Ehsan Modiri is a researcher at the Department of Hydrosystem Modelling , Helmholtz Centre for Environmental Research (UFZ), Germany. His work focuses on climate change impacts on hydrological systems, drought monitoring, and environmental modeling using advanced computational frameworks. Affiliation: UFZ - Helmholtz Centre for Environmental Research Department: Hydrosystem Modelling Research Themes: Climate Change, Droughts, Hydrological Forecasting, Water Resource Management Research Interests: Modiri specializes in understanding hydrological responses to climate change, particularly in drought dynamics and soil moisture variability. His work bridges observational data with sophisticated modeling frameworks to improve predictability of water balance components under warming scenarios. Scientific Contributions: Recent publications highlight his role in developing high-resolution drought simulations, evaluating hydrological model performance, and analyzing groundwater responses to global warming. He participates in large-scale European hydrological projects and collaborates on climate-hydrology integration initiatives.
Edriss S. Titi is a University Distinguished Professor and Arthur Owen Professor of Mathematics at Texas A&M University within the College of Arts & Sciences. His research focuses on nonlinear partial differential equations, applied mathematics, and geophysical fluid dynamics. He leads studies on fluid mechanics, atmospheric and oceanic dynamics, data assimilation, and control theory. His work often addresses mathematical rigor in modeling complex systems like climate dynamics and turbulent flows. Research Interests: Nonlinear PDEs and their applications Fluid dynamics and turbulence Data assimilation algorithms Climate and ocean modeling Infinite-dimensional dynamical systems Recent publications emphasize Navier-Stokes equations , primitive equations , and data assimilation in chaotic systems . His methodologies bridge theoretical analysis and computational modeling, with applications to weather prediction and geophysical flows. Collaborations include the Institute for Applied Mathematics and Computational Science (IAMCS) at Texas A&M. Notable contributions include rigorous analysis of global well-posedness for oceanic models and development of CDAnet, a physics-informed deep learning framework for fluid flow downscaling.
Professor Bharath Ganapathisubramani is a faculty member in the Department of Aeronautics and Astronautics at the University of Southampton. He holds the title of Professor of Experimental Fluid Mechanics and leads research on aerodynamic/hydrodynamic phenomena relevant to transportation, energy, and autonomous systems. His work is supported by funding from EPSRC, EU programs, and industry partners like Rolls-Royce and Huawei. He is affiliated with the National Wind Tunnel Facility and serves as an Associate Editor for Experiments in Fluids and Flow . Education: B.Tech in Naval Architecture (IIT Madras, 1999), M.S. and Ph.D. in Aerospace Engineering (University of Minnesota, 2004), followed by postdoctoral research at the University of Texas at Austin. Administrative Roles: Head of Aero/Astro Department (2019–2022), Deputy Head of School for Research (2018–2019). Research focuses on experimental methods to predict/control fluid flows, including boundary layer manipulation, flow control for aerodynamics/aeroacoustics, and data-driven approaches using machine learning. Current projects explore rough wall turbulence, flapping foil energy harvesting, and advanced diagnostics like FoRMS facilities. Awards include the ERC Starting Grant (2012) and Fellowships from the Royal Aeronautical Society/AIAA. He advises ~6–7 PhD/undergraduate projects annually, emphasizing experimental design (wind/water tunnels) and CFD modeling in aerospace/energy sectors. External engagements include invited speaking roles at conferences like the International Symposium on PIV (2013) and advisory roles in international research boards.
Alexandros Kontogiannis is a research fellow at the University of Cambridge, Department of Engineering, specializing in fluid dynamics and applied mathematics. His work combines Bayesian inference, machine learning, and physics-informed algorithms to solve inverse problems in magnetic resonance velocimetry (MRV) and fluid-structure interaction. EPSRC National Fellow in Fluid Dynamics Member of Energy, Fluids and Turbomachinery Division Research Focus: Development of digital twin frameworks that integrate MRV data with Navier-Stokes equations to reconstruct flowfields, infer rheological parameters in non-Newtonian fluids, and estimate hidden quantities like pressure and wall shear stress. Key innovations include: Physics-informed compressed sensing for sparse MRV data Simultaneous boundary shape and flowfield estimation Bayesian turbulence model parameter learning Scientific Awards: ASME Fluids Engineering Division Graduate Student Scholar (2021) Technical Chamber of Greece (TEE) Award (2018) Limmat Foundation Academic Excellence (2017) Mentzelopoulos Scholarship for international studies (2017) Greek State Scholarships Foundation Award (2012) Key Contributions: Algorithms for 3D flow reconstruction with adaptive discretization, viscous signed distance field regularization, and multi-objective aerodynamic shape optimization. His methodologies enable 27x reductions in MRI scanning time while maintaining diagnostic accuracy.
