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Achilles K. TertikasView profile

Professor

• Partial Differential Equations
• Functional Analysis
• Inequalities (Hardy, Sobolev)
• +4 more

Achilles K. Tertikas is a Professor in the Department of Mathematics & Applied Mathematics at the University of Crete, where he has maintained an active teaching and research career spanning multiple decades. His professional activities are centered at the University Campus in Heraklion, Crete, where he maintains office E 306 and holds regular office hours on Tuesdays and Wednesdays. Professor Tertikas' research focuses on advanced mathematical analysis, particularly in the areas of partial differential equations, functional analysis, and various forms of inequalities including Hardy and Sobolev inequalities. His work demonstrates a consistent pattern of investigating optimal constants, boundary behaviors, and geometric properties in mathematical inequalities. Over his career, he has developed significant expertise in analyzing elliptic equations, spectral theory, and geometric aspects of functional inequalities. The trajectory of his publication record shows a sustained focus on improving and refining fundamental inequalities in mathematical analysis. His recent work (2023-2025) continues to push boundaries in understanding geometric Hardy inequalities on Heisenberg groups, Liouville properties for weighted anisotropic equations, and best constants for various inequality forms. A distinctive feature of his research is the consistent collaboration with leading mathematicians like Gerassimos Barbatis, Stathis Filippas, and Luisa Moschini across multiple publications. As an educator, Professor Tertikas has taught a comprehensive range of mathematics courses at the University of Crete including Plane and Space, Infinite Calculus, Partial Differential Equations, Analysis, Ordinary Differential Equations, Complex Analysis, and Set Theory across multiple academic years from 2010 through the upcoming 2024-2025 academic year. His teaching portfolio demonstrates expertise across both pure and applied mathematical disciplines, with particular emphasis on analysis and differential equations. Professor Tertikas maintains an active scholarly presence with ongoing research projects, recent publications, and current teaching responsibilities, indicating a vibrant and continuing academic career at the University of Crete.

Ramesh GarimellaView profile

Teaching Professor

• Functional Analysis
• Operator Theory

Dr. Ramesh Garimella is a Teaching Professor in the Department of Mathematics at Drexel University with over 25 years of teaching experience, specializing in undergraduate mathematics education. His core teaching principle focuses on motivating students and developing mathematical thinking and problem-solving skills. Education includes: PhD, Mathematics, University of Toledo (1986) MS, Mathematics, Andhra University (1981) Research focuses on Functional Analysis and Operator Theory, particularly solving operator equations arising from differential equations like KdV. His published works explore mathematical structures in Banach algebras and theoretical approaches to biological systems. Beyond teaching, he has secured grants for teacher training programs and STEM outreach initiatives for middle/high school students. Previously served as department chair at University of Central Arkansas and tenured professor at Tennessee Technological University.

Martin HutzenthalerView profile

Professor

• Stochastic Analysis
• Numerical Methods for PDEs
• High-Dimensional Systems
• +2 more

Martin Hutzenthaler is a Professor in the Faculty of Mathematics at the University of Duisburg-Essen, leading the Stochastic Analysis research group. His office is located in Essen, Germany. His research centers on stochastic analysis and numerical methods, particularly multilevel Picard techniques for solving high-dimensional semilinear PDEs without the curse of dimensionality. Additional interests include stochastic differential equations, deep learning approximations, and functional inequalities. Recent publications focus on overcoming computational limitations in high-dimensional systems through novel approximation techniques. He serves as Associate Editor for the SIAM Journal on Numerical Analysis and offers student positions for tutorial development and algorithm implementation.

