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Andrew GardView profile

Assistant Professor

• Applied Mathematics
• Statistics
• Quantitative Botany
• +3 more

Andrew Gard is an Assistant Professor of Mathematics at Lake Forest College, affiliated with the Mathematics and Computer Science Department within the College of Arts and Sciences. He holds a PhD and BS/BA in Mathematics and Philosophy from The Ohio State University, graduating with honors (magna cum laude, Phi Beta Kappa). His research focuses on applied mathematics, statistics, and humanistic data science, with notable contributions to quantitative botany and error propagation analysis. He actively develops R programming tools for ecological data analysis, as evidenced by his work on the 'fqar' package. Dr. Gard teaches a range of courses from introductory calculus to advanced topics in probability, statistical programming, and mathematical statistics. His scholarly work spans pure and applied mathematics, including contributions to the American Mathematical Monthly and Rocky Mountain Journal of Mathematics. He has also led educational initiatives such as inquiry-based calculus pedagogy and curriculum design for STEM programs. His grant proposals include a NSF-funded research initiative on pursuit-evasion problems ($297K) and a Title III grant enhancing STEM opportunities ($800K). He co-edited the 2013 Midstates Conference proceedings and remains active in both theoretical mathematics and its practical applications in botany and education.

Jamie PommersheimView profile

Professor

• Quantum Computing
• Algebraic Geometry
• Number Theory
• +4 more

Jamie Pommersheim is the Katharine Piggott Professor of Mathematics at Reed College, where he has served since 2004. His academic journey includes post-doctoral positions at the Institute for Advanced Study, MIT, and UC Berkeley, as well as faculty roles at New Mexico State University and Pomona College. He specializes in algebraic geometry, number theory, topology, and quantum computation, with recent focus on quantum learning algorithms and Fourier analysis on polytopes. His research spans diverse areas such as toric varieties, quantum query complexity, and geometric combinatorics. He co-authored the influential 2010 number theory textbook *Number Theory: A Lively Introduction* with Tim Marks and Erica Flapan. Pommersheim also teaches advanced courses like Topics in Geometry and mentors high-ability students through programs like Johns Hopkins' CTY and Hampshire College Summer Studies in Mathematics. His recent publications explore algebraic constructions, area relations in geometry, and quantum computational models. While no formal awards are listed, his work reflects sustained contributions to theoretical mathematics and education. Pommersheim’s academic service includes leadership in curriculum development and outreach, bridging advanced research with pedagogical innovation.

Volker MehrmannView profile

Professor

• Applied and Numerical Linear Algebra
• Analysis and Numerics of Differential-Algebraic Equations
• Numerical Solution of Differential Equations
• +3 more

Volker Mehrmann is a Professor of Mathematics (Numerical Mathematics) at Technische Universität Berlin since 2000. He holds a C4-Professorship and has held leadership roles in major research initiatives such as the DFG Research Center MATHEON and the Einstein Center ECMath. His research focuses on applied and numerical linear algebra, differential-algebraic equations, control theory, and industrial mathematics. Mehrmann earned his Ph.D. from Universität Bielefeld (1982) and habilitation (1988). He has authored influential textbooks, including Lineare Algebra , and serves as Editor-in-Chief of Linear Algebra and its Applications . He is a SIAM Fellow, ERC Advanced Grant recipient, and member of prestigious academies like acatech and Academia Europaea. Education: Diplom (1979), Ph.D. (1982), Habilitation (1988) from Universität Bielefeld. Key Roles: Speaker of DFG Research Center MATHEON (2008–2014) President of GAMM (2011–2016) and EMS (2017–2022) Research Highlights: Model reduction, port-Hamiltonian systems, and energy-based modeling Control of complex systems and industrial applications His recent work emphasizes interdisciplinary projects in energy systems (e.g., BMBF-EiFer, Hybrid Energy Networks) and numerical methods for large-scale systems. Mehrmann’s contributions span theoretical advancements and practical implementations, bridging fundamental mathematics with real-world challenges.

• Applied Mathematics
• Data Science
• Partial Differential Equations
• +7 more

Dejan Slepčev is a Professor and Associate Dean for Faculty and Graduate Affairs at the Mellon College of Science, Department of Mathematical Sciences, Carnegie Mellon University. His research focuses on applied analysis, with applications to data science, collective behavior of particle systems, and energy-driven systems. He holds a Ph.D. from the University of Texas at Austin. His research interests include partial differential equations, calculus of variations, optimal transportation, and their applications to machine learning tasks such as clustering and classification. He develops mathematical frameworks for analyzing variational and PDE-based problems on random data samples, improving algorithms for data science. He also studies systems of interacting particles, particularly those modeling collective behavior in biological and physical systems, and energy-driven systems involving pattern formation and interface evolution. His work bridges theoretical analysis and computational methods, addressing challenges in data analysis, image processing, and the dynamics of complex systems. Key contributions include studies on spectral clustering, graph-based semi-supervised learning, and the continuum limits of discrete variational problems. Education: Ph.D., University of Texas at Austin.

