Find Academic Supervisors

Monowar BhuyanView profile

Associate Professor

• Machine Learning
• Anomaly Detection
• Systems and AI Security
• +6 more

Monowar Bhuyan is an Associate Professor in the Department of Computing Science at Umeå University, Sweden, leading the Cyber Analytics and Learning Group within ADSLab. He holds a Ph.D. in Computer Science from Tezpur University and has held academic positions at Assam Kaziranga University and Umeå University. His research focuses on machine learning, anomaly detection, edge AI, and distributed systems security. He has secured over 35 MSEK in grants from WASP, STINT, and EU Horizon programs. Education Ph.D. in Computer Science and Engineering, Tezpur University (2014) M.Tech. in Information Technology, Tezpur University (2009) B.E. in Computer Science and Engineering, IETE (2007) Research Interests Distributed/Federated/Responsible Machine Learning Cybersecurity and Anomaly Detection in Edge Clouds AI for DDoS Defense and Cyber Resilience Edge AI and Serverless Computing Recent Contributions His recent work addresses secure federated learning, DDoS attack detection in cloud-edge systems, and responsible AI. Key publications include novel frameworks for VSI-DDoS detection and federated learning optimizations. Awards & Grants Best Paper Awards at ICONIP 2023 and ACM ICACCI 2012 WASP NEST Grant (AIR2 Project, 5 MSEK) EU Horizon Europe Grant (SovereignEdge.Cognit, 8.27 MSEK) Lab & Collaborations He leads the Cyber Analytics and Learning Group (ADSlab), collaborating with institutions like KTH, Linköping University, and Nara Institute of Science and Technology (NAIST). The lab focuses on AI-driven security solutions for distributed systems.

Mihyun KangView profile

Professor

• Combinatorics
• Discrete Probability
• Algorithms
• +3 more

Mihyun Kang is a Professor in the Institute of Discrete Mathematics at Graz University of Technology (TU Graz), leading the Combinatorics Group. She holds significant academic positions and has been recognized with a Heisenberg Fellowship (German Research Foundation) and a Friedrich Wilhelm Bessel Research Award (Alexander von Humboldt Foundation). Her research focuses on combinatorics, discrete probability, and algorithms, with a specialization in random graph theory. She contributes to editorial boards, including Random Structures & Algorithms . Her research interests emphasize high-dimensional graphs, percolation processes, and structural properties of random graphs. She explores topics such as phase transitions, graph enumeration, and algorithmic applications in combinatorial problems. Her work bridges theoretical foundations with practical applications in algorithm design and probabilistic modeling. Dr. Kang’s scientific achievements include pioneering studies on hypergraphs, bootstrap percolation, and the evolution of random graph processes. Her recent publications highlight advancements in understanding graph components, connectivity thresholds, and universality phenomena in random structures. She collaborates widely, contributing to conferences and international initiatives like the SFB “Discrete random structures: enumeration and scaling limits.” Her professional activities include advising doctoral students and supervising research projects at TU Graz. She leads the Combinatorics Group, which hosts the Graz Combinatorics Seminar and actively engages in academic outreach. Her contributions extend to textbook authorship, including Diskrete Mathematik für die Informatik , and she maintains an active presence in discrete mathematics education and research.

YooJung ChoiView profile

Assistant Professor

• Probabilistic Machine Learning
• Tractable Probabilistic Modeling
• Algorithmic Fairness
• +6 more

YooJung Choi is an Assistant Professor at the School of Computing and Augmented Intelligence, Arizona State University. Her research focuses on probabilistic machine learning, trustworthy AI, and tractable probabilistic modeling. She holds a PhD in Computer Science from UCLA and has been recognized with awards like the Cisco Research Award and Simons-Berkeley Research Fellowship. Education: PhD in Computer Science (University of California, Los Angeles). Research interests include probabilistic reasoning, fairness, robustness, and interpretability. Her work bridges theoretical foundations and practical applications, with contributions to probabilistic circuits and optimal transport. Notable achievements include co-organizing TPM 2024 and presenting at venues like NeurIPS and AAAI. She actively promotes fairness in AI through frameworks that address label bias and discrimination patterns. Awards: Cisco Research Award, Simons-Berkeley Fellowship, AAAI 2023 New Faculty Highlights. Teaching: Courses on artificial intelligence, machine learning, and research supervision. Service: Tutorial leadership on probabilistic circuits, workshop organization, and academic talks globally.

