Find Academic Supervisors

• Positional games
• Discrete geometry
• Computational geometry
• +3 more

Milos Stojakovic is a Full Professor in the Department of Mathematics and Informatics at the Faculty of Sciences, University of Novi Sad, Serbia. He has held this position since 2016, following appointments as Associate Professor (2011-2016) and Assistant Professor (2006-2011) at the same institution. Since 2018, he has led the Foundations of Computer Science research group. His academic foundation includes dual Bachelor's degrees in Mathematics and Computer Science (1999), a Master's degree in Computer Science (2001), and a Ph.D. in Computer Science from ETH Zurich (2005) under Emo Welzl and Tibor Szabó. Dr. Stojakovic's research focuses on: Positional games and their variants Discrete and computational geometry Discrete random structures Combinatorial algorithms Graph theory and applications His extensive publication record reveals a consistent focus on positional games, with recent work exploring Maker-Breaker games, Avoider-Enforcer games, and Constructor-Blocker games. His research bridges combinatorics, game theory, and computational geometry, with notable contributions to hypergraph coloring, graph searching algorithms, and geometric matchings. He co-authored the seminal book 'Positional Games' (2014) in the Oberwolfach Seminars series. Dr. Stojakovic has received significant recognition including the 'Dr Z. Đinđić Award' for best young scientist in Vojvodina (2008) and the 'Best Student of University of Novi Sad Award' (1998/99). He has successfully mentored three PhD students to completion: Mirjana Mikalački (2014, 'Positional games on graphs'), Marko Savić (2018, 'Efficient algorithms for discrete geometry problems'), and Jelena Stratijev (2023, 'Strong positional games'). His research has been supported by various grants, including the 1 million RSD grant accompanying the Dr Z. Đinđić Award. As head of the Foundations of Computer Science group, Dr. Stojakovic leads research in theoretical computer science and discrete mathematics. He has organized international workshops including multiple editions of the Novi Sad Workshop on Foundations of Computer Science (NSFOCS) and specialized workshops at the Oberwolfach Research Institute for Mathematics.

Adolfo R. EscobedoView profile

Associate Professor

• Mathematical Programming
• Power Systems Operations
• Circular Economy
• +4 more

Adolfo R. Escobedo is an Associate Professor in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University. His research focuses on mathematical programming, power systems operations, circular economy, computational social choice, and computational linear algebra. He has received prestigious awards including the NSF CAREER Award (2024) and the INFORMS MIF Early Career Award (2023). His work bridges theoretical advancements in optimization algorithms with practical applications in energy systems, crowd intelligence, and STEM education equity. Education: PhD in Industrial Engineering from Texas A&M University (2016), BA in Mathematics from California State University-Los Angeles (2009). Affiliations: Member of the College of Engineering, actively contributing to interdisciplinary research and education initiatives. His research emphasizes robust numerical guarantees in optimization solvers, with a focus on overcoming computational bottlenecks in linear and mixed-integer programming. Recent projects include developing open-source tools for reliable optimization software and launching inclusive STEM education programs targeting underrepresented minorities. Escobedo’s work also addresses societal challenges such as sustainable power systems and waste management through data-driven models. His publications highlight contributions to rank aggregation, crowd wisdom enhancement, and exact matrix factorization techniques. Beyond academia, he advocates for diversity in STEM through recruitment programs and research internships. Key Grants: NSF CAREER Award (2024), CRII: III Grant (2019). Labs/Teams: Leads research groups focused on optimization algorithms and computational social choice, collaborating with interdisciplinary teams in energy and environmental systems.

