University of Twente. His work bridges nanotechnology, quantum physics, and unconventional computing, focusing on novel hardware for machine learning and brain-inspired systems. Full Professor, Nano Electronics, MESA+ Institute, University of Twente Research Interests: His research lies at the intersection of nanoelectronics and cognitive computing. He investigates disordered dopant-atom networks in silicon , gold nanoparticle assemblies , and in-materio computing to develop energy-efficient, adaptive hardware. His work explores how physical systems can inherently perform computation, bypassing traditional von Neumann architectures. Publication Trends: Recent articles (2023–2025) highlight a shift toward practical implementations of neuromorphic computing, including speech recognition in physical systems and systematic reviews of brain-inspired architectures. Earlier works focus on fundamental transport mechanisms in nanostructures. Collectively, his publications reveal a trajectory from basic physics of nanodevices to applied neuromorphic engineering and software frameworks like Brains-PY. Scientific Contributions: While specific awards are not listed, his high-impact publications in journals like Nature and Physical Review Applied , along with continuous funding and supervision of research, indicate significant recognition in the field. Advising and Grants: He has supervised at least 29 research projects, including PhD and postdoctoral work, as indicated by 'Supervised Work (29)'. He leads or participates in externally funded projects related to nanotechnology and neuromorphic computing, evidenced by collaborative publications and datasets. Labs and Teams: He is a key member of the MESA+ Institute for Nanotechnology at the University of Twente, working within the Nano Electronics group. He collaborates extensively with researchers in physics, materials science, and computer science, particularly on projects involving unconventional computing and nanofabrication.

• Cryptography
• Information Security
• Symmetric Encryption
• +11 more

Leonie Simpson is an Associate Professor in the School of Computer Science at Queensland University of Technology (QUT), Faculty of Science. Her research and teaching are centered on cryptography and information security, with a strong focus on symmetric encryption, key management, and secure communications. Her research interests include: Symmetric encryption algorithms for real-time communications Authenticated encryption and security-efficiency trade-offs Boolean functions in cipher design for cyber attack resistance Information security management and threat information sharing IoT and vehicular communication security Leonie has published over 80 articles and three book chapters on symmetric ciphers. Her recent publications emphasize lightweight cryptography for connected vehicles, anomaly detection in key management via metadata, and secure frameworks for post-quantum migration. These works reflect a consistent trend toward practical, efficient, and resilient cryptographic solutions for emerging technologies. She has supervised 12 PhD and 1 Master’s student to completion and currently mentors 5 research students. Her grants include leadership in the Cyber Security CRC and an ARC project on secure stream ciphers. She also serves on the editorial board of the journal Cryptography and has chaired major conferences like ACISP 2013 and 2023. Notable professional roles include: Theme Leader, Resilient Systems, Cyber Security CRC Editorial Board Member, Cryptography journal Program Co-chair, ACISP 2013 and 2023 Referee for top journals and conferences in cryptography She has led industry security analyses and served as an expert witness in a Federal Court case on cryptographic telecommunications. Her teaching includes unit coordination for Cyber Security and previous development of multiple information security and cryptography units at QUT.

Abdelhamid TayebiView profile

Professor

• Control Systems Theory
• Robotics
• Autonomous Vehicles
• +7 more

Abdelhamid Tayebi is a Distinguished Professor and Research Chair in the Department of Electrical and Computer Engineering at Lakehead University. He also holds a courtesy professorship at Western University. His research focuses on control systems, autonomous navigation, and robotics, with applications to UAVs and multi-agent systems. Key roles include Graduate Coordinator and founder/director of the Robotics & Automatic Control Laboratory. Education: B.Sc. in Electrical Engineering, Ecole Nationale Polytechnique (1992) M.Sc. in Robotics, Université Pierre & Marie Curie (1993) Ph.D. in Robotics and Automatic Control, Université de Picardie Jules Verne (1997) Research Interests: Control systems theory, iterative learning control, autonomous vehicles, UAV navigation, hybrid feedback control, and nonlinear state estimation. His work emphasizes provable stability guarantees and real-world applications. Recent Articles Trends: Focus on hybrid control for obstacle avoidance, distributed estimation in multi-agent systems, and nonlinear observers for inertial navigation. Publications span IEEE Transactions on Automatic Control , Automatica , and robotics conferences like CDC and ACC. Awards: 2024 Distinguished Instructor Award (Lakehead University's top teaching honor) 2023 IEEE Fellow and EIC Fellow NSERC Discovery Accelerator Supplements (2013) Top 2% Highly Cited Scientist (Stanford, 2019–) Students & Labs: Advised over 20 PhD/MSc students and postdocs. Current research group includes postdocs Mayur Sawant (autonomous robotics) and Ishak Cheniouni (navigation algorithms). Notable former students include Miaomiao Wang (geometric observers) and Mouaad Boughellaba (multi-agent systems). Grants & Editorial Work: Holder of NSERC grants and Lakehead Research Chairs (2012–2027). Associate Editor for Automatica , IEEE Transactions on Control Systems Technology , and others. Active in reviewing for top journals/conferences.

