Haohan Wang is an Assistant Professor at the University of Illinois Urbana-Champaign's School of Information Sciences, with affiliations to the Carl R. Woese Institute for Genomic Biology and the National Center for Supercomputing Applications. His research focuses on developing trustworthy machine learning methods for computational biology and healthcare applications , emphasizing robustness , causality , and interpretability in vision-based models. Recent research trends in his work include: Large Language Model interactions with biomedical challenges (GenoAgent, GenoTex) Adversarial security in language and vision models (Guard, Jailbreakzoo) Genomic data analysis through robust machine learning frameworks (Precision Lasso, Kernel Mixed Models) Interactive toolkits like Robustar for data annotation and model training Scientific awards include recognition as Baidu's Top 50 AI+X Rising Young Scholars (2022), Best Paper Honorable Mention at WSDM 2023, and Broad Institute's Next Generation status (2019). Current projects explore AI-made scientists for biomedical discovery and Robustar development for GUI-based robust vision learning.
Dr. Feliks Nüske is a Max Planck Group Leader at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, leading the Data-driven Modeling of Complex Physical Systems research group. He also holds a position as Guest Professor at Freie Universität Berlin (2023-2024). His research bridges applied mathematics, data science, and molecular simulation to develop novel algorithms for understanding complex physical systems at the molecular level. Dr. Nüske's educational background includes: Ph.D. in Mathematics from Freie Universität Berlin (2012-2017) Postdoctoral research at Universität Paderborn (2019-2022) and Rice University (2017-2019) His research focuses on developing data-driven methods that combine physical insights with machine learning to extract meaningful information from molecular simulation data. Key areas include Koopman operator theory for analyzing nonlinear dynamical systems, dimensionality reduction techniques, tensor methods for efficient computation, and kinetically consistent coarse-graining approaches. His work enables more efficient modeling of complex molecular processes that would otherwise be computationally prohibitive. Dr. Nüske's publication record shows a consistent trajectory of advancing both the theoretical foundations and practical applications of data-driven modeling in molecular science. His recent work emphasizes error analysis for data-driven models, control of stochastic systems, tensor-based dimensionality reduction, and methods to preserve kinetic properties in coarse-grained models. These contributions address critical challenges in scaling molecular simulations to biologically relevant timescales and system sizes. Dr. Nüske actively collaborates with researchers worldwide and has established partnerships with leading institutions including Freie Universität Berlin, Rice University, and TU Ilmenau. His collaborative network spans multiple disciplines, connecting mathematicians, chemists, and computational scientists. Dr. Nüske advises several Ph.D. students at the Max Planck Institute, including Vahid Nateghi, Lei Guo, Minakshi Verma, and Hauke Sprink. He has organized workshops on Uncertainty Quantification for molecular systems and regularly presents at major international conferences including SIAM MS, MTNS, and IMSI workshops. His research group continues to push the boundaries of what's possible in computational molecular science through innovative mathematical approaches.
Martin Eigel is a Researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) , specializing in numerical methods for stochastic partial differential equations, uncertainty quantification, and machine learning applications in computational mathematics. His work bridges tensor networks, Bayesian inversion, and quantum simulations. Research Interests : Adaptive stochastic Galerkin finite element methods Low-rank tensor approximations for high-dimensional problems Machine learning integration with PDE solvers Quantum circuit simulation techniques Bayesian inverse problems and error control Key Article Trends : His recent publications focus on merging deep learning architectures (e.g., ResNet, CNNs) with stochastic and tensor-based numerical methods for solving parametric PDEs, Bayesian inversion, and quantum systems. Topics include Hamilton-Jacobi-Bellman equations, Langevin dynamics, and risk-averse optimization under uncertainty.