Ravi MontenegroView profile

Professor

• Markov Chains
• Cryptography
• Combinatorics
• +3 more

Ravi Montenegro is a Professor and Department Chair in the Department of Mathematics & Statistics at the University of Massachusetts Lowell (UML), within the Kennedy College of Sciences. He also serves as the GPS Math Coordinator. His research focuses on Markov chain convergence rates, combinatorics, isoperimetric inequalities, and cryptographic algorithms like Pollard's Rho and Kangaroo methods for discrete logarithm problems. Montenegro holds a Ph.D. in Mathematics from Yale University (2002) and a BS in Mathematics from the California Institute of Technology (1995). Education Background: Ph.D. in Mathematics, Yale University (2002) B.S. in Mathematics, California Institute of Technology (1995) Research Interests: Markov chain mixing times and convergence rates Cryptanalysis and birthday attack algorithms Combinatorial optimization and graph theory Isoperimetric inequalities and geometric bounds Notable Works: Developed rigorous analyses of Pollard’s Rho and Kangaroo methods Contributed to spectral profile and conductance-based mixing time bounds Explored evolving set processes and canonical path methods for Markov chains Awards and Honors: Japan Society for the Promotion of Science (JSPS) Invitation Fellowship (2013) NSF-VIGRE Postdoctoral Fellowship (2002) NSF-VIGRE Graduate Fellowship (1999) Professional Contributions: Guided multiple research papers on discrete logarithm algorithms and Markov chain theory Active in academic leadership roles, including graduate program coordination

Yury PersonView profile

Professor

• Discrete Mathematics
• Random Graph Theory
• Computer Science
• +3 more

Yury Person is a University Professor and Head of the Large Networks and Random Graphs Group at Technische Universität Ilmenau's Department of Mathematics. He previously served as Endowed Junior Professor at the Carl Zeiss Foundation (2018–2021) and Junior Professor of Discrete Mathematics and its Applications at Goethe University Frankfurt (2013–2018). His research focuses on identifying patterns in large discrete structures, leveraging the interplay of structure and randomness, with applications in computer science, particularly property testing. Education: Mathematics Degree from Technical University of Munich; PhD in Computer Science from Humboldt University of Berlin (2010) PostDoc: Institute of Mathematics at Free University of Berlin Research interests include discrete mathematics, random graph theory, and algorithm design. He has conducted research stays at institutions like the Institute for Pure and Applied Mathematics (Los Angeles), Hebrew University Jerusalem, and Universidade de São Paulo. Recognized for teaching excellence, he was nominated for the 1822 University Award for Excellence in Teaching in 2017. Professional contributions include a fellowship in the Main-Campus-educator program (2014–2016) and leadership of the Large Networks and Random Graphs Group at TU Ilmenau.

Shanshan LvView profile

Assistant Professor

• Statistical Inference
• Fiducial Inference
• Small Sample Methods
• +3 more

Shanshan Lv is an Assistant Professor in the Department of Statistics at Truman State University. She holds a Ph.D. in Applied Statistics (2019) from the University of Louisiana at Lafayette, with additional master’s degrees in Applied Statistics (2017), Pure Mathematics (2015), and a bachelor’s in Applied Mathematics (2012) from Guizhou University of Finance and Economics. Her research focuses on fiducial inference for discrete distributions, statistical computing, and small sample inference methods. Current work emphasizes fiducial inference applications, with plans to expand into continuous distributions and machine learning domains. She also explores prediction intervals for discrete distributions and confidence interval methodologies for population size estimation using capture-recapture data. Her publications span topics like hypothesis testing for continuous distributions, ratio estimation for normal means, and finite population proportion estimation. Research trends show a strong emphasis on developing robust statistical methodologies with practical applications in ecology, engineering, and computational statistics. No scientific awards or grants are explicitly mentioned in the provided text. She has no listed advisees, and no specific labs/teams are referenced in her profile.

• Computational Algebraic Geometry
• Combinatorics
• Commutative Algebra
• +4 more

