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Andrea BraidesView profile

Professor

• Calculus of Variations
• Gamma-convergence
• Homogenization
• +7 more

Andrea Braides is a Full Professor of Mathematical Analysis at the University of Rome Tor Vergata. He holds a dual affiliation with SISSA, Trieste. His research focuses on Calculus of Variations, Homogenization, and Discrete Systems, with applications to materials science and fracture mechanics. He has authored influential books like Gamma-convergence for Beginners and contributed to over 200 publications. His work bridges discrete and continuous models, addressing problems in phase transitions, lattice systems, and geometric measure theory. Affiliations: University of Rome Tor Vergata (Department of Mathematics), SISSA, Trieste. Education: Laurea in Mathematics (University of Pisa, 1983), PhD in Mathematics under Ennio De Giorgi at Scuola Normale Superiore. Research Interests: Braides' work emphasizes variational methods, Γ-convergence, and homogenization. He explores energy relaxation in discrete systems, phase transitions in Ising models, and microstructure analysis in materials. His recent studies include long-range interactions, anti-phase boundaries, and stochastic homogenization. Publications: His 150+ articles span topics like non-local energies, discrete-to-continuum limits, and ferromagnetic systems. Notable recent work includes studies on modulated phases in Ising systems (2025) and closure theorems for Γ-convergence (2024). Awards: Tullio Levi Civita Prize (2015), Timoshenko Fellowship (2004), Invited Speaker at ICM 2014. Advising & Leadership: Supervised 11 PhD theses, including those by R. Alicandro and M. Cicalese. Organized major conferences like the School on Multiple-scale Problems (2001) and the EMS Summer School (2018). Served on editorial boards of Archive for Rational Mechanics and Analysis and Networks and Heterogeneous Media . Labs/Teams: Coordinates research groups on variational problems, homogenization, and discrete systems at the University of Rome Tor Vergata and SISSA. Collaborates internationally, including with Oxford and TU Munich on multiscale phenomena.

• Operations Research
• Logistics Optimization
• Inventory Management
• +5 more

Elia Balugani is an Associate Professor at the Department of Engineering Sciences and Methods of the University of Modena and Reggio Emilia. His research focuses on operations research, logistics optimization, inventory management, renewable energy systems, and multi-criteria decision analysis. He holds positions at both the Reggio Emilia Technopole and Morselli Pavilion campuses. His work integrates advanced methodologies like robust ordinal regression, machine learning, and sensor technologies for industrial applications. Key research areas include: Development of decision support systems for sustainable manufacturing and logistics Ergonomic risk assessment using Kinect technology Optimization of supply chain processes through Six Sigma and combinatorial algorithms Circular economy strategies for waste management and additive manufacturing adoption Recent publications emphasize innovative approaches in: Multicriteria decision analysis with FITradeoff and PROMETHEE methods Smart city projects integrating tourism and urban infrastructure Energy efficiency in industrial retrofitting through data-driven modeling He maintains active collaborations in: Industrial safety and ergonomic assessment Environmental quality evaluation for buildings Big data applications in healthcare and urban planning

Mahdi ImaniView profile

Assistant Professor

• Machine Learning
• Control Theory
• Learning Theory
• +3 more

Mahdi Imani is an Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a courtesy appointment in the Khoury College of Computer Sciences. He holds a PhD in Electrical Engineering from Texas A&M University (2019), and MSc and BSc degrees in Electrical and Mechanical Engineering from the University of Tehran (2014 and 2012, respectively). His research focuses on machine learning, control theory, Bayesian statistics, and signal processing, with applications in gene regulatory networks, network security, and human-AI collaboration. Dr. Imani has received prestigious awards, including the NIH NIBIB Trailblazer Award (2022), the NSF CISE Career Award (2020), and the Outstanding Associate Editor Award from IEEE Transactions on Neural Networks and Learning Systems (2023 and 2024). He serves as an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Vehicular Technology, and is a Senior Member of IEEE. His research projects include DARPA-funded work on verified probabilistic reasoning in mixed reality systems, NSF-funded statistical inference methods, and ONR-funded studies on human-AI team synergy. He leads a lab focused on developing scalable Bayesian methods and reinforcement learning techniques for complex systems.