Dr. Hussam Al Daas is a Postdoctoral Research Fellow at the Max Planck Institute for Dynamics of Complex Technical Systems since January 2019, specializing in numerical algorithms for large-scale scientific computing. His work bridges theoretical numerical analysis and high-performance implementation. Education: PhD in Applied Mathematics, Inria-Paris and Sorbonne University (2015-2018), funded by TOTAL Master in Fundamental and Applied Mathematics, Paris-Sud University (2012-2014) His research centers on developing communication-avoiding algorithms for sparse linear systems and tensor computations, with emphasis on robust preconditioners (algebraic two-level Schwarz, domain decomposition), Krylov subspace methods, and low-rank approximations. He addresses critical challenges in parallel scalability through rigorous complexity analysis and memory-efficient implementations, particularly for distributed-memory architectures. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Algebraic multilevel preconditioners achieving robustness for ill-conditioned sparse systems, (2) Theoretical communication lower bounds with optimal algorithm design for tensor/matrix operations, and (3) Novel extensions of Krylov methods for sequences of shifted systems and tensor train decompositions. These contributions target reservoir simulation, PDE-constrained optimization, and high-dimensional data problems. Funded by TOTAL during his PhD and currently by the Max Planck Society, his work shows no evidence of student advisement or external grant leadership. He contributes to the Computational Methods in Systems and Control Theory group, focusing on scalable solvers for complex dynamical systems through collaborative software development and algorithmic innovation.
Todd Young is a Professor and Chair in the Department of Mathematics at Ohio University , part of the College of Arts and Sciences . He is also a member of the Quantitative Biology Institute and the Infectious and Tropical Disease Institute . His research integrates dynamical systems theory with biological modeling, particularly in cell cycle regulation, clustering phenomena in yeast, and biomedical informatics. Education: Ph.D. in Mathematics, Georgia Institute of Technology, 1995 M.S. in Mathematics, University of California at Riverside, 1991 M.S. in Engineering Mechanics, University of Kentucky, 1987 B.S. in Mathematics, University of Kentucky, 1985 Research Interests: Todd Young's work centers on the qualitative theory of ordinary and random differential equations , with applications in cell cycle dynamics , biological feedback systems , clustering in populations , and binary classification in biomedical informatics . He develops mathematical models to understand how feedback mechanisms lead to synchronization in yeast and how dynamical systems principles apply to neural and immune responses. His research spans pure theory, such as bifurcation and ergodic theory, to applied problems in public health, like ventilator-associated pneumonia detection. Research Trends: His recent publications reveal a strong focus on mathematical biology , particularly in modeling the cell cycle with feedback and clustering behavior . He increasingly integrates numerical methods and tensor approximation dynamics into his work, collaborating across disciplines in physics, engineering, and medicine. His articles frequently appear in journals like SIAM Journal on Applied Dynamical Systems , Journal of Mathematical Biology , and Nonlinearity , reflecting a blend of rigorous analysis and biological relevance. Scientific Awards and Roles: Joint Editor-in-Chief, Dynamical Systems journal Recipient of the College of Arts and Sciences “Dean's Outstanding Teacher Award” Editorial Board memberships in Discontinuity, Nonlinearity and Complexity and Annual Review Chaos Theory, Bifurcations & Dynamical Systems Founding member of the Quantitative Biology Institute Active member of SIAM, AMS, and MAA Advising and Grants: Todd Young has advised numerous Ph.D., M.S., and undergraduate students, many of whom have pursued academic or data science careers. His research has been funded by the National Institutes of Health (NIH) and the National Science Foundation (NSF) , notably through the NIH-NIGMS R01GM090207 grant on mathematical biology. He emphasizes student training through structured research participation, including exploratory and paid assistantships. Labs and Teams: He leads the Dynamics in Biology Research Group , which includes students and collaborators working on projects ranging from theoretical dynamics to computational biology. The group fosters interdisciplinary collaboration, particularly with biologists and medical researchers at Ohio University and beyond.
