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Lex SchrijverView profile

Professor

• Discrete Mathematics
• Optimization
• Algorithms
• +3 more

Lex Schrijver is a Professor of Mathematics at the University of Amsterdam since 1990 and a CWI-Fellow at Centrum voor Wiskunde en Informatica (CWI) since 1989. His research focuses on discrete mathematics, optimization, and algorithms, with significant contributions to graph theory, combinatorics, and coding theory. Research Interests: Discrete Mathematics, Optimization, Algorithms, Graph Theory, Combinatorics, Coding Theory. Awards: EURO Gold Medal (2015) Honorary Doctorate from Eotvos Lorand University (2011) Franz Edelman Award (2008) SIGMA Prize (2008) John von Neumann Theory Prize (2006) Spinoza Prijs (2005) Ridder in de orde van de Nederlandse Leeuw (2005) Lanchester Prize (2004, 1986) Fulkerson Prize (2003, 1982) Van Dantzig Prize - SIAM / MPS (2003) Eredoctoraat from University of Waterloo (2002) Projects: Involved in the Networks project, funded by the Netherlands Organisation for Scientific Research (NWO). Professional Activities: Fellow of the American Mathematical Society (AMS) and SIAM.

Cristian DobreView profile

Associate Professor

• Semidefinite Programming
• Operations Research
• Combinatorial Optimization
• +2 more

Cristian Dobre is an Associate Professor at the Tilburg School of Economics and Management , affiliated with the Department of Econometrics and Operations Research at Tilburg University. His research focuses on Semidefinite Programming , Operations Research , and Combinatorial Optimization . Research Interests: Specializing in symmetry exploitation in copositive programs, semidefinite relaxations for combinatorial problems, and applications in matrix algebra. Publications: His work includes key contributions to Mathematical Programming and Discrete Applied Mathematics , with a doctoral thesis on structured combinatorial optimization. Projects: Participated in semidefinite programming research projects between 2006 and 2011 as a researcher.

Bastian von Harrach-SammetView profile

Professor

• Inverse Problems
• Numerical Analysis
• Partial Differential Equations
• +3 more

Prof. Dr. Bastian von Harrach-Sammet is a W3 Professor for Numerics of Partial Differential Equations at Goethe University Frankfurt, where he has served as Dean of the Department of Computer Science and Mathematics since January 2024. He studied mathematics and physics at the University of Mainz as a scholarship holder of the German National Academic Foundation, earned his doctorate in 2006, and held postdoctoral positions at the Johann Radon Institute (Austria) and Yonsei University (South Korea). His academic trajectory includes professorships at TU Munich (2010–2011), University of Würzburg (2011–2013), and University of Stuttgart (2013–2015) before joining Goethe University. His research focuses on inverse problems , numerical analysis of PDEs , and optimization , with applications in medical imaging (e.g., electrical impedance tomography) and computational mechanics. Key themes include monotonicity-based reconstruction, convex optimization for inverse coefficient problems, and stability analysis. Publications emphasize theoretical rigor in inverse problems, featuring advances in Calderón-type problems, fractional Schrödinger equations, and stochastic inclusion detection. Recent work (2023–2025) explores convex reformulations of inverse problems and computational methods for elasticity/tomography. Awards & Honors: Robert Bartnik Visiting Fellowship (2017) MediaV Young Researcher Award (2016) Dissertationspreis der Freunde der Universität Mainz e.V. (2007) General Membership, Institute for Mathematics and Its Applications (2006) He is a liaison lecturer for the German Academic Scholarship Foundation, co-editor of journals ( Inverse Problems , Communications on Analysis and Computation ), and co-founder/treasurer of the Inverse Problems International Association. No specific grants, labs, or student advisees are detailed in the source text.

• Mathematical Optimization
• Stochastic Processes
• Control Theory
• +3 more

Rodolfo Antonio Rios Zertuche is a Research Fellow in the Department of Mathematics and Statistics at UiT The Arctic University of Norway, actively contributing to the research groups Applied and Computational Algebra and MASCOT. His work bridges theoretical mathematics with computational applications across optimization and stochastic systems. His research focuses on Mathematical Optimization, Stochastic Processes, and Dynamical Systems, with significant contributions to variational problem relaxations, sums-of-squares hierarchies, and subgradient method dynamics. Recent investigations examine convexity in measure-theoretic frameworks and convergence properties in polynomial optimization. Publications from 2022-2024 reveal a concentrated trend toward optimization theory, particularly in occupation measure relaxations, correlative sparsity convergence rates, and stochastic approximation techniques. These works demonstrate increasing sophistication in handling nonsmooth functions and probabilistic frameworks through measure-theoretic approaches. Rios Zertuche maintains strong international collaborations with researchers from institutions including LAAS-CNRS (Toulouse) and Czech Technical University, evidenced by co-authorships with Didier Henrion and Milan Korda. His involvement in the MASCOT research group supports interdisciplinary projects connecting algebraic methods with computational mathematics.

