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Saugata BasuView profile

Professor

• Real Algebraic Geometry
• Computational Algebra and Geometry
• Theoretical Computer Science
• +5 more

Saugata Basu is a Professor of Mathematics and Computer Science at Purdue University, with Mathematics as his home department. He holds a joint appointment and has a distinguished academic career, previously serving at the Georgia Institute of Technology, University of Michigan, and IBM T. J. Watson Research Center. His research spans real algebraic geometry, computational algebra and geometry, theoretical computer science, and applied topology. He has authored influential works such as the textbook Algorithms in Real Algebraic Geometry and contributed to foundational results in semi-algebraic geometry and computational complexity. Education: PhD from New York University's Courant Institute (1996) and BTech in Computer Science from IIT Kharagpur (1991). He has held visiting positions at institutions including Université de Rennes, Institute Henri Poincaré, and the Mathematical Sciences Research Institute in Berkeley. Research interests include O-minimal structures, computational topology, and algorithmic semi-algebraic geometry. His work bridges theoretical mathematics with applications in computer science, cryptography, and optimization. Notable awards include the Sloan Fellowship (2003–2005) and NSF Career Award (2002–2007). Grants and collaborations span topics like symmetry in algebraic geometry, quantum computations, and topological data analysis. He frequently presents at leading conferences such as FOCS, STOC, and SIAM Algebraic Geometry.

Stefan SteinerbergerView profile

Professor

• Analysis
• Applied Mathematics
• Combinatorics
• +5 more

Stefan Steinerberger is a Professor in the Department of Mathematics at the University of Washington, Seattle. He holds a PhD from the University of Bonn (2013) and specializes in Analysis, with research interests spanning Partial Differential Equations, Spectral Theory, Harmonic Analysis, and applications to Data Science and Mathematical Physics. His work often bridges theoretical and applied mathematics, addressing problems in geometry, combinatorics, and numerical methods. Education: PhD in Mathematics, University of Bonn, 2013 Research Interests: Steinerberger’s research focuses on Analysis, with emphasis on PDEs, Spectral Theory, and Harmonic Analysis. He explores interdisciplinary connections, such as applying analytic techniques to graph theory, optimization, and data science. Specific themes include eigenvalue estimates, dynamical systems, and the interplay between geometry and combinatorial structures. Awards & Recognition: While specific awards are not listed, his extensive publication record and affiliations with prestigious journals indicate significant contributions to the field. His work frequently appears in top-tier journals like Advances in Mathematics , Journal of Functional Analysis , and SIAM Journal on Discrete Mathematics . Teaching & Outreach: He teaches courses in the Department of Mathematics and is involved in outreach initiatives such as the Washington Experimental Mathematics Lab (WXML) and the UW Math Circle. His courses and research often emphasize problem-solving and real-world applications. Labs/Teams: Active in collaborations across mathematical disciplines, including work with researchers in combinatorics, probability, and numerical analysis. He maintains a personal website with resources on his research, teaching, and open problems.

Matthew A MastenView profile

Associate Professor

• Econometrics
• Causal Inference
• Identification
• +3 more

Matthew A. Masten is an Associate Professor of Economics at Duke University, affiliated with the Trinity College of Arts & Sciences. He holds a PhD from Northwestern University (2013). His research focuses on econometrics, causal inference, and robustness/sensitivity analysis in treatment effect estimation. He has received the NSF CAREER Award (2020–2025) and was tenured at Duke in 2021. His work emphasizes identification under weak assumptions and develops methods for assessing sensitivity to unconfoundedness and unobserved confounding. He has contributed Stata packages like ivcrc and tesensitivity for econometric analysis. His recent articles explore minimax-regret treatment rules, exogeneity assumptions in potential outcomes, and sensitivity analysis techniques. He teaches advanced econometrics courses (ECON 703D, ECON 899) and leads research on correlated random coefficients and instrumental variable models. His grants include foundational work on sensitivity analysis in econometrics. Key awards/grants: NSF CAREER Award (2020–2025), Tenured Faculty Recognition (2021). His research bridges theoretical econometrics with practical applications, emphasizing robustness and methodological innovation.