Dr Sean Anderson is a Senior Lecturer at the Department of Automatic Control and Systems Engineering , University of Sheffield , with over 15 years of experience in interdisciplinary research spanning robotics, control systems, and computational biology. He earned his MEng and PhD from the University of Sheffield, focusing on control systems and chemical engineering. Education: MEng in Control Systems Engineering, University of Sheffield (2001) PhD in Chemical and Process Engineering, University of Sheffield (2005) Research Interests include: Bioinspired robotics Adaptive and optimal control in biological systems Nonlinear system identification Computational neuroscience Acoustic and visual sensor fusion for localization His recent publications highlight innovations in robotic localization in hazardous environments, interpretable deep learning for control systems, acoustic sensing technologies, and data-driven modeling of complex systems. Key projects involve autonomous navigation in pipe networks, turbulence modeling, and biomedical signal processing. Grants and Funding: He has secured major grants from EU H2020 (£4M), EU FP7 (£2.9M), and EPSRC (£5.7M), focusing on bioinspired control algorithms, robotic safety, and infrastructure assessment. Teaching: He leads the ACS61011 Deep Learning module, emphasizing practical applications in robotics and signal processing.
Professor Massimiliano Tani Bertuol is a distinguished academic specializing in economics at UNSW Canberra's School of Business, where he has served as Professor since 2015. His professional affiliations extend beyond UNSW as he is an Associate Investigator/Member at CEPAR; Ageing Futures; uDASH; AI Institute; and Cyber security (IFCYBER). Additionally, he maintains international connections as a Research Fellow at the Institute for the Future of Labor (IZA) in Germany since 2005, an Associate Member at Macquarie University's Centre for Workforce Futures since 2018, and a Research Fellow at the Global Labor Organization (GLO) in Maastricht since 2016. His educational background reflects a strong foundation in economics and business, having earned a PhD in Economics from the Australian National University (2003), a Master of Science in Economics from the London School of Economics (1992), and a Bachelor's degree in Business/Economics from Bocconi University in Milan, Italy (1989). His academic journey has positioned him as a leading researcher in human capital economics with international recognition. Professor Tani Bertuol's research centers on human capital development and its economic implications. His work examines how human capital can be fostered, efficiently transferred internationally through migration, and how it affects productivity, innovation, and economic growth at both firm and national levels. His research spans multiple regions including Australia, Europe, the US, Africa, and China, with particular focus on migration economics, labor market outcomes, and the economic impacts of education and skills. His current research agenda includes non-pecuniary incentives, behavioral/financial decisions in China, occupational licensing, language skills and economic assimilation, AI-human interactions in health contexts, and labor mobility and productivity. Analysis of his recent publications reveals significant interdisciplinary trends bridging economics with public health, environmental science, and technology. His work connects migration dynamics with economic outcomes, examines household financial behaviors through gender lenses, and investigates the complex relationships between environmental factors like air pollution and economic activities including education investment and entrepreneurship. More recent work explores AI applications in health and the economic implications of pandemic responses, demonstrating his ability to address contemporary challenges through rigorous economic analysis. 2023: UNSW ARC Postgraduate Council (Arc PGC) award for excellence in research supervision 2011: Vice-Chancellor Award for Teaching Excellence 2011: Faculty Award for Teaching Excellence for teaching economics Professor Tani Bertuol has successfully supervised 4 PhD students to completion, with 1 submitted dissertation and 5 currently under supervision. His active research program is supported by significant grant funding including an ARC Linkage Project (2023-27) on regional Australia's skills shortages and high-skill refugees' employment ($354,811), an ARC Discovery Project (2019-23) on migrant aging and wellbeing ($478,000), and a NUW Alliance grant (2021-23) on hearing screening and academic outcomes ($73,367). He serves as Associate Editor for Social Indicators Research and Higher Education Research & Development, contributing to scholarly discourse in his fields of expertise. His teaching portfolio includes courses in data analytics, finance, and professional executive education focused on cost-benefit analysis and data communication. He teaches ZBUS2333 Data Analytics and Visualisation, ZBUS8105 Finance and Investment Appraisal, and ZBUS8149 Introduction to Finance, demonstrating his commitment to developing the next generation of economics professionals with both theoretical knowledge and practical skills.
