Steffen Winter is a Lecturer (PD Dr.) at the Institute of Stochastics, Karlsruhe Institute of Technology (KIT). His research specializes in Fractal Geometry, Geometric Measure Theory, Stochastic Geometry, and Dynamical Systems. He leads the DFG-funded project Scaling of curvature measures and the modified Weyl-Berry conjecture and serves as Principal Investigator for project 12 ( Morphometric Roughness of Nanostructured Surfaces ) within the DFG Priority Programme 2265 (Random Geometric Systems). Winter's work explores the mathematical foundations of fractals, stochastic processes, and geometric measurements. Key themes include Minkowski content, curvature measures, self-similar sets, percolation models, and applications to materials science and geoscience. Recent publications emphasize fractal dimensionality, surface roughness quantification, and stochastic convergence in complex systems. He teaches advanced courses including Stochastic Geometry , Fractal Geometry , and Markov Chains , and mentors students through seminars and proseminars. No awards or research grants besides DFG projects are documented.
Dr. Yue Wu is a Lecturer in the Department of Mathematics and Statistics at the Faculty of Science, University of Strathclyde. She is actively engaged in research and teaching, with a strong focus on stochastic and numerical analysis. She is affiliated with the Alan Turing Institute as a Visiting Researcher and collaborates internationally on advanced mathematical and data science projects. Research Interests: Numerical analysis for stochastic (partial) differential equations (SDEs/SPDEs) Random periodic solutions and their numerical approximation Rough path theory and signature methods Applications in machine learning, data science, and engineering systems Her recent work bridges pure stochastic analysis with practical applications in AI, autonomous systems, and industrial diagnostics. She employs advanced mathematical tools such as log-signatures and randomized numerical schemes to solve complex real-world problems. Publication Trends: Dr. Wu's recent publications (2022–2025) show a strong trend toward integrating stochastic numerics with machine learning. Her work spans theoretical convergence analysis of numerical schemes, feature extraction using rough paths, and PDE-informed deep learning. There is a clear interdisciplinary focus, connecting mathematics with engineering and computer science. Scientific Awards: Strathclyde & TU Braunschweig Joint Collaborative Funding Recipient (2023) ICIAM2023 Financial Support Scheme 2 Recipient (2023) Turing Network Development Award: Trailblazers Competition Recipient (2022) Lower Saxony – Scotland Tandem Fellowship Recipient (2022) Advising and Grants: Dr. Wu is accepting PhD students and offers projects in stochastic numerics and rough path applications. She has secured funding as Principal Investigator (e.g., International Exchanges Round 3) and Co-investigator (e.g., AI-based asteroid navigation project with ESA). Her grants reflect strong international collaboration and interdisciplinary innovation. Labs and Teams: While no specific lab name is mentioned, Dr. Wu is part of active research networks including the Alan Turing Institute and collaborates with teams in aerospace, data science, and applied mathematics. She organizes seminars and workshops, indicating leadership in her research community.
Asuman Aksoy is a Crown Professor of Mathematics and George R. Roberts Fellow at Claremont McKenna College (CMC), Department of Mathematical Sciences. She holds a B.Sc. from the University of Ankara (Turkey), M.S. from Middle East Technical University, and Ph.D. from the University of Michigan. Her research focuses on Functional Analysis , Metric Geometry , and Operator Theory , with contributions to quasi-Banach spaces, approximation theory, and Banach space geometry. Her work includes studies on Baire category theorem applications, hypercyclic operators, and R-tree embeddings. Awards include the MAA Tensor Summa Grant (2013), MAA Distinguished Teaching Award (2006), and Claremont Graduate University’s Institute of Mathematical Sciences Award (2022). She has secured grants like the Fletcher Jones Grant (2009–2011) and Presidential Awards at CMC. Her publications reflect interdisciplinary strengths in functional analysis and geometry, with over 30 articles in journals like Advances in Operator Theory and Journal of Mathematical Analysis and Applications . She has authored books on real/complex analysis and problem-solving in real analysis. Her research emphasizes theoretical rigor while fostering education through grants and mentorship, contributing to both pure mathematics and its pedagogical applications.