Prasad TetaliView profile

Professor

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

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

Julio Enrique Castrillon-CandasView profile

Academic

• Uncertainty Quantification
• Partial Differential Equations
• Integral Equations
• +5 more

Julio Enrique Castrillon-Candas is a Research Assistant Professor at Boston University's Department of Mathematics and Statistics within the College of Arts & Sciences. He is a prominent member of the Probability and Statistics research group, focusing on computational mathematics and uncertainty quantification. His academic journey includes a Ph.D. in electrical engineering and computer science from MIT. His research spans several interconnected fields including Uncertainty Quantification, Partial Differential Equations, Integral Equations, Fast Multi-Level Kriging for large spatial datasets, Fast Radial Basis Function Interpolation, and Machine Learning applications. His work bridges theoretical mathematics with practical computational approaches for solving complex problems in science and engineering. Analysis of his recent publications reveals a strong focus on developing efficient computational methods for uncertainty quantification, particularly through multi-level approaches to spatial statistics and stochastic collocation methods for PDEs with random domains. His research demonstrates a consistent progression from foundational mathematical theory to practical implementations for large-scale problems. Dr. Castrillon-Candas has secured significant funding including an NIH grant titled 'Stochastic Dynamic Modeling of Cellular Protein Interactions' (Award Number: 1R01GM131409-01, $323,280) and an NSF/DOE AMPS grant on 'Uncertainty Quantification for Stochastic Analysis of Electrical Power Networks' (Award Number: 1736392, $229,279). He maintains an active research collaboration network with prominent academics including Raul Tempone (KAUST), Fabio Nobile (EPFL), Marc G. Genton (KAUST), and Rio Yokota (Tokyo Institute of Technology). His work has been presented at numerous invited talks at prestigious institutions including Harvard University, Tufts University, and MIT.

• Machine Learning
• Statistics
• Probability Theory
• +3 more

Dr. Steffen Grünewälder is a Senior Lecturer in Statistics at the University of York, Department of Statistics. Previously, he held roles as Senior Lecturer at Newcastle University (2022-2024) and Lecturer at Lancaster University (2014-2022). His research focuses on statistical learning theory, kernel methods, and reinforcement learning. He has contributed to advancements in Gaussian processes, Markov processes, and fairness in machine learning. His work spans theoretical developments and practical applications, including compressed empirical measures, stochastic processes, and algorithmic fairness. Key research interests include Bayesian methods, computational statistics, and optimization techniques in machine learning. Dr. Grünewälder’s recent publications explore topics such as deep Gaussian processes, Markov chain analysis, and reinforcement learning algorithms. He has collaborated on interdisciplinary projects, including ecological studies on animal behavior. He can be contacted via email at steffen.grunewalder@york.ac.uk .

• Structural Health Monitoring
• Wind Energy
• Graph Neural Networks
• +3 more

Dr. Gregory Duthé is a researcher at the ETH Zürich in the Structural Mechanics and Monitoring department. His work bridges computational modeling and structural diagnostics with applications in renewable energy systems. Research contributions include: Development of Graph Neural Networks for aerodynamic flow reconstruction Advancements in wake-induced load estimation for wind farms Innovations in unsupervised fault detection for offshore turbine systems Key publication trends show focus on physics-informed machine learning (2025), multi-agent infrastructure decision support (2025), and leading edge erosion modeling (2021). He combines graph-based architectures with structural health monitoring techniques across wind energy applications. Contact: duthe@ibk.baug.ethz.ch

Alina ChertockView profile

Professor

• Numerical Analysis
• Computational Fluid Dynamics
• Hyperbolic Conservation Laws
• +4 more

Alina Chertock serves as the Head of the Department of Mathematics at North Carolina State University (NCSU), where she leads academic and research initiatives in computational mathematics. Her work focuses on developing advanced numerical methods for hyperbolic conservation laws, fluid dynamics, and models involving uncertainty quantification. Chertock’s research integrates finite-volume, particle, and hybrid methods to address complex phenomena such as chemotaxis, magnetohydrodynamics, and shallow water systems. She has contributed to high-resolution schemes for stiff detonation waves and stochastic collocation techniques for nonlinear PDEs with uncertainties. Her research interests span computational fluid dynamics, numerical analysis, and applied mathematics, with applications in geophysical flows, multiphase systems, and biological models. Notable contributions include well-balanced path-conservative schemes for rotating flows and divergence-free flux methods for magnetohydrodynamics. Chertock collaborates actively with institutions on NSF-funded projects, including structure-preserving methods for atmospheric and shallow water models. Her publications emphasize adaptive algorithms, error mitigation in stochastic systems, and the integration of machine learning for wave equation analysis. Chertock’s work bridges theoretical developments with practical applications, addressing challenges in computational efficiency and accuracy across diverse scientific domains.