Bernd Sturmfels is a full Professor in the Department of Mathematics at the University of California, Berkeley. He has held academic positions at leading institutions including the Max Planck Institute for Mathematics in the Sciences in Leipzig. His research spans Computational Algebraic Geometry, Combinatorics, Algebraic Statistics, and Nonlinear Algebra. He has mentored over 60 PhD students and postdoctoral scholars, many of whom have become prominent researchers in their fields. Current research projects include tropical geometry, phylogenetics, and applications to computational biology and quantum chemistry. Sturmfels' teaching record includes courses ranging from introductory mathematics to advanced topics in algebraic geometry, optimization, and combinatorics. Notable courses include Math 270: Metric Algebraic Geometry (Spring 2023) and specialized graduate seminars on nonlinear algebra and tropical geometry. He frequently collaborates internationally, contributing to interdisciplinary projects at the intersection of mathematics, computer science, and physics. His work emphasizes the interplay between theoretical developments and computational methods, with applications to systems biology, statistics, and engineering. Sturmfels has authored influential textbooks such as Gröbner Bases and Convex Polytopes and co-founded the Mathematical Research Institute at Berkeley. He maintains active research collaborations and serves on editorial boards of leading journals in algebraic geometry and computational mathematics.

Odile BellenguezView profile

Professor

• Computer Science
• Ethics in Technology
• Operations Research
• +3 more

Professor Odile Bellenguez is a faculty member in the Automation, Production and Computer Sciences department at IMT Atlantique. Her work focuses on integrating ethical considerations into algorithm design, particularly in personnel scheduling and decision support systems. She holds a University Diploma in Philosophy from Université Paris Nanterre, enhancing her interdisciplinary approach. Her research bridges computer science, discrete mathematics, and social sciences, with collaborations in sociology, psychology, and management science. Notable projects include co-supervising a thesis on ethical inclusion in healthcare scheduling and developing fair nurse rostering algorithms. She also pioneered a philosophy-infused framework for algorithmic ethics, addressing fairness and equity in automated systems. Professor Bellenguez actively engages in pedagogy, designing courses that teach ethical implications of algorithms through case studies like traffic navigation and workforce scheduling. Her recent work emphasizes cross-disciplinary dialogue to address societal challenges posed by algorithmic systems. Her contributions span over 50 publications, with a focus on scheduling optimization (RCPSP variants, nurse rostering) and ethical frameworks for automated decision-making. She plans to host a summer school for PhD students exploring ethics in algorithmics.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +6 more

Matthew Watson is a researcher at Durham University , Department of Computer Science, UK. His work spans Computer Science , Artificial Intelligence , and Human-Computer Interaction , with a focus on autonomous systems , semantic search , and pedagogical tools . Research Trends : Recent publications include applications of machine learning in adaptive cruise control , UAV-based wildlife tracking , and deep learning frameworks like KerasCV/KerasNLP. Earlier work involved mathematical combinatorics and cognitive modeling in dialogue systems. Collaborations : Co-authored with Navid Mohajer, Darius Nahavandi, Ashok K. Krishnamurthy, and others in domains like robotics, bioinformatics, and software engineering. Publications include 13 peer-reviewed articles from 2008–2025, covering topics in control systems , knowledge graphs , and algorithm design .

Esty KelmanView profile

Research Fellow

• Theoretical Computer Science
• Computational Complexity
• Probabilistically Checkable Proofs
• +2 more

Esty Kelman is a Research Fellow jointly affiliated with the Massachusetts Institute of Technology (CSAIL) and Boston University, working in theoretical computer science. Her research focuses on computational complexity, sublinear algorithms, property testing, and Boolean function analysis. She was previously a PhD student at Tel Aviv University advised by Muli Safra and participated in the Simons Institute's Causality program at UC Berkeley. Her work has been recognized through the Simons Research Fellowship. Research interests span probabilistically checkable proofs, combinatorics, and adversarial models in algorithm design. Publications demonstrate consistent focus on theoretical computer science foundations, with recent advances in property testing methods and combinatorial mathematics. Collaborative work frequently appears in top venues including FOCS, ITCS, and SODA.