Adam M RosenView profile

Professor

• Econometrics
• Instrumental Variable Methods
• Tobit Models
• +4 more

Adam M Rosen is a Professor of Economics at Duke University's Trinity College of Arts & Sciences. His research focuses on econometric methodology, particularly instrumental variable (IV) methods, partial identification, and discrete choice modeling. He holds a Ph.D. from Northwestern University (2006). Rosen's work spans theoretical advancements in IV models, Tobit models, and structural estimation of economic games, with applications to policy evaluation and applied microeconomics. His recent publications emphasize extending IV frameworks to nonlinear settings and addressing identification challenges with imperfect instruments. He has contributed to software tools for econometric analysis, including Stata packages for intersection bounds and imperfect instrument applications. Rosen also serves as Director of Graduate Studies in Economics at Duke and teaches advanced courses in econometrics. Key affiliations: International Research Staff at CeMMAP, International Research Fellow at IFS. Notable contributions include 'Generalized Instrumental Variable Models' (Handbook of Econometrics) and 'Partial Identification in Applied Research.' Research interests include bounding wage distributions, robust analysis of short panels, and counterfactual analysis. His work often bridges econometric theory and practical applications, with a focus on causal inference under incomplete information.

• Mathematical Finance
• Stochastic Processes
• Financial Mathematics
• +3 more

Dr. Anna Aksamit is a Lecturer in the School of Mathematics and Statistics at the University of Sydney. Her research focuses on mathematical finance and stochastic processes, particularly the theoretical foundations of information modeling in financial markets. She specializes in enlargement of filtration theory, arbitrage detection, and stochastic control under uncertainty. Notably, she co-authored the book Enlargement of Filtration with Finance in View (2017), which explores applications of filtration theory in financial modeling. Her work includes collaborations on projects such as 'Information and Uncertainty in Financial Modelling' (2024) and 'Can green investors drive the transition to a low emissions economy?' (2022), funded by the University of Sydney and the Australian Research Council. She has published extensively in top-tier journals like Finance and Stochastics and Stochastic Processes and their Applications , addressing topics ranging from robust pricing-hedging duality to sensitivity analysis of stochastic processes. Aksamit’s contributions span both theoretical advancements (e.g., martingale representations, random times decomposition) and applied finance (e.g., green finance frameworks, mean-field games for sustainable finance). No scientific awards are explicitly listed in her profile, though her research has been recognized through multiple grant awards. She holds a doctoral thesis titled Random Times, Enlargement of Filtration and Arbitrages (2014) from Université d'Evry Val d'Essonne.

Federico BugniView profile

Professor

• Econometric Theory
• Partially Identified Models
• Functional Data Analysis
• +4 more

Federico Bugni is a Professor of Economics and Director of Graduate Studies at Northwestern University's Weinberg College of Arts & Sciences, Department of Economics. He holds a PhD from Northwestern University (2008) and previously served as Professor and Director of Graduate Studies at Duke University (2013-2021). His research focuses on econometric theory, particularly in inference for partially identified models, functional data analysis, dynamic games, and covariate-adaptive randomization methods. Education includes a PhD and MA from Northwestern University (2008, 2005), and B.A./M.A. from Universidad de San Andrés, Argentina (2001/2002). Awards include Fellow of the International Association for Applied Econometrics (2021) and NSF grants totaling over $500k. He has advised numerous PhD students and serves as Associate Editor for journals like Quantitative Economics and Journal of Econometrics. His work addresses challenges in causal inference, experimental design, and microeconometric modeling.

