Prof. Dr. Dominik Schwarz is a faculty member at the Faculty of Physics , Bielefeld University. His research focuses on Cosmology and Particle Physics , particularly in the areas of Dark Energy , Dark Matter , Cosmological Inflation , and Large-Scale Structure Formation . He contributes to projects like the International LOFAR Telescope Consortium and the SFB-TRR 211 on strongly interacting matter. APART Fellow of Austrian Academy of Sciences Humboldt Fellow CERN Fellow His recent work explores the cosmic dipole anisotropy , axion density perturbations , and multi-wavelength cosmic web mapping . He also advances data science infrastructure through the PUNCH4NFDI consortium.
Leonardo Chamorro is a Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign (UIUC), with affiliations in Earth Science and Environmental Change, Aerospace Engineering, and Civil and Environmental Engineering. His research focuses on fluid dynamics, renewable energy systems, and turbulence modeling. He holds a Ph.D. in Civil Engineering from the University of Minnesota (2010) and has held academic positions at UIUC since 2013, advancing to Full Professor in 2024. Chamorro's work spans experimental and theoretical investigations of wind and hydrokinetic energy, geophysical flows, and particle dynamics. His research group, the Renewable Energy & Turbulent Environment Group (RE-TE-G), explores topics like tidal flow multifractality, vortex dynamics, and bio-inspired robotics. Key achievements include Nature and Lab on a Chip cover articles, and contributions to turbulence modeling for tidal energy systems. He has received awards such as the Best Paper Award in Energies (2018) and recognition for pandemic-related research (2021). His editorial roles include associate editorships at journals like Journal of Renewable and Sustainable Energy and Frontiers in Energy Research . Chamorro has supervised numerous graduate students and postdocs, contributing to over 150 peer-reviewed publications since 2009.
Prof. Dr. Stefan Luther is a Max Planck Research Group leader (W2, tenured since 2013) at the Max Planck Institute for Dynamics and Self-Organization, Göttingen, and an Honorarprofessor at the Faculty of Physics, University of Göttingen. He holds adjunct roles as Adjunct Associate Professor at Cornell University (2009–2012) and Northeastern University (2016–2018), and serves as DZHK-Professor at the Institute of Pharmacology and Toxicology, University Medical Center Göttingen. His research focuses on nonlinear spatiotemporal dynamics in excitable biological media, particularly cardiac arrhythmias. He pioneered 4D imaging of heart function and developed algorithms for optogenetic and electrical control of arrhythmias. Translational efforts span basic research to preclinical and clinical studies. Education includes a Diplom in Physics (1997) and PhD (2000) from Georg-August-University, Göttingen. Postdoctoral training followed at the University of Twente (2001–2004) and Cornell University’s LASSP (2004–2006). His lab, the Biomedical Physics group, explores electromechanical coupling in cardiac systems and develops novel therapeutic approaches. Collaborations include work on computational modeling, uncertainty quantification in dynamical systems, and fluid dynamics of multiphase flows.
Jan de Gier is a Professor at the School of Mathematics and Statistics, The University of Melbourne . He is also the Founding Director of MATRIX , Australia’s residential research institute in the mathematical sciences, and a former Deputy Director and Chief Investigator in the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) . Additionally, he co-founded the Australian and New Zealand Association for Mathematical Physics (ANZAMP) in 2011 and served as its inaugural Chair. His research focuses on solvable lattice models at the intersection of mathematical physics and statistical mechanics . Key areas include the application of quantum integrability , algebraic structures like the Yang-Baxter equation, Hecke algebras, and quantum groups, as well as analytical methods such as complex analysis and elliptic curves. His work bridges pure and applied mathematics through connections between enumerative combinatorics , representation theory , and real-world phenomena like traffic flow modeling via exclusion processes . The 15 most recent articles reflect his expertise in integrable systems , non-equilibrium statistical mechanics , and algebraic combinatorics . Topics span Macdonald polynomials , stochastic duality , quantum spin chains , and traffic modeling , with methodologies involving matrix product forms , exact solutions , and critical phenomena analysis. He has contributed to editorial efforts through the AustMS Gazette and MATRIX Annals, and has been involved in public science communication via opinion pieces on mathematics funding and applications. His work emphasizes the importance of fundamental research in driving technological innovation, as highlighted in media articles discussing pi calculation , zero-knowledge proofs , and mathematics education .
