Find Academic Supervisors

Ioannis CaragiannisView profile

Professor

• Algorithmic Game Theory
• Social Choice Theory
• Fair Division
• +4 more

Ioannis Caragiannis is a Professor at the Department of Computer Science, Aarhus University. His research focuses on algorithmic game theory, fair division, mechanism design, and social choice theory. He explores computational aspects of fairness in resource allocation, voting systems, and multi-agent systems. His work often addresses theoretical challenges in designing efficient and fair algorithms with provable guarantees. Key research areas include social choice mechanisms, metric distortion in collective decisions, clustering algorithms with fairness constraints, and truthful mechanisms for budget allocation and facility assignment. He has contributed to understanding the complexity of Pareto-optimal and envy-free outcomes, as well as the efficiency of serial dictatorship mechanisms. His recent work investigates welfare-optimal allocations under computational constraints and the metric properties of sortition-based decision-making. Caragiannis has published extensively in top venues like AAAI, ICML, and SAGT. Notable contributions include studies on proportional fairness in non-centroid clustering, randomized learning-augmented auctions, and the distortion of sortition systems. His research bridges theoretical computer science with practical applications in fair resource distribution and collective decision-making processes.

Glyn Williams-JonesView profile

Professor

• Physical Volcanology
• Glaciovolcanism
• Geothermal Systems
• +3 more

Glyn Williams-Jones is a Professor and Co-Director of the Centre for Natural Hazards Research at Simon Fraser University's Department of Earth Sciences. His work bridges physical volcanology, glaciovolcanism, and geothermal systems with Indigenous knowledge integration. Current research explores magma dynamics in the Cascade Volcanic Arc Develops machine learning tools for volcano monitoring Integrates Indigenous oral histories with geological analyses Notable projects include studying Canada's deadliest volcanic eruption (Tseax, ~1700 CE) and developing hazard assessments for glacier-capped volcanoes. His lab employs advanced geophysical techniques like gravity modeling and seismic signal analysis. Students under his supervision work on topics spanning structural geology, glaciovolcanic cave systems, and volcanic risk communication. Recent publications highlight applications of machine learning in volcano-seismic data analysis (2025), Indigenous-Western science co-creation (2024), and glaciovolcanic void dynamics (2024). He actively collaborates with institutions like the Instituto Geofísico de la Escuela Politécnica Nacional in Ecuador.

Fan WeiView profile

Assistant Professor

• Extremal Combinatorics
• Probabilistic Combinatorics
• Theoretical Computer Science
• +3 more

Fan Wei is an Assistant Professor of Mathematics and Computer Science at Duke University, affiliated with the Trinity College of Arts & Sciences. She holds a PhD in Mathematics from Stanford University (2019), a Master of Advanced Study from Cambridge University (UK), and a Bachelor's degree from MIT. Her research focuses on extremal and probabilistic combinatorics, with applications to computer science, particularly in analyzing large networks using tools from probability, analysis, and algebra. Education: PhD in Mathematics, Stanford University, 2019 Master of Advanced Study, Mathematics, University of Cambridge, UK Bachelor's degree in Mathematics, MIT Research Interests: Extremal combinatorics, probabilistic combinatorics, graph theory, theoretical computer science, and applications to network analysis. Recent work includes studies on undecidability in graph homomorphism densities, independent transversals, and irregularity strength conjectures. Publications: Her work spans over 30 peer-reviewed articles, focusing on graph theory, combinatorial optimization, and algorithmic applications. Notable topics include undecidability in combinatorial problems, spectral graph theory, and phase transitions in graph properties. Awards & Funding: NSF Award DMS-2401414 (2023–2028) NSF Award DMS-1953958 (2020–2023) Simons Foundation Algorithms & Geometry Unit Fellowship (2020) Teaching & Mentoring: Teaches combinatorics courses at Duke, leads a research group, and mentors students in REU programs. Previously taught discrete mathematics and calculus. Labs/Teams: Active in Duke's combinatorics research community, collaborates internationally on projects such as the Barbados Graph Theory Workshop. Hosts a minicourse on regularity methods in combinatorics.

