Marcus Herrmann is a Professor of Aerospace and Mechanical Engineering at Arizona State University's School for Engineering of Matter, Transport and Energy. He is also affiliated with the Center for Negative Carbon Emissions. His research focuses on fluid mechanics, multiphase flows, atomization processes, and numerical methods for discontinuous interfaces. Herrmann holds a PhD in Mechanical Engineering from RWTH Aachen University (2001) and a Diplom (1995). His career includes a postdoctoral fellowship at Stanford University's Center for Turbulence Research (CTR) and a visiting scientist position at the University of Technology Eindhoven, Netherlands. He has secured major grants from NASA, NSF, and industry partners like Honeywell, focusing on atomization modeling, supersonic crossflows, and turbulence simulations. Research interests span computational fluid dynamics, multiphase flow simulation, and LES/DNS methodologies. His recent work emphasizes high-fidelity numerical techniques for particle-resolved simulations and phase interface dynamics. Teaching includes courses like MAE 561 (Computational Fluid Dynamics) and MAE 384 (Advanced Math Methods for Engineers). He actively advises students through research and dissertation roles. Notable projects include modeling wax deposition in pipelines and developing novel approaches for interface dynamics in turbulent flows. His work bridges fundamental fluid mechanics with industrial applications like combustion systems and porous media modeling.
Olof Runborg is a Professor in Numerical Analysis at the Royal Institute of Technology (KTH) in Stockholm, Sweden. He works in the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH. His research focuses on developing and analyzing numerical methods for partial differential equations, particularly for wave propagation problems. His educational background includes: MSc in Electrical Engineering from KTH (1992) BSc in Economics & Business Administration from SSE (1994) PhD in Numerical Analysis/Applied Mathematics from KTH (1998) Postdoc at Paris VI University (1999) Postdoc at Princeton University's Program in Applied and Computational Mathematics (2000-2001) Became Docent at NADA in 2003 Appointed Professor at KTH in 2010 Professor Runborg's research centers on the numerical treatment of partial differential equations, with special emphasis on wave propagation problems. His work spans multiple areas including high-frequency waves, multiscale phenomena, numerical homogenization, uncertainty quantification, multiresolution analysis, Gaussian beams, and mesh generation. A recurring theme in his research is developing methods to solve computationally expensive problems more efficiently while maintaining accuracy. His approach often involves coupling different numerical methods or reformulating equations to create more efficient computational approaches. His research has applications across physics and engineering domains where wave phenomena are important. An analysis of his recent publications (2015-2025) shows continued focus on high-frequency wave propagation with expanding applications to areas like the Landau-Lifshitz equation for magnetic materials and elastic wave propagation. His work bridges theoretical numerical analysis with practical computational techniques, often developing novel methods like the WaveHoltz iteration for solving the Helmholtz equation. The publications demonstrate increasing attention to uncertainty quantification and multiscale methods while maintaining strong theoretical foundations in error analysis. Professor Runborg teaches several courses at KTH including Numerical Methods (basic course), Applied Numerical Methods, Numerical Algorithms for Data-Intensive Science, and Selected Topics in Numerical Analysis II. He serves as examiner for degree projects in Scientific Computing. His teaching reflects his research expertise in numerical methods and computational mathematics, providing students with both theoretical foundations and practical implementation skills. Beyond his core research, Professor Runborg has engaged in interesting side projects, such as his analysis of 'Numbers on the Web' where he investigated the frequency of numbers 11-1000 in web content, revealing patterns related to dates, time, computer systems, and cultural phenomena. This demonstrates his broader interest in data analysis and computational approaches to understanding patterns in information.