Simone Fior is a Lecturer at the Department of Environmental Systems Science , ETH Zürich , focusing on ecological genetics and plant adaptation. Their research integrates genomic, quantitative genetics, and ecological field experiments, particularly on Dianthus (Caryophyllaceae) along altitudinal and climatic gradients. Recent work explores climate-induced range shifts, local adaptation, and genomic responses to environmental changes. Professional experience includes roles at ETH Zürich since 2013 (Senior Assistant, Postdoc) and prior positions at the Edmund Mach Foundation (2009-2012) and University of Insubria (2007-2008). Education spans a PhD in Plant Biology (University of Milan, 2007) and an MSc in Natural Sciences (University of Milan, 2003). Simone co-organizes the Bioinformatics for Adaptation Genomics Winter School . Key research areas include adaptive divergence , polygenic adaptation , climate change biology , and phylogenomics . Articles emphasize genomic selection signatures, functional-structural modeling, and ecological-genetic interactions. Notable collaborations involve Jake Alexander, Alex Widmer, and interdisciplinary teams at ETH Zurich.
Lars Nordström is a Professor at the Division of Electric Power and Energy Systems within KTH Royal Institute of Technology, Stockholm, Sweden. His work bridges control systems , communication networks , and power systems , with a focus on future architectures, functionality, and quality aspects of ICT for power grid operations. He has led initiatives such as the Swedish Centre of Electric Power Engineering and served as Thematic Leader for Smartgrids in KIC InnoEnergy. In 2014, he was a Visiting Professor at Washington State University. Education : Ph.D., MSc.EE Nordström's research explores the intersection of smart grids , machine learning , and cybersecurity for power systems. Key areas include: Wide-Area Monitoring and Control (WAMC) systems Decentralized control strategies for DC microgrids Impedance modeling using neural networks Data-driven methods for islanding detection ICT reliability and protocol design for grid operations His recent publications emphasize machine learning applications in power systems, including LSTM networks for EV charging management, graph attention networks for stability monitoring, and digital twin approaches for cyber-attack mitigation. These works span disciplines such as Smart Grids, Power Electronics, and Data Science. Scientific Recognitions : Senior Member, IEEE Senior Member, CIRED Senior Member, Cigre Past Chairman, Swedish IEC TC57 Mirror Committee Nordström actively teaches and examines graduate courses like Communication and Control in Electric Power Systems and Computer Applications and Machine Learning in Electric Power Systems . His work influences industry practices through collaborations on digital substations, energy market analysis, and resilience strategies.
Evelyn Lake is an Associate Professor in the Department of Radiology and Biomedical Imaging at Yale University, with affiliations to the Wu Tsai Institute and Yale Biomedical Imaging Institute. Her research focuses on functional neuroimaging, particularly using rodent models to study brain connectivity, neurovascular coupling, and disorders like Alzheimer’s disease and autism spectrum disorder. PhD in Medical Biophysics, University of Toronto (2016) Postdoctoral Associate, Yale University (2019) BSc (Honors) in Biophysics, University of Guelph (2010) Her work spans multimodal imaging techniques (e.g., simultaneous Ca2+ and fMRI), examining how genetic mutations (e.g., PTEN, KATNAL2) affect cerebrospinal fluid dynamics and neuronal connectivity. She investigates longitudinal changes in Alzheimer’s models and methodological aspects of animal neuroimaging, such as sample size optimization and awake imaging protocols. Recent publications highlight her contributions to understanding autism-related white matter abnormalities, transdiagnostic connectome predictive modeling, and the impact of neurovascular uncoupling in awake mice. Collaborations include Todd Constable, Francesca Mandino, Xilin Shen, and Xenophon Papademetris.
Yuxin Chen is a Professor at the University of Pennsylvania , holding joint appointments in the Department of Statistics and Data Science and the Department of Electrical and Systems Engineering . Prior to UPenn, he was an Assistant Professor at Princeton University (2017-2021) and a Postdoctoral Researcher at Stanford University (2015-2017). His research spans statistics, optimization, reinforcement learning theory, diffusion models, and information theory , with a focus on theoretical foundations and practical algorithms for machine learning. Education : Ph.D. in Electrical Engineering (Stanford, 2015), M.S. in Statistics (Stanford, 2013), M.S. in Electrical and Computer Engineering (UT Austin, 2010), B.E. in Electrical/Microelectronics (Tsinghua, 2008). Research Interests encompass theoretical and applied aspects of machine learning, including nonconvex optimization , sample complexity analysis , low-dimensional adaptation , and generative modeling . His work bridges mathematical rigor with real-world applications, particularly in scientific imaging and high-dimensional data analysis. Scientific Awards include the SIAM Activity Group on Imaging Science Best Paper Prize (2024) Alfred P. Sloan Fellowship (2022) NSF Career Award (2022) Google Research Scholar Award (2022) IEEE Transactions on Power Electronics Prize Paper Award (2024) Advising and Grants : He has mentored numerous students who have transitioned to academic roles at institutions like UIUC and UW-Madison. His research is supported by grants from the NSF , Amazon , and Google , with recent projects focusing on controllable diffusion models and efficient reinforcement learning algorithms .