Vin de Silva is a Professor of Mathematics and Statistics at Pomona College, part of The Claremont Colleges, since 2005. His work focuses on applying geometry and topology to data analysis, sensor networks, and machine learning. He holds a D.Phil. from Oxford University and a Master of Arts from Cambridge University. Research interests include Applied Algebraic Topology, Machine Learning, and Topological Data Analysis. His notable contributions span persistent homology, sensor network coverage, and tensor decomposition. He has collaborated with leading researchers like Robert Ghrist and Gunnar Carlsson. Recent publications include work on Zigzag Persistent Homology (2009), sensor network algorithms (2007), and tensor rank analysis (2008). His research emphasizes geometric insights in interdisciplinary problems. Awards: 2012: Pomona College Wig Distinguished Professor Award for Excellence in Teaching 2007: Scientific American SciAm 50 Award for sensor network coverage algorithms Teaches courses like Combinatorics, Differential Equations/Modeling, and Topics in Geometry and Topology. His grants and lab activities focus on computational topology and data science applications.
Giorgio Bacci is an Associate Professor at the Department of Computer Science , Aalborg University , Denmark. He is a member of the Distributed, Embedded, and Intelligent Systems (DEIS) research group led by Prof. Kim G. Larsen. He earned his Ph.D. in Computer Science from the University of Udine (2013) under Prof. Marino Miculan, following M.Sc. and B.Sc. degrees in Computer Science from the same university (2008 and 2005, both 110/110 cum laude ). Research Interests include: Behavioural Metrics : Quantitative methods for system equivalence and approximation. Model Synthesis and Automata Learning : Automated construction of models for probabilistic/stochastic systems. Analysis of Cyber-Physical Systems : Formal verification techniques for real-time and hybrid systems. Process Algebras : Nondeterministic, probabilistic, and stochastic process calculi. Semantics of Programming Languages : Formal models for probabilistic and concurrent computation. Scientific Service includes roles as PC member for LICS 2025 , ICALP 2025 , and co-chair for EXPRESS/SOS 2025 and GandALF 2025 . He has contributed to 35 publications in areas like probabilistic automata , Markov processes , and bigraphical models , with a focus on computational complexity and behavioral distances . Awards include: Teacher of the Year 2022/2023 (Department of Computer Science, Aalborg University) Best Paper Award at CALCO 2021 .
Tselil Schramm is an Assistant Professor in the Department of Statistics at Stanford University, with courtesy appointments in Computer Science and Mathematics. She is actively engaged in research and teaching in theoretical computer science and statistics. Department: Department of Statistics School: School of Humanities and Sciences University: Stanford University Office: CoDa E254 Email: tselil@stanford.edu She earned her PhD from UC Berkeley under Prasad Raghavendra and Satish Rao, followed by postdoctoral work at Harvard and MIT with Boaz Barak, Jon Kelner, Ankur Moitra, and Pablo Parrilo. Her research lies at the intersection of theoretical computer science and statistics, focusing on high-dimensional estimation, information-computation tradeoffs, sum-of-squares algorithms, and random graph theory. She develops algorithms for statistical problems and investigates the boundaries between what is statistically possible and what is computationally feasible. Her recent publications span topics including the overlap-gap property, discrepancy algorithms, robust message passing, semidefinite programming, spectral clustering, and random geometric graphs, appearing in top venues such as STOC, FOCS, COLT, NeurIPS, and The Annals of Statistics. She teaches a range of courses, including Introduction to Statistics (STATS 60), Theory of Statistics II (STATS 300B), and Machine Learning Theory (STATS 214 / CS 228M), reflecting her expertise in both foundational and advanced statistical theory. Runner-up for Best Paper at COLT 2021 Invited to STOC 2022 special issue of SICOMP Invited to SODA 2016 special issue of ACM Transactions on Algorithms Invited to CCC 2019 special issue of Theory of Computing Tselil Schramm advises and collaborates with numerous students and researchers, including Shuangping Li, Misha Ivkov, and Siqi Liu. She has been involved in multiple research grants and projects, particularly in the areas of high-dimensional inference and algorithmic robustness. Her work often bridges theoretical guarantees with practical algorithmic design. She is affiliated with Stanford’s theoretical computer science and statistics research groups, contributing to a vibrant academic environment. Her future work is expected to further explore the limits of efficient computation in statistical settings, with potential applications in machine learning, signal processing, and network analysis.