Michael J. LindseyView profile

Assistant Professor

• Computational Methods
• Numerical Linear Algebra
• Optimization
• +19 more

Michael J. Lindsey is an Assistant Professor in the Department of Mathematics at the University of California, Berkeley, and a Faculty Scientist at Lawrence Berkeley National Laboratory. His research focuses on computational methods driven by Numerical Linear Algebra , Optimization , and Randomization , particularly for High-Dimensional Scientific Computing in quantum many-body problems and applied probability. University : UC Berkeley (Assistant Professor since 2022) Lab Affiliation : Mathematics Group at Lawrence Berkeley National Laboratory Email : lindsey@berkeley.edu His work includes Semidefinite Relaxation for quantum and classical problems, Monte Carlo Sampling techniques, and Tensor Networks for high-dimensional functions. He has pioneered Variational Embedding theory with guaranteed energy bounds and scalable solvers for quantum systems. Recent publications span Quantum Chemistry , Machine Learning , and High-Dimensional Probability , with applications to Electronic Structure , Molecular Dynamics , and Optimal Transport . He received the 2024 Hellman Fellowship and the 2019 SIAM Student Paper Prize . Teaching includes graduate and undergraduate courses in numerical analysis and applied mathematics at UC Berkeley and New York University. He also organizes the HDSC Seminar on high-dimensional scientific computing.

Pedro FelzenszwalbView profile

Professor

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +2 more

Pedro Felzenszwalb is a Professor of Engineering and Computer Science at Brown University , with a research focus spanning computer vision, artificial intelligence, machine learning, and algorithms. Born in Rio de Janeiro, Brazil, he earned his BS in Computer Science from Cornell University (1999) and MS/PhD in EECS from MIT (2001/2003). He previously held a faculty position at the University of Chicago (2004-2011) before joining Brown in 2011. Education : PhD in EECS, MIT (2003) MS in EECS, MIT (2001) BS in Computer Science, Cornell University (1999) His research integrates computer vision and AI, emphasizing scalable algorithms for object recognition, image segmentation, and probabilistic modeling. Key methodologies include deformable part models, belief propagation, and dynamic programming. His work has significant applications in early vision tasks, scene understanding, and geometric constraints in 3D object recognition. Pedro’s publications demonstrate a trajectory from foundational graph/image algorithms (2004-2006) to advanced machine learning approaches (2010-2023), with recurring themes in optimization, clustering, and multiscale modeling. Notable journals include Journal of the ACM , IEEE Transactions , and Communications of the ACM . Scientific Awards : ACM Grace Murray Hopper Award IEEE Technical Achievement Award PASCAL Visual Object Challenge Lifetime Achievement Prize Longuet-Higgins Prize NSF CAREER Award He has received NSF funding for projects including Graph Cut Algorithms (2012-2015) and Object Recognition with Hierarchical Models (2008-2013). At Brown, he teaches graduate courses in machine learning, linear systems, and pattern recognition.

Dmitriy (Tim) KuniskyView profile

Assistant Professor

• Probability
• Statistics
• Mathematical Data Science
• +7 more

Dmitriy (Tim) Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University's Whiting School of Engineering. He is also affiliated with the Data Science and AI Institute, the Department of Mathematics, and the Algorithms and Complexity Group at Johns Hopkins. Dr. Kunisky received his bachelor's degree in mathematics from Princeton University, worked as a software engineer for Google, earned his PhD in mathematics from the Courant Institute at NYU under the supervision of Afonso Bandeira and Gérard Ben Arous, and was a postdoctoral associate in computer science at Yale University before joining Johns Hopkins. His research broadly concerns how probability theory and mathematical statistics interact with computational complexity and the theory of algorithms. He investigates the mathematical phenomena that govern the power and limitations of algorithms processing massive and high-dimensional inputs, drawing on asymptotic statistics, convex geometry, random matrix theory, statistical physics, and representation theory. His work includes studying convex relaxation algorithms on combinatorial optimization problems, computational intractability in high-dimensional statistics, pseudorandomness, and experimental approaches to number theory and combinatorics. His recent publications demonstrate a consistent focus on the intersection of computational complexity, statistical inference, and random matrix theory. There's a clear trajectory from theoretical foundations to practical algorithmic applications, with particular emphasis on information-computation gaps, spectral methods, and the sum-of-squares hierarchy. His work often bridges theoretical computer science with statistical physics approaches. Dr. Kunisky actively advises graduate students at Johns Hopkins, including PhD candidates in Applied Mathematics and Statistics. He has taught courses on Random Matrix Theory in Data Science and Statistics, Probability Theory, Sum-of-Squares Optimization, and Modern Probability for Theoretical Computer Science, demonstrating his commitment to both research and education in mathematical data science.