Jozsef BaloghView profile

Professor

• Combinatorics
• Graph Theory
• Extremal Combinatorics
• +5 more

Jozsef Balogh is a Professor in the Department of Mathematics at the University of Illinois, holding the J. Andrew and Susan Langan Scholar title. He is affiliated with the College of Liberal Arts & Sciences and focuses on Combinatorics, Graph Theory, and related areas. His research emphasizes extremal combinatorics, additive combinatorics, and the development of the container method for hypergraphs, which has applications in Ramsey theory and theoretical computer science. Education: Ph.D. in Mathematics from the University of Memphis (2001). He has held prestigious fellowships including the Simons Fellowship (2013–2014) and Marie Curie Fellowship (2013–2017), and received the George Pólya Prize in Combinatorics (2016) from SIAM. His NSF CAREER Award (2008–2013) supported work in modern combinatorics. Research highlights include resolving conjectures on maximal triangle-free graphs, maximal sum-free sets, and union-free sets using the container theorem. His work bridges probabilistic methods and combinatorial structures, influencing areas like discrete geometry and algorithm design. Recent publications address extremal graph problems, Ramsey-type tilings, and probabilistic thresholds in combinatorial systems. Awards and grants reflect his impactful contributions, while his advising and mentorship are central to his academic role. Ongoing research focuses on expanding applications of container methods and exploring new frontiers in extremal combinatorics.

Nathan M. DunfieldView profile

Professor

• 3-dimensional geometry and topology
• Hyperbolic geometry
• Geometric group theory
• +3 more

Nathan M. Dunfield is a Professor of Mathematics at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the College of Liberal Arts & Sciences. He holds a PhD from the University of Chicago and has held positions at Harvard University and the California Institute of Technology before joining UIUC in 2007. His research focuses on 3-dimensional geometry and topology, hyperbolic geometry, and their connections to number theory and geometric group theory. Dunfield is a Fellow of the American Mathematical Society (since 2013) and collaborates with researchers in physics, computer science, and algebraic geometry. Education History: PhD from the University of Chicago (20th century), prior roles at Harvard and Caltech. Current research interests include experimental mathematics, topological invariants, and applications of geometric techniques to problems in algebra and number theory. He develops computational tools like the SnapPy software for studying 3-manifolds. Key contributions include work on essential surfaces in 3-manifolds, random 3-braids, and algorithmic topology. Dunfield advises current PhD students Brevan Ellefsen, Lara Lahey, and Shana Li, and has mentored numerous graduated students. His work integrates theoretical insights with computational methods, exemplified by collaborations on projects like the Virtual Haken Conjecture and spatial graph enumeration in engineering systems. He maintains active research through grants and collaborations, including contributions to open-source software and participation in interdisciplinary initiatives. Dunfield’s labs and teams involve computational topology projects, such as the SnapPy development and CompuTop collaborations, advancing both theoretical understanding and practical tools in low-dimensional topology.

Kay KirkpatrickView profile

Professor

• Quantum Mechanics
• Statistical Mechanics
• Probability Theory
• +6 more

Kay Kirkpatrick is a full Professor of Mathematics and Physics at the University of Illinois, Urbana-Champaign. She holds affiliations with the Department of Mathematics and the Department of Physics within the College of Liberal Arts & Sciences. Her roles include being a Blackwell Scholar and Helen Corley Petit Scholar. Kirkpatrick’s research focuses on quantum and statistical mechanics, nonlinear quantum equations, and applications to physics and biology, including dark matter dynamics, biophysical systems, and computational models of biological computation. She has contributed to understanding Bose-Einstein condensation, topological insulators in microtubules, and the channel capacity of ribosomes. Education: Ph.D. in Mathematics from UC-Berkeley (2007), B.S. in Mathematics from Montana State University. Additional training includes a Study in a Second Discipline Fellowship in Neuroscience (2020–2021). She has held postdoctoral positions at MIT and NYU, and visiting roles at CRM Montréal and MIT. Research interests span quantum statistical mechanics, nonlinear Schrödinger equations, Gibbs measures, and computational biology. Notable awards include the Simons Fellowship, NSF CAREER Award, and Tenney Peck Teaching Award. Kirkpatrick has mentored students and led projects in percolation theory and phase transitions through Illinois Geometry Lab initiatives. Her recent publications (2022–2024) address topics like Bose-Einstein condensation in dark matter, ribosome channel capacity, and fractional quantum models. She teaches advanced courses in machine learning and partial differential equations, emphasizing interdisciplinary applications and effective communication.