Stefano NoventaView profile

Research Fellow

• Mathematical Psychology
• Psychometrics
• Psychophysics
• +7 more

Stefano Noventa is a Research Fellow at the Methods Center, Department of Social Sciences, Faculty of Economics and Social Sciences, University of Tübingen. He has held multiple postdoctoral positions at the University of Tübingen and previously at the University of Verona and the University of Padova. Education: Ph.D. in Cognitive Psychology, University of Padova (2011) M.Sc. in Physics, University of Padova (2006) Studies in Physics, University of Padova (1999–2006) International Visiting Graduate Student, University of Toronto (2009, 2010) Dr. Noventa's research lies at the intersection of mathematical psychology, psychometrics, and psychophysics, with a focus on developing and unifying quantitative models of human cognition and assessment. His work integrates Item Response Theory (IRT) and Knowledge Space Theory (KST) to create more robust frameworks for educational and psychological measurement. He investigates latent variable models, probabilistic knowledge structures, and the identifiability of complex psychometric models, often applying these to domains such as education, organizational psychology, and entrepreneurship. His recent publications (2020–2024) demonstrate a strong trend toward theoretical integration, particularly in bridging cognitive diagnosis models with traditional psychometric frameworks. The articles emphasize mathematical rigor, model generalization, and empirical validation, with applications in both cognitive science and applied psychology. Topics include the unification of assessment models, parameter estimation under local dependence, and the modeling of intuitive physical reasoning. Scientific Awards: No awards or honors listed in the provided text. Dr. Noventa has not been explicitly mentioned as an advisor to students, but he has served as a corresponding author and collaborator on multiple research projects, indicating a leadership role in research teams. He has been involved in a DFG-funded project (GLI NON-NORM) since 2019, suggesting active grant participation. His work is highly collaborative, involving researchers from Germany, Italy, Austria, and Canada. Labs and Research Groups: Methods Center, University of Tübingen Hector Institute of Education Science and Psychology, University of Tübingen Center of Assessment, University of Verona Department of General Psychology, University of Padova

• Distributed control
• Autonomy and Control
• Aerospace Systems
• +6 more

Dengfeng Sun is a Professor and Associate Head of the Gambaro Graduate Program in the School of Aeronautics and Astronautics at Purdue University. His research focuses on distributed control systems, autonomy, resilient networks, and air traffic management. Sun holds a B.Eng. from Tsinghua University, an M.S. from The Ohio State University, and a Ph.D. from UC Berkeley. His work spans advanced air mobility, UAV trajectory planning, and stochastic optimization for large-scale systems. Key contributions include resilient UAV traffic control, distributed state estimation algorithms, and fault detection methods for navigation systems. Sun's research has been published in top journals like IEEE Transactions on Intelligent Transportation Systems and Transportation Research Part E. Education: B.Eng., Tsinghua University (2000) M.S., Ohio State University (2002) Ph.D., UC Berkeley (2008) He advises on cutting-edge projects integrating robotics, autonomous systems, and cloud-based traffic modeling. His lab develops solutions for urban air mobility, emergency medical UAV networks, and next-generation air traffic control systems. Notable collaborations include work with NASA and industry partners on continuous descent approach procedures and metroplex routing paradigms. Sun's work bridges theoretical control systems with practical applications in aviation and infrastructure optimization.

