Marina Khismatullina is an Assistant Professor of Econometrics at Erasmus School of Economics, Erasmus University Rotterdam. She specializes in nonparametric statistics, time series analysis, and machine learning applications in econometrics. Her research develops novel statistical methods for analyzing economic and epidemiological time series data. Recent publications explore neural network volatility forecasting, dimensionality reduction of housing indices, and nonparametric comparison of COVID-19 epidemic trends. She contributes to the R package 'MSinference' for multiscale analysis of time series data, enabling rigorous statistical inference for nonparametric trend functions.
Corsini Alessandro is a Full Professor at the Department of Engineering, Sapienza University of Rome, specializing in renewable energy systems, computational fluid dynamics, and turbomachinery. His research focuses on optimizing offshore wind energy systems, hydrogen storage, and sustainable energy communities. He leads projects on wind turbine aerodynamics, fluid-structure interaction, and machine learning applications in engineering. Key research areas include wake dynamics in offshore wind farms, adaptive turbine blade design, and the integration of renewable energy technologies into urban and island systems. His work addresses challenges in energy efficiency, environmental impact mitigation, and innovative solutions for sustainable power generation. Recent studies explore technology roadmaps for energy sectors, desalination in renewable energy communities, and predictive modeling of material erosion in turbines. He collaborates on experimental testing of wave energy converters and machine learning-driven analysis of energy systems. Corsini has contributed to advancements in computational fluid dynamics, including variational multiscale methods and surrogate modeling for noise prediction. His interdisciplinary approach bridges engineering, environmental science, and data-driven innovation.
Dr. Guannan Hu is a Researcher at the Department of Meteorology, University of Reading, School of Mathematical, Physical and Computational Sciences. With a focus on data assimilation and numerical weather prediction, their work addresses critical challenges in high-impact weather forecasting. Research Interests: Specializing in data assimilation methodologies, observation impact assessment, and computational efficiency improvements for weather prediction systems. Their work spans convection-permitting models, error covariance estimation, and multiscale modeling. Publication Trends: Recent research (2021–2025) explores advanced computational methods like localized fast multipole algorithms, observation error covariance estimation, and ensemble-based sensitivity analysis for hazardous weather prediction. Key themes include optimizing data assimilation frameworks and improving extreme weather forecasting accuracy. Collaborative Projects: Active participant in the DARE (Data Assimilation for the REsilient city) initiative (2020–2022) and the Centaur Publications program. Former Principal Researcher in the DARE Pilot Project (2021).
Marta D'Elia is an Adjunct Professor at Stanford's Institute for Computational and Mathematical Engineering (ICME), specializing in Scientific Machine Learning and nonlocal modeling. Her research develops data-driven algorithms for multiscale/multiphysics simulations, integrating numerical analysis, uncertainty quantification, and fractional calculus. Core applications include subsurface transport, turbulence modeling, image processing, and materials science. She leads innovations in nonlocal operator regression, physics-informed neural networks, and fractional Laplacian formulations. Current work focuses on embedded machine learning for constitutive modeling, Bayesian uncertainty frameworks, and computational homogenization. D'Elia pioneered approaches for nonlocal-to-local model coupling and fractional Helmholtz decompositions, advancing simulation capabilities for anomalous transport phenomena. Her Ph.D. in Applied Mathematics (Emory University) underpins rigorous mathematical foundations, while collaborations with national labs address high-performance computing implementations. Research contributes to open-source scientific software and computational mathematics education through ICME courses on numerical methods and machine learning.