Lennart Risthaus serves as a Researcher at the Department of Engineering Mathematics within the School of Civil Engineering at the University of Duisburg-Essen, Germany. He joined the university in September 2023 after previously working as a Researcher at the Institute of Engineering Mechanics, Continuum Mechanics Division at the Karlsruhe Institute of Technology (KIT) from February 2021 to August 2023. His academic appointments demonstrate a consistent trajectory in computational mechanics research within German technical universities. Dr. Risthaus completed his Bachelor's degree in Mechanical Engineering with a focus on Continuum Mechanics (2014-2018) and Master's degree in Mechanical Engineering with majors in Medical Technology and Applied Mechanics (2018-2021), both from the Karlsruhe Institute of Technology. His educational journey included an Erasmus exchange semester at the Royal Institute of Technology (KTH) in Stockholm and practical experience through internships at Reden B.V. in the Netherlands and Admedes GmbH in Germany, where he worked on finite element simulations and material testing. His research specializes in advanced computational techniques for material science, particularly FFT-based homogenization methods in micromechanics. Risthaus has developed innovative approaches for implementing Dirichlet boundary conditions in FFT-based computational frameworks and pioneered applications of tensor-train formats to enhance computational efficiency. His work bridges theoretical mathematics with practical engineering applications, focusing on solving complex boundary value problems in material behavior analysis. An analysis of his publication record reveals a clear research trajectory toward increasingly sophisticated computational methods for micromechanical simulations. His recent work demonstrates growing expertise in thermal homogenization problems and the integration of tensor-train methods with traditional FFT approaches. The consistent publication in high-impact journals like Computational Mechanics and International Journal for Numerical Methods in Engineering indicates recognition within the computational mechanics community. Risthaus actively contributes to the academic community through presentations at major international conferences including the GAMM Annual Meetings, ECCOMAS Young Investigators Conference, and the International Conference on Computational Plasticity (COMPLAS). His teaching responsibilities include leading exercises and tutorials for Mathematics courses for Civil Engineering students at both undergraduate and graduate levels, demonstrating his commitment to engineering education alongside his research activities.
Robert Crittenden is a Professor of Cosmology at the University of Portsmouth's Institute of Cosmology and Gravitation (ICG), part of the Faculty of Technology. He holds additional roles as Faculty Director of Postgraduate Research and PhD Supervisor. His academic journey includes a PhD from the University of Pennsylvania (1993) under Paul Steinhardt, followed by postdoctoral positions at Princeton University and the Canadian Institute for Theoretical Astrophysics (CITA). He joined the ICG in 2002 as a Reader in Cosmology and was promoted to Professor in 2014. Affiliations: Institute of Cosmology and Gravitation, University of Portsmouth. Education: B.S., University of Maryland; Ph.D., University of Pennsylvania (1993). Research Interests: Crittenden's work focuses on understanding cosmic structure formation and evolution through observations of the cosmic microwave background (CMB), galaxy distribution, and weak gravitational lensing. He is renowned for pioneering studies on the integrated Sachs-Wolfe effect linking galaxies and CMB anisotropies, providing independent evidence for dark energy. His research spans cosmology, CMB physics, large-scale structure, dark energy, early universe phenomena, and magnetic fields in the cosmos. Publications: His recent work explores dark energy dynamics using DESI and eBOSS data, gravitational lensing effects, and relativistic cosmological simulations. Key themes include galaxy clustering, BAO analysis, and cosmological constraints from multiwavelength surveys. Grants & Advising: Supervised over 15 PhD theses, reflecting his role in training early-career cosmologists. His research is supported by grants focusing on observational cosmology and theoretical frameworks for dark energy. Labs/Teams: Active in the ICG's dark energy and gravitational lensing research groups, contributing to international collaborations like the Dark Energy Survey and DESI projects.
Nadia Gosselin is a Full Professor in the Department of Psychology at the University of Montreal and Scientific Director of the Centre d'études avancées en médecine du sommeil (CÉAMS). She holds a PhD in clinical neuropsychology from Université de Montréal and completed postdoctoral training in neuroimaging at McGill University. Her research focuses on the long-term effects of traumatic brain injury (TBI) on cognition, sleep, and circadian rhythms, as well as the relationship between obstructive sleep apnea (OSA) and mild cognitive impairment. She leads major research projects funded by the Canadian Institutes of Health Research (CIHR) and the Canada Research Chairs program. Education: PhD in Neuropsychology (2007, Université de Montréal); Postdoctoral Fellowship in Neuroimaging (2011, McGill University). Research interests include: Neurocognitive and sleep disturbances following TBI Clinical and neurobiological mechanisms of OSA Idiopathic hypersomnia pathophysiology Neuroimaging techniques (MRI, EEG, SPECT) Cerebral plasticity and recovery processes Key grants: CIHR Project Grant: Targeting Sleep to Optimize Brain Recovery (2017-2023) Canada Research Chair in Sleep Disorders and Brain Health (2020-2025) American Academy of Sleep Medicine Foundation Award (2020-2023) Her team includes postdoctoral fellows, PhD students, and clinical collaborators addressing sleep-brain interactions in aging populations and neurological disorders. She has supervised over 10 doctoral students and published widely on sleep neurobiology and TBI recovery mechanisms.