Buddika PeirisView profile

Associate Professor

• Order Restricted Inference
• Meta-Analysis
• Bayesian Statistics
• +3 more

Buddika Peiris is an Associate Professor of Teaching in the Department of Mathematical Sciences at Worcester Polytechnic Institute (WPI), where he also serves as the Coordinator of the Applied Statistics Master's Program and the Statistics Consulting Lab. His academic career at WPI has progressed from Postdoctoral Fellow (2014-2016) to Assistant Teaching Professor (2016-2021) and now to Associate Professor of Teaching (2021-present). BS in Mathematics from University of Sri Jayewardenepura (2005) MS in Mathematical Statistics from Southern Illinois University, Carbondale (2010) PhD in Mathematical Statistics from Southern Illinois University, Carbondale (2014) Dr. Peiris's research focuses on developing new statistical methodologies with applications across various fields. His primary research areas include Order Restricted Inference, Meta-Analysis, Bayesian Statistics, and Actuarial Science. His work addresses complex problems in public health, weather forecasting, Food Science, and various industries where traditional 'ad hoc' methods are reaching their limits. His teaching philosophy emphasizes clear communication of statistical concepts, exposing students to statistical analysis structures, and teaching effective communication of statistical results to diverse audiences. His publication record demonstrates consistent contributions to statistical methodology, particularly in constrained regression models, meta-analysis techniques, and Bayesian approaches. His work spans theoretical developments with practical applications in biomedical research, environmental studies, and industrial settings. The publications show a progression from foundational work on order restricted inference to more complex applications involving circular-linear regression and meta-analysis of cylindrical time series data. As an educator, Dr. Peiris has supervised numerous graduate students through WPI's Master's program, with projects spanning healthcare analytics, financial applications, environmental modeling, and industrial statistics. His teaching portfolio includes both undergraduate and graduate courses in probability, mathematical statistics, regression analysis, experimental design, and specialized topics in statistical methodology. Through his role as Coordinator of the Statistics Consulting Lab, he facilitates connections between statistical expertise and real-world problems across disciplines. His current research continues to develop constrained prediction intervals, diagnostic tests in regression, and applications of statistical methodology to forensic analysis and plant science.

Margaret A. ReaddyView profile

Professor

• Algebraic Combinatorics
• Discrete Geometry
• Topology
• +2 more

Margaret A. Readdy is a Professor of Mathematics at the University of Kentucky, where she has been a faculty member since Fall 2000. She is a member of the Discrete Mathematics group within the Department of Mathematics. Her academic journey includes a PhD in Mathematics followed by a two-year postdoctoral fellowship at Laboratoire de Combinatoire et d'Informatique Mathématiques (LACIM) at Université du Québec à Montréal (UQAM), and a three-year Visiting Assistant Professorship at Cornell University. She has held numerous prestigious visiting positions including at the Institute for Advanced Study in Princeton (1998-1999 and 2010-2011), Stockholm University, MIT (2006-2007), and Princeton University (2014-2015). Professor Readdy's research focuses on algebraic combinatorics, particularly the interactions of combinatorics with algebra, topology, discrete geometry, and number theory. Her work explores the deep connections between combinatorial structures and other mathematical fields, with significant contributions to the study of polytopes, root systems, Coxeter groups, and flag enumeration. She has developed important insights into the cd-index, Eulerian posets, and combinatorial identities arising from representation theory. Her research is supported by NSF grant DMS-2247382, continuing her long-standing record of externally funded research. Analysis of Professor Readdy's recent publications (2016-2024) reveals a consistent focus on combinatorial structures with geometric interpretations. Her work often centers on polytopes (particularly the Legendre polytope), triangulations, and the combinatorial properties of Coxeter arrangements. She frequently collaborates with Richard Ehrenborg and other mathematicians, producing results that bridge discrete geometry, algebraic combinatorics, and topology. A notable theme in her recent work is the application of combinatorial methods to solve geometric problems, such as the n-dimensional pizza theorem, and the exploration of connections between different combinatorial structures through bijections and enumerative techniques. Involved with Women and Mathematics program that won the AMS 2019 Award for Mathematics Programs that Make a Difference Guest editor for March 2018 Notices of the AMS special issue for Women's History Month Featured on the cover of the March 2018 Notices of the AMS Professor Readdy actively mentors students, currently advising PhD students Will Gustafson and Ben Reese. She has received consistent research support from the National Science Foundation, including current grant DMS-2247382. She co-organizes the KOI Combinatorics Lectures (funded by NSF DMS 2435236), which brings together researchers from Kentucky, Ohio, and Indiana. She has organized the Discrete CATS Seminar since 2000 (with some interruptions), fostering a vibrant research community in combinatorics at the University of Kentucky. Professor Readdy is deeply involved in academic service and outreach. She has been a key participant in the Women and Mathematics (WAM) Program at the Institute for Advanced Study and Princeton University since 2017, serving as Academic Program Manager (2017-2019) and on the WAM Committee (2020-2022). She co-founded the Math Ambassadors program at the University of Kentucky and has organized multiple Julia Robinson Math Festivals. Her commitment to promoting mathematics extends to K-12 education through presentations at local schools and participation in the Kentucky American Water Science Fair.