• Structural Engineering
• Resilient Infrastructure Design
• Fire Safety Engineering
• +6 more

Maria Garlock is the Daniel Tsui Professor in Engineering at Princeton University, serving as Co-Director of the Program in Architecture and Engineering and Head of Forbes College. Her roles also include membership in the Executive Committee of the Council on Science and Technology, Associated Faculty in the School of Architecture, and Associated Faculty in the Program in Latin American Studies. Garlock holds a PhD in Structural Engineering (Lehigh University, 2002), an MS in Civil Engineering (Cornell University, 1993), and a BS in Civil and Environmental Engineering (Lehigh University, 1991). Her research focuses on resilient structural design for extreme hazards like fires, earthquakes, and storm surges. She explores both isolated and cascading multi-hazard scenarios while also analyzing historical structural designs (e.g., Félix Candela’s thin-shell concrete umbrellas) and improving STEM education for non-technical majors through innovative teaching methods, including MOOCs and scale model exhibitions. Recent work emphasizes coastal defense systems using hyperbolic-paraboloid forms and steel-concrete girder performance under shear stress. Garlock has received notable honors including the ASCE SEI Fellowship (2016 T.R. Higgins Lectureship), President’s Award for Distinguished Teaching (2012), and the Emerson Electric Co. Faculty Advancement Award (2006). In education and grants, she teaches courses like Structures and the Urban Environment and Advanced Design of Steel/Concrete Structures , and has secured government funding for STEM literacy initiatives. Her research collaborations include the BRITE Pivot project and studies on Cuba’s historic National School of Ballet domes. She also leads efforts in deploying kinetic umbrellas as flood barriers and advancing probabilistic models for fire fragility in multi-hazard contexts. Garlock’s work bridges engineering and art, exemplified by her preservation studies of Candela’s architectural masterpieces and pedagogical innovations that emphasize creativity in structural design.

Rebecca NugentView profile

Professor

• Clustering Methodology
• Record Linkage
• Educational Data Mining
• +5 more

Rebecca Nugent is the Stephen E. and Joyce Fienberg Professor of Statistics & Data Science and Department Head at Carnegie Mellon University. She holds a PhD in Statistics from the University of Washington (2006), an MS in Statistics from Stanford (2006), and a BA in Mathematics, Statistics, and Spanish from Rice University (2002). Her research spans clustering methodology , record linkage , educational data mining , public health , and semantic organization , with a focus on high-dimensional data and adaptive learning environments. She leads the Integrated Statistics Learning Environment (ISLE) and Corporate Capstone programs, emphasizing low-barrier data platforms for education and industry collaboration. Academic Roles : Department Head, Carnegie Mellon; Affiliated Faculty, Block Center for Technology and Society Research Grants : NSF (2017-2019), NIH (2018), Carnegie Mellon ProSEED/Simon Initiative (2020, 2018), Berkman Fund (2014) Her 15 most recent publications focus on data science pedagogy, clustering algorithms, record linkage applications in historical and medical data, educational data mining, and semantic organization studies. Awards include the ASA Waller Education Award (2015) and the William H. and Frances S. Ryan Award (2015) . She mentors a diverse group of PhD, Master's, and undergraduate students, with alumni pursuing careers in academia, industry, and sports analytics.

• Swarm Robotics
• Artificial Intelligence
• Collective Decision Making
• +5 more

Nicolas Cambier is a Visiting Professor at Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Artificial Intelligence department and the Network Institute. His research focuses on swarm robotics, collective behavior, and evolutionary systems. He explores topics like emergent communication, modular robotics, and prosociality in robotic swarms. Key areas include task-driven language evolution, adaptive decision-making, and environmental interaction in constrained environments. His work bridges theoretical models with practical implementations, emphasizing self-organization and cultural evolution in synthetic systems. Recent contributions address challenges in heterogeneous swarms, skill acquisition in modular robots, and decision-making without prior knowledge. He collaborates widely, with publications in IEEE Robotics and Automation Letters, Nature Communications, and top conferences like GECCO and Distributed Autonomous Robotic Systems. Research interests span robotics, artificial intelligence, and evolutionary computation, with applications to both theoretical frameworks and real-world robotic systems. His studies often involve agent-based simulations and embodied evolution approaches to understand complex collective phenomena.