Professor Tobias Weinzierl holds the position of Professor in the Department of Computer Science at Durham University. He is the Co-director of the Institute for Data Science (IDAS) and leads the Scientific Computing research group. His expertise includes high-performance computing, parallel algorithms, and scientific computing, with a focus on numerical methods and adaptive mesh refinement techniques. Research interests encompass high-performance computing architectures, parallel algorithm design, and the development of scalable numerical solvers for hyperbolic partial differential equations (PDEs). He has contributed to projects like ExaHyPE, an engine for exascale simulations of wave phenomena, and has authored influential books such as *Principles of Parallel Scientific Computing* and *A Framework for Parallel PDE Solvers on Multiscale Adaptive Cartesian Grids*. He has held roles including inaugural director of the Master in Scientific Computing and Data Analysis (MISCADA) and has reviewed for the European High Performance Computing Joint Undertaking (EuroHPC JU). His work emphasizes resilience in numerical software, compiler optimizations, and energy-efficient computing. Key contributions include the Peano software framework for adaptive grid traversals, and research on task-based parallelism, GPU offloading, and fault tolerance in HPC systems. Current research focuses on exascale computing, multiscale optimisation, and the application of parallel computing to astrophysics and fluid dynamics. Labs/Teams: Scientific Computing research group, ExaHyPE project team Grants/Projects: PI/Co-I on ExCALIBUR projects, H&ES installations
Tomas Dohnal is a Professor at the Institute of Mathematics of Martin Luther University Halle-Wittenberg , Germany, since 2018. His research focuses on Nonlinear Partial Differential Equations (PDEs) , Dispersive Waves , Bifurcation Theory , and Wave Propagation in Periodic Structures . He has held academic positions at Technical University Dortmund, Karlsruhe Institute of Technology, ETH Zurich, and University of New Mexico. Research Interests : Nonlinear PDEs, Surface Plasmon Polaritons, Gap Solitons in Photonic Crystals, Spectral Problems, Rigorous Asymptotics, Numerical Analysis Grants : DFG grants on nonlinear wavepacket asymptotics and moving gap solitons in periodic media Students : Supervised PhD students including Maximilian Hanisch, Matthias Ionescu-Tira, and Daniel Tietz; Master students at multiple institutions Software : Co-developer of the PDE2PATH MATLAB package for bifurcation analysis Publications span topics in Maxwell equations with interfaces, PT-symmetric problems, homogenization of periodic media, and nonlinear wave dynamics. His work often bridges rigorous mathematical analysis with numerical methods. Teaching includes courses on Dispersive PDEs, Asymptotic Methods, Nonlinear Analysis, and Wave Propagation. He has taught at TU Dortmund, Karlsruhe Institute of Technology, and Martin Luther University.
Prof. Dr. Tobias Preußer is a Professor of Mathematical Modelling of Medical Processes at the School of Computer Science and Engineering, Constructor University Bremen gGmbH. His research focuses on mathematical modeling in biomedical processes, numerical analysis, image processing, and scientific visualization. He holds a PhD in Mathematics from the University of Duisburg-Essen (2001-2003), a Diploma in Mathematics from the University of Bonn (1994-1999), and completed an exchange semester in applied mathematics at New York University. His academic roles include Deputy Institute Director and Head of Modeling and Simulation at Fraunhofer MEVIS, General Manager at TechsoMed GmbH, and Visiting Assistant Professor at the University of Bremen. His work emphasizes interdisciplinary collaboration, particularly in systems biology and medical imaging applications. Key research interests include partial differential equations, bio-medical process simulation, anisotropic diffusion techniques, and multiscale methods. He has contributed to advancements in radiofrequency ablation modeling, liver pharmacokinetics simulations, and uncertainty quantification in medical visualization. His publications span computational biology, medical physics, and visualization techniques, with notable contributions to virtual liver modeling and stochastic collocation methods for optimal control problems. He has led collaborative projects involving academic and industrial partners, advancing both theoretical and applied aspects of mathematical modeling in healthcare.
Anton Klimovsky is a Senior Lecturer at the University of Würzburg since 2022, affiliated with the Applied Stochastics department. His career includes visiting professorships at the University of Stuttgart (2022) and the University of Bonn (2014), along with lecturing at the University of Duisburg-Essen (2013-2022). Education : Habilitation (2019), Ph.D. in Mathematics (2008, TU Berlin, advisor: Anton Bovier), M.Sc. in Applied Mathematics (2002, Kharkiv National University, advisor: Mariya Shcherbina). Klimovsky’s research focuses on stochastic models of complex systems , including statistical mechanics of disordered systems , range-free spin glasses , and stochastic processes on evolving networks . His work bridges high-level heuristic ideas from statistical physics (e.g., cavity method, renormalization group) with rigorous probabilistic techniques. The 15 most recent articles highlight his expertise in disordered systems , hierarchical structures , population genetics , and network dynamics . Key trends include analysis of phase transitions in branching Brownian motion, renormalization of Lambda-Cannings processes, and Gaussian fluctuation theory in random energy models. Scientific awards include the Marie Curie Intra-European Fellowship (2010-2012) DFG Ph.D. Fellowship (2003-2005) National Academy of Sciences of Ukraine Ph.D. Fellowship (2002-2003) He has held visiting positions at Leiden University , EURANDOM , and Technische Universität Berlin , and has taught courses on Stochastic Processes , Markov Chains , and Spin Glasses . His grants include DFG funding for stochastic processes on evolving networks (2019-2024) and collaborative projects on hierarchical self-organization (2020-2026).