Dr. Igor V. Pivkin is a Full Professor at the Institute of Computing within the Faculty of Informatics at the Università della Svizzera italiana (USI) in Lugano, Switzerland. His academic journey includes degrees from Novosibirsk State University (B.Sc./M.Sc. Mathematics), Brown University (M.Sc. Computer Science and Ph.D. Applied Mathematics), and postdoctoral research at MIT's Department of Materials Science and Engineering. His research focuses on multiscale/multiphysics modeling , numerical methods , and large-scale simulations of biological and physical systems. Key areas include biophysics, cellular/molecular biomechanics, stochastic modeling, and coarse-grained molecular simulations. He leverages high-performance computing (HPC) and particle-based methods to address complex biological phenomena. His work spans diverse applications, from understanding cellular mechanosensitivity and biofilm engineering to modeling cancer cell behavior and red blood cell dynamics in the spleen. His contributions bridge computational science, biotechnology, and biomedical research. He has published extensively in top-tier journals, with recent work advancing automated biofilm analysis, deep learning for microbial classification, and systems biology approaches to metal bioleaching. His lab collaborates on interdisciplinary projects, emphasizing computational innovation for real-world biological challenges.
Agnes Desolneux is a CNRS Research Director at the Borelli Centre (formerly CMLA) and a Professor attached to the Mathematics Department at ENS Paris-Saclay. Education: PhD in Applied Mathematics (2000) from ENS Cachan Habilitation in Applied Mathematics (2010) from Université Paris Descartes Her research focuses on image analysis via statistical methods , particularly a contrario approaches, image restoration, texture synthesis, Determinantal Point Processes (DPP), optimal transport, Gaussian mixtures, geometry of random field excursions, shot-noise models, and mathematical modeling of visual perception through Gestalt theory. The articles extracted reflect her expertise in applied mathematics and computer vision , with recent works (2025-2020) on optimal transport algorithms, DPP applications, multiscale texture analysis, and stochastic modeling in medical imaging. Keywords span machine learning, probability theory, medical imaging, and computer vision . She has no listed scientific awards but has authored influential works including the book From Gestalt Theory to Image Analysis: A Probabilistic Approach (Springer, 2008) and Pattern Theory: the stochastic analysis of real-world signals (AK Peters, 2010).
Yuri Bazilevs is the E. Paul Sorensen Professor of Engineering at Brown University's School of Engineering and Co-Director of the Mechanics of Undersea Science and Engineering Center. His research focuses on computational mechanics, isogeometric analysis, fluid-structure interaction, and high-performance computing. Prior to Brown, he held positions at UC San Diego, where he advanced to Full Professor in 2014 after a rapid tenure. He earned his PhD in 2006 and postdoc training in computational engineering at UT Austin's ICES. Research interests span computational fluid dynamics, solid mechanics, and advanced discretization methods like isogeometric analysis (IGA) and meshfree approaches. He has developed novel formulations for complex phenomena such as underwater explosions, composite material failure, and hypersonic flow dynamics. His work integrates cutting-edge numerical methods with practical engineering applications in aerospace, energy, and biomedical systems. Recent publications highlight advancements in IGA for architected materials, RKPM-based crack modeling, and stabilized formulations for compressible flows. His contributions bridge theoretical mechanics with computational innovation, addressing challenges in multiphysics coupling and large-scale simulations. Collaborations span academia and industry, emphasizing practical validation and real-world impact. Bazilevs' expertise includes variational multiscale methods, peridynamics for fracture mechanics, and immersive particle methods for fluid-structure interaction. His work has been applied to wind turbine aerodynamics, gas turbine optimization, and cardiovascular flow analysis. He actively contributes to computational infrastructure development, such as the tIGAr software framework for IGA automation.
Lise Vermeersch is a Chemistry doctoral student and researcher at the Faculty of Sciences and Bioengineering Sciences, Vrije Universiteit Brussel. Her work focuses on polymer chemistry, computational chemistry, and materials science, particularly in self-healing and recyclable polymer networks. She collaborates on projects funded by the Research Council, exploring Diels-Alder reaction kinetics and molecular dynamics. Vermeersch has supervised multiple master's theses and participates in academic committees, conferences, and outreach activities like the New Bauhaus 2025 initiative. Her research integrates computational predictions with experimental synthesis to develop dynamic materials. Her projects include 'Accelerating the Diels-Alder kinetics of self-healing polymer networks' (2022–2026) and 'Backup mandate Research Council: Understanding and accelerating Diels-Alder kinetics' (2021–2022). She has published extensively on topics such as Lewis acid catalysis, hydrogen-bond effects, and quantum chemical analysis of Diels-Alder reactions. Her contributions bridge theoretical insights with practical applications in sustainable materials. Vermeersch has advised students on projects like 'Benchmarking Multiscale Model of Self-Healing Materials' and 'Predicting global and local reactivity descriptors.' She actively engages in academic events, including the Chemistry Day 2023 as a chair and talks at workshops like TADA. Her work emphasizes interdisciplinary approaches to material design and sustainability.