Cecil Dybowski is the Francis Alison Professor in the Department of Chemistry and Biochemistry at the University of Delaware, where he serves as Associate Chair for Undergraduate Studies. His research program bridges physical chemistry, materials science, and cultural heritage conservation, with a focus on nuclear magnetic resonance (NMR) spectroscopy of solid materials. Dr. Dybowski received his B.S. in Chemistry (honors) from the University of Texas at Austin in 1969 and his Ph.D. in Chemistry (Chemical Physics) from the same institution in 1973. Following a Research Fellowship in Chemical Engineering at Caltech (1973-1976), he joined the University of Delaware faculty, progressing from Assistant Professor (1976-1982), to Associate Professor (1982-1986), to Professor (1986-present), and finally to Francis Alison Professor (2015-present). His research centers on NMR spectroscopy of solid and semi-solid materials, particularly focusing on heavy-atom spin-1/2 nuclei such as $^{207}$Pb, $^{199}$Hg, and $^{119}$Sn. His group has developed sophisticated computational methods using density functional theory to predict NMR chemical shieldings, incorporating relativistic effects through the Zeroth Order Regular Approximation (ZORA). A significant portion of his recent work involves collaboration with the Metropolitan Museum of Art to study lead soap formation in historical paintings, applying NMR techniques to understand degradation mechanisms in cultural artifacts. An analysis of his recent publications reveals a strong focus on cultural heritage science, particularly the application of NMR to study paint degradation and conservation. His work spans fundamental NMR theory, computational chemistry, and practical applications in pharmaceutical analysis and art conservation, demonstrating remarkable interdisciplinary reach while maintaining technical depth in magnetic resonance. Among his numerous honors are the Francis Alison Award (2015), Eastern Analytical Symposium Award for Achievements in Magnetic Resonance (2010), and Gold Medal from the New York Section of the Society for Applied Spectroscopy (2010). He is also a Fellow of the American Association for the Advancement of Science, American Institute of Chemists, and Society for Applied Spectroscopy. Dr. Dybowski has maintained active research funding throughout his career, with recent projects focusing on water transport in paint films, lead soap formation mechanisms, and computational prediction of NMR parameters for inorganic nuclides. His laboratory has trained numerous graduate students and postdocs who have gone on to careers in academia, industry, and cultural heritage institutions. The Dybowski Research Group operates state-of-the-art NMR facilities at the University of Delaware, with capabilities for solid-state NMR of challenging nuclei, including specialized techniques for heavy metals. The group maintains active collaborations with the Metropolitan Museum of Art, Brookhaven National Laboratory (for synchrotron X-ray studies), and several pharmaceutical companies for materials characterization projects.
Sergey Dolgov is an Associate Professor in the Department of Mathematical Sciences at the University of Bath. His research focuses on developing tensor decomposition methods for high-dimensional problems in numerical analysis, stochastic modeling, and quantum computing. Dr. Dolgov leads projects funded by the EPSRC including work on tensor methods for dynamic programming and Bayesian inverse problems. He is affiliated with the Centre for Mathematics and Algorithms for Data (MAD) and co-organizes workshops on computational mathematics for quantum technologies. His research interests center on tensor networks, high-dimensional optimization, rare event simulation, and quantum algorithm design. Recent work applies tensor train decompositions to problems in epidemiology, fluid dynamics, and material science, significantly reducing computational complexity. Dolgov's publications demonstrate consistent focus on tensor-based numerical methods for solving partial differential equations, epidemic modeling, and quantum computing applications. Key trends include the development of efficient algorithms for high-dimensional data and uncertainty quantification. He leads the Tensor Computation Research Group and collaborates internationally on projects involving quantum-inspired numerical methods. Current work explores tensor networks for solving Navier-Stokes equations and optimizing neural network architectures.