Weilin LiView profile

Assistant Professor

• Applied and Computational Harmonic Analysis
• Super-Resolution
• Signal Processing
• +4 more

Weilin Li is an Assistant Professor in the Department of Mathematics at the City College of New York (CUNY). His research focuses on applied and computational harmonic analysis, with emphasis on super-resolution, quantization methods, and signal processing. He holds a PhD in Mathematics from the University of Maryland, College Park, and was previously a Courant Instructor at New York University. Education PhD in Mathematics, University of Maryland, College Park (201X) Courant Instructor, New York University, 201X-201X Research Interests Dr. Li's work bridges pure mathematics and applied sciences, with key areas including nonharmonic Fourier analysis, spectral super-resolution, and compressed sensing. His methods address challenges in signal reconstruction under quantization constraints and subspace estimation techniques. Recent Trends in Publications Recent work explores optimality of spectral estimation algorithms (e.g., Gradient-MUSIC), stability of Fourier matrices, and applications of scattering transforms in hyperspectral imaging. His research often intersects with machine learning, particularly in understanding approximation capabilities of neural networks under quantization. Awards & Grants Recipient of the 2021 Charles Chui Young Researcher Best Paper Award Funded by NSF, PSC-CUNY, and City College Foundation grants Academic Engagement Co-organizer of the One World MINDS Seminar, CUNY GC Harmonic Analysis and PDE Seminar, and CUNY analysis learning seminars. Active in mentoring graduate students, including Ben Tupper who joined the PhD program in 2025.

Matiyasevich YuriView profile

Professor

• Diophantine equations
• Computability theory
• Algorithms
• +1 more

Yuri Matiyasevich is a Russian mathematician and computer scientist affiliated with the Steklov Institute of Mathematics (Leningrad/St.Petersburg Branch) since 1980, where he serves as head of the Laboratory of Mathematical Logic. He also holds part-time professorships at Leningrad/St.Petersburg State University (since 2003) and previously at Polytechnical Institute (1980-1981). His research focuses on Diophantine equations, Computability theory, Algorithms, and Decidability problems. Full member, Russian Academy of Sciences (2008) Corresponding member, Bavarian Academy of Sciences (2007) Docteur Honoris Causa, Université Pierre et Marie Curie (2003) Humboldt Research Award (1997) Markov Prize, Academy of Sciences of the USSR (1980) His recent work involves computational approaches to Riemann's zeta function, probabilistic reformulations of graph theory problems, and algorithmic analysis of Diophantine representations. He has published extensively on connections between number theory and computational models, including studies on exponential Diophantine equations and their applications. Notable contributions include definitive solutions to Hilbert's Tenth Problem through Diophantine representations of recursively enumerable sets, and computational experiments supporting the Riemann Hypothesis. His publications span multiple languages and cover intersections between mathematical logic, number theory, and theoretical computer science.

Maryna ViazovskaView profile

Professor

• Sphere packing
• Fourier analysis
• Automorphic forms
• +3 more

Maryna Viazovska is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), renowned for solving the sphere packing problem in dimensions 8 and 24. She holds a PhD from the Max Planck Institute for Mathematics and the University of Bonn (2013), with earlier degrees from Technical University Kaiserslautern (M.Sc., 2007) and Kyiv National Taras Shevchenko University (B.Sc., 2005). Her research focuses on sphere packing, optimal point configurations, Fourier analysis, automorphic forms, and number theory. Notable achievements include proving the E8 lattice's optimality in 8 dimensions and the Leech lattice in 24 dimensions, leveraging modular forms and interpolation techniques. Key awards include the Fields Medal (2022), Fermat Prize (2020), and Salem Prize (2016). She has held positions at Humboldt University of Berlin (BMS Dirichlet Postdoc), Institut des Hautes Études Scientifiques, and Princeton University. Her publications span top journals like Annals of Mathematics and address interdisciplinary topics such as superconductivity and combinatorial geometry. Viazovska’s work bridges pure mathematics with applications in coding theory and physics.