Frank PinskiView profile

Professor

• Computational Chemistry
• Materials Science
• Theoretical Physics
• +3 more

Frank Pinski is a Professor in the Department of Physics at the University of Cincinnati. His research focuses on developing computational tools to study reaction pathways and energy barriers in molecular systems, particularly addressing rare events in barrier-limited processes. He has contributed to hybrid Monte Carlo algorithms, path sampling methods, and statistical mechanics frameworks to analyze complex systems like alloys and quantum fluids. Education: Ph.D. in Physics from the University of Minnesota (1977). Research Interests: Computational chemistry, materials science, and theoretical physics. Specific areas include molecular dynamics simulations, transition path sampling, and first-principles calculations of atomic correlations in metallic alloys. His work addresses challenges in sampling rare events and developing efficient algorithms for high-dimensional systems. Grants: NSF Grant DMR-9531223-002 (1996–1999): $245,000 for first principles calculations of atomic correlations in metallic alloys. Department of Education Grant P200A50053-97 (1996–1999): $146,777 for physics graduate fellowships. NSF Grant PHY 1005530 (2010–2013): $437,445 for enabling GPU-based physics research. Advising & Grants: Served as Principal Investigator (PI) on two major grants and collaborator on a third, focusing on computational methodologies and material science applications. Labs/Teams: Collaborates with students and researchers on computational tools for molecular systems, though no specific lab names are listed.

Jan NagelView profile

Professor

• Probability Theory
• Random Walks
• Random Matrices
• +1 more

Jan Nagel is Professor of Stochastics at TU Dortmund University's Department of Mathematics. His research explores probability theory with focus areas including random walks in random environments, random matrix theory, and stochastic processes. Recent publications investigate the dynamic behavior of viscoelastic structures with random material properties, sum rules in mathematical physics, and functional central limit theorems for random matrices. Dr. Nagel completed his doctorate in Mathematics at Ruhr University Bochum (2010) following a Diplom degree (2008). His academic trajectory includes positions as Assistant Professor at TU Dortmund (2019-2023), research fellowship at TU Eindhoven, and visiting professorships at LMU Munich. His work bridges theoretical mathematics with applications in statistical mechanics and disordered systems.

Zhe ShandianView profile

Associate Professor

• Artificial Intelligence
• Machine Learning (Theory and Modeling)
• Physics-informed Machine Learning
• +7 more

Zhe Shandian is an Associate Professor at the Kahlert School of Computing, University of Utah. His research focuses on advancing machine learning methodologies, particularly in physics-informed machine learning, operator learning, and Bayesian methods. He explores applications in differential equations, multi-omics data, and engineering simulations. His work emphasizes scalable algorithms and data efficiency in complex systems. Education details are not explicitly provided in the text, but his professional trajectory aligns with a strong computational and mathematical background. Research interests include developing novel frameworks for neural operators, symbolic regression, and functional Bayesian decomposition. Key research trends from his articles highlight advancements in physics-guided AI models, multi-fidelity simulation, and scalable kernel-based methods. His contributions span domains like material science, cancer prediction, and traffic flow modeling. No scientific awards are mentioned in the provided text. Zhe Shandian has not listed any advisees, and specific grants or labs are not detailed here. His work often involves collaboration across disciplines, leveraging both theoretical and applied machine learning techniques.

Niklas LundströmView profile

Associate Professor

• Mathematical Biology
• Partial Differential Equations
• Optimal Switching
• +5 more