Prof. Hansjörg Kutterer is a Professor and Dean at the KIT-Department of Civil Engineering, Geo and Environmental Sciences at Karlsruhe Institute of Technology (KIT). His primary affiliation is with KIT's Department of Civil Engineering, Geo and Environmental Sciences. He leads geodetic research initiatives focusing on Earth observation systems, atmospheric modeling, and geophysical data analysis. His research emphasizes advanced applications of GNSS, InSAR, and satellite gravimetry for monitoring climate-related phenomena such as water vapor dynamics, terrestrial water storage changes, and ground motion patterns. Key projects include developing machine learning-enhanced models for tropospheric delay corrections and integrated water vapor estimation in the Upper Rhine Graben region. Prof. Kutterer actively contributes to international geodetic frameworks like the Global Geodetic Observing System (GGOS), particularly through DA-CH regional collaborations. His work bridges geodetic methodologies with interdisciplinary challenges in climate science and environmental engineering. He oversees departmental operations as Dean, fostering innovation in geospatial education and infrastructure. His technical expertise spans geodetic deformation analysis, statistical robust estimation, and the integration of geophysical models with observational data.
Andrea Garzelli is a Full Professor at the University of Siena's Department of Information Engineering and Mathematical Sciences. His research focuses on remote sensing image processing, particularly in optical and SAR sensor technologies, image fusion, and spatial resolution enhancement. He holds teaching roles in 'Fundamentals of Signal Processing and Telecommunications' and 'Statistical Signal Processing.' He earned his Ph.D. in Computer Science and Telecommunication Engineering from the University of Florence. Notably, he was recognized as a World's Top 2% Scientist by Stanford University for 2019–2023 and his career-long contributions. He served as President of the University of Siena's Quality Assurance Committee (2016–2021) and currently coordinates the graduate program in Computer and Information Engineering. Research interests include satellite data analysis (e.g., Sentinel-2, PRISMA), hypersharpening techniques, and environmental monitoring. His work bridges theoretical advancements (e.g., pansharpening algorithms) and practical applications like urban land classification and vegetation index enhancement. Recent articles emphasize reproducibility, meta-analysis, and synthetic data generation through GANs. Awards: World's Top 2% Scientists (2019–2023 & career). Grants/Advising: Supervises remote sensing theses; no specific grants mentioned. Labs/Teams: Leading research in the department's remote sensing and signal processing groups.
Eirik Valseth is an Associate Professor of Scientific Computing at the Norwegian University of Life Sciences (NMBU), Department of Data Science. He holds concurrent roles as a research associate at the Oden Institute, University of Texas at Austin, and an affiliated researcher at Simula Research Laboratory (Department of Numerical Analysis and Scientific Computing). His expertise lies in advanced finite element methods for PDEs with applications in flood modeling and hydropower systems. Current Affiliation: NMBU (Norwegian University of Life Sciences) Secondary Affiliations: Oden Institute (UT Austin), Simula Research Laboratory Research interests span numerical methods for challenging PDE systems, including: Stabilized finite element formulations Hurricane storm surge and riverine flood modeling Hydropower infrastructure analysis Computational mechanics and applied mathematics His recent publications (2024–2025) emphasize flood risk assessment (compound flooding, dam breaks, dredging impacts), advanced numerical methods (isogeometric analysis, stochastic finite elements, graph-grammar algorithms), and environmental applications (pollution transport, pathogen distribution, mosquito population dynamics after hurricanes). Key trends include cross-disciplinary integration of physics-aware machine learning and robust hydrodynamic simulation tools. Valseth's work extends to software development (e.g., WAVEx for spectral wave models, SWEMniCS for coastal circulation) and large-scale modeling frameworks like the ADCIRC unstructured mesh model for US coasts. Collaborative projects involve institutions such as University of Texas at Austin, Simula, and NOAA.