Alexandru ZaharescuView profile

Professor

• Number Theory
• Analytic Number Theory
• Modular Forms
• +5 more

Alexandru Zaharescu is a Professor of Mathematics at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Department of Mathematics in the College of Liberal Arts & Sciences. His research focuses on Number Theory, encompassing analytic, algebraic, and geometric aspects with connections to modular forms, L-functions, prime distribution, and partition theory. He holds a Ph.D. from Princeton University (1995). Education: Ph.D. in Mathematics, Princeton University, 1995 Zaharescu's research interests include the Riemann Hypothesis, Farey series, exponential sums, and applications to cryptography and discrete mathematics. Recent work explores geometric interpretations of number-theoretic phenomena, such as angular distributions of lattice points and properties of modular curves. His studies on prime partitions and pseudo-differentiable functions bridge analysis and number theory. His publications reflect a focus on advanced topics like L-functions, divisor functions, and modular forms. Key contributions include proofs of Pollock’s conjectures on icosahedral numbers and statistical analyses of Fermat quotients. Awards: AMS Fellow (2017), LAS Teaching Excellence Award (2010), Center for Advanced Study Associate (2004–05, 2013–14) Zaharescu’s work often intersects with algebraic geometry and combinatorics, addressing problems in both pure and applied mathematics. His research on cyclotomic fields and p-adic valuations contributes to foundational number theory.

Bogdan SavchynskyyView profile

Researcher

• Combinatorial Optimization
• Computer Vision
• Machine Learning
• +3 more

Dr. Bogdan Savchynskyy is a Senior Researcher and Group Leader at the Computer Vision and Learning Lab, Heidelberg University. His academic journey includes roles at TU Dresden and the Heidelberg Collaboratory for Image Processing. He holds a Ph.D. in Computer Science from National Technical University of Ukraine (Kiev Polytechnic Institute). Savchynskyy's research focuses on combinatorial optimization, graphical models, and their applications in computer vision and machine learning. Education: PhD in Computer Science, National Technical University of Ukraine (2009) Postdoc, Heidelberg Collaboratory for Image Processing (2009–2014) His research interests include large-scale combinatorial optimization, multi-graph matching, and unsupervised learning. Notable achievements include developing state-of-the-art graph matching solvers and securing DFG grants for projects like OPTEMA and UMDISTO. He has published extensively in top venues such as CVPR, ICCV, and AAAI. Dr. Savchynskyy supervises students in projects involving optimization techniques and teaches courses like Applied Combinatorial Optimization . His lab hosts regular seminars on convex and combinatorial optimization. Awards: DFG Grants (2016, 2022, 2024) Outstanding Reviewer Awards (ICML 2022, CVPR 2016/2015) Best Student Paper Award at CVPR 2014 Key projects include the Graph Matching Benchmark and Tracking-by-Assignment , advancing bioimaging and computer vision applications. His lab fosters collaboration in optimization and interdisciplinary research.

Alan FriezeView profile

Professor

• Combinatorics
• Computer Science
• Random Graphs
• +6 more

Alan Frieze is a University Professor at Carnegie Mellon University , affiliated with the Department of Mathematical Sciences. His research spans combinatorics, random graphs, algorithms, and probabilistic methods. Academic Rank: Professor Department: Mathematical Sciences Emails: frieze@cmu.edu, af1p@andrew.cmu.edu, alan@random.math.cmu.edu Research Interests: Frieze's work focuses on combinatorics, random graph theory, algorithm design, and probabilistic models. His recent publications address Hamiltonian cycles, rainbow subgraphs, and stochastic processes in graph theory. Scientific Awards: He has received prestigious honors including the Fulkerson Prize (1991), Guggenheim Fellowship (1997), and fellowships from the AMS and SIAM. Teaching Activities: Frieze has taught courses such as Random Graphs, Combinatorics, and Doctoral Thesis Research at Carnegie Mellon University.