Joel MillerView profile

Associate Professor

• Infectious Diseases
• Network Theory
• Stochastic Analysis and Modelling
• +7 more

Joel Miller is an Associate Professor in Mathematics & Statistics at La Trobe University, School of Computing Engineering and Mathematical Sciences. His research focuses on infectious disease modelling, network theory, and ethical considerations in public health policy. He has held academic positions at Monash University, Penn State University, and the University of British Columbia Centre for Disease Control. Miller earned his PhD from the University of Cambridge and has contributed to significant projects, including the Australian Centre for AI in Medical Innovation and federal studies on pandemic response. His teaching includes courses on Vector Calculus, Infectious Disease Modelling, and Network Epidemiology. Notable awards include the 2021 La Trobe honourable mention for research impact and the RAPIDD Postdoctoral Fellowship. Education: PhD (University of Cambridge), MMath (University of Cambridge), BS (Harvard Mudd College). Research interests span epidemic dynamics, network structures, stochastic simulation algorithms, and ethical frameworks in computational modelling. He has led grants including a 2020 federally-funded study on pandemic testing capacity and contributed to the National COVID plan for Remote/Indigenous communities. Scientific Awards: Larkins Fellowship, RAPIDD Postdoctoral Fellowship, and multiple fellowships. His work has been featured in journals like PLOS Computational Biology and Science Advances. Teaching and Advising: Supervised numerous PhD/Masters students and developed courses like Dynamics of Spreading Processes on Networks. His mentorship includes projects on hepatitis C epidemiology, hypergraph contagion, and AI-driven network structure analysis. Professional Activities: Editorial roles in journals like PNAS and Science Advances, and media collaborations with outlets like Reuters and the New York Times.

Marius TibaView profile

Lecturer

• Geometric Inequalities
• Probability
• Combinatorics
• +5 more

Dr. Marius Tiba is a Lecturer in Pure Mathematics at King's College London, part of the Analysis group within the Department of Mathematics. His academic journey includes a PhD from the University of Cambridge (2021, supervised by Béla Bollobás), a postdoctoral position at IMPA (Rio de Janeiro), and a Titchmarsh Research Fellowship at the University of Oxford (2022–2024). His research focuses on geometric problems rooted in analysis, probability, and combinatorics, with a special emphasis on stability of geometric inequalities, covering systems, Ramsey theory, and Littlewood polynomials. He holds a BA and Master of Mathematics from Trinity Hall, University of Cambridge. His teaching includes lecturing on Complex Analysis and supervising dissertations on Stability of Geometric Inequalities and Ramsey theory. He currently co-supervises PhD student Albert Rehak at KCL and Zhaoyang Liu at the London School of Geometry and Number Theory. His research outputs include groundbreaking work on geometric inequalities (e.g., Brunn-Minkowski stability), multicolour Ramsey numbers, and the existence of flat Littlewood polynomials. He collaborates extensively with leading mathematicians like Alessio Figalli and Robert Morris. His work bridges pure mathematics fields, often applying probabilistic and combinatorial techniques to geometric problems. Key research themes include quantitative stability estimates for functional inequalities, extremal combinatorics, and additive number theory. His contributions have appeared in top journals like the Annals of Mathematics, Journal of the European Mathematical Society, and Inventiones Mathematicae.

Therese BiedlView profile

Professor

• Graph Drawing
• Algorithms for Planar Graphs
• Computational Geometry
• +3 more

Therese Biedl is a Professor at the University of Waterloo's Department of Computer Science. Her research focuses on graph drawing, algorithms for planar and near-planar graphs, computational geometry, and theoretical computer science. She holds a Ph.D. from Rutgers University (1997) and a Dipl.-Math from the Technical University of Berlin (1996). Her work emphasizes geometric representations of graphs, algorithm optimization for special graph classes, and visibility representations. Key research interests include rectangular duals, graph embeddings on surfaces (cylinder/torus), and efficient algorithms for 1-planar graphs. She explores topics such as matching problems in planar graphs, geometric curve separation, and polygon decomposition. Her recent publications (2023–2025) address graph connectivity, visibility layouts, and parameterized complexity in embedded graphs. Notably, her articles analyze structural properties of graphs (e.g., basis numbers, independence numbers) and geometric constraints (e.g., outer-string representations). While no scientific awards are listed, her contributions to graph theory and algorithm design are substantial. No advising/grant details are provided in the text, but her involvement in conferences like GD 2018 highlights academic engagement.