Hailiang LiuView profile

Professor

• Computational and Applied Mathematics
• Deep Learning
• Mathematical Biology
• +7 more

Hailiang Liu is a Professor of Mathematics at Iowa State University, with a courtesy appointment in Computer Science and membership in the Translational AI Center (TrAC). He also serves as the Data Science/AI Coordinator in the Department of Mathematics. Currently, he is on leave as a Program Director in the Applied Mathematics Program at the National Science Foundation (NSF). Dr. Liu holds a Ph.D. in Applied Mathematics from the Chinese Academy of Sciences (1995), an M.Sc. from Tsinghua University (1988), and a B.Sc. in Math Education from Henan Normal University (1984). His research focuses on computational and applied mathematics, including mathematical modeling via PDEs, numerical methods, scientific computing, and their intersections with deep learning and data-driven approaches. Key areas include: Deep Learning: Neural ODEs, selection dynamics, and optimal transport-based algorithms Mathematical Biology: Population dynamics, ecological dispersal, and aggregation modeling Computational Methods: Discontinuous Galerkin (DG) schemes, energy-stable numerical methods, and Gaussian beam methods Kinetic Theory: Photon scattering, Bose-Einstein condensation, and hyperbolic balance laws His work has been supported by the NSF and DOE. Notable contributions include structure-preserving numerical schemes, critical threshold analysis in hyperbolic systems, and data-driven optimal control frameworks.

Volodymyr MininView profile

Professor

• Biostatistics
• Infectious Disease Modeling
• Phylogenetics
• +4 more

Volodymyr Minin is a Professor in the Department of Statistics at the University of California, Irvine, where he also serves as Associate Director of the Infectious Disease Initiative and Honors Program Director for the School of Information & Computer Sciences. Additionally, he is Co-Director of the Irvine Summer Institute in Biostatistics and Undergraduate Data Science (ISI-BUDS), which recently welcomed its fourth cohort of 16 undergraduate students from across the country. Professor Minin leads an active research group focused on developing and applying statistical methods for infectious disease modeling, phylogenetics, and population dynamics. His group holds weekly journal club meetings on Fridays where members present and discuss recent research papers related to stochastic and statistical modeling approaches in epidemiology and evolutionary biology. His research interests span Biostatistics , Infectious Disease Modeling , Phylogenetics , Bayesian Statistics , Population Dynamics , Epidemiological Modeling , and Stochastic Processes . Minin's recent publications demonstrate a strong methodological focus with practical public health applications, particularly in infectious disease dynamics. His work integrates multiple data sources including genetic sequences, case reports, and wastewater surveillance to improve epidemiological inference. Key themes include addressing reporting delays in real-time analyses, developing more accurate methods for estimating the effective reproduction number, and investigating transmission dynamics of pathogens like SARS-CoV-2 and tuberculosis. Professor Minin has mentored numerous students through their academic careers, including Catalina Medina who recently defended her dissertation and is starting as an Assistant Professor at CSU Channel Islands, and students like Jessalyn Sebastian and Christian Bernal Zelaya who have received recognition at conferences like WNAR 2025 where Christian won the best student oral presentation award. His former students include Andy Magee, Jon Fintzi, Michael Karcher, Jason Xu, Jane Lange, Peter Chi, Julia Palacios, and Soyoung Ryu. His research group actively collaborates with public health organizations and participates in initiatives like the Infectious Disease Initiative at UCI. The group maintains an active GitHub presence with repositories documenting their journal club activities and sharing resources for stochastic and statistical modeling. The group's work has important implications for understanding and controlling infectious disease outbreaks through advanced statistical modeling approaches.

Philip MoczView profile

Researcher

• Multiphysics simulations
• Cosmology
• Galaxy formation and evolution
• +7 more