• Operator Algebras
• Random Matrices
• Privacy-Preserving Data Synthesis
• +4 more

March Boedihardjo is an Assistant Professor in the Department of Mathematics at Michigan State University (MSU). His academic journey includes postdoctoral roles at ETH Zurich (2022-2023), the University of California, Irvine (2021-2022), and UCLA (2016-2021). He earned his Ph.D. in Mathematics from Texas A&M University (2012-2016) under advisors Bill Johnson and David Kerr, and holds a B.Sc./M.Phil. from Hong Kong Baptist University (2007-2011). Boedihardjo's research focuses on operator algebras, random matrices, and privacy-preserving data synthesis. His work bridges functional analysis and modern data science, with contributions to covariance loss minimization, max-sliced Wasserstein distances, and privacy-utility tradeoffs in synthetic data. He has presented at venues like Carnegie Mellon's Probability/Math Finance Seminar and the International Conference on Machine Learning (ICML). His teaching includes advanced courses like MTH 890: Readings in Mathematics. His research outputs span foundational mathematics (e.g., C*-algebra representations on ℓp spaces) and applied topics (e.g., differentially private synthetic data generation). While no specific awards are listed, his active conference participation reflects scholarly engagement in operator theory, probability, and data science intersections.

Wesley PegdenView profile

Professor

• Discrete Mathematics
• Probabilistic Combinatorics
• Combinatorial Game Theory
• +7 more

Wesley Pegden is a Professor in the Department of Mathematical Sciences at Carnegie Mellon University, affiliated with the Mellon College of Science. He holds a Ph.D. from Rutgers University. His research focuses on Discrete Mathematics, including probabilistic combinatorics, combinatorial game theory, graph theory, and discrete geometry. A central theme of his work is the Abelian sandpile model, where he has explored fractal patterns and connections to Apollonian circle packings. Collaborations with Lionel Levine and Charles Smart have advanced understanding of the sandpile's geometric properties through novel constructions of integer superharmonic functions. His work extends to applications in network science, epidemiological modeling, and statistical sampling biases (e.g., analyzing the Bangladesh mask trial). Notably, he contributed to studies on gerrymandering and congressional districting, including an expert analysis of Pennsylvania's district map. Awards include the Sloan Research Fellowship. Research Highlights: Abelian sandpile fractal geometry, algorithmic local lemma extensions, and random graph dynamics. Key Collaborations: Lionel Levine, Charles Smart, Alan Frieze.

Prasad TetaliView profile

Professor

• Discrete Mathematics
• Probability Theory
• Theory of Computing
• +6 more

Prasad Tetali is the Alexander M. Knaster Professor and Department Head of the Department of Mathematical Sciences at Carnegie Mellon University (CMU), located in Pittsburgh, PA. He also holds adjunct professorships at Emory University (Math/CS) and the Georgia Institute of Technology (Math/CoC). His academic journey includes a Ph.D. from the Courant Institute of Mathematical Sciences, NYU, and postdoctoral research at AT&T Bell Labs. Education: Ph.D. (1991), Courant Institute of Mathematical Sciences, NYU M.S. (1987), Indian Institute of Science, Bangalore, India Postdoctoral Appointments: Mathematical Sciences Research Center, AT&T Bell Labs Research Interests: Dr. Tetali's work focuses on Discrete Mathematics, Probability Theory, and Theoretical Computing. Key areas include Markov chains, isoperimetry, combinatorics, computational number theory, and algorithm design. His contributions span foundational theory and applications in optimization, statistical physics, and network analysis. Publications & Trends: His research has led to influential papers on topics like mixing times of Markov chains, entropy inequalities, and combinatorial optimization. Notable works include foundational studies on the Potts model, the Swendsen-Wang algorithm, and sharp threshold phenomena in number theory. Awards & Recognition: AAAS Fellow SIAM Fellow (Inaugural Class, 2009) Fellow of the American Mathematical Society AMS Fellow (Inaugural Class, 2012) Georgia Tech’s Regents Professor Advising & Grants: Dr. Tetali has mentored 11 PhD students and numerous postdocs. His grants include leadership roles in initiatives such as the SIAM Activity Group on Discrete Mathematics and the ACO PhD Program at Georgia Tech. He has also held editorial roles at top journals like SIAM Journal on Discrete Mathematics and Random Structures & Algorithms. Labs & Collaborations: His work often intersects with interdisciplinary teams, including contributions to distributed algorithms, network science, and computational mathematics. He actively collaborates with researchers in computer science, physics, and applied mathematics.