Dr. Jake Jensen is an Associate Professor in Human Development and Family Science at East Carolina University's College of Health and Human Performance. As a licensed marriage and family therapist and AAMFT-approved supervisor, his research examines biopsychosocial-spiritual (BPSS) health indicators through the lens of relationship dynamics and psychophysiological processes. Research Focus: Relationship work between romantic partners and social networks, LGBTQ+ health, integrated care outcomes, and psychophysiological responses in couples. Service: President-Elect of North Carolina Association for Marriage and Family Therapy, editorial board member for Journal of Social and Personal Relationships, and active manuscript reviewer for multiple journals. Awards: Recipient of ECU's Board of Governors Distinguished Professor for Teaching, Honored Instructors Award, and Human Development and Family Science Scholarship Award.
Prof. Dr.-Ing. David E. Rival is a full Professor at the Institute of Fluid Mechanics within the Faculty of Mechanical Engineering at Technische Universität Braunschweig. His research spans interdisciplinary domains at the intersection of experimental fluid dynamics, data assimilation, network science, and bio-inspiration, with applications in renewable energy systems and bio-mimetic engineering. Former Associate Professor at Queen’s University, Canada Doctoral work on dragonfly flight aerodynamics at TU Darmstadt Alexander von Humboldt research fellowship recipient (2020) Postdoctoral associate at MIT studying shape morphing in nature Research chair at University of Calgary on atmospheric sensing His work focuses on unsteady flow phenomena, bio-inspired design, and advanced measurement techniques. Key projects include: Co-chairing NATO AVT task group on flow separation International collaborations with AFOSR, NATO, and ONR Development of cost-effective flow-tracking sensors for natural environments Investigations into shear-thinning suspension dynamics and vortex ring behavior Recent publications demonstrate a strong emphasis on: Large-scale particle tracking with natural light and UAVs Machine learning for sparse data reconstruction in fluid flows Soft coastal protection methods and ecohydraulics Advanced sensing techniques for atmospheric and industrial applications Scientific Awards: 2020: Alexander von Humboldt Research Fellowship Notable research achievements include textbook authorship on Biological and Bio-Inspired Fluid Dynamics (Springer) and media features in The Nature of Things (David Suzuki) and Discovery Channel’s Daily Planet .
Nan Chen is an Associate Professor at the Department of Mathematics, University of Wisconsin-Madison, and a faculty affiliate of the Institute for Foundations of Data Science (IFDS), a multi-University TRIPODS Phase II Initiative. His research spans applied mathematics with applications in atmosphere-ocean science, climate dynamics, and data science. Education: PhD from Courant Institute of Mathematical Sciences (CIMS) and Center of Atmosphere and Ocean Science (CAOS), New York University (NYU), May 2016 Postdoc research associate at CIMS, NYU (June 2016-May 2018) Master's degree from School of Mathematical Sciences, Fudan University, Shanghai Undergraduate in Mechanical Engineering, Fudan University, Shanghai Visited Department of Scientific Computing at Florida State University working with Dr. Max Gunzburger and Dr. Xiaoming Wang Nan Chen's research focuses on contemporary applied mathematics, particularly modeling complex systems, stochastic methods, numerical algorithms, and data science. He specializes in uncertainty quantification (UQ), data assimilation, and developing statistically accurate algorithms to address the curse of dimensionality in large-dimensional complex dynamical systems with strong non-Gaussian features. His work has significant applications in atmosphere-ocean science, including predicting phenomena such as the Madden-Julian Oscillation (MJO), monsoons, El Niño Southern Oscillation (ENSO), and sea ice dynamics. He has also extended his research to material science, neuroscience, and other complex systems. His recent publications demonstrate expertise in inverse problems, wave equations, numerical methods, and data compression techniques that blend mathematical theory with practical applications. Dr. Chen has authored a book titled "Stochastic Methods for Modeling and Predicting Complex Dynamical Systems --- Uncertainty Quantification, State Estimation, and Reduced-Order Models" published by Springer, and a tutorial paper "Taming Uncertainty in a Complex World: The Rise of Uncertainty Quantification — A Tutorial for Beginners" in the Notices of the AMS. Professional Activities: Organizing "Data Meets Dynamics: Workshop on Data Assimilation for Complex Systems and Applications" (August 21-22, 2025) Author of two articles in Elsevier's Reference Module in Earth Systems and Environmental Sciences Participant in Wisconsin Science and Computing Emerging Research Stars (WISCERS) program Judge for Outstanding Student Paper Award (OSPA) program at American Geophysical Union (AGU) fall meetings Involved in Madison Experimental Mathematics Lab (MXM Lab) Dr. Chen actively mentors undergraduate students for research during semesters and summers, encouraging them to present at the UW undergraduate symposium. He also offers reading and independent study courses for interested undergraduates. He is currently seeking highly motivated PhD students to join his research group with possible Research Assistantship support.