Ian DurhamView profile

Professor

• Quantum Physics
• Relativity
• Thermodynamics
• +3 more

Ian Durham is a Professor of Physics at Saint Anselm College, specializing in foundational questions in quantum mechanics, relativity, and the philosophy of science. He holds a Ph.D. in Mathematics from the University of St. Andrews, an MSc in Applied Physics from Johns Hopkins University, and a BSc in Mechanical Engineering from the University of Buffalo. His research explores quantum information theory, relativistic quantum systems, and the interplay between thermodynamics and quantum mechanics. Dr. Durham is an active member of professional societies including the American Physical Society (APS), where he served as Treasurer of the Topical Group on Quantum Information (GQI) from 2012-2014, and the Royal Astronomical Society (RAS). He co-founded the APS GQI's publication The Quantum Times . His recent work addresses topics like quantum superposition in consciousness, observer-independent frameworks in relativity, and the philosophical implications of quantum theory. His awards include election to the Foundational Questions Institute (FQXi) and recognition for editorial contributions to quantum research. Durham's publications span theoretical physics, philosophy of science, and educational strategies for teaching quantum concepts. Notable themes include quantum contextuality, entanglement's role in thermodynamics, and historical interpretations of physics.

Michael VerdicchioView profile

Associate Professor

• Computer Science Education
• Software Engineering
• Algorithms
• +3 more

Dr. Michael Verdicchio is an Associate Professor of Computer Science at The Citadel, affiliated with the Swain Family School of Science and Mathematics. He joined the institution in 2011 and holds a PhD and BSE in Computer Science from Arizona State University. His research focuses on computer science education, software engineering, algorithms, and previously on computational biology and bioinformatics. He also serves as the Graduate Program Director for Computer Science and chairs the graduate curriculum committee. His professional affiliations include Senior Member of the Association for Computing Machinery (ACM) and Program Evaluator for ABET's Computing Accreditation Commission. Dr. Verdicchio teaches a wide range of courses, from introductory computer science to advanced graduate topics like software testing and deployment. His educational contributions include adapting curricula for emerging technologies like generative AI and creating specialized courses such as DevOps. He has also led initiatives to ensure continuity in course delivery during disruptions like hurricanes and pandemics. His research spans pedagogical innovations, including automated assessment methods and interdisciplinary undergraduate research experiences. He has published extensively on topics ranging from network routing algorithms to gene regulatory networks in cancer research. His work bridges theoretical computer science with practical applications in education and bioinformatics. Dr. Verdicchio’s service roles include leadership in accreditation processes, curriculum design, and program assessment. He collaborates with external institutions like the College of Charleston and actively participates in national conferences, contributing to the advancement of computing education standards.

Jaijeet RoychowdhuryView profile

Professor

• Machine Learning
• Cyber-Physical Systems
• Novel Computational Paradigms
• +4 more

Jaijeet Roychowdhury is a Professor of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. He specializes in machine learning, novel computational paradigms, and the analysis/simulation of cyber-physical, electronic, and biological systems. His research group pioneered self-sustaining oscillator-based Ising machines and contributed to oscillator phase macromodeling, nonlinear system reduction, and open-source prototyping platforms like MAPP. Education: B.Tech., Electrical Engineering, Indian Institute of Technology (IIT) Kanpur, 1987 M.S., Electrical Engineering & Computer Science, UC Berkeley, 1989 Ph.D., Electrical Engineering & Computer Science, UC Berkeley, 1993 Research Interests: Machine learning integration with physical systems Innovative computational hardware (e.g., oscillator-based Ising machines) Oscillator networks for unconventional computing Nonlinear dynamical systems analysis Analog circuit simulation and verification Awards & Recognition: 2019 Bell Labs Prize (with Tianshi Wang) 2009 IEEE Fellow 2019-2023 Bakar Foundation Awards Bell Labs' Extraordinary Achievement Award (1996) Advising & Industry: Notable advisee: Tianshi Wang (Bell Labs Prize co-winner) Cofounder of Berkeley Design Automation (acquired by Mentor Graphics) Leadership roles at AT&T Bell Labs, Bell Labs, and CeLight Inc. Lab & Tools: Leads the Roychowdhury Research Group, developing MAPP (Model and Algorithm Prototyping Platform) and PHLOGON phase-based logic frameworks.