• Uncertainty Quantification
• Civil Engineering Systems
• Resilience Engineering
• +5 more

Rui Teixeira is an Assistant Professor in the School of Civil Engineering at University College Dublin (UCD). He leads UCD's Centre for Critical Infrastructure Research (CCIR) and focuses on Uncertainty Quantification, Safety, and Risk in civil engineering systems, with applications to infrastructure resilience. His research emphasizes reliability analysis, multi-fidelity modeling, and AI-driven risk assessment. Education: MSc in Civil Engineering, University of Porto, Portugal PhD in Civil Engineering, Trinity College Dublin Professional Certificate in University Teaching and Learning, UCD Research Interests: Development of novel reliability analysis techniques Resilience of infrastructure systems Artificial intelligence applications for risk assessment Probabilistic system evaluation and safety standards Grants & Projects: Smart Enforcement of Transport Operations (SETO), Horizon Europe (2023–2026) Optimality-Tracking Civil Engineering Systems, Enterprise Ireland (2023–2025) Floating Offshore Wind Dynamic Cables (FlOWDyn), Sustainable Energy Authority of Ireland (2024–2027) Teaching: Coordinates courses such as 'Civil Engineering Systems' and 'Design of Structures 1'. Labs/Teams: Director of the Centre for Critical Infrastructure Research (CCIR), focusing on interdisciplinary approaches to infrastructure resilience.

• Decision Making
• Risk Literacy
• Cognitive Psychology
• +5 more

Dr. Jinan Allan is an Assistant Professor at Clemson University's College of Behavioral, Social and Health Sciences, Department of Psychology. She holds a PhD in Cognitive Psychology from the University of Oklahoma (2021), an MS from the same university (2018), and a BS in Mathematics-Statistics from Michigan Technological University (2015). She completed a postdoctoral fellowship at the Max Planck Institute for Human Development. Her research focuses on cognitive abilities, statistical numeracy, and their impact on decision-making in contexts like medical scenarios, cybersecurity, and extreme weather risks. She is affiliated with the Media Forensics Hub and teaches courses on judgment and decision-making. Education: PhD Cognitive Psychology, University of Oklahoma, 2021 MS Cognitive Psychology, University of Oklahoma, 2018 BS Mathematics-Statistics, Michigan Technological University, 2015 Research emphasizes behavioral science and psychometric methods to develop decision-making metrics. Key themes include risk communication for tornado/hurricane warnings, climate change judgments, and cybersecurity decision-making. Her work bridges cognitive psychology with real-world applications in disaster preparedness and public health. Recent publications analyze extreme weather response patterns, numeracy's role in science comprehension, and probabilistic risk communication strategies. She is currently reviewing graduate student applications for 2025-2026. Affiliations include the Media Forensics Hub at Clemson University, indicating interdisciplinary collaboration in digital security and information integrity.

Justin ThalerView profile

Associate Professor

• Algorithms
• Computational Complexity
• Zero-Knowledge Proofs
• +3 more

Justin Thaler is an Associate Professor in the Department of Computer Science at Georgetown University, researching algorithms and computational complexity with focus on probabilistic proof systems, verifiable computation, and streaming algorithms. Education: PhD Computer Science, Harvard University BS Computer Science and Mathematics, Yale University Research Interests: Develops protocols for verifying computations (including zero-knowledge proofs), analyzes the power of low-degree polynomials, and designs efficient streaming/sketching algorithms for large datasets. Publications: Research advances theoretical foundations of proof systems, with recent work on SNARKs, lookup arguments, and Fiat-Shamir security. Authored the monograph 'Proofs, Arguments, and Zero-Knowledge'. Advising & Labs: Advises PhD students in theoretical computer science. Contributes to open-source projects including DataSketches library of streaming algorithms. Currently on leave at a16z crypto research.