Sebastian Krämer is a researcher at the Institute for Geometry and Practical Mathematics at RWTH Aachen University, working under the supervision of Prof. Markus Bachmayr and Prof. Lars Grasedyck. His research focuses on tensor networks, low-rank approximations, and numerical methods for high-dimensional problems. He has made significant contributions to the field of tensor train formats and rank minimization techniques. Dr. Krämer's research interests span tensor networks, low-rank approximations, numerical linear algebra, high-dimensional approximation, tensor train formats, and machine learning optimization. His work centers on developing efficient algorithms for tensor decompositions, particularly focusing on alternating least squares methods, iteratively reweighted least squares approaches, and geometric constraints for tensor singular values. His research bridges theoretical numerical analysis with practical applications in high-dimensional data processing and scientific computing. His publication record shows a consistent output of high-quality work in top numerical analysis journals, with recent publications in 2024 demonstrating ongoing active research. His work demonstrates expertise in both theoretical aspects of tensor decompositions and practical implementation of numerical algorithms. He has developed several open-source toolboxes for tensor network arithmetic and tensor train feasibility problems, which have been widely used by the research community. Dr. Krämer has been actively involved in teaching, serving as a lecturer and assistant for various mathematics courses at RWTH Aachen, including Numerical Mathematics for mathematicians and civil engineers. He has also contributed to specialized research schools on high-dimensional approximation and deep learning, developing course materials and providing instruction. His professional activities include regular participation in the GAMM conference since 2017 and peer review activities for SIAM journals since 2015.
Kassa Darge, MD, PhD, DTM&P, FSAR, FESUR, is Chair of the Department of Radiology and Radiologist-in-Chief at Children’s Hospital of Philadelphia (CHOP). He is a Professor of Radiology at the Perelman School of Medicine at the University of Pennsylvania and holds the William L. Van Alen Endowed Chair in Pediatric Radiology. He also serves as an Honorary Professor of Radiology at Addis Ababa University in Ethiopia, reflecting his deep commitment to global health education and outreach. Dr. Darge earned his medical degree from Addis Ababa University and the University of Heidelberg, completed radiology residency and pediatric radiology fellowship at Heidelberg, and conducted research with the World Health Organization in tropical medicine. His career spans leadership roles in Germany and the U.S., including serving as Chair of Pediatric Radiology at the University of Wuerzburg before joining CHOP. His research focuses on advanced body imaging, particularly in magnetic resonance and ultrasound modalities. Key areas include functional MR urography (fMRU), contrast-enhanced ultrasound (CEUS), and innovative pediatric imaging techniques that reduce radiation exposure. He has published over 200 papers and led numerous educational initiatives. Dr. Darge’s recent publications emphasize non-invasive, radiation-free imaging solutions for pediatric conditions such as ARPKD, inflammatory bowel disease, necrotizing enterocolitis, and urinary tract disorders. His work integrates technical innovation with clinical applicability, advancing global standards in pediatric radiology. 2016 RSNA Honored Educator Award 2011 CHOP Mentor Award 2008 Schinz Medal, Swiss Radiological Society Multiple educational and research awards from RSNA, SPR, SAR, and ESUR Fellow of SAR, ESUR, and other leading radiology societies Dr. Darge has mentored dozens of trainees and established the CHOP Radiology International Education Outreach pediatric radiology fellowship in Ethiopia. He leads a mentoring program for junior faculty and has secured multiple grants to support research and global outreach. His leadership extends to editorial roles in journals like Pediatric Radiology and European Radiology , and active participation in societies including SPR, RSNA, and WFPI. He is involved in key institutional initiatives such as the Global Health Center, FUSED Program for Ultrasound, and Pediatric Kidney Stone Center at CHOP, driving innovation in clinical care, education, and global health equity.