Professor V. Radu Craiu is a distinguished faculty member in the Department of Statistical Sciences within the Faculty of Arts and Science at the University of Toronto. He has served as Chair of the Department for 5 years (2018-2022 and 2023-2024) after joining as an Assistant Professor in 2001, being promoted to Associate Professor in 2006 and to Full Professor in 2013. Ph.D. in Statistics (2001) - University of Chicago M.S. in Mathematics (1996) - University of Bucharest B.S. in Mathematics (1995) - University of Bucharest Professor Craiu's research spans multiple domains of statistics with particular expertise in computational methods. His work has evolved from foundational research on Markov chain Monte Carlo samplers to broader applications in Bayesian statistics, copula models, statistical genetics, and more recently, astronomy. His research demonstrates both theoretical depth and practical applications across diverse fields including genetics, ecology, and astrophysics. His recent publications show a strong focus on advancing computational methodologies while addressing complex real-world problems. The research trends reveal increasing interdisciplinary collaboration, particularly with astronomers working on radio transients and stellar flares, while maintaining strong contributions to core statistical methodology in areas like copula modeling, MCMC algorithms, and dimension reduction. Fellow of the American Statistical Association (2022) Fellow of the Institute of Mathematical Statistics (2020) Faculty Affiliate of the Vector Institute (2020) CJS Award for 'Likelihood Inflating Sampling Algorithm' (2019) CRM-SSC prize from Centre de Recherches Mathematiques and Statistical Society of Canada (2016) Elected Member of the International Statistical Institute (2015) Professor Craiu has supervised numerous doctoral students whose work spans statistical genetics, computational methods, and copula modeling. His editorial service includes positions as Contributing Editor for the IMS Bulletin and Associate Editor for multiple prestigious journals including Harvard Data Science Review, Journal of Computational and Graphical Statistics, Statistics Surveys, The Canadian Journal of Statistics, and Statistical Methods and Applications. His research has been supported by various grants that have enabled extensive collaborations across disciplines.
Gemma de les Coves is an ICREA Research Professor at the Departament d'Enginyeria of Universitat Pompeu Fabra (Barcelona) and holds an Associate Professorship at the University of Innsbruck (Austria). Her research bridges quantum physics, mathematical theory, and philosophy, focusing on universality, undecidability, and interdisciplinary frameworks. She leads the Mathematical Quantum Physics research group in Innsbruck and has received prestigious awards including the START Prize (2020) and the ICREA professorship (2024). Education & Academic Path: PhD in Theoretical Physics (University of Innsbruck, 2011) Postdoc at Max Planck Institute for Quantum Optics (2011–2016) Assistant Professor (University of Innsbruck, 2018–2023) ICREA Research Professor (2024–present) Research Interests: Universality in physical and computational systems Undecidability in quantum models and formal languages Mathematical foundations of quantum theory Interdisciplinary connections between physics, philosophy, and culture Awards & Recognition: START Prize (FWF, 2020) Elise Richter Fellowship (2016–2018) Emmy Noether Visiting Fellowship (Perimeter Institute, 2016) Advisees & Grants: Supervised PhD students include Tobias Reinhart, Andreas Klingler, and Mirte van der Eyden Recipient of the START Prize grant (FWF) Labs & Outreach: Runs the Mathematical Quantum Physics group at Innsbruck Active in science communication via YouTube, podcasts, and public lectures
Tom Braeckevelt is a Research Fellow at Ghent University, Belgium, working within the computational materials science group led by Prof. Veronique Van Speybroeck. Based at Tech Lane Ghent Science Park (Technologiepark 46, Zwijnaarde), he collaborates extensively with experimental teams including Prof. Johan Hofkens (photophysics) and Prof. Sara Bals (electron microscopy), bridging theoretical modeling with advanced characterization techniques to solve stability challenges in next-generation photovoltaics. Education: PhD in Materials Science, Ghent University (2018). Dissertation: Designing 2D hybrid organic-inorganic perovskites for game-changing photovoltaics , supervised by Prof. Veronique Van Speybroeck and Dr. Kurt Lejaeghere. His research centers on perovskite stability mechanisms through multiscale computational modeling (DFT, machine learning potentials, molecular dynamics) integrated with experimental validation (TEM, GIWAXS, spectroscopy). Key focus areas include phase transition kinetics, strain engineering, doping strategies, and interfacial design for cesium lead halide perovskites. This work addresses critical barriers to commercial solar cell deployment, particularly ambient-condition stability and efficiency retention. From 2019-2025, Dr. Braeckevelt co-authored 13 high-impact publications including Science (2019), Nature Communications (2022), and ACS Nano (2025), demonstrating progression from fundamental phase transition studies to machine learning-enhanced stability solutions. Recent work expands into covalent organic frameworks and rare-event sampling algorithms, reflecting methodological diversification while maintaining photovoltaic applications as the core driver. No scientific awards or fellowships were documented in the source materials. Supported by institutional research grants at Ghent University, Dr. Braeckevelt has presented findings at 8+ international conferences including PSCO19 (Lausanne), ICAMM (Rennes), and DFT2022 (Brussels). His invited talk at the 2025 Eindhoven Psiflow workshop highlights growing recognition in machine learning for perovskites. No student supervision roles were indicated in current position. He operates within Ghent's integrated materials research ecosystem, contributing to cross-disciplinary projects that combine computational prediction with nanoscale characterization to accelerate renewable energy technology development.