Carlo Alberto Furia is an Associate Professor and Vice Dean at the Faculty of Informatics, Università della Svizzera italiana (USI). He is affiliated with the Software Institute, where he leads the ATOM research group. His academic journey includes prior roles as an Associate Professor at Chalmers University of Technology and a Senior Researcher at ETH Zurich’s Chair of Software Engineering. PhD in Computer Science, Politecnico di Milano Master of Science in Computer Science, University of Illinois at Chicago Laurea in Computer Science and Engineering, Politecnico di Milano His research centers on formal methods for software engineering, aiming to enhance software correctness, reliability, and quality through rigorous techniques. Key areas include automated program verification, contract-based development, loop invariant inference, and empirical evaluation using Bayesian data analysis. He emphasizes practical applicability and automation in formal methods. His recent publications reflect a strong focus on program analysis at the bytecode level, multilingual software analysis, automated repair of Android security issues, and empirical methodologies. These works span topics such as JVM substitutability, exception behavior in Java bytecode, and information flow security, demonstrating a consistent thread in improving software robustness through formal and automated techniques. He is actively involved in the software engineering research community as an Associate Editor of the Empirical Software Engineering (EMSE) journal and as a Program Committee member for major conferences including FASE, FM, ASE, ICSE, and CauSE. Carlo Furia has advised multiple research projects and supervised student theses. He has led and contributed to funded research initiatives, particularly in program analysis and verification. His group has developed tools such as AutoProof and other software artifacts available through the ATOM software page. He regularly teaches courses such as Software Analysis, Programming Fundamentals, and Software Design & Modeling. He leads the ATOM research group, which focuses on advancing automated techniques for software testing, analysis, and verification. The group develops practical tools and conducts empirical studies to validate research outcomes.
Bingzhang Chen is a Senior Lecturer in the Department of Mathematics and Statistics at the University of Strathclyde, Faculty of Science. He previously held positions as a Chancellor’s Fellow at the same institution, a researcher at the Japan Agency of Marine-Earth Science and Technology (JAMSTEC), and was affiliated with Xiamen University and Mount Allison University. His academic journey began with a PhD from the Hong Kong University of Science and Technology. Education: PhD in Trophic interactions within the microbial food web, Hong Kong University of Science and Technology (Awarded 2009) His primary research interests lie at the intersection of biological oceanography and theoretical ecology, with a strong focus on ecosystem modeling. He investigates how biodiversity, particularly of phytoplankton, influences marine ecosystem functioning such as primary production and the biological carbon pump. A central theme in his work is understanding the differential temperature sensitivity between autotrophs and heterotrophs, a question that bridges statistical analysis, metabolic theory, and Earth system science. His recent publications highlight a consistent trend in developing and applying individual-based models (e.g., PIBM 1.0), analyzing large datasets on plankton thermal responses, and studying the impacts of climate change and anthropogenic activities (like nutrient input) on marine microbial communities across diverse regions from the South China Sea to the North Pacific and Scottish coastal waters. His work often combines modeling with observational data to address fundamental ecological questions. Scientific Awards: David Cushing Prize (2015) from the Journal of Plankton Research New Century Excellent Talent (2012) from the Ministry of Education of China Dr. Chen is actively involved in research supervision, currently guiding five PhD students. He has been the Principal Investigator on multiple research projects funded by organizations such as the Leverhulme Trust, FILAMO, and the National Science Foundation. His expertise in programming (R, Fortran, MATLAB) underpins his methodological approach. He also contributes to the scientific community as an Associate Editor for the prestigious journal Limnology and Oceanography . His work is associated with efforts to understand and model invasive species dynamics, such as the spread of Sargassum muticum in Scottish waters, and he is involved with external advisory groups like the MASTS Marine Artificial Intelligence Forum.
Xiaocheng Shang is an Associate Professor in Mathematical Optimisation and Data Science at the University of Birmingham's School of Mathematics. His research focuses on numerical methods for stochastic differential equations, with applications in computational mathematics, statistics, physics, and data science. He is affiliated with the Optimisation and Numerical Analysis Group, Statistics and Data Science Group, and the Institute for Data and AI. Shang holds a PhD in Applied and Computational Mathematics from the University of Edinburgh (2016) and completed postdocs at the University of Edinburgh, Brown University, and ETH Zurich before joining Birmingham in 2019. His academic achievements include fellowships from The Alan Turing Institute, the LMS Emmy Noether Fellowship, and the EUniWell Leadership Fellowship. He has secured funding from EPSRC, the Royal Society, and the Isaac Newton Institute. Shang is actively involved in supervising PhD students and co-organizing research initiatives such as the Data Science and Computational Statistics Seminar. Research interests include structure-preserving integrators, Bayesian sampling techniques, and machine learning applications in dynamical systems. His work bridges numerical analysis, probability theory, and multiscale modeling in materials science. Recent projects involve neural networks for complex dynamical systems and numerical algorithms for deterministic/stochastic systems.
