Find Academic Supervisors

• Financial Engineering
• Financial Risk Management
• Applied Mathematics
• +4 more

Dan Pirjol is a Teaching Associate Professor at the School of Business, Stevens Institute of Technology. He holds a PhD in Theoretical Physics from the University of Mainz and an MS in Physics from the University of Bucharest. His research focuses on financial engineering, derivatives pricing, hedging, and risk management, employing asymptotic analysis and applied probability. Key areas include financial risk management, applied mathematics, numerical methods, and stochastic modeling of financial markets. He has industry experience in roles at Merrill Lynch, Markit Partners, and JP Morgan, specializing in model risk management and derivatives modeling. His publications span topics such as Asian options valuation, volatility modeling, GARCH risk analysis, and stochastic processes. He actively contributes to professional service roles, including co-editing a special issue of Risks and refereeing for journals like SIAM Journal on Financial Engineering. He teaches courses in financial engineering, market microstructure, and algorithmic trading. No scientific awards are listed, and no advising/grant details are provided. His work emphasizes quantitative finance and practical applications of theoretical models.

Daniel AhlbergView profile

Associate Professor

• Probability Theory
• Spatial Stochastic Processes
• Percolation Theory
• +3 more

Daniel Ahlberg is an Associate Professor in the Department of Mathematics at Stockholm University. His research utilizes probability theory to study spatial random processes including percolation models, random growth systems, branching processes, and combinatorial optimization problems. Current investigations focus on phase transitions in percolation, noise sensitivity of random systems, and geometric properties of random metrics. Research has established fundamental results on sharpness of phase transitions in continuum percolation, scaling limits of Boolean functions, and asymptotic behavior of first-passage percolation models. Recent work examines chaos phenomena in random growth models and statistical properties of geodesics in disordered media. Funded by multiple Swedish Research Council grants including a current research project grant (2022-2025). Recipient of the Ruth and Nils-Erik Stenbäck Foundation stipend (2022). Supervises doctoral students in probability theory and stochastic processes. Developed educational materials on probability fundamentals and Markov chain mixing. Teaches courses in probability theory, stochastic processes, and discrete mathematics.

Joris PinkseView profile

Professor

• Econometrics
• Industrial Organization
• Antitrust Economics
• +3 more

Joris Pinkse is a Professor of Economics at Pennsylvania State University, specializing in econometrics, industrial organization, and antitrust economics. His research focuses on nonparametric estimation, discrete choice models, and auction theory. He previously served as an Associate Professor at the University of British Columbia and holds a PhD from the London School of Economics (1994) and an MSc from Tilburg University. His work includes developing the GruMPS computing package for demand estimation and advancing methods for auction models with shape constraints. He has contributed to top journals like Econometrica and Journal of Econometrics , and his 2014 Raymond Lombra Award highlights his scholarly impact. Teaching spans econometrics, probability, and thesis advising. Education: PhD (London School of Economics), MSc (Tilburg University) Research Themes: Demand Estimation, Auction Analysis, Weak Identification, Industrial Policy Professional Contributions: Editorial roles at five economics journals, including Econometrica

Matthew KahleView profile

Professor

• Combinatorics
• Probability Theory
• Geometric Group Theory
• +3 more

Matthew Kahle is a Professor in the Department of Mathematics at The Ohio State University, where he has served since 2011. His research focuses on the interplay between topology, geometry, probability, and combinatorics, with applications to statistical physics and geometric group theory. He holds a PhD from the University of Washington (2007) and has held positions at Stanford University and the Institute for Advanced Study. Kahle advises multiple PhD students and has mentored numerous postdocs. His honors include the Simons Fellowship, NSF CAREER Award, and Sloan Research Fellowship. Education: PhD (2007) in Mathematics from the University of Washington; MS (2001) in Mathematics from Colorado State University. Earlier studies include a BS from the University of Chicago. Research interests emphasize stochastic topology, configuration spaces, and random simplicial complexes. He explores how probabilistic methods reveal structural properties in geometric and combinatorial systems. His work on persistent homology and topological phase transitions has influenced computational topology and statistical mechanics. Key awards include the 2019 Mercator Fellowship, AMS Fellowship, and Simons Fellowship. His Erdős number is 2, reflecting collaborations in combinatorics and graph theory. Kahle has also engaged in visiting roles at institutions worldwide, including TU Berlin and Queen Mary University of London. Advising spans over a dozen PhD students and postdocs, contributing to their success in academic and applied mathematics. His grants include NSF funding for research on topological phases and configuration space topology. Collaborative efforts with international researchers highlight his global impact in mathematical science. Labs/Teams: Kahle leads a research group focused on algebraic topology and its applications, collaborating with postdocs and students on projects ranging from random geometric complexes to topological data analysis.