Philip Mocz is a computational research scientist at the Flatiron Institute's Center for Computational Astrophysics, part of the Simons Foundation. His work focuses on developing scalable, high-performance multiphysics simulation software with a growing emphasis on integrating modern AI techniques and automatic differentiability. Previously, he was a Computational Physicist at Lawrence Livermore National Laboratory, where he specialized in designing high-order Arbitrary Lagrangian-Eulerian (ALE) finite element methods for magnetohydrodynamics (MHD) simulations on heterogeneous computing architectures as part of the Multiphysics on Advanced Platforms Project (MAPP). Dr. Mocz earned his Ph.D. in Astrophysics from Harvard University in 2017 under Lars Hernquist, where he developed a finite-volume moving mesh magnetohydrodynamics algorithm applied to study structure formation and magnetic field amplification, integrating his solvers into the Arepo simulation code. Prior to his doctorate, he received an A.B. in Mathematics and Astrophysics from Harvard in 2012. His research spans multiphysics simulations, cosmology, galaxy formation, black hole physics, turbulence, numerical methods, and AI integration in computational astrophysics. A significant focus involves cosmological simulations of alternative dark matter candidates, particularly fuzzy dark matter. His work bridges theoretical astrophysics with high-performance computing, developing novel simulation frameworks that incorporate modern computational techniques. Dr. Mocz's publication record reveals a strong trajectory in computational methods for astrophysical problems, with increasing integration of AI techniques in recent years. His research demonstrates expertise across multiple domains including quantum mechanics applications to cosmology, turbulence modeling, and the development of advanced simulation algorithms. The interdisciplinary nature of his work connects astrophysics with computer science and applied mathematics. He maintains an active educational presence through his blog featuring approximately 100-line Python tutorials on scientific computing at the undergraduate level, published on Medium and followed on Twitter. His educational materials cover fundamental computational methods including finite difference approaches, Riemann solvers, and differentiable simulations using JAX. Dr. Mocz has served as a Teaching Fellow for Harvard courses including Astronomy 151 (Astronomical Fluid Dynamics), Applied Computation 274 (Computational Fluid Dynamics), and Applied Mathematics 205 (Advanced Scientific Computing). His outreach activities include mentoring for the LLNL DSTI Research Program, NASA Cosmic Origins Transitional Leadership Team, and Princeton Astrophysics Undergraduate Summer Research Program. Originally from Hawaii, he enjoys outdoor activities when not working. His professional presence includes active GitHub repositories (pmocz), a personal website (pmocz.github.io), and engagement on Bluesky (@philipmocz.bsky.social), where he shares insights about computational physics and scientific software development.

Laleh TafakoriView profile

Senior Lecturer

• Extreme Value Theory
• Dependence Modelling via Copulas
• Time Series Analysis
• +8 more

Dr. Laleh Tafakori is a Senior Lecturer in Statistics and Analytics at RMIT University’s School of Science, based at the City Campus in Australia. Her research focuses on extreme value theory, dependence modeling via copulas, time series analysis, machine learning, financial econometrics, and stochastic processes. She actively supervises research projects in areas such as diabetes prediction, climate modeling, and network analysis. Her teaching interests include applied analytics, statistical inference, and stochastic processes. Dr. Tafakori’s work bridges statistical theory and real-world applications, particularly in healthcare, finance, and environmental science. Recent projects include predicting diabetes onset, analyzing extreme precipitation patterns, and developing machine learning models for risk assessment. She collaborates internationally, with studies conducted in Saudi Arabia, Oman, and Europe. Research Highlights: Predictive modeling in healthcare, network influence analysis, and climate data forecasting. Supervision Interests: Machine learning applications, time series modeling, and complex systems analysis.

Solesne BourguinView profile

Associate Professor

• Stochastic Analysis
• Fractional Brownian Motion
• Malliavin Calculus
• +6 more

Solesne Bourguin is an Associate Professor in the Department of Mathematics and Statistics at Boston University. She is a member of the Probability and Statistics research group. Her research focuses on advanced topics in stochastic analysis, including stochastic dynamical systems, fractional Brownian motion, Malliavin calculus, Stein's method, and free probability theory. She teaches graduate and undergraduate courses such as Probability Theory II and Linear Algebra. Her research interests emphasize the theoretical foundations of stochastic processes, with applications to nonlinear statistics and random matrices. Recent work explores quantitative analysis of stochastic iterative algorithms and Gaussian approximations in high-dimensional settings. She has contributed to understanding fluctuation dynamics in multiscale systems driven by fractional Brownian motion and has published in leading journals like the Electronic Journal of Probability and Stochastic Processes and their Applications. Bourguin's recent publications highlight her expertise in topics like functional Gaussian approximations, moderate deviation principles, and spherical Poisson waves. These studies often combine techniques from Malliavin calculus and Stein's method to derive precise probabilistic estimates. She also investigates the limiting behavior of correlated Wishart matrices in high-dimensional regimes, contributing to random matrix theory. Scientific awards and grants are not explicitly listed in the provided information. She actively participates in academic activities, including organizing Boston University's Statistics and Probability Seminar Series. Her teaching reflects her commitment to both foundational and advanced mathematical education, spanning topics from linear algebra to stochastic processes.