Ullrich KötheView profile

Associate Professor

• Machine Learning
• Neural Networks
• Explainable AI
• +6 more

Prof. Dr. Ullrich Köthe is an Associate Professor and group leader in the Visual Learning Lab Heidelberg at the University of Heidelberg . He focuses on Explainable Machine Learning , leveraging Invertible Neural Networks to enhance transparency and utility in image analysis and medical applications . He also maintains the widely used VIGRA image analysis library. Education: PhD in Informatics, University of Hamburg, 2000 Habilitation in Informatics, University of Hamburg, 2008 His research interests center around machine learning , image analysis , and scientific computing , particularly the development of robust algorithms for medical imaging , computer vision , and life sciences . His work on invertible neural networks and parameter-free segmentation has led to significant advancements in the field. Recent publications highlight his contributions to Bayesian inference , neural network interpretability , and stochastic modeling , with applications ranging from disease outbreak dynamics to connectomics . Key trends include generative models , parameter-free segmentation , and likelihood-free inference . Scientific Awards: DAGM 2003 Main Prize DAGM Best Paper Award 2008 He has supervised numerous Master and Bachelor theses in machine learning and image analysis , with teaching roles in Advanced Machine Learning and Explainable AI . His collaborative research grants include funding from HARMAN International (2024). He leads the Explainable Machine Learning subgroup and has contributed to open-source software projects like ilastik and VIGRA , which are critical tools in bioimage analysis .

• Industrial Internet of Things
• Digital Manufacturing
• Industry 4.0
• +3 more

Prem Prakash Jayaraman is Professor and Director of the Factory of the Future and Digital Innovation Lab at Swinburne University, leading digital transformation initiatives for manufacturing and smart cities. His research develops IoT architectures for industrial applications with over $7.5M in external funding. Key projects include the Advanced Manufacturing Industry 4.0 Hub providing SME digital readiness assessments, 5G-enabled smart city solutions for road maintenance, and industrial IoT systems for quality control. His research spans edge computing, digital twins, and cyber-physical systems with applications in food processing, logistics, and healthcare. Professor Jayaraman has published extensively on IoT middleware, sensor sharing marketplaces, and quality-aware autonomous systems. He leads Swinburne's strategic partnerships in Industry 4.0 and contributes to national initiatives through ARC-funded research hubs on digital manufacturing.

Roger ColbeckView profile

Professor

• Quantum Cryptography
• Quantum Foundations
• Device-Independent Protocols
• +3 more

Professor Roger Colbeck is a faculty member in the Department of Mathematics at the University of York, where he serves as Head of Section for Applied Mathematics. His research focuses on quantum information theory and foundational questions in quantum mechanics. He holds a PhD from the University of Cambridge and has held positions at ETH Zurich, the Perimeter Institute for Theoretical Physics, and as a Junior Research Fellow at Homerton College, Cambridge. His research explores interdisciplinary areas including device-independent quantum cryptography, quantum random number generation, uncertainty relations, and causal models in quantum theory. He investigates the implications of quantum mechanics for information processing, such as secure computations and protocols leveraging relativistic principles. Professor Colbeck has contributed to projects like the EPSRC Quantum Communications Hub and collaborates internationally. His work often bridges theoretical foundations with practical applications, such as improving cryptographic security through device-independent approaches. He advises three doctoral students and offers PhD projects in quantum cryptography, random number generation, or quantum foundations. His research has been published in top journals, including Physical Review Letters , and he frequently presents at conferences and seminars on topics like nonlocality and causal structures.