Philip Matchett WoodView profile

Professor

• Probability Theory
• Combinatorics
• Random Matrices
• +3 more

Philip Matchett Wood is a Professor in the Department of Mathematics at Harvard University. His research focuses on probability theory, combinatorics, and their applications, with particular emphasis on random matrices, Markov chains, structural behavior in sumsets, and computational methods. He has contributed to foundational work in random matrix theory, including spectral analysis of non-backtracking matrices and universality principles for sparse matrices. His work often bridges theoretical insights with computational exploration, as seen in his development of Maple packages for combinatorial bijections and probabilistic algorithms. Research Interests: Probability Theory, Combinatorics, Random Matrices, Markov Chains, Computational Mathematics, Geometry Grants: Supported by NSA grants (H98230-16-1-0301 and H98230-14-1-0149) from 2014–2018, focusing on random matrix theory and spectral analysis. Publications: Over 20 peer-reviewed articles, including work on outlier detection in random matrix products, computational number theory, and universality in spectral distributions. Contributions: Developed Maple tools for combinatorial bijections (e.g., Fibonacci, ZeckFibBijections), advancing algorithmic approaches to enumerative combinatorics. He is actively involved in teaching, such as Math 22A: Vector Calculus and Linear Algebra I in Fall 2024. His interdisciplinary interests include applying probability to clinical studies (e.g., heart rate variability under anesthesia) and recreational mathematics, exploring topics like the mathematics of Rubik's Cube and origami constructions for angle trisection.

Peter KvamView profile

Assistant Professor

• Cognitive Psychology
• Decision Making
• Machine Learning
• +4 more

Dr. Peter Kvam is an Assistant Professor in the Department of Psychology at The Ohio State University, specializing in Cognitive Psychology and Decision Psychology. He holds a PhD from Michigan State University and has held visiting and postdoctoral positions at institutions including the Max Planck Institute in Berlin and the University of Tasmania. His research focuses on the cognitive mechanisms underlying decisions, particularly in complex, dynamic environments, leveraging machine learning and computational models. He previously served as faculty at the University of Florida before joining Ohio State in 2024. Education: PhD in Psychology (Michigan State, 2017), MA (Michigan State, 2014), BS in Psychology and BA in Mathematics/Sociology (Indiana University, 2012). Research interests include decision-making dynamics, computational modeling, and applications of AI to psychology. His work spans topics such as multi-alternative decisions, pricing strategies, and the cognitive basis of polarization. Notable awards include the William K. Estes Early Career Award (2024) and APS Rising Star Award (2023). Advising and grants include mentoring graduate students and securing funding from the University of Florida and Ohio State. His lab, Cognition & Decision Modeling, explores decision processes through interdisciplinary methods. He teaches courses on judgment, decision-making, and psychological modeling.

• Petroleum Geoscience
• Reservoir Engineering
• Seismic Data Analysis
• +3 more

Asghar Shams is an Associate Professor at Heriot-Watt University's School of Energy, Geoscience, Infrastructure and Society, affiliated with the Institute for GeoEnergy Engineering. His research focuses on reservoir characterization, seismic data analysis, and application of artificial intelligence in petroleum engineering. He has extensive experience in carbonate reservoir modeling, fluid flow dynamics, and integration of geophysical data with reservoir engineering workflows. Research interests include reservoir simulation, production optimization, fracture network modeling, and machine learning applications. His work spans global basins like the Sirt Basin (Libya), Gulf of Mexico, and Middle East carbonate systems. He has published over 52 peer-reviewed articles and actively participates in international conferences such as SEG and EAGE. Professional activities include membership in committees at Imperial College London and Italy's Istituto Nazionale di Oceanografia e di Geofisica Sperimentale. His research emphasizes bridging geoscience and engineering through advanced computational methods.

• Combinatorics
• Number Theory
• Discrete Mathematics
• +3 more

Pavel Shuldiner is a Research Fellow (Postdoctoral Fellow) at the University of Waterloo, working under the supervision of Wayne Oldford. His appointment spans from May 1, 2023, to October 31, 2024. He can be reached at pavel.shuldiner@uwaterloo.ca and is located in room M3 3102. Shuldiner's research focuses on advanced combinatorial problems, number theory, and discrete mathematics. His work explores topics such as clique covers in graph theory, modular arithmetic properties of partitions, and noncommutative symmetric functions. His recent studies include investigations into Bernoulli sums and their probabilistic interpretations, as well as the structural characterization of colored m-ary partitions. His publications reflect a deep engagement with theoretical mathematics, spanning from extremal graph theory to algebraic combinatorics. He has contributed to understanding residue classes of partition numbers and developing Pieri rule-based frameworks for Schur-like bases in noncommutative symmetric function spaces. Shuldiner has not been listed as having formal advisees, but his research aligns with collaborative efforts in combinatorics and theoretical computer science. His work is supported through his postdoctoral fellowship under Oldford’s supervision, though specific grants are not detailed in the provided information.