Bin YuView profile

Professor

• Machine Learning
• Statistics
• Veridical Data Science
• +5 more

Bin Yu is a Chancellor’s Professor at the University of California, Berkeley, affiliated with the Departments of Statistics and Electrical Engineering & Computer Sciences. Her research focuses on statistics, machine learning theory, and methodologies for high-dimensional data analysis, with interdisciplinary applications in genomics, neuroscience, and remote sensing. PhD in Statistics, UC Berkeley (1990) MA in Statistics, UC Berkeley (1987) BS in Mathematics, Peking University (1984) Her work includes pioneering the Predictability, Computability, and Stability (PCS) framework for veridical data science, emphasizing responsible AI development and reproducibility. Recent publications address transformer interpretability, domain adaptation, and medical AI ethics. Scientific awards include: U.S. National Academy of Sciences Member (2014) American Academy of Arts and Sciences Member (2013) Guggenheim Fellow (2006) IMS Rietz Lecturer (2016) COPSS Elizabeth L. Scott Award (2018) Research support includes collaboration with Eva Leach (eva.leach@berkeley.edu) and leadership roles at the Berkeley Artificial Intelligence Research Lab (BAIR) and the Microsoft Lab on Statistics and Information Technology in China.

Armeen TaebView profile

Assistant Professor

• Selective Inference
• Multiple Testing
• Graphical Models
• +7 more

Armeen Taeb is an Assistant Professor in the Department of Statistics at the University of Washington . Previously, he was a postdoctoral fellow at ETH Zürich under the ETH Foundations of Data Science, mentored by Peter Bühlmann. He earned his PhD in Electrical Engineering at Caltech under Venkat Chandrasekaran's supervision. Research Interests : His work bridges optimization and statistics , focusing on Graphical and latent-variable modeling Provably optimal causal model learning False positive error control in non-traditional settings Domain adaptation Applications in physical sciences Article Trends : His publications span causal inference (2022-2025), graphical models (2017-2025), convex optimization (2018-2025), and statistical robustness (2020). Recent work (2025) addresses extremal graphical modeling and selective inference challenges. Scientific Awards : ETH Zürich Foundations of Data Science Postdoctoral Fellowship (2019-2021) Caltech Resnick Institute Fellowship (2016-2018) W. P. Carey & Co. Prize for Applied Mathematics (2020) Caltech Graduate Fellowship (2013-2014) Grants : National Science Foundation DMS-2413074 (PI), University of Washington Royalty Research Fund (PI). Service : President of the Institute of Mathematical Statistics New Researcher Group; co-organized IMS New Researchers Conferences (2024-2025).

• Coding Theory
• Discrete Geometry
• Finite Geometry
• +5 more

Sascha Kurz is an Associate Professor at the Mathematical Institute of the Faculty of Mathematics, Physics and Computer Science at the University of Bayreuth, Germany. His research focuses on discrete structures, coding theory, voting systems, and combinatorial optimization. Professor Kurz's primary research interests span several interconnected fields in discrete mathematics and its applications: Coding Theory : With a focus on divisible codes, subspace codes, and constant dimension codes, his work explores the theoretical foundations and practical applications of error-correcting codes. Discrete Geometry : His research in finite geometry, particularly on arcs in projective spaces and vector space partitions, contributes to both theoretical understanding and coding applications. Game Theory and Voting Systems : He investigates power indices, weighted voting games, and their applications to political science and decision-making processes. Combinatorial Optimization : His work includes network coding, subspace packings, and algorithmic approaches to classification problems in coding theory. Analysis of Professor Kurz's recent publications reveals a strong focus on the intersection of coding theory and discrete geometry. His work consistently addresses fundamental questions about code parameters, classifications, and constructions, with particular attention to divisible codes and their properties. There's a clear progression from theoretical foundations to computational methods, as evidenced by his increasing use of computer-assisted classification techniques. His research demonstrates significant contributions to understanding the structure of linear codes, subspace codes, and their geometric interpretations. Professor Kurz has made notable contributions to both theoretical and applied aspects of his fields, collaborating with researchers across Europe and beyond. His work bridges pure mathematics with practical applications in information theory and network communication.