Alexander BarvinokView profile

Professor

• Computational Complexity
• Algorithms
• Algebra
• +6 more

Alexander Barvinok is a Professor in the Department of Mathematics at the University of Michigan, Ann Arbor. His office is located in East Hall (4066 East Hall), where he has been conducting research and teaching advanced courses in computational mathematics since receiving his Ph.D. from Leningrad State University in 1988. Professor Barvinok's research focuses on computational complexity and algorithms in algebra, geometry and combinatorics. He is particularly interested in connections between various notions of phase transition in statistical physics, analytical properties of partition functions and computational complexity. His work bridges theoretical mathematics with practical computational approaches, exploring how physical phenomena can inform algorithmic design and analysis. His research spans convex geometry, combinatorial optimization, and the computational aspects of polynomial systems. His recent publications (2016-2024) demonstrate a consistent focus on partition functions, computational aspects of convex bodies, and approximation algorithms for counting problems. He has made significant contributions to understanding the zeros of partition functions in statistical physics models, developing efficient volume estimation algorithms for polyhedra, and creating polynomial-time approximation schemes for problems previously thought to be computationally intractable. His work frequently connects algebraic properties of polynomials with computational feasibility. Professor Barvinok has authored several influential textbooks including "A Course in Convexity" (AMS Graduate Studies in Mathematics, 2002), "Integer Points in Polyhedra" (Zurich Lectures in Advanced Mathematics, 2008), and "Combinatorics and Complexity of Partition Functions" (Springer, 2016). He regularly teaches advanced graduate courses such as Math 669 on specialized topics including "Combinatorics, Geometry and Complexity of Integer Points" and "Topics in Convexity," with his lecture notes often evolving into significant research contributions.

Giovanni FantuzziView profile

Assistant Professor

• Optimization
• Dynamical Systems
• Fluid Mechanics
• +4 more

Giovanni Fantuzzi serves as a W1 Professor (equivalent to Assistant Professor) in the Department of Mathematics at Friedrich-Alexander University Erlangen-Nuremberg. He leads research within the FAU DCN-AvH Chair for Dynamics, Control, Machine Learning and Numerics under the Alexander von Humboldt Professorship framework, holding office in Room 03.318 with contact details including giovanni.fantuzzi@fau.de and +49 9131 85-67134. His educational background includes a PhD and Master of Engineering in Aeronautics from Imperial College London, supplemented by a research position in Engineering Science at the University of Oxford during his doctoral studies. Key academic milestones are documented through his ORCID, Google Scholar, and LinkedIn profiles. Fantuzzi's research program integrates mathematical analysis with computational optimization to solve nonlinear differential equations, focusing on deriving a priori scaling laws for heat transport and developing provable numerical schemes for PDE-constrained optimization. His methodology bridges convex optimization, polynomial optimization, and dynamical systems theory, with recent applications extending to transformer neural networks and sentiment analysis through hardmax mechanisms. Current teaching includes Data-driven methods for dynamical systems and Polynomial optimization and applications for WS 24/25. Analysis of his 15 most recent publications reveals dominant trends in fluid mechanics (particularly convection and heat transfer), polynomial optimization techniques, and data-driven dynamical systems analysis. His work consistently applies convex optimization frameworks to derive rigorous bounds in physical systems while expanding into machine learning applications like transformer model analysis. Geophysical Fluid Dynamics Fellowship at WHOI (2015) EPSRC Doctoral Prize Fellowship (2018) Imperial College Research Fellowship Fantuzzi's research program is supported by prestigious fellowships including the Imperial College Research Fellowship and EPSRC Doctoral Prize. His academic service includes organizing the FAU MoD Lecture & Workshop on AI for maths and maths for AI (June 2025) and co-hosting the #MLPDES25 Machine Learning and PDEs Workshop. He actively supervises research within the FAU DCN-AvH group, focusing on polynomial optimization applications in dynamical systems and PDEs. As core faculty in the FAU DCN-AvH Chair, Fantuzzi collaborates within a multidisciplinary team specializing in dynamics, control, machine learning, and numerical methods. The group maintains strong international connections through workshops like the Oberwolfach Seminar on Polynomial Optimization for Nonlinear Dynamics and participates in conferences including CIN-PDE and Nečas Seminar on Continuum Mechanics, driving innovation at the intersection of mathematics and computational physics.