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

Nick Gravin is a Professor at the School of Information Management and Engineering (SIME) of Shanghai University of Finance and Economics (SHUFE), affiliated with the Institute for Theoretical Computer Science (ITCS). His research focuses on Theoretical Computer Science, Discrete Mathematics, and Probability Theory, particularly in algorithmic auction design, equilibria computation in Algorithmic Game Theory, and online algorithms. Before joining SHUFE in 2017, he held postdoctoral positions at MIT-CSAIL (2016) and Microsoft Research New England (2013–2015). He holds dual PhDs: one in Mathematics from the Steklov Institute (Russia) under Dmitri Karpov, and another in Computer Science from Nanyang Technological University (Singapore), advised by Dmitrii Pasechnik, Edith Elkind, and others. His work bridges fundamental algorithmic challenges and practical market design, emphasizing mechanisms for online resource allocation, budget constraints, and auction optimization. Over 15 recent publications span prophet inequalities, secretary problems, and competitive analysis in dynamic environments. Gravin’s contributions include novel pricing strategies, nonadaptive algorithms for matching problems, and frameworks for Bayesian online settings. His research also explores the interplay between cardinal and ordinal data in algorithmic decision-making. No awards or grants are explicitly listed in the provided text. His advising role is not detailed, though his postdoctoral mentorship suggests involvement in training researchers. Current research continues to address theoretical and applied aspects of computational economics and combinatorial optimization.

Alistair StewartView profile

Research Fellow

• Theoretical Computer Science
• Algorithms
• Machine Learning
• +3 more

Alistair Stewart is a Research Fellow in the Department of Computer Science at the University of Southern California (USC), working with Professor Ilias Diakonikolas. Previously, he completed his PhD at the University of Edinburgh under the supervision of Professor Kousha Etessami, focusing on algorithms for infinite-state stochastic models. His research interests span theoretical computer science, algorithms, machine learning, and probability theory. Notable areas include robust statistical estimation, distribution learning, algorithmic game theory, and probabilistic systems analysis. His work has been published in top conferences such as SODA, FOCS, COLT, and STOC. Key contributions include advancements in high-dimensional robust estimation, efficient learning algorithms for discrete distributions, and analysis of stochastic models like branching processes and context-free grammars. No scientific awards are explicitly mentioned in the provided texts. His academic trajectory includes collaborations with leading researchers in theoretical computer science, and his PhD thesis addressed foundational problems in stochastic algorithmics.

• Probability Theory
• Random Discrete Structures
• Scaling Limits
• +7 more

Igor Kortchemski is a CNRS researcher at the Department of Mathematics (DMA) of École Normale Supérieure (ENS Paris) and a lecturer in the Department of Applied Mathematics at École Polytechnique. His research focuses on probability theory , scaling limits of random discrete structures , Galton-Watson trees , and Lévy processes , particularly studying continuous limits of random discrete models. University : École Normale Supérieure (ENS Paris) and École Polytechnique Roles : Researcher and lecturer He has taught courses on discrete mathematics , condensation phenomena in random trees , and advanced probability topics since 2017, with lecture notes and exercises available online. Contact: igor.kortchemski@math.cnrs.fr , igor.kortchemski@ens.psl.eu , igor.kortchemski@polytechnique.edu .

Nikola KuzmanovskiView profile

Research Fellow

• Commutative Algebra
• Extremal Combinatorics
• Discrete Mathematics
• +3 more

Nikola Kuzmanovski is a Kenna Postdoctoral Research Fellow in the Department of Mathematics at the University of Notre Dame. He holds a B.S. from the University of Wisconsin-Superior (2018), an M.A. in Mathematics from the University of Nebraska-Lincoln (2020), and a Ph.D. in Mathematics from the same institution (2024). His research focuses on extremal combinatorics, commutative algebra, and their interdisciplinary applications, particularly exploring connections between algebraic structures and combinatorial problems. His work includes contributions to isoperimetric problems, algebraic combinatorics, and discrete mathematics. Key publications address topics like local-global principles in algebra, combinatorial methods in graph theory, and the development of novel algebraic frameworks. Kuzmanovski’s research often bridges theoretical foundations with practical applications, reflecting his expertise in both algebraic and combinatorial domains. No scientific awards are explicitly mentioned in the provided text. His professional activities do not include listed advising roles or grants, though his postdoctoral position at Notre Dame underscores his active involvement in advanced research. He is affiliated with the Algebra and Algebraic Geometry, Discrete Math, Operations Research, and Probability research groups at the university.