Chun Liu is Chair and Professor of Applied Mathematics at the Department of Applied Mathematics, Illinois Institute of Technology (IIT), within the College of Computing. His research focuses on Nonlinear Partial Differential Equations , Complex Fluids , and Multiscale Modeling , with applications in electrophysiology and materials science. He earned a Ph.D. from New York University’s Courant Institute, an M.S. from Duke University, and a B.S. from Fudan University. Prof. Liu leads projects on General Diffusion Systems , Ion Channel Dynamics , and Viscoelastic Fluids . He has secured grants from NSF, BSF, and DAAD for research in energetic variational approaches, multiscale materials modeling, and biomolecular systems. Key contributions include the development of Poisson-Boltzmann models , coarse-grained dynamics , and energetically stable numerical methods . He serves on editorial boards for Communications in Mathematical Sciences , SIAM Journal on Mathematical Analysis , and others. His work bridges applied mathematics with engineering and biophysics, addressing challenges in fluid mechanics, ion transport, and nonlinear systems.
Lande Liu is a Senior Lecturer in Chemical Engineering at the University of Huddersfield's School of Applied Sciences. Previously, he held a Lectureship at the University of Manchester (2010-2014), and earlier worked as an industrial consultant and research fellow at Leeds and Sheffield Universities. His academic journey began with a MEng in Chemical Engineering and a PhD in kinetic theory of aggregation from Sheffield (2004), preceded by a visiting PhD at Twente University (2002). Education: PhD in Chemical Engineering (University of Sheffield, 2004) Visiting PhD (Twente University, 2002) MEng in Chemical Engineering (Tsinghua University, 1999) BSc in Applied Mathematics (Tsinghua University, 1996) Liu's research focuses on multi-scale particle interactions (molecular to granular) using kinetic theory of aggregation, with applications spanning nanotechnology, pharmaceutical engineering, and sustainable chemical processes. His work aligns with UN Sustainable Development Goals for environmental protection and industrial innovation. Recent publications examine particle deposition in turbulent flows, enhanced heat exchanger designs, and nanofluid stabilization techniques. He teaches core chemical engineering topics including transport phenomena, unit operations, and process design. Active in collaborative research, Liu has partnered with institutions across Europe on projects involving spectroscopy, ultrasonics, and dynamic modeling. His technical expertise includes particle size analysis, tomography, and computational simulation of complex systems.
Simone D. Castellarin is a Professor in the Department of Applied Biology at the University of British Columbia's Faculty of Land and Food Systems, and holds the Canada Research Chair Tier 2 in Viticulture. His research focuses on the molecular and physiological mechanisms governing berry ripening and composition in grapes, blueberries, and raspberries, with emphasis on genomic regulation under environmental stressors like heatwaves and drought. PhD in Plant Biology from the University of Udine (2007) Postdoctoral training at Hochschule Geisenheim University and University of California Davis Research areas include terpene biosynthesis, jasmonate signaling, cuticular wax dynamics, and agronomic strategies for climate change mitigation. Key projects involve remote sensing for vineyard zoning , hormone application effects , and genetic studies of berry quality traits . His work spans collaborations with industry bodies like the BC Wine and Grape Council and academic partners including the Cantu Lab at UC Davis. Recent publications highlight genomic analyses of terpene synthases, water deficit impacts on metabolites, and postharvest quality assessments. Awards include the 2009 Rudolf Hermanns Prize for viticultural research. He supervises numerous PhD and Master's students, and leads the Castellarin Lab at UBC's Wine Research Centre.
Arnab Sen is an Associate Professor at the School of Mathematics, University of Minnesota. His research focuses on probability theory and discrete harmonic analysis, with emphasis on models from statistical physics such as spin glasses, random graphs, random matrices, and random polynomials. PhD in Statistics, UC Berkeley (2010), advised by Steven N. Evans and Elchanan Mossel Postdoctoral Fellow, Statistical Laboratory, University of Cambridge His research spans discrete probability , statistical physics , and random matrix theory , addressing topics like disorder chaos in spin glasses, eigenvalue distributions, and quantum percolation. He has taught graduate and undergraduate courses including Random Matrix Theory , Introduction to Stochastic Processes , and Multivariable Calculus . His recent publications analyze spin glass models, random matrices, and combinatorial systems.