Marco ValtortaView profile

Professor

• Artificial Intelligence
• Bayesian Networks
• Causal Inference
• +5 more

Marco Valtorta is a Professor and Graduate Director in the Department of Computer Science and Engineering at the University of South Carolina’s Molinaroli College of Engineering and Computing. He specializes in Artificial Intelligence, with a focus on normative reasoning under uncertainty, Bayesian networks, causal models, and computational complexity. His work includes developing algorithms for structure learning in graphical models, causal inference, and applications in multiagent systems. Education: Ph.D., Computer Science, Duke University (1987) M.A., Computer Science, Duke University (1984) Laurea, Electrical Engineering, Politecnico di Milano (1980) Research Interests: Dr. Valtorta’s work integrates logical and probabilistic reasoning, with contributions to causal models, chain graphs, and adversarial machine learning. His funded projects include collaborations with the Office of Naval Research (ONR), IARPA, and the U.S. Department of Agriculture (USDA). Notable collaborations include applying Bayesian networks to healthcare and developing frameworks for trustworthiness assessment in AI systems. Grants & Collaborations: Multi-institution IARPA project on Wigmorean/Bayesian networks for argumentation ONR-funded research on Markov properties of directed hypergraphs with Dr. Linyuan Lu Causal analysis for performance modeling of configurable systems His recent publications emphasize causal inference in AI, automated evaluation of text and sentiment analysis systems, and robustness of foundation models. He has pioneered algorithms for learning chain graphs and addressing adversarial attacks in probabilistic models.

Gourab RayView profile

Associate Professor

• Probability Theory
• Statistical Physics
• Stochastic Processes
• +3 more

Gourab Ray is an Associate Professor in the Department of Mathematics and Statistics at the University of Victoria, Faculty of Science. He holds a PhD from the University of British Columbia, Vancouver. His research focuses on the intersection of probability theory, geometry, and mathematical physics, particularly large-scale patterns in stochastic models inspired by physics. Key areas include random planar maps, random walks, lattice spin models, dimer models, Gaussian free field properties, and Liouville quantum gravity. Recent work emphasizes establishing Gaussian free field-like behaviors in dimer models across various graphs and surfaces. He teaches courses such as MATH 236: Introduction to Real Analysis and MATH 555: Topics in Probability. His publications span leading journals including Inventiones Mathematicae , Annals of Probability , and Probability Theory and Related Fields . Notable contributions include studies on unimodular hyperbolic triangulations, half-planar map classifications, and conformal invariance in dimer models. No specific awards are listed for Dr. Ray, though his work has been recognized in peer-reviewed venues. He actively contributes to academic service, including roles on graduate committees and research collaborations. His research group engages with theoretical and applied aspects of probability theory, often bridging discrete and continuous mathematical frameworks.

• Algorithmic Game Theory
• Computational Social Choice
• Artificial Intelligence
• +3 more

Edith Elkind is a Professor of Computer Science at the University of Oxford and a Non-Tutorial Fellow at Balliol College (leaving October 2024). Her research focuses on algorithmic game theory and computational social choice. She joined Oxford in 2013 after roles at Nanyang Technological University (Singapore), Princeton University (PhD 2005), and postdoctoral positions at the University of Warwick, University of Liverpool, Hebrew University of Jerusalem, and University of Southampton. Her work addresses voting systems, cooperative games, and complexity analysis. Key contributions include studies on multiwinner elections, justified representation, and coalition formation. She has advised six PhD/Master’s students and authored/co-authored over 30 publications. Notable works include Properties of multiwinner voting rules (2017) and Justified representation in approval-based committee voting (2017), advancing fairness and efficiency in voting mechanisms. Her research spans theoretical computer science and AI, with applications in social choice theory and game dynamics. Collaborations include projects on foundational AI and computational social choice. She has contributed to foundational texts like Computational Aspects of Cooperative Game Theory (2011).

• Epistemology
• Formal Epistemology
• Philosophy of Science
• +3 more

William Roche is a Professor of Philosophy at Texas Christian University (TCU), affiliated with the AddRan College of Liberal Arts. He holds a Ph.D., M.S., and B.S. in Philosophy from The Ohio State University and the University of Utah. His primary research focuses on epistemology, formal epistemology, and philosophy of science, with additional interests in logic, philosophy of language, and philosophy of mind. Roche teaches courses such as Critical Reasoning, Symbolic Logic I, Introduction to Epistemology, and Advanced Issues in Epistemology. His work explores topics like foundationalism, Bayesian confirmation theory, and probabilistic support structures. Recent publications include discussions on the perils of parsimony, evidence screening-off mechanisms, and infinite regresses in epistemological frameworks. His research bridges formal methods with traditional philosophical inquiry, addressing questions in scientific methodology and epistemic justification. Roche’s contributions often engage with debates in cognitive science and the logical underpinnings of knowledge claims.