Fei CaoView profile

Visiting Assistant Professor

• Interacting Particle Systems
• Partial Differential Equations
• Theoretical Computer Science
• +3 more

Fei Cao is a Visiting Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on the interplay between interacting particle systems and partial differential equations, exploring how microscopic agent dynamics lead to macroscopic phenomena. He also investigates theoretical foundations of machine learning algorithms, particularly stochastic gradient descent. Education: Ph.D. in Applied Mathematics, Arizona State University (2017–2022) Bachelor's degree in Applied Mathematics, Southwestern University of Finance and Economics, China (2013–2017) Research Interests: Interacting particle systems and their PDE limits Mathematical analysis of wealth distribution models Opinion dynamics and collective behavior Theoretical computer science and machine learning mechanisms His work bridges stochastic agent-based models with deterministic PDE frameworks, with recent focus on econophysics and gradient-based learning algorithms. Publications Trends: Recent work emphasizes multi-phase economic models, mean-field analysis of opinion dynamics, and PDE perspectives on generative AI. His research combines analytical rigor with numerical methods to study complex systems. No scientific awards explicitly listed. Advising no formal students listed. Active in departmental research activities. Labs/Teams: Affiliated with the Department of Mathematics and Statistics at UMass Amherst, collaborating on interdisciplinary projects in applied mathematics and theoretical computer science.

Wuzhe XuView profile

Visiting Assistant Professor

• Applied Mathematics
• Computational Mathematics
• Machine Learning
• +4 more

Wuzhe Xu is a Marshall H. Stone Visiting Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst, mentored by Prof. Yulong Lu. He holds a Ph.D. in Mathematics from the University of Minnesota Twin Cities (2022), supervised by Prof. Li Wang. His research focuses on applied mathematics, computational methods, and interdisciplinary applications in fluid dynamics, partial differential equations, and machine learning. Education: Ph.D. in Mathematics, University of Minnesota Twin Cities, 2022 Research Interests: Dr. Xu develops advanced computational techniques for solving multiscale kinetic equations, diffusion models, and physics-guided machine learning frameworks. His work addresses challenges in fluid dynamics, radiative transfer, and long-time prediction of evolution equations. He emphasizes asymptotic preserving schemes and transfer learning applications to ensure numerical stability and accuracy across diverse physical systems. Publications Trends: His recent work (2021–2025) highlights innovations in generative downscaling of PDE solvers, super-resolution techniques in fluid dynamics, and mathematical modeling of granular media. Earlier studies (2013–2014) explored epidemiological trends of infectious diseases in Guangxi Province, China. Awards & Recognition: Notable for award-winning teaching and research excellence in applied mathematics. Advising & Grants: Active in mentoring students through interdisciplinary projects. Current grants and collaborations focus on advancing computational methods for fluid dynamics and machine learning applications. Labs/Teams: Collaborates closely with the Applied Mathematics Research Group at UMass Amherst and maintains ties with Prof. Li Wang's team at the University of Minnesota.

• Mathematical Statistics
• Machine Learning
• Kernel Methods
• +8 more

Dr Dalia Chakrabarty is a Reader in Statistical Data Science at the Department of Mathematics, University of York. Previously, she held positions as Senior Lecturer at Newcastle University and Lecturer at Lancaster University. Her research focuses on probabilistic methods, Bayesian inference, and machine learning applications in fields such as medicine, astronomy, and materials science. She specializes in kernel methods, random graph analysis, and causal forecasting. Notable contributions include developing methodologies for uncertainty quantification and non-parametric learning. Her academic career includes a Royal Society Dorothy Hodgkin Fellowship and supervision of students like Kane Warrior. Dr Chakrabarty's work bridges theoretical statistics and real-world challenges, with publications spanning journals like Plos One and Artificial Intelligence in Medicine . She also authored the textbook Supervised Learning: Mathematical Foundations & Real-world Applications (2024, CRC Press). Research Highlights: Inter-graph distance metrics for medical data analysis Bayesian state-space modeling of galactic dynamics High-dimensional data applications in oncology and materials science Collaborations: Maintains ties with Brunel Mathematics for PhD supervision and international research networks. Contact: Email dalia.chakrabarty@york.ac.uk , Tel: +44 (0)1904 32 1486

• Quantum Information
• Functional Analysis
• Operator Algebras
• +3 more

Li Gao is a Professor at the School of Mathematics and Statistics, Wuhan University. His research bridges functional analysis and quantum information, with a focus on operator algebras and noncommutative analysis. Previously, he held positions at the University of Houston (tenure-track assistant professor, 2021–2023), Technical University of Munich (postdoc, 2021), and Texas A&M University (postdoc, 2018–2020). He earned his Ph.D. in Mathematics from the University of Illinois at Urbana-Champaign (2018) and a Bachelor's degree from Wuhan University (2012). His research explores quantum entanglement, quantum algorithms, and entropy inequalities in quantum systems. He actively organizes seminars like the Yangtze Analysis Seminar (2025), East Lake Quantum Seminar (2024), and undergraduate Quantum Information Seminars. Recent teaching includes Functional Analysis (Spring 2025) and Linear Algebra A. Key research themes include logarithmic Sobolev inequalities, quantum channel properties, and the interplay between noncommutative geometry and quantum information. His work addresses foundational questions in quantum computing, such as Shor's algorithm and Bell inequality violations, as seen in student-led seminar discussions on topics like quantum entanglement and Heisenberg uncertainty principles. Advising includes mentoring postdocs and Ph.D. students in interdisciplinary projects. He collaborates across departments like the Li Yinan School of Artificial Intelligence and Guanglin School of Mathematics. Current initiatives focus on quantum machine learning and cutting-edge quantum communication protocols.