Jean-Pierre Fouque is a Professor in the Department of Statistics and Applied Probability (PSTAT) at the University of California, Santa Barbara. His research focuses on stochastic processes, financial mathematics, systemic risk, and reinforcement learning, with a particular emphasis on mean field games and multi-scale stochastic models. He explores applications in portfolio optimization, risk management, and algorithmic finance. His work combines theoretical advancements in stochastic analysis with practical applications in economics and finance. Notable contributions include developing models for systemic risk in financial networks, analyzing reinforcement learning algorithms in mean-field frameworks, and studying stochastic volatility effects in derivatives pricing. Recent research trends include integrating deep learning techniques for systemic risk quantification, advancing multi-scale asymptotic methods for portfolio optimization, and investigating strategic interactions in financial systems using game-theoretic approaches. His publications frequently address topics such as stochastic volatility calibration, optimal investment strategies under uncertainty, and the dynamics of financial markets under stress scenarios. Dr. Fouque has contributed to foundational textbooks and edited volumes on systemic risk and mean field games. His interdisciplinary work bridges probability theory, mathematical finance, and computational methods, impacting both academic research and practical risk management practices.
Thomas Yizhao Hou is the Charles Lee Powell Professor of Applied and Computational Mathematics at the California Institute of Technology, where he has served as a faculty member since 1998 and as Executive Officer of Applied and Computational Mathematics from 2000-2006. His research spans fundamental mathematical problems with significant implications for fluid dynamics and computational science. Hou received his B.S. in Mathematics from South China University of Technology in 1982, followed by an M.S. in 1985 and Ph.D. in 1987 from UCLA under the supervision of Prof. Bjorn Engquist. His academic journey includes positions at the Courant Institute and the Institute for Advanced Study before joining Caltech. Hou's research focuses on multiscale analysis and computation, interfacial problems, stochastic PDEs and uncertainty quantification, and the Millennium Problem concerning global regularity of 3D incompressible Euler and Navier-Stokes equations. His work on adaptive data analysis has led to significant methodological innovations. His research is characterized by the integration of rigorous mathematical analysis with computational approaches to tackle problems that have resisted traditional methods. His recent publications reveal a consistent focus on singularity formation in fluid equations, particularly the Euler and Navier-Stokes equations, with increasing sophistication in analyzing potential blowup scenarios. His work spans theoretical analysis, numerical verification, and the development of innovative mathematical frameworks for multiscale problems. Member of the National Academy of Sciences (2024) William Benter Prize in Applied Mathematics (2024) SIAM Ralph E. Kleinman Prize (2023) SIAM Outstanding Paper Prize (2018) Fellow of the American Mathematical Society (2012) Fellow of the American Academy of Arts and Sciences (2011) Hou has served in significant editorial roles including Founding Editor-in-Chief of the SIAM Journal on Multiscale Modeling and Simulation and Co-Editor-in-Chief of Research in Mathematical Sciences. His professional service includes membership on the SIAM Council and leadership roles at the Institute of Mathematics and its Applications. His research has been supported by numerous grants focusing on multiscale modeling, fluid dynamics, and computational mathematics.
Dr. Cristina Rosell is a Professor and Head of the Department of Food and Human Nutritional Sciences at the Faculty of Agricultural and Food Sciences, University of Manitoba. Her research focuses on grain-based food innovation, starch properties, and sustainable bakery processes. Education: PhD and BSc in Pharmacy from Universidad Complutense de Madrid, Spain Dr. Rosell specializes in cereal science, enzymatic food modification, and gluten-free product development. Her current projects include optimizing bakery processes with computational tools, tracing rice supply chains via blockchain, and integrating bioactives into gluten-free foods. Her work bridges food chemistry, nutritional science, and industrial bakery technology. Her recent research trends emphasize gluten-free bread using legume and root starches, starch-plant compound interactions , and sustainable food processing via microwave-assisted extraction, high-pressure treatments, and fermentation. Key subfields include dough rheology, polyphenol bioactivity, and techno-functional ingredient development. Dr. Rosell’s teaching includes graduate seminars in food science (HNSC 7130). She supervises graduate students in breadmaking, starch hydrolysis, and functional food design. Her lab explores grain quality, enzymatic treatments, and bakery product optimization through interdisciplinary approaches combining chemistry, nutrition, and process engineering.