Niklas Lundström serves as an Associate Professor in the Department of Mathematics and Mathematical Statistics at Umeå University, Sweden. His academic profile centers on interdisciplinary mathematical research with applications spanning ecology, energy systems, and image processing, supported by continuous publication activity through 2025. His primary research domains include Mathematical Biology (focusing on predator-prey dynamics, population modeling, and pest control), Partial Differential Equations (specializing in p-harmonic functions, variational inequalities, and nonlocal obstacle problems), and Optimal Switching (applied to hydropower management and stochastic control). Lundström investigates nonlinear phenomena in ecological systems, develops mathematical frameworks for image despeckling, and analyzes dynamical behaviors in engineering contexts like generator rotor dynamics. Analysis of his 15 most recent publications (2025-2016) reveals consistent emphasis on nonlinear PDE theory with practical applications. Key trends include analytical solutions for Lotka-Volterra systems, well-posedness studies for variable-exponent equations in image processing, and optimal control strategies for hydropower operations. His work demonstrates strong connections between abstract mathematical theory and real-world problems in sustainability and public health. Lundström actively participates in three university research groups: Mathematical Biology, Mathematical Modeling and Analysis, and Partial Differential Equations. He currently leads the research project "Viscosity solutions to systems of variational inequalities related to multi-modes switching problems" (2019-2025), indicating sustained grant funding. No scientific awards or student advisement details are documented in the provided materials.

Andreas KarageorghisView profile

Professor

• Numerical Analysis
• Scientific Calculations
• Boundary Element Methods
• +3 more

Andreas Karageorghis is a Professor in the Department of Mathematics and Statistics at the University of Cyprus, affiliated with the School of Natural and Applied Sciences. His research focuses on numerical analysis, scientific computations, and boundary element methods, particularly employing the Method of Fundamental Solutions (MFS). He holds a DPhil from Oxford University, preceded by a BA and MA. His work emphasizes solving complex elliptic boundary value problems, inverse scattering, and geometric reconstruction. Notable contributions include advancements in MFS algorithms for biharmonic equations, axisymmetric systems, and polyharmonic problems. He has also explored hybrid meshless methods and RBF neural networks for nonlinear problems. Key applications span fluid mechanics (e.g., Brinkman equations), elasticity, and acoustic scattering. His research addresses challenges like void detection, shape identification, and steady-state heat conduction. Karageorghis has published extensively in top journals like SIAM and IMA Numerical Analysis, with over 40 years of continuous academic contribution. His methodological innovations include matrix decomposition algorithms, efficient Kansa-type MFS formulations, and non-iterative approaches for inverse problems. Ongoing work includes optimal parameter selection for RBF methods and extending MFS to higher-order PDE systems.

Vyron VellisView profile

Associate Professor

• Analysis
• Geometric Function Theory
• Geometric Measure Theory
• +5 more

Vyron Vellis is an Associate Professor in the Department of Mathematics at the University of Tennessee, Knoxville (UTK), within the College of Arts and Sciences. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and has held postdoctoral positions at the University of Jyväskylä, Finland, and the University of Connecticut. His research focuses on Analysis , Geometric Function Theory , Geometric Measure Theory , and Fractal Geometry . Education: Ph.D., Mathematics, University of Illinois at Urbana-Champaign (2009) M.S., Mathematics, University of Illinois at Urbana-Champaign (2008) B.S., Mathematics, Aristotle University of Thessaloniki, Greece (2006) Research interests span metric geometry, quasiconformal mappings, fractal structures, and geometric embeddings . His work often explores the interplay between analysis and topology in metric spaces. Recent publications highlight advancements in quasisymmetric geometry, Hölder parametrization, and bi-Lipschitz embeddings. No scientific awards are explicitly listed. No advising or grant information is available. His work contributes to foundational studies in geometric analysis and metric space theory.

Mo LiView profile

Assistant Professor

• Statistics
• Categorical Time Series
• Changepoint Detection
• +4 more

Mo Li is an Assistant Professor in the Department of Mathematics at the University of Louisiana at Lafayette since August 2023. He holds a Ph.D. in Systems Modeling and Analysis (2021) from Virginia Commonwealth University, an M.S. in Operations Research (2018) from the same institution, and dual B.S. degrees in Biology and Statistics (2015) from the University of New Mexico and Biology from Northwest University, China. His postdoctoral research at Johns Hopkins Bloomberg School of Public Health focused on statistical applications in microbiome and T-cell receptor sequencing data analysis. His research interests span categorical time series, changepoint detection, and statistical methodologies in environmental and biological sciences. He actively collaborates with interdisciplinary teams and mentors graduate/undergraduate students in research projects. Key areas of expertise include microbiome studies, TCR sequencing, and developing statistical tools for complex data analysis.