• Turbulence
• Nonlinear Systems
• Fluid Dynamics
• +4 more

Miguel Bustamante is an Associate Professor at the School of Mathematics and Statistics , University College Dublin (UCD). He holds roles such as Head of Research and Innovation (2013–2014), Head of Subject in Applied and Computational Mathematics (2018–2021), and is an elected member of UCD's Academic Council (2016–2018). His research focuses on turbulence, nonlinear systems, fluid dynamics, and wave interactions , with applications in plasmas, quantum superfluids, and industrial mathematics. He has collaborated internationally, including visits to institutions like the Chinese Academy of Sciences and the Fields Institute. Research Interests: Turbulence and extreme events in fluids Nonlinear energy transfer mechanisms (e.g., precession resonance) Applications in water waves, plasmas, and atmospheric dynamics Mathematical biology and industrial problem-solving Awards & Recognition: UCD Teaching and Learning Award (2020) Highest-viewed paper in Fluids (2021) Invited speaker at international conferences (e.g., British Applied Mathematics Colloquium) Professional Activities: Member of EUROMECH and the Irish Mathematical Society Reviewer for multiple funding bodies (NSERC, FWF-Austria, etc.) Organizer of the 2nd European Fluid Dynamics Conference (2025)

Taoyang WuView profile

Lecturer

• Computational Biology
• Phylogenetic Networks
• Combinatorics
• +3 more

Taoyang Wu is a Lecturer in Computing Sciences at the University of East Anglia's School of Computing Sciences. His research bridges computational biology, combinatorics, and algorithm design, with focus on mathematical modeling of biological systems and evolutionary processes. Dr. Wu's work encompasses: Development of combinatorial models for phylogenetic networks Statistical analysis of tree-shaped structures in evolution Algorithmic solutions for biological data analysis Mathematical approaches to plant stress signaling His publications demonstrate consistent methodological focus on discrete mathematics applied to biological questions. Recent work includes asymptotic enumeration of phylogenetic network classes, distribution analyses of subtree patterns, and graph-theoretic approaches to biological structures.

• Mathematical Optimization
• Nonlinear Optimization
• Mixed-Integer Optimal Control
• +3 more

Christian Kirches is a full professor at the Institute for Mathematical Optimization within the Carl-Friedrich-Gauß-Fakultät (Faculty of Mathematics, Technische Universität Braunschweig). His research focuses on nonlinear optimization , mixed-integer optimal control , and robust optimization for dynamic systems. He was awarded the Klaus-Tschira prize (2011) for public science communication and the Hengstberger prize (2014) for junior researchers, and received an ERC Consolidator Grant (2022) for his work on optimization under uncertainty. Alumni of Heidelberg University (Diploma, Doctorate, Habilitation) Former resident associate at Argonne National Laboratory and postdoctoral appointee at the University of Chicago Leader of a junior research group (2013–2017) at Heidelberg University His recent publications highlight advancements in mixed-integer nonlinear programming , real-time control systems , and optimization for sustainable energy and transportation . He collaborates with researchers on projects like wind farm control, hydrogen aviation networks, and chromatography process optimization. His methodological work on sum-up rounding , trust-region algorithms , and combinatorial integral approximation has been published in journals such as SIAM Journal on Optimization, Mathematical Programming, and IEEE Control Systems Letters. Kirches also serves as area coordinator for Optimization Online and was associate editor for OR Spectrum (2022–2024). Scientific Awards: Klaus-Tschira Prize (2011) Hengstberger Prize (2014) ERC Consolidator Grant (2022) He is an elected member of the COIN-OR initiative and contributes to open-source optimization software. His lab at TU Braunschweig develops algorithms for dynamic systems under uncertainty, with applications in energy management, autonomous traffic, and industrial processes.