Chee YapView profile

Professor

• Computer algebra
• Symbolic-numeric computation
• Robotics
• +5 more

Chee Yap is a Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University, where he has been a faculty member since 1979. He previously held positions at the University of Southern California and has served as a Visiting Professor at several prestigious institutions including Oxford University, Seoul National University, and the Chinese Academy of Sciences. Dr. Yap received his BS in Computer Science & Mathematics (Double Major) from MIT in 1975 and his PhD in Computer Science from Yale University in 1980. His academic journey began at the Boys' Wing, Royal Military College in Malaysia. His research spans several areas in theoretical computer science and computational mathematics, with primary focus on Computer algebra and symbolic-numeric computation , Robotics , Visualization , Theory of algorithms , Exact computation , Complexity theory , and Computational geometry . His work bridges theoretical foundations with practical implementations, exemplified by his development of the Core Library for exact computation and the G-Vis System for visualization. Dr. Yap's publication record shows a consistent focus on robust geometric computation, algorithmic motion planning, and exact numerical methods. His recent work (2021-2026) demonstrates continued innovation in parameter estimation for differential equations, subdivision models, path planning algorithms, and root clustering techniques. His research maintains a strong connection between theoretical computer science and practical applications in robotics and scientific computing. Among his notable honors are the CAS President's International Visiting Scientist Fellowship (2018), KIAS Scholar award (2006-2009), Lester R. Ford Award (1990), and Fulbright Scholar Award (1988). He has also received multiple ACM Recognition of Service Awards for his conference leadership. Dr. Yap has been continuously funded by the National Science Foundation since 1984, securing approximately 20 grants valued at about $5 million. He has mentored 17 PhD students and 4 postdocs throughout his career. His editorial service includes long tenures on the boards of SIAM Journal of Computing (1985-2002), Journal of Computer and System Sciences (1986-2014), and Journal of Symbolic Computation (1988-2004), among others. He is the principal architect of the Core Library (http://cs.nyu.edu/exact) and co-architect of the G-Vis System (http://cs.nyu.edu/visual), and holds two visualization patents. His leadership in the academic community is evident through his roles as General Chair of the 42nd ISSAC conference and various program committee positions for major conferences including FOCS, ISSAC, SoCG, and WAFR.

• Machine Learning
• Computational Fluid Dynamics
• Turbulence Modeling
• +3 more

Romit Maulik is an Assistant Professor at the College of Information Sciences and Technology, Pennsylvania State University, and holds a joint appointment as a faculty member at Argonne National Laboratory. His research focuses on integrating machine learning with computational physics, particularly in turbulence modeling, fluid dynamics, and high-performance computing. He leads projects such as the Interdisciplinary Scientific Computing Laboratory (ISCL), which develops AI-driven algorithms for applications like weather modeling and fusion energy. Education: Ph.D. from Oklahoma State University under Dr. Omer San, specializing in computational fluid dynamics. Postdoctoral work at Argonne National Laboratory with Prasanna Balaprakash and Bethany Lusch on scalable machine learning for scientific computing. Research interests include data-driven closure models, reduced-order modeling, and interpretable AI for fluid flow reconstruction. He is associated with the DeepHyper project for neural architecture search and has contributed to open-source tools like TensorFlowFoam and PythonFOAM. Recent achievements include awards from the NERSC AI4Science program for GPU hours and recognition for students like Xuyang Li and Dibyajyoti Chakraborty. His work bridges advanced computing, physics, and AI to solve complex multiscale problems.

Zafer DoganView profile

Assistant Professor

• Machine Learning
• Data Analytics
• Signal Processing
• +5 more

Zafer Dogan is an Assistant Professor in the Department of Electrical and Electronics Engineering at Koç University, where he leads the Machine Learning Information Processing (MLIP) research group and serves as Deputy Director of the Koç University İşbank AI (KUIS AI) Research Center. He holds a Ph.D. from École Polytechnique Fédérale de Lausanne (EPFL) and a B.Sc. from Middle East Technical University (METU). Before joining Koç University, he worked as a postdoctoral researcher at Harvard University's John A. Paulson School of Engineering and Applied Sciences (2016–2019). His research bridges theoretical advancements in machine learning, optimization, and signal processing with practical applications in high-dimensional data mining, AI-driven imaging (e.g., biomedical imaging), and inverse problem solutions. Key areas include generative models, image super-resolution, neural network dynamics, and domain-specific interpolation techniques. His work emphasizes trustworthy AI, perceptual-quality image enhancement, and biomedical applications like neuroimaging analysis. Notable contributions include developing the MMSR framework for class-specific image super-resolution, wavelet-domain losses for artifact reduction, and leveraging vision-language models for trustworthy super-resolution. His articles span domains from theoretical ML to applied biomedical imaging solutions. Dr. Dogan’s academic journey includes postdoctoral training at Harvard and a strong focus on interdisciplinary research at the intersection of engineering and AI. He currently leads initiatives at KUIS AI, advancing applied AI research and innovation.