Angelantonio Tafuni is an Assistant Professor at the School of Applied Engineering and Technology, New Jersey Institute of Technology (NJIT). His research focuses on computational fluid dynamics, smoothed particle hydrodynamics (SPH), and aerospace engineering applications. He leads projects simulating autonomous systems and human-agent interactions, funded by the National Science Foundation (NSF). His work spans multiscale modeling, cryogenic fluid management in space systems, and numerical simulation techniques. Recent research includes advancements in domain decomposition strategies for SPH, CFD modeling of cryogenic propellant tanks during parabolic flights, and development of flight-ready sensors for cryogenic systems. Collaborations involve international teams and industry partners in aerospace and computational engineering. Dr. Tafuni has secured a major NSF grant (2022–2025) titled 'Frameworks: Simulating Autonomous Agents and the Human-Autonomous Agent Interaction.' His publications emphasize innovative numerical methods and their application to real-world engineering challenges, with a strong focus on aerospace and fluid dynamics.
Trisha Sain is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University. She earned her PhD in Civil Engineering (2008), MSc in Civil Engineering (2003), and BE in Civil Engineering (2001) from Indian institutions. Joined Michigan Tech in August 2016 Previous Assistant Professor at North Carolina A&T State University (2013-2016) Postdoctoral research at University of Michigan (2011-2013) and Technical University of Catalunya (2009-2011) Her research focuses on multiscale/multiphysics modeling of material behavior, including: Fracture, damage, and impact in polymers and composites Biomedical degradation of metallic implants Polymer curing process modeling Phase-field fracture analysis Uncertainty quantification in material models Current trends in her publications include: Developing phase-field models for complex crack propagation in composites Studying thermo-oxidative degradation of polymers Investigating 3D printed polymer architectures Analyzing chemically strengthened glass fracture Creating predictive models for polymer curing Understanding viscoplastic damage in semicrystalline polymers She is supported by grants from: Air Force Office of Scientific Research Army Research Office Her work combines computational modeling with experimental validation, focusing on predictive material behavior under various loading and environmental conditions.
Dr. Pawan Goyal is a Senior AI Engineer/Researcher at appliedAI Initiative GmbH, focusing on generative AI and physics-informed machine learning. Previously, he led the Physics-Enhanced Machine Learning team at the Max Planck Institute for Dynamics of Complex Technical Systems, where he developed surrogate modeling techniques integrating physics into AI. His research spans dynamical systems, model reduction, and materials science, emphasizing stability and interpretability of models. Education: PhD in Applied Mathematics (Max Planck Institute, 2018), M.Tech and B.Tech in Engineering Design (IIT Madras). His work bridges computational science and AI, addressing challenges in engineering design and materials discovery. Key interests include generative AI for design, physics-informed neural networks, and reduced-order modeling for complex systems. Awards: Dr.-Klaus-Körper Prize (GAMM, 2019), Best Ph.D. Thesis (Otto-von-Guericke University, 2018).
Gabriel Potirniche is a Professor and Associate Dean at the University of Idaho's College of Engineering, with affiliation to the Department of Mechanical Engineering. He holds a Ph.D. in Mechanical Engineering from Mississippi State University (2003), dual M.S. degrees from Polytechnic University of Bucharest (Mechanics, 1998; Transportation, 1999), a B.B.A. from Academy of Economic Studies (1998), and a B.S. in Transportation from Polytechnic University of Bucharest (1995). His research focuses on computational solid mechanics, fracture/fatigue behavior of metals, thermoelectric material performance, and high-temperature material deformation modeling. Key areas include: Finite Element Method applications Creep-fatigue interaction mechanisms Nanostructured thermoelectrics Plasticity-induced crack closure Recent publications demonstrate strong focus on fatigue crack growth modeling in high-temperature alloys (2019-2024), with applications in nuclear reactor components and energy systems. His work combines experimental validation with advanced computational simulations. Awards include: University Mid-Career Faculty Award (2015) Orr Early Career Award, ASME (2007) Award for Excellence in Multiscale Modeling (2005) Research has been funded by Department of Energy, National Science Foundation, and Department of Defense. Current projects involve creep-fatigue characterization in nuclear reactor alloys and thermoelectric device development.