Rafael OliveiraView profile

Assistant Professor

• Computational Complexity
• Algebraic Geometry
• Optimization Algorithms
• +1 more

Rafael Oliveira is an Assistant Professor at the University of Waterloo's Cheriton School of Computer Science, conducting research from office DC 1313. His work explores fundamental connections between complexity theory, optimization, and geometry. Research spans: Algebraic complexity barriers Geometric combinatorics Efficient tensor algorithms Proof complexity frameworks His publications demonstrate consistent focus on mathematical foundations of computation , with recent advances in polynomial identity testing, interactive proofs, and geometric optimization. Article analysis shows recurring themes in algebraic complexity (40%), combinatorial geometry (30%), and optimization (30%).

Luke SchaefferView profile

Assistant Professor

• Quantum Computing
• Theoretical Computer Science
• Combinatorial Game Theory
• +4 more

Luke Schaeffer is an Assistant Professor at the University of Waterloo, affiliated with the Cheriton School of Computer Science and the Institute for Quantum Computing (IQC). His academic journey includes a BMath and MMath from Waterloo, a PhD from MIT under Scott Aaronson, and postdoctoral positions at Waterloo and UMD. His research bridges quantum computing and theoretical computer science, with focuses on quantum circuit complexity, classical simulation of quantum systems, and combinatorics on words. Notable projects include studying the Clifford group's structure, low-depth quantum vs. classical circuits, query complexity of regular languages, and fermion-to-qubit encodings. He also explores non-quantum areas like combinatorial game theory and cellular automata. His work on interactive quantum advantage protocols demonstrated separations between quantum and classical circuit models, including results against AC⁰[p] and NC¹ classes. He co-developed sample-optimal classical shadow algorithms for pure states and classified Clifford gates over qubits into 57 distinct classes. Education: BMath and MMath, University of Waterloo PhD in Computer Science, MIT (advisor: Scott Aaronson) Key Research Themes: Quantum advantage and circuit complexity Automata and combinatorics on words Classical simulation techniques for quantum systems Algorithmic foundations of quantum computing He advises students in the Cheriton School of Computer Science, emphasizing quantum computing and theoretical computer science. His contributions include foundational results in quantum-classical separations and decidability of word properties via first-order logic.

Meral TosunView profile

Professor

• Singularities in Algebraic Geometry
• Resolution of Singularities
• Topology of Singularities
• +4 more

Meral Tosun is a Professor in the Department of Mathematics at Galatasaray University, Istanbul, Turkey. She holds a DEA in Mathematics from Université Paris VII (1994) and a Ph.D. in Mathematics from Université d'Aix-Marseille I (1998), advised by Prof. Lê Dũng Trang. Her research focuses on singularities in algebraic geometry, including resolution and deformations of singularities, topology of singularities, classification of singularities, Lie algebras related to surface singularities, toric geometry, and tropical geometry. Education: DEA in Mathematics, Université Paris VII, Paris, France (1994) Ph.D. in Mathematics, Université d'Aix-Marseille I, Marseille, France (1998) Research Interests: Her work explores the interplay between algebraic geometry and singularity theory, with emphasis on resolution algorithms, topological properties of singularities, and their connections to Lie algebras and toric geometry. Notable contributions include studies on rational triple points, non-isolated singularities, and applications of toric methods in surface singularities. Recent Research Trends: Recent articles highlight advancements in minimal embedded resolution techniques (e.g., toric methods for rational triple points), exploration of Lojasiewicz exponents in surfaces, and the role of Lie algebras in singularity classification. She has also contributed to geometric invariant theory and quantum key distribution in turbulence channels. Awards & Grants: No specific awards listed, but her extensive postdoctoral fellowships (ICTP, UNAM) and supervisory roles indicate sustained academic recognition. She has advised 9 graduate students and mentored 6 post-doctoral researchers at institutions including Galatasaray University and Gebze Technical University. Labs & Collaborations: Active in organizing workshops and schools at Galatasaray University, contributing to Turkish mathematical communities like the Nesin Mathematics Village and Istanbul Mathematical Sciences Center (IMBM).