Julie Dethier is a Research Fellow at the University of Liège, affiliated with the Faculty of Applied Sciences and Montefiore Institute. Her research bridges cellular neuroscience and network dynamics, with focus on pathological rhythms in Parkinson's disease and brain-machine interface development. She completed her Ph.D. in 2015 under Prof. Sepulchre's supervision. Her academic journey includes: Ph.D. in Systems and Modeling (2015), University of Liège with Princeton University research stay (2014-2015) Master of Science in Bioengineering (2011), Stanford University (Brains in Silicon lab) Master of Applied Sciences in Biomedical Engineering (2010, summa cum laude), University of Liège Bachelor of Applied Sciences (2008, summa cum laude), University of Liège Dr. Dethier investigates how cellular feedback mechanisms generate pathological beta oscillations in basal ganglia circuits, disrupting motor function in Parkinson's disease. Her computational approaches model the transition from unicellular rhythms to network-level oscillations, with implications for deep-brain stimulation therapies. She integrates electrophysiological data with dynamical systems theory to explain robustness and modulation in neural circuits. Her publication record demonstrates evolving expertise from brain-machine interface hardware (2011-2012) to fundamental neural mechanism studies (2013-2015). Key themes include spiking neural network decoders, cellular feedback loops, and pathological oscillation generation. Work spans computational modeling, neuromorphic engineering, and translational neuroscience with applications in neuroprosthetics. Major recognitions include: WBI excellence grant for Princeton research (2014) LEAR Foundation Fellowship for Cambridge research (2013) Audience Award at ULg thesis competition (2013) IEEE EMBS Best Poster Award (2011) Fulbright Honorary Fellowship and Rotary International Fellowship (2010) Funded by competitive F.R.S.-FNRS and international fellowships, her research involved cross-institutional collaboration with Princeton, Cambridge, and Stanford teams. While no formal advisees are listed, her publications reflect mentorship through co-supervised projects and conference presentations. Current work extends her doctoral thesis on multiscale neural dynamics. She maintains active ties with the Systems and Modeling Research Unit at Liège, Brains in Silicon lab at Stanford, and participates in Benelux neuroscience networks through the Montefiore Institute.
Michael Levin is a Vannevar Bush Professor and Distinguished Professor at Tufts University, affiliated with the School of Arts and Sciences (Department of Biology) and School of Engineering (Biomedical Engineering). His research focuses on bioelectricity, developmental biology, and collective intelligence. He leads the Allen Discovery Center and the Tufts Center for Developmental and Regenerative Biology. Education: PhD in Genetics from Harvard Medical School (1996); BS in Computer Science and Biology from Tufts University (1992). Research Interests: Integrates developmental biology, computer science, and cognitive science to study morphogenesis, regeneration, and cancer. Explores bioelectric signaling, synthetic organisms, and AI-driven discovery. Key areas include regenerative medicine, cancer reprogramming, and collective intelligence in biological systems. Publications: Over 600 articles, with recent work on xenobots, neuroevolution, and bioelectric therapies. Themes include bioelectric control of form, AI in biology, and collective intelligence. Awards: INNS Donald O. Hebb Award, AAAS Fellow, and Vox Future Perfect 50 List recognition. Frequently invited to speak at conferences on biology, AI, and consciousness. Advising & Labs: Mentored numerous postdocs and students, including pioneers in bioelectricity and synthetic biology. Lab focuses on interdisciplinary approaches to biological pattern formation and regeneration.