Kartik Iyer is an Assistant Professor jointly appointed in the Department of Physics and Department of Mechanical and Aerospace Engineering at Michigan Tech. He earned his PhD in Aeronautics from the Georgia Institute of Technology, followed by postdoctoral appointments at the University of Rome and New York University. Research focuses on Atmospheric Physics, Turbulence, Thermal Convection, and Numerical Simulations. Director of the Iyer Research Lab , specializing in supercomputing and turbulent flow analysis. His scientific work spans turbulence theory, scalar transport, and computational methods, with over 15 recent publications in top-tier journals. Key areas include the Zeroth Law of Turbulence, circulation statistics, bifractal scaling, and subgrid-scale modeling. Despite his focus on high-performance computing, no specific awards or honors are mentioned in the provided data. Dr. Iyer teaches courses related to atmospheric physics and heat transfer, emphasizing interdisciplinary approaches between physics and engineering disciplines.
Wotao Yin is a Professor of Mathematics at the University of California, Los Angeles, with a distinguished research career spanning over two decades in optimization theory and its applications. His work bridges theoretical mathematics with practical applications in machine learning, image processing, and signal analysis. As a leading researcher in optimization algorithms, he has made significant contributions to the development of methods like ADMM (Alternating Direction Method of Multipliers), proximal algorithms, and decentralized optimization techniques. Department: Department of Mathematics School: College of Letters and Science University: University of California, Los Angeles Yin's research focuses on developing efficient algorithms for large-scale optimization problems, with particular expertise in convex and nonconvex optimization, distributed and decentralized optimization, and mathematical foundations of machine learning. His work has profound implications for image reconstruction, signal processing, and modern machine learning systems. He has pioneered methods for handling sparse data, non-smooth objectives, and constrained optimization problems that arise in real-world applications. An analysis of his recent publications reveals a strong trend toward addressing optimization challenges in machine learning, particularly in federated learning, attention mechanisms, and nonconvex problem structures. His work demonstrates a consistent pattern of bridging theoretical optimization with practical machine learning applications, developing algorithms that balance computational efficiency with theoretical guarantees. Recent papers show increasing focus on heterogeneous data settings, large language model optimization, and fundamental limitations of optimization methods in complex learning scenarios. Throughout his career, Professor Yin has mentored numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. His collaborative network spans multiple institutions worldwide, with particularly strong connections to researchers in China and across the United States. His work has been supported by various funding agencies recognizing the fundamental importance of optimization theory for advancing computational science. Professor Yin leads a vibrant research group focused on mathematical optimization and its applications, where students and collaborators work on cutting-edge problems at the intersection of mathematics, computer science, and engineering. The group maintains strong connections with both theoretical and applied research communities, participating in major conferences across optimization, machine learning, and computational mathematics.
Jun.-Prof. Dr. Christian Dreßler is the head of the Theoretical Solid State Physics Group at Technische Universität Ilmenau since September 2022. He holds an academic background in Chemistry from the University of Leipzig and completed his PhD at Martin Luther University Halle-Wittenberg, focusing on cross-scale approaches to proton conduction and intermolecular interactions. He also studied Mathematics during his PhD. His research emphasizes molecular dynamics simulations of ion transport in energy materials, including proton transport in fuel cell membranes and lithium-ion transport in battery electrodes. He developed a multiscale approach to model proton transport in nanostructured/porous materials and plans to extend this to semiconductor/water interfaces. Upcoming teaching includes a quantum mechanics lecture and future courses on molecular dynamics. Education: Bachelor/Master in Chemistry (University of Leipzig) PhD in Theoretical Chemistry (Martin Luther University Halle-Wittenberg) Additional studies in Mathematics Research Interests: Focuses on simulating atomic/molecular motion using molecular dynamics, particularly ion transport in energy materials. Current projects include optimizing multiscale methods for proton transport in nanostructured materials and expanding into semiconductor/water interface simulations. His work bridges theoretical physics, materials science, and computational chemistry.