Tom BohmanView profile

Professor

• Extremal Combinatorics
• Probabilistic Combinatorics
• Discrete Mathematics
• +3 more

Tom Bohman is a Professor of Mathematical Sciences at Carnegie Mellon University, affiliated with the Mellon College of Science. His research focuses on extremal and probabilistic combinatorics, exploring discrete structures inspired by mathematics, information theory, statistical physics, and computer science. He holds a Ph.D. from Rutgers University and has held postdoctoral positions at MIT and the Mathematical Sciences Research Institute (MSRI). Bohman's work includes studies on random graph processes, such as the triangle-free process and Hamilton cycles in random graphs, as well as hypergraph coloring and Ramsey numbers. His publications span over three decades, addressing topics like coprime matchings, lonely runner conjectures, and dynamic concentration phenomena in combinatorial systems. He teaches advanced courses in combinatorics and discrete mathematics, including Graph Theory and Random Structures & Algorithms. His research has contributed to understanding phase transitions in random processes and the interplay between combinatorial structures and probabilistic methods. While no awards are explicitly listed, his extensive publication record and editorial roles (e.g., with Random Structures & Algorithms ) highlight his academic impact. Bohman's work often bridges theoretical foundations with algorithmic applications, influencing both pure and applied combinatorics.

• Distributed Computing
• Graph Algorithms
• Theoretical Computer Science
• +3 more

Yannic Maus is a Professor at Graz University of Technology, affiliated with the Institute of Algorithms and Theory and the Institute of Software Engineering and Artificial Intelligence. His research focuses on distributed computing, graph algorithms, and theoretical computer science. He holds a PhD and multiple bachelor’s and master’s degrees in Computer Science. His work emphasizes distributed graph coloring, locality in algorithms, and massively parallel computing. He has contributed to foundational results in distributed algorithms, including optimal edge coloring and coloring hyperbolic random graphs. His research bridges theoretical insights with practical distributed systems challenges. Education: PhD in Natural Sciences (Dr.rer.nat.), B.Sc. and M.Sc. in Computer Science Key research interests include distributed algorithms for graphs, Lovász Local Lemma applications, and algorithmic efficiency in dynamic networks. His publications explore topics like ruling sets in trees, exponential speedups in MPC models, and adaptive coloring techniques for sparse graphs. He has also investigated the algorithmic small-world phenomenon and connectivity in forests using deterministic approaches. Yannic Maus’s work often addresses theoretical lower bounds and upper limits in distributed computing, with applications to real-world networked systems. His contributions span conferences like DISC and SoCG, focusing on both foundational problems and their algorithmic solutions.

Alexis BoulinView profile

Researcher

• Extreme value theory
• Unsupervised learning
• Stationary time series
• +1 more

Alexis Boulin is a Research Assistant at Ruhr University Bochum under the Chair of Mathematical Statistics , part of the Stochastics Group Bücher . He is affiliated with the Faculty of Mathematics and contributes to research in extreme value theory, unsupervised learning, and high-dimensional statistics. Education: He completed his thesis, Partitionnement des variables de séries temporelles multivariées selon la dépendance de leurs extrêmes , at Université Côte D'Azur (2024). His work focuses on statistical methodologies for multivariate time series and extreme events. Publications include submissions on max-stable random vectors and model-based clustering techniques. He maintains a research profile at aleboul.github.io and is reachable at IB 2/169, Universitätsstraße 150, 44780 Bochum by appointment.