Prof. Dr. Jonathan Bedford is a leading researcher in physical geodesy at Ruhr-Universität Bochum's Institute of Geology, Mineralogy and Geophysics. Previously, he worked at the German Research Centre for Geosciences (GFZ) in Potsdam and the Free University of Berlin. His research focuses on subduction zone dynamics, coseismic/postseismic deformation, and machine learning applications in geophysics. University of Leeds (BSc Geosciences) Colorado School of Mines (MS Geosciences) Free University of Berlin (PhD 2015) His work spans: Subduction zone mechanics and earthquake cycles Viscoelastic relaxation and afterslip modeling Machine learning for earthquake prediction Geodetic data analysis with GPS and InSAR Fault interaction and seismic hazard assessment Power-law rheology in crustal deformation Research trends from his publications show emphasis on: Pre-earthquake deformation patterns (wobbling, gradual unlocking) Postseismic processes (afterslip, viscoelastic relaxation, poroelasticity) Integration of geodetic and seismic data Physics-based and data-driven earthquake analog models Notable collaborations include GFZ Potsdam, Free University of Berlin, and Chilean institutions. His work combines numerical modeling with observational data to understand megathrust earthquake mechanisms and improve seismic hazard assessments.
Dr. Liangping Li is an Associate Professor in the Department of Geology and Geological Engineering at South Dakota School of Mines & Technology. He holds a Ph.D. from Technical University of Valencia and an M.S. from China University of Geoscience, with expertise in hydrogeology, groundwater modeling, and geothermal energy systems. Education: M.S., China University of Geoscience; Ph.D., Technical University of Valencia His research focuses on integrating machine learning with groundwater modeling, data assimilation, geostatistics, and optimization of geothermal energy systems. He has pioneered methods combining generative adversarial networks (GANs) and ensemble smoother techniques for inverse modeling in complex aquifers. Recent publications highlight his work on extremal optimization for well placement, progressive growing GANs for facies modeling, and stochastic inversion of fracture networks. His research trends emphasize computational innovation in subsurface flow simulation and sustainable groundwater management. Scientific Awards: NSF RII Track-4 Grant, NSF REU Site Grant, BLM Environmental Monitoring Grant, and appointments as Associate Editor for Advances in Water Resources and Mathematical Geosciences . Dr. Li teaches courses in groundwater engineering, statistical methods, and environmental field camp, while mentoring graduate and undergraduate researchers in subsurface energy and water resource projects.
Ahmed M. Attia is a computational mathematician at the Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA. He is also a member of the Laboratory for Applied Mathematics and Numerical Software (LANS) at Argonne. Previously, he was a postdoctoral researcher at Argonne and a research fellow at SAMSI, with affiliation to the Department of Mathematics at North Carolina State University. Education: Ph.D. in Computer Science and Applications, Virginia Tech, 2016 M.S. in Statistics and Computer Science, Mansoura University, 2008 B.S. in Mathematics, Statistics and Computer Science, Mansoura University, 2004 His research spans computational science and engineering, focusing on data assimilation, uncertainty quantification, optimal experimental design, PDE-constrained optimization, Bayesian inference, and high-performance computing . He integrates machine learning and statistical methods into scientific computing frameworks. His work enables robust and scalable solutions for inverse problems in complex physical systems. The primary trend in his recent publications centers on the development of PyOED, an open-source framework that unifies variational and Bayesian data assimilation with optimal experimental design, featuring novel optimization and machine learning solvers. This work bridges applied mathematics, computational science, and software engineering. Scientific Awards: No awards explicitly mentioned. Advising and Grants: Ahmed has mentored and collaborated with researchers such as Abhijit Chowdhary and Shady E. Ahmed on the PyOED project. His research is supported by the U.S. Department of Energy (DOE), particularly through the Office of Science and the Advanced Scientific Computing Research (ASCR) program. Labs and Teams: He is an active member of the Laboratory for Applied Mathematics and Numerical Software (LANS) at Argonne National Laboratory, contributing to national efforts in applied mathematics and scientific computing.