Hala GhousseiniView profile

Professor

• Mathematics Education
• Teacher Development
• Educational Research
• +3 more

Hala Ghousseini is the John Grover Harvey Professor of Math Education and Professor in the Department of Curriculum and Instruction at the University of Wisconsin-Madison. Her research focuses on teacher learning, particularly how professional education designs can support equitable and responsive teaching practices. She emphasizes the role of mathematical representations in children’s learning and teacher collaboration. Education: PhD in Mathematics Education, University of Michigan (2008) MA in Mathematics Education, American University of Beirut (1996) BS in Mathematics, American University of Beirut (1989) Teaching Diploma in Secondary Mathematics Education, American University of Beirut (1989) Her research interests include teacher professional development, classroom discourse, and the intersection of mathematics education with equity. Recent work explores how collaborative learning environments and surgical coaching models can improve educational and medical practices. Awards: John Grover Harvey Professorship (2019–2024) Spencer Postdoctoral Fellowship (2012–2014) 2014 Outstanding JTE Article Award (AACTE) Grants/Advising: Ghousseini’s work is supported by grants focusing on teacher education and interdisciplinary collaboration. She advises on projects integrating surgical coaching principles into educational settings. Media Contributions: Featured in the Big Ten Network’s Out of the Blue series and the Journal of Teacher Education Insider for her insights on teacher education.

• Ergodic Theory
• Dynamical Systems
• Pure Mathematics

Charles Walkden is a Lecturer in Pure Mathematics at the School of Mathematics, University of Manchester, where he has been employed since 1997 after completing his PhD at the University of Warwick under Bill Parry. His academic career spans over two decades with continuous contributions to mathematical research and education. Education: PhD in Mathematics, University of Warwick (1997) Dr. Walkden's research specializes in Ergodic Theory and Dynamical Systems, with significant work on iterated function systems, skew products, and hyperbolic systems. His investigations frequently integrate probability theory to analyze limit laws, invariant measures, and spectral properties within dynamical frameworks, advancing theoretical understanding of complex systems. His publication record reveals a sustained focus on contractive dynamical systems, particularly examining limit theorems for iterated maps, structural properties of skew products, and topological dynamics. This body of work demonstrates methodological consistency in applying operator theory and stochastic analysis to solve problems in pure mathematics. Scientific Awards: No awards documented in available sources. Dr. Walkden has supervised three PhD students (Sara Santos 2005, Ian Morris 2006, and current student Andrew Moss) and serves as Programme Director for the MSc in Pure Mathematics. He actively seeks new PhD candidates interested in ergodic theory, reflecting his commitment to academic mentorship and program development. He contributes to the Analysis and Dynamical Systems research group within the University of Manchester's Data Science Institute, collaborating on interdisciplinary projects that connect pure mathematics with computational applications.

• Continuous Optimization
• Machine Learning
• Design and Analysis of Optimization Algorithms
• +3 more

Jiajin Li is an Assistant Professor in the Operations and Logistics Division at the UBC Sauder School of Business, concurrently affiliated with the Institute of Applied Mathematics (IAM) at the University of British Columbia. He holds a BSc in Statistics from Chongqing University and a PhD in Systems Engineering and Engineering Management from the Chinese University of Hong Kong (CUHK). Prior to joining UBC, he was a postdoctoral researcher at Stanford University's Department of Management Science and Engineering. His research focuses on continuous optimization , design and analysis of optimization algorithms , and machine learning , with emphasis on nonsmooth/nonconvex optimization, minimax problems, and distributionally robust optimization. His work bridges theoretical foundations with applications in graph data analysis and algorithmic stability. He teaches courses including Logistics and Operations Management (BCom), Seminar on Theoretical Developments in Management (PhD), and Optimization Theory and Applications (PhD). He actively seeks PhD students with strong mathematical or coding backgrounds through UBC Sauder or IAM programs. Key research contributions include advancements in primal-dual balancing for minimax optimization, stability evaluation via distributional perturbation analysis, and outlier-robust algorithms for graph data. His methodologies often integrate optimal transport theory and subdifferential analysis. He is affiliated with multiple research communities, including the IEEE and conferences like NeurIPS, ICML, and ICLR. His work emphasizes algorithmic robustness and scalability in complex optimization landscapes.