Yilun ShangView profile

Associate Professor

• Complex Systems
• Network Science
• Nonlinear Dynamics
• +7 more

Yilun Shang is an Associate Professor in the Department of Computer and Information Sciences at Northumbria University. Previously, he held an Associate Professor position at Tongji University's School of Mathematical Sciences (2014–2018). His research interests span complex systems, network science, and nonlinear dynamics. He earned a PhD in Applied Mathematics from Shanghai Jiao Tong University in 2010, followed by postdoctoral appointments at institutions including the University of Texas at San Antonio and the Hebrew University of Jerusalem. Shang's work focuses on three core areas: (1) properties of complex networks (e.g., robustness, percolation, epidemic models), (2) mathematical properties of random graph models (e.g., degree distribution, connectivity, algebraic indices), and (3) nonlinear dynamics in multi-agent systems (e.g., synchronization, consensus algorithms). He has organized international conferences and delivered invited talks on topics like Estrada indices in random graphs. His academic achievements include the 2016 Dimitrie Pompeiu Prize and a presentation at the 2018 International Congress of Mathematicians. He advises postgraduate students in complex networks and systems and investigates resilient consensus protocols, graph metrics, and topological indices with applications in chemistry and engineering.

Laura SanitàView profile

Associate Professor

• Combinatorial Optimization
• Approximation Algorithms
• Network Design
• +3 more

Laura Sanità is an Associate Professor in the Department of Computing Sciences at Bocconi University, Milan, Italy. Previously, she held positions at TU Eindhoven (2020–2022) and the University of Waterloo, Canada, where she was an Assistant Professor (2012–2017) and later Associate Professor (2017–2020). She earned a Bachelor’s and Master’s in Management Engineering from Università di Roma Tor Vergata (2003–2005), followed by a PhD in Operations Research from Università Sapienza di Roma (2009). Her postdoctoral work (2009–2011) was at EPFL’s Discrete Optimization Group. Her research focuses on Combinatorial Optimization , Approximation Algorithms , Network Design , and Algorithmic Game Theory . Key contributions include advancements in node connectivity augmentation, graph stabilization, and polytope diameter analysis. She has received prestigious awards such as the NWO-VIDI Award (Netherlands), NSERC Discovery Accelerator Supplements, and the Early Researcher Award (Ontario). Laura co-organizes the Bocconi Theory Day (May 2024) and serves on program committees for conferences like SODA, ESA, and IPCO. She is an Associate Editor for Mathematical Programming , Mathematics of Operations Research , and Operations Research Letters . Current advisees include PhD students Sean Kafer, Dylan Hyatt-Denesik, and Lucy Verbeck. Her work bridges theoretical foundations and practical applications, with notable publications in Mathematical Programming , SIAM Journal on Optimization , and Operations Research . Recent projects explore stabilization of capacitated matching games and iterative randomized rounding techniques for combinatorial problems.

Robert B. EllisView profile

Associate Professor

• Combinatorics
• Graph Theory
• Adaptive Coding Theory
• +3 more

Robert B. Ellis is an Associate Professor of Applied Mathematics at Illinois Institute of Technology (IIT), serving as Program Director for the Bachelor of Science in Data Science. His academic journey includes a Ph.D. in Mathematics from the University of California, San Diego. Ellis specializes in combinatorics, graph theory, adaptive coding theory, and probabilistic methods, with notable contributions to random geometric graphs and liar games. He has held roles like Project NExT Fellow and received awards such as the 2023 Michael J. Graff Award for Innovation in Teaching and IIT IPRO Outstanding Faculty Award. Education: Ph.D. in Mathematics, University of California, San Diego Research Interests: Focuses on asymptotic analysis of codes, liar games' connections to packing/covering problems, random geometric graphs in wireless networks, probabilistic methods, and algorithm design. His work bridges theoretical mathematics with applications in data science and network systems. Publications & Grants: Over 20 peer-reviewed articles in journals like Journal of Combinatorics and Algorithmica . Secured grants from NSA Young Investigator and AFRL programs. Advised graduate students on topics like graph partitioning and group testing matrices. Awards: 2023 Michael J. Graff Award for Innovation in Teaching IIT IPRO Outstanding Faculty Award (2009) Project NExT Fellow (2005-2006) Teaching: Courses include Calculus II, Discrete Mathematics, and Applied Algebra. Emphasizes innovative pedagogy through Menger Day talks and IPRO interdisciplinary projects.