Ian StevensonView profile

Associate Professor

• Computational Neuroscience
• Statistical Analysis of Neural Data
• Neural Dynamics
• +5 more

Ian Stevenson is an Associate Professor in the Department of Psychological Sciences at the University of Connecticut. He earned his Ph.D. from Northwestern University in 2011 and leads the UConn Statistical Neuroscience Lab. His affiliations include the Behavioral Neuroscience Program, Department of Biomedical Engineering, and CT Institute for the Brain and Cognitive Sciences. Stevenson's research bridges computational neuroscience and neural data analysis with two primary focus areas: Developing computational tools to model neural dynamics, interactions, and adaptation mechanisms Linking neural activity patterns to perception, behavior, and learning processes His recent publications demonstrate strong trends in Bayesian neural decoding methods and synaptic plasticity modeling, with applications spanning auditory processing, neural connectivity inference, and educational neuroscience. Computational approaches dominate his research, particularly for analyzing high-dimensional neural datasets. He maintains active collaborations through shared data repositories and tools for neural data analysis. His lab is currently seeking undergraduate and graduate students interested in computational neuroscience research.

Wumaier MaimaitiyimingView profile

Teaching Professor

• Scientific Computing
• Numerical analysis
• Optimization

Dr. Wumaier Maimaitiyiming is a Teaching Professor in the Department of Mathematics and Statistics at Missouri University of Science and Technology. Previously, he served as an Adjunct Assistant Professor (postdoc) in the Computational Applied Mathematics group at UCLA's Mathematics Department. He holds a PhD from Iowa State University (2020), and M.S. and B.S. degrees from Xinjiang University. Education: Ph.D. in Applied Mathematics, Iowa State University, 2020 M.S. in Computational Mathematics, Xinjiang University, 2010 B.S. in Computational Mathematics, Xinjiang University, 2007 Research Interests: Focuses on Scientific Computing, Numerical analysis, and Optimization. His work emphasizes developing efficient numerical schemes with energy stability and positivity preservation, particularly for electrochemical models like Poisson-Nernst-Planck systems. These schemes address multi-dimensional systems and interaction potentials in diffusion processes. Publications: Recent articles highlight advancements in computational methods for Poisson-Nernst-Planck equations, including energy-stable algorithms and reduced models. His work bridges theoretical analysis with practical applications in electrochemistry and diffusion dynamics. Scientific Awards: No awards explicitly listed. Advising & Grants: No students or grants mentioned. Labs/Teams: Previously affiliated with UCLA's Computational Applied Mathematics group during his postdoc.

• Probability Theory
• Stochastic Processes
• Functional Limit Theorems
• +7 more

Prof. Alexander Iksanov is a Professor in the Operations Research Department at the Faculty of Computer Science and Cybernetics, Taras Shevchenko National University of Kyiv, Ukraine. His career includes roles as Associate Professor (2002–2008), Deputy Dean of the Faculty of Cybernetics (2004–2006), and Head of the Operations Research Department since 2014. He leads the project 'Asymptotic and structural analysis of stochastic models of population dynamics' (2019–2021). Education: 1995: Bachelor’s/Master’s degree in Cybernetics from Taras Shevchenko National University of Kyiv 2000: PhD in Physics and Mathematics 2008: Habilitation in Physics and Mathematics Research Interests: Focuses on stochastic processes, random discrete structures (compositions, coalescents, branching processes), functional limit theorems, renewal theory applications, and asymptotic analysis of complex systems. Explores regeneration properties in discrete and continuous stochastic models, including perturbed random walks, random Dirichlet series, and generalized convexity analysis. Methodological Expertise: Shot noise processes, regenerative compositions, perpetuities, branching random walks, probabilistic number theory, and convex stochastic geometry. Future Projects: Includes asymptotic theory of perturbed random walks, analysis of random Dirichlet series, discounted perpetuities, generalized convexity, random vp-trees, mod-phi convergence, and complex systems analysis. Grants & Leadership: Headed project 19БФ015-01 (2019–2021). Active in collaborative research groups and international publications.