Christopher Pittenger is the Elizabeth Mears and House Jameson Professor of Psychiatry at Yale School of Medicine. He holds secondary appointments as Professor of Psychology and in the Child Study Center, with additional roles as Director of the Clinical Neuroscience Research Unit and Deputy Chair for Translational Research in Psychiatry. Yale School of Medicine Yale Department of Psychiatry Child Study Center Interdepartmental Neuroscience Program Dr. Pittenger's research spans basal ganglia function, obsessive-compulsive disorder (OCD) pathophysiology, Tourette syndrome modeling, and psychedelic science applications for mental health. His lab employs genetically modified mice to investigate habit formation mechanisms and translational approaches to neuropsychiatric treatment. Recent publications demonstrate his interdisciplinary focus: 2025: Machine learning applications in OCD research 2024: Psychedelic medicine integration in residency training 2024: Neuroimmune mechanisms in pediatric neuropsychiatric syndromes 2024: Prefrontal astrocyte-mediated depression models Scientific recognition includes: Senior Researcher Award (2016) Eva King Killam Award (2015) NARSAD Independent Investigator Award (2015) Fellow of three major medical colleges Clinical trials under his leadership explore: Psilocybin dosing for OCD Neuroimaging biomarkers for treatment response Novel pharmacological interventions
Veronique Van Speybroeck is a Full Professor at Ghent University within the Faculty of Engineering and Architecture, a position she has held since October 2012. She also serves as Research Professor at Ghent University since October 2007 and is the Head of the Center for Molecular Modeling (CMM), which she co-founded in 2000. The CMM brings together approximately 40 researchers from the faculties of Science and Engineering and Architecture to model molecules, materials, and processes at the nanoscale. Van Speybroeck graduated as an engineer in physics from Ghent University in 1997 and obtained her Ph.D. in 2001 on theoretical simulations of chemical reactions. After her Ph.D., she received a postdoctoral fellowship from the National Fund for Scientific Research Flanders and conducted research at various foreign institutes. Her research focuses on first principle kinetics and molecular dynamics simulations of complex chemical transformations in nanoporous materials, particularly zeolites, Metal-Organic Frameworks (MOFs), and Covalent Organic Frameworks (COFs). She has built substantial expertise in modeling nanoporous materials for catalysis and adsorption, with applications inspired by and performed in close collaboration with experimental groups. Her work strongly emphasizes modeling realistic materials and processes under operating conditions. Analysis of her recent publications reveals a strong focus on computational modeling of reticular materials, with particular emphasis on machine learning potentials for accurate simulations, understanding confined water behavior in zeolites, and developing photocatalytic frameworks for environmental applications. Her research increasingly integrates advanced computational techniques with experimental validation to tackle complex problems in materials science and catalysis. Dr. Karl Wamsler innovation award (2023) Francqui prize in exact sciences (2024) ERC Starting grant (2010) ERC Consolidator grant (2015) ERC Advanced grant (2025) Full member of the Royal (Flemish) Academy for Science and the Arts of Belgium (KVAB) Van Speybroeck leads a research team comprising approximately 5 postdoctoral researchers, 13 PhD students, and 5-10 Master's students annually. Her research has been supported by multiple European Research Council grants, including an ERC Starting grant in 2010, an ERC Consolidator grant in 2015, and an ERC Advanced grant in 2025. She also participates in various activities to enhance the impact of science on society, having served on the STEM-platform from 2012-2017 to promote Science, Technology, Engineering, and Mathematics training and careers. The Center for Molecular Modeling (CMM), which Van Speybroeck heads, is a multidisciplinary research group composed of about 40 researchers. The CMM's mission is to model molecules, materials, and processes at the nanoscale by bringing together physicists, chemists, and (bio-)engineers. This collaborative approach is central to the CMM's scientific excellence in molecular modeling.
Jonas Kusch is an Associate Professor at the Department of Data Science, Norwegian University of Life Sciences, specializing in numerical analysis and its applications in scientific computing and machine learning. His work focuses on dynamical low-rank approximation, particularly in developing low-rank neural networks with geometry-aware training algorithms that respect the differential geometry of matrix manifolds. Research spans computational quantum mechanics , radiation transport , and machine learning . Key contributions include energy-stable integrators for kinetic equations and multi-fidelity optimization algorithms for fission criticality. Publications emphasize low-rank methods for solving inverse problems, time-dependent systems, and uncertainty quantification in hyperbolic equations.