Manfred DrosteView profile

Professor

• Automata Theory
• Formal Languages
• Quantitative Logics
• +4 more

Manfred Droste is a Professor at the Institute of Computer Science at the University of Leipzig since 2004. He previously held positions at TU Dresden, University of Dortmund, and University of Essen. Research interests: Automata theory, logic, algebraic models for concurrent systems, domain theory, and model theory. Key affiliations: Academia Europaea member (2011), Vice-speaker of DFG-Research Training Group 'QuantLA' Supervised 8 PhD students including Dietrich Kuske (TU Ilmenau) and Paolo Boldi (University of Milano) Recent Publications focus on weighted automata, formal power series, and quantitative logics with applications in real-time systems and XML processing. 2020: Dr. h.c. from Immanuel Kant Baltic Federal University 2018: Visiting professorship at ENS Cachan 2012: Catedra de Excelencia at URV Tarragona Active in conference organization including 17 international workshops and 6 Dagstuhl seminars. Editorial board member for multiple journals including Journal of Automata, Languages and Combinatorics and Forum Mathematicum . Leads research projects with DFG and DAAD funding including international collaborations in China, Serbia, and Thailand.

Tomasz TkoczView profile

Associate Professor

• Analysis
• Probability
• Convex Geometry
• +5 more

Tomasz Tkocz is an Associate Professor in the Department of Mathematical Sciences at Carnegie Mellon University (CMU), affiliated with the Mellon College of Science. He holds a Ph.D. from the University of Warwick and has held postdoctoral positions at Princeton University and the Mathematical Sciences Research Institute (MSRI). His research focuses on analysis, probability, and convex geometry, with emphasis on high-dimensional phenomena, geometric inequalities, and information theory. Tkocz has taught courses ranging from undergraduate calculus to graduate-level topics in complex analysis, functional analysis, and probability. Notable contributions include work on Gaussian inequalities, convex body sections, and entropy-related problems. Education: Ph.D. in Mathematical Sciences, University of Warwick Postdoctoral Fellowships: Princeton University, MSRI Research Interests: Analysis of high-dimensional geometric structures Probabilistic methods in convex geometry Entropy and concentration inequalities Random matrices and log-concave distributions Recent Publications Trends: Tkocz’s recent work explores geometric stability phenomena (e.g., simplex and polydisc slicing), entropy interpretations of anti-concentration, and sharp inequalities for random variables. His 2025 papers extend tail bounds for uniform sums and reverse Hölder inequalities, reflecting ongoing contributions to probabilistic and geometric analysis. Teaching & Outreach: Tkocz has instructed over a decade of courses at CMU, including Complex Analysis, Functional Analysis, and Real Analysis. His materials emphasize rigorous problem-solving and applications of abstract mathematical concepts.

Chris FewsterView profile

Professor

• Quantum Field Theory
• General Relativity
• Mathematical Physics
• +3 more

Professor Chris Fewster is a faculty member in the Department of Mathematics at the University of York, serving as Deputy Head of Department for Research. His academic journey includes a PhD from the University of Cambridge and postdoctoral roles at Bern and York before joining as a lecturer in 1998. He has held significant university roles, including membership of the Senate and leadership roles in teaching and assessment committees. His research focuses on mathematically rigorous formulations of quantum field theory (QFT) in curved spacetimes, with four main areas: measurement theory in QFT, quantum energy inequalities (QEIs), locally covariant QFT, and Hadamard states. He has supervised numerous PhD students, including notable works on QEIs and locally covariant frameworks. Recent publications (2021–2025) emphasize advancements in quantum measurement frameworks, energy inequalities, and foundational aspects of QFT. His work bridges functional analysis, geometry, and category theory, addressing paradoxes and applications in quantum information. Active in collaborations and grants, his research group explores mathematical physics and quantum information. Awards: None explicitly listed. Supervision and grants include leadership in projects like the EPSRC-funded 'Symmetry and measurement: a foundation for semi-local quantum physics.' His lab affiliation is the Mathematical Physics and Quantum Information Research Group.