Jay Gopalakrishnan is a Professor of Mathematics and the Maseeh Distinguished Chair at Portland State University's Department of Mathematics. His research focuses on scientific computation, numerical analysis, finite elements, and multigrid methods with applications in optics and mechanics. He has contributed significantly to the development of hybridizable discontinuous Galerkin (HDG) methods and the Discontinuous Petrov-Galerkin (DPG) framework, emphasizing structure-preserving numerical techniques. His work spans diverse domains including microstructured optical fibers, spacetime tents for hyperbolic systems, and eigenvalue cluster computations. Notable research activities include developing accurate computational tools for leaky modes in fibers, stability analysis of acoustic waveguides, and bone mineralization models. He advises doctoral students at institutions such as Intel Corporation, The MathWorks, and James Madison University. His teaching includes advanced numerical analysis courses and undergraduate mathematical computing. Collaborations with researchers like L. Demkowicz and J. Schöberl highlight his interdisciplinary impact in computational mathematics and engineering.
Angelo Bongiorno is an Associate Professor of Chemistry at the College of Staten Island, CUNY, since 2015. Previously, he held faculty positions at Georgia Tech’s School of Chemistry and Biochemistry (2008–2014) and School of Physics (2014–2015). He earned a B.S. and M.S. in Physics from the University of Milan (Italy) and a Ph.D. in Physics from the École Polytechnique Fédérale de Lausanne (Switzerland). His research focuses on computational and theoretical studies of materials, particularly 2D materials like graphene, defects in solids, elastic properties, and energy-related applications. Educations: B.S. Physics, University of Milan, 1995 M.S. Materials Science, University of Milan, 1995 Ph.D. Physics, École Polytechnique Fédérale de Lausanne, 2003 Research Interests: Dr. Bongiorno applies density functional theory (DFT) to investigate materials under extreme conditions, such as graphene-based ultra-hard carbon films, elastic coupling in layered systems, and proton transport in solid-oxide fuel cells. His work bridges computational chemistry, solid-state physics, and nanotechnology, with applications in energy storage and nanomaterials. Articles: His most recent publications (2020–2024) explore nonlinear elasticity of amorphous materials, oxygen activation in enzymes, and mechanical properties of 2D boron nitride under pressure. These studies highlight his expertise in interdisciplinary material science. Labs/Teams: Active collaborations span computational modeling and experimental validation of material properties, often involving international teams in Europe and the U.S.
Monique Laurent is a Tilburg University professor and senior researcher at CWI (Centrum Wiskunde & Informatica), focusing on discrete mathematics and optimization . Her work bridges algebra, geometry, and computer science to solve complex combinatorial and polynomial optimization problems. Part-time full professor at Tilburg University (since 2009) Group leader of Networks and Optimization at CWI (2005-2016) Current member of CWI Management Team Research Focus: Semidefinite programming hierarchies, noncommutative polynomial optimization, quantum information theory, and matrix factorization. Her recent work explores applications in quantum entanglement , graph parameters , and combinatorial data analysis . Key Publications: 15+ articles from 2017-2024 address topics like copositive matrices , sum-of-squares convergence , and hypergraph optimization . Collaborations span institutions in the Netherlands, France, Germany, and the U.S. Awards: 2023 Khachiyan Prize SIAM Fellow (2017) KNAW member (2018) EUROPT Fellow (2021) Grants & Projects: Leads EU-funded initiatives TENORS (2024) and POEMA (2019), with prior NWO and Marie Curie grants. Organizes international workshops on polynomial optimization and quantum information.