Dr. Sander Los is an Associate Professor at the Faculty of Behavioural and Movement Sciences (Department of Cognitive Psychology), Vrije Universiteit Amsterdam. He earned his PhD in 1994 with a thesis on 'On the origin of mixing costs: Exploring information processing in pure and mixed blocks of trials' under Prof. Andries Sanders. His research focuses on temporal dynamics of preparatory processes, co-developing the formalized Multiple Trace Theory (fMTP) to explain temporal preparation across time scales (seconds to days). His work integrates cognitive psychology, neuroscience, and computational modeling to explore attentional mechanisms, statistical learning, and spatiotemporal dynamics. Education: PhD in Cognitive Psychology (VU Amsterdam, 1994), postdoctoral research at VU Amsterdam, progressing to Assistant Professor before his current role. Key research areas include visual attention, response inhibition, and long-term memory. He has published over 40 peer-reviewed articles and serves on editorial boards for journals like Attention, Perception, and Psychophysics and Acta Psychologica . Research Interests: His studies investigate how humans prepare for upcoming events temporally and spatially, with recent work on statistical learning guiding visual attention and computational frameworks for temporal preparation. Collaborations emphasize interdisciplinary approaches to understanding attention allocation and neural underpinnings of timing. Grants & Advising: No explicit grants listed, but active in training students (1 supervised PhD thesis). His courses include Methodology, Research Methods, and Practical Skills for Researchers at VU Amsterdam. Labs/Teams: Works closely with colleagues on the fMTP model and statistical learning projects, emphasizing team-based computational and experimental psychology.
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.
Brian Swingle is an Adjunct Assistant Professor in the Department of Physics at the University of Maryland. He holds affiliations with the Condensed Matter Theory Center, Joint Center for Quantum Information and Computer Science, and Maryland Center for Fundamental Physics. His research focuses on quantum information theory, quantum gravity, and entanglement renormalization in many-body systems. Swingle earned his Ph.D. in Physics from MIT in 2011. His work explores connections between quantum entanglement and spacetime geometry, with contributions to holography, topological quantum liquids, and quantum chaos. Notable research includes demonstrating how entanglement patterns can encode gravitational dynamics, developing renormalization group approaches for topological phases, and analyzing quantum complexity in holographic systems. His teaching includes Physics 603: Methods of Statistical Physics. Key publications address holographic wormholes, entanglement renormalization techniques, and quantum many-body dynamics. Swingle collaborates with institutions like JQI and has been featured in podcasts discussing black hole physics and quantum information.
Franklin Goldsmith serves as Associate Professor of Engineering within Brown University's School of Engineering, where his research bridges fundamental chemical kinetics with practical combustion applications. His work directly impacts energy conversion technologies and emission reduction strategies through rigorous investigation of reaction mechanisms. His academic foundation includes: PhD in Chemical Engineering from Massachusetts Institute of Technology (2010) BS in Chemical Engineering from North Carolina State University (2003) BA in Chemistry from University of North Carolina at Chapel Hill (1998) Goldsmith's research program centers on radical reaction kinetics and low-temperature oxidation phenomena , employing both computational master equation modeling and experimental techniques like shock tube spectroscopy and synchrotron photoionization. His investigations into non-Boltzmann energy distributions and pressure-dependent rate coefficients have established new frameworks for understanding ignition chemistry. The Thermochemistry for Combustion Database project exemplifies his commitment to foundational data resources for the field. Analysis of his publication record reveals three dominant research thrusts: (1) detailed kinetic modeling of hydrocarbon oxidation, particularly propane systems; (2) development of computational methodologies for pressure-dependent rate estimation; and (3) fundamental studies of radical-molecule interactions. His work consistently integrates high-precision experimental validation with theoretical frameworks, as evidenced by collaborations with national laboratories. Goldsmith teaches Brown's core chemical engineering curriculum including ENGN 1120 (Reaction Kinetics and Reactor Design) and ENGN 1130 (Chemical Engineering Thermodynamics), alongside specialized graduate courses in heterogeneous catalysis (ENGN 2751) and chemically reacting flow (ENGN 2910Q). His educational approach emphasizes the connection between molecular-scale kinetics and reactor design principles. His research group maintains active collaborations with Argonne National Laboratory (Klippenstein), MIT (Green), and Sandia National Laboratories (Taatjes), focusing on multiscale informatics for complex reaction systems. Current projects investigate biomass-derived fuel combustion and catalytic partial oxidation mechanisms using spatially resolved experimental techniques.