• Real Algebraic Geometry
• Optimization
• Computational Algebra
• +8 more

Cordian Benedikt Riener is a Professor in Mathematics at UiT The Arctic University of Norway, Tromsø, and serves as Pro-Dean for PhD education at the Faculty of Science and Technology. He leads the Algebra group and is a co-director of the Lie-Størmer Center for Fundamental Structures in Computational and Pure Mathematics. His academic roles include chairing the Norwegian Mathematics Council and serving on the Abel Prize board. Habilitation in Mathematics (2018, University of Konstanz) Magister Artium in Philosophy (2013, Goethe University Frankfurt) PhD in Mathematics (2011, Goethe University Frankfurt) Master in Pure Mathematics (2005, Université de Bordeaux 1) Diplom in Mathematical Economics (2006, Universität Ulm) Riener’s research bridges mathematics and theoretical computer science, focusing on computational complexity, real algebraic geometry, and polynomial optimization. His work includes characterizing positive polynomials via sums of squares, studying moment problems, and developing semidefinite relaxation methods. He explores symmetry in algebraic structures, such as Specht ideals and Weyl group orbits, with applications in discrete geometry and spectral graph theory. His recent publications emphasize symmetric semi-algebraic sets, trigonometric polynomial optimization, and tropical geometry. The 15 most recent articles span 2023-2025, addressing topics like symmetric nonnegative forms, orbit space descriptions, and algorithmic symmetry reduction. Keywords include Real Algebraic Geometry, Optimization, and Computational Complexity, with subfields such as Sums of Squares, Spectral Bounds, and Tropical Geometry. Aalto Science Institute Fellow (2012-2016) Zukunftskolleg Associate Fellow (2011-2012) Riener contributes to academic service as chair of the Norwegian Mathematics Council and Abel Prize board member. He has organized conferences like ISSAC 2023 and Nordic Combinatorial Conference 2022. His projects include POEMA (Horizon 2020) and SymRAG, with leadership in the Lie-Størmer Center and MASCOT collaborations.

Aljaž ZalarView profile

Assistant Professor

• Real Algebraic Geometry
• Truncated Moment Problems
• Matrix Polynomials
• +3 more

Assistant Professor Aljaž Zalar is affiliated with the University of Ljubljana at the Faculty for Computer and Information Science . His research focuses on Real Algebraic Geometry , Truncated Moment Problems , and Matrix Polynomials , with applications in Operator Theory and Positive Linear Maps . PhD in Mathematics, University of Ljubljana (2017) MSc and BSc in Mathematics, University of Ljubljana (2013, 2011) Zalar's work bridges theoretical mathematics and computational applications, including copositive matrices , positive semidefinite matrix completions , and noncommutative polynomial positivity . His recent projects address truncated moment problems on curves and algebraic structures in optimization . His publications from 2016–2025 span journals like Linear Algebra and its Applications , SIAM Journal on Applied Algebra and Geometry , and Integrable Equations and Operator Theory , emphasizing polynomial operator analysis and matrix inequalities . Zalar supervises postdoctoral and graduate students, including PhD candidate Rajkamal Nailwal and Igor Zobovič, and mentors undergraduate researchers. He leads the ARIS grant project J1-60011 on real algebraic geometry approaches to moment problems.

Rakesh VenkatView profile

Assistant Professor

• Theoretical Computer Science
• Approximation Algorithms
• Hardness of Approximation
• +1 more

Rakesh Venkat is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad. His research focuses on Theoretical Computer Science, including approximation algorithms, hardness of approximation, and communication complexity. Education : Ph.D., Tata Institute of Fundamental Research (TIFR), Mumbai. Research Trends : His work addresses fundamental challenges in algorithm design, such as optimizing cache misses, improving clustering algorithms, analyzing graph expansion, and exploring embedding techniques. Publications span top-tier conferences like APPROX, FSTTCS, ICALP, and ITCS, with collaborations at institutions including HUJI, TIFR, and IIT-Bombay. Teaching : Courses taught include Approximation Algorithms, Advanced Data Structures, Discrete Mathematics, and Spectral Graph Theory.