Bill Hirsch serves as a Teaching Professor in the Physics Department at Marquette University, teaching core undergraduate courses including introductory physics for scientists/engineers and health professions, classical mechanics, electricity and magnetism (Parts 1 & 2), general relativity, and particle physics. His academic background includes a BS in Astrophysics from Pennsylvania State University, an MS with computational nuclear physics research from Indiana University of Pennsylvania, and a PhD from Wake Forest University focused on theoretical quantum gravity. His dissertation examined quantum effects of fermion fields in black hole and wormhole spacetimes. Dr. Hirsch's research centers on extreme spacetime curvature phenomena, investigating solutions to Einstein's field equations, neutron star/magnetar structure, and the plausibility of wormholes through semi-classical general relativity. His work bridges mathematical physics with quantum field theory in curved spacetimes, particularly analyzing fermion field interactions near astrophysical compact objects. His publication record demonstrates consistent focus on quantum gravitational effects, developing computational methods for stress-energy tensor calculations in static spherically symmetric spacetimes. This research contributes to understanding whether quantum effects might prevent the formation of astrophysical objects with extreme curvature.
Professor Norbert Schuch is a full Professor of Physics and Mathematics at the University of Vienna, where he leads the Research Group "Quantum Information and Quantum Many-Body Physics" at both the Faculty of Physics and Faculty of Mathematics. He joined the University of Vienna in October 2020 after serving as a tenured Research Group Leader at the Max-Planck-Institute of Quantum Optics in Garching, Germany and as a Lecturer at the Technical University Munich. Prior to that, he held a Tenure-Track-Professor position at the Institute for Quantum Information at RWTH Aachen University. Professor Schuch's research focuses at the intersection of Quantum Information and Computation with the Physics of Complex Quantum Many-Body Systems. His work combines mathematical, physical, and computational approaches to understand quantum correlations in many-body systems. Key research areas include tensor networks (such as Matrix Product States and Projected Entangled Pair States), topological order, entanglement theory, quantum algorithms, and quantum complexity theory. His interdisciplinary approach integrates methods from physics, mathematics, and theoretical computer science to address fundamental questions about quantum systems. His recent publications show a continued focus on tensor network theory and applications, with particular emphasis on topological phases, entanglement structure, quantum algorithms, and computational aspects of quantum many-body systems. His work spans mathematical foundations, physical applications, and computational implementations, demonstrating the cross-disciplinary nature of his research program. As an educator, Professor Schuch teaches courses on Quantum Information, Quantum Computing, and Quantum Algorithms, as well as specialized topics like Entanglement in Quantum Many-Body Systems. He actively supervises PhD students, postdocs, and master's students in his research group, which maintains strong connections with the international quantum information community.
Michael J. Lindsey is an Assistant Professor in the Department of Mathematics at the University of California, Berkeley, and a Faculty Scientist at Lawrence Berkeley National Laboratory. His research focuses on computational methods driven by Numerical Linear Algebra , Optimization , and Randomization , particularly for High-Dimensional Scientific Computing in quantum many-body problems and applied probability. University : UC Berkeley (Assistant Professor since 2022) Lab Affiliation : Mathematics Group at Lawrence Berkeley National Laboratory Email : lindsey@berkeley.edu His work includes Semidefinite Relaxation for quantum and classical problems, Monte Carlo Sampling techniques, and Tensor Networks for high-dimensional functions. He has pioneered Variational Embedding theory with guaranteed energy bounds and scalable solvers for quantum systems. Recent publications span Quantum Chemistry , Machine Learning , and High-Dimensional Probability , with applications to Electronic Structure , Molecular Dynamics , and Optimal Transport . He received the 2024 Hellman Fellowship and the 2019 SIAM Student Paper Prize . Teaching includes graduate and undergraduate courses in numerical analysis and applied mathematics at UC Berkeley and New York University. He also organizes the HDSC Seminar on high-dimensional scientific computing.