• Computational Complexity
• Quantum Computing
• Semidefinite Programming
• +3 more

Rajat Mittal serves as an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. His academic credentials include a PhD from Rutgers University (2011) with a dissertation on semidefinite programming applications in query complexity, supervised by Mario Szegedy, and a B.Tech from IIT Bombay (2004) in Computer Science and Engineering. Dr. Mittal's research program centers on Computational Complexity, Quantum Computing, and Semidefinite Programming, representing the cutting edge of theoretical computer science. His work bridges abstract theoretical concepts with practical algorithmic applications, particularly in quantum information processing and complexity analysis. The departmental affiliation with Computer Science and Engineering places his research within a strong technical framework that supports both theoretical exploration and practical implementation. His publication record demonstrates consistent contributions to top-tier theoretical computer science conferences including FOCS, ICALP, and QIP, with a clear trajectory of advancing knowledge in quantum algorithms and computational complexity theory. These works collectively establish semidefinite programming as a powerful tool for addressing fundamental questions in computational theory. Within the academic community at IIT Kanpur, Dr. Mittal contributes through specialized courses on semidefinite programming applications in complexity theory and advanced linear programming topics, shaping the next generation of theoretical computer scientists in India's premier technical institution. His office is located in CS 202 within the Department of Computer Science and Engineering at IIT Kanpur, maintaining active engagement with students, colleagues, and the broader research community through his accessible contact information.

Frits C.R. SpieksmaView profile

Professor

• Combinatorial Optimization
• Operations Research
• Scheduling
• +7 more

Prof. Frits C.R. Spieksma is a full professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e) , where he leads research within the Combinatorial Optimization Group. He has held academic positions at Maastricht University, KU Leuven, and the University of British Columbia, and has been at TU/e since 2018. Education: M.Sc. in Econometrics, University of Groningen (1987) Ph.D. in Operations Research, Maastricht University (1992) Research Focus: His work lies at the intersection of combinatorial optimization and real-world applications . Key themes include: Scheduling and clustering problems, especially in sports tournaments Organ allocation optimization for Eurotransplant Assignment and transportation problems Approximation algorithms and graph-theoretic optimization Scientific Service & Leadership: Founder and ex-Chair, EURO Working Group OR in Sports Member, Steering Committees of MAPSP and MathSports International President, EURO (Association of European Operational Research Societies) Former Vice-President, IFORS Former President, Belgian Society of Operations Research (ORBEL) Editorial Boards: Associate Editor, 4OR (2015–present) Associate Editor, Journal of Quantitative Analysis in Sports (2014–present) Associate Editor, Operations Research Letters (2008–2024) Former Associate Editor, INFORMS Transactions on Education , OMEGA , Computers & Operations Research , IIE Transactions , Naval Research Logistics PhD Supervision & Mentoring: He has supervised more than 25 PhD theses at KU Leuven and TU/e, many of whom now hold academic positions worldwide. Conference & Workshop Organisation: Recent leadership roles include General Chair of IPCO 2022 (Eindhoven), organiser of Benders Day 2024 , and co-organiser of the Dagstuhl Seminar on Fairness in Scheduling and Resource Allocation (March 2025).

• Algorithms
• Combinatorial Optimization
• Geometric Optimization
• +5 more

Dr. Joachim Spoerhase is a Lecturer at the Department of Computer Science , University of Liverpool , specializing in algorithm design and combinatorial optimization. His research focuses on approximation algorithms for clustering, network design, and geometric optimization problems. PhD in Computer Science (2010) and Habilitation (2017) from the University of Würzburg Former Research Associate at the Max Planck Institute for Informatics Research positions at Aalto University and University of Wroclaw His recent work explores high-dimensional data structures, polyline bundle simplification, and robust clustering frameworks. He also investigates hardness of approximation and theoretical limits in algorithm design. The University of Liverpool serves as his primary affiliation, with contributions to both computational geometry and machine learning. Notable research trends include interdisciplinary applications in network design, geometric optimization, and interpretable AI frameworks. Dr. Spoerhase teaches modules like Computer Networks (COMP211) and contributes to algorithmic theory development.