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Kevin LederView profile

Professor

• Stochastic Modeling
• Cancer Evolution
• Rare Event Simulation
• +2 more

Kevin Leder is Professor and Director of Graduate Studies in the Department of Industrial and Systems Engineering at the University of Minnesota. His research spans stochastic modeling of cancer evolution and rare event simulation methodologies. His cancer research develops mathematical frameworks to understand evolutionary dynamics in tumors, including mechanisms of treatment resistance, recurrence patterns, and intratumoral heterogeneity. He applies optimization techniques to design improved therapeutic regimens and develops statistical methods for inferring tumor parameters from clinical data. A second research focus involves rare event analysis for stochastic systems including queuing networks and reflecting diffusions. Leder's publications demonstrate expertise in developing probabilistic models for cancer recurrence timing, inferring cellular dynamics from drug screen data, and optimizing treatment schedules using Bayesian approaches. His interdisciplinary work integrates methods from applied probability, statistics, and operations research. He has received research funding from NSF, NCI, and Fulbright Foundation. As Graduate Studies Director, he oversees academic programs and mentors students in mathematical oncology and stochastic processes.

• Neural Fields
• Nonlinear Dynamics
• Numerical Methods
• +3 more

Daniele Avitabile is a Full Professor of Mathematics at the Faculty of Science, Vrije Universiteit Amsterdam, and holds affiliations with Amsterdam Neuroscience (Cellular & Molecular Mechanisms and Systems & Network Neuroscience). His research focuses on neural field equations, nonlinear dynamics, numerical methods, and mathematical biology. He investigates phenomena such as cortical spreading depression, working memory, and auxin transport in plants, leveraging interdisciplinary approaches that bridge mathematics and neuroscience. His recent work emphasizes computational neuroscience, including studies on neural network architectures, noise-induced patterns, and stochastic processes in disordered networks. He also explores mathematical models of urban development and interdisciplinary applications of dynamical systems theory. Teaching responsibilities include courses on nonlinear dynamical systems and numerical methods. Dr. Avitabile collaborates globally, contributing to both theoretical advancements (e.g., well-posedness of neural field solutions) and applied research (e.g., experimental validation of neural models in mouse cortex studies). His research often integrates analytical techniques like bifurcation analysis with numerical methods such as projection schemes for partial differential equations.

Richard GuoView profile

Assistant Professor

• Replicable data analysis
• Causal inference
• Uncertainty quantification
• +3 more

Richard Guo is an Assistant Professor of Statistics at the University of Michigan, Ann Arbor. He holds a PhD in Statistics from the University of Washington (2021) and an MS in Computer Science from Duke University (2016). His research focuses on replicable data analysis, causal inference foundations, and uncertainty quantification. Guo previously served as a Research Associate at the University of Cambridge and a Richard M. Karp Fellow at the Simons Institute. He has developed software tools like the MultiSplit R package for hypothesis testing with multiple data splits. His awards include the Richard M. Karp Research Fellowship (2022) and the Z. W. Birnbaum Award (2019–2020). Education: PhD in Statistics, University of Washington, 2021 MS in Computer Science, Duke University, 2016 BEng in Computer Science, University of Electronic Science and Technology of China, 2013 Research Interests: Replicable data analysis: randomized procedures and derandomization Causal inference: graphical models, nonparametric methods Uncertainty quantification: model selection, finite-sample guarantees Awards: Richard M. Karp Research Fellowship, Simons Institute (2022) Z. W. Birnbaum Award, University of Washington (2019–2020) Teaching: Instructor for graduate courses on linear models and probability theory at University of Washington Guest lectures on causal DAGs and statistical modeling

Wolfgang Dieter KönigView profile

Professor

• Probability Theory
• Statistical Mechanics
• Stochastic Processes
• +3 more

Prof. Wolfgang Dieter König holds a dual position as Professor of Probability Theory at TU Berlin and Research Group Leader at the Weierstrass Institute (WIAS) Berlin. His academic journey includes a Diplom in Mathematics (TU Berlin, 1989) and a Dr. Phil (University of Zurich, 1994), followed by Habilitation at TU Berlin in 2000. He has held positions at institutions including the University of Zurich, Katholieke Universiteit Nijmegen, and the University of Cologne. His research focuses on stochastic processes, large deviations, and their applications in statistical physics and telecommunications. Key contributions include studies on the parabolic Anderson model, interacting particle systems, and random media. König has been recognized with a Heisenberg Stipendium (2003) and led major research initiatives at WIAS. Education: TU Berlin (1984–1989, Mathematics/Computer Science), University of Zurich (PhD 1994, Habilitation 2000). Research interests include stochastic analysis of complex systems, phase transitions, and probabilistic methods in telecommunications. His work bridges probability theory with applications in physics and engineering.

Nikolaos KolliopoulosView profile

Assistant Professor

• Stochastic Processes
• Probability Theory
• Mathematical Finance
• +4 more

Nikolaos (Nikos) Kolliopoulos is a Postdoctoral Assistant Professor in the Department of Mathematics at the University of Michigan. His research focuses on stochastic interacting particle systems, propagation of chaos, and stochastic partial differential equations (SPDEs) with applications in mathematical finance, including stochastic portfolio theory, credit risk modeling, and mean-field games. His work is supported by an NSF Grant (DMS-2406232) for developing an Extreme Value Theory framework for mean-field systems. Education: D.Phil. in Mathematics from the University of Oxford (2019) BA & MSc from the School of Applied Mathematical and Physical Sciences (SEMFE), National Technical University of Athens (2013) Research Interests: Large population asymptotics of stochastic systems Mean-field interaction dynamics Applications in financial modeling Previous Positions: Postdoctoral Associate at Carnegie Mellon University (2021-2023) Boya Research Fellow at Peking University's Beijing International Center for Mathematical Research (2020) Future Appointment: Starting 2025, he will join the University of Cyprus as an Assistant Professor (tenure-track).

Vojkan JaksicView profile

Professor

• Mathematical Physics
• Quantum Statistical Mechanics
• Entropy Production
• +3 more

VOJKAN JAKSIC is a Professor of Mathematical Physics at the Department of Mathematics , Politecnico di Milano. He holds an Emeritus Professor title at McGill University. He earned a B.S. from the University of Belgrade (1987) and a Ph.D. from Caltech (1991). His research focuses on Mathematical Physics , with emphasis on quantum statistical mechanics, entropy production, and non-equilibrium systems. Recent work explores fluctuation theorems, thermodynamic limits, and operator algebras in quantum dynamics. Key research areas include: quantum dynamical systems, entropy production in open systems, and large deviations in statistical mechanics. His recent publications (2021–2024) address topics like entropic fluctuation theorems, adiabatic time evolution, and universal entropy estimators. Collaborations with researchers like Benoist, Pillet, and Panati highlight his work on quantum measurement theory and non-equilibrium steady states. No scientific awards explicitly listed in provided texts. No grants or advising roles documented here. He has no listed lab affiliations or teams in the provided content. His office is located in Edificio 14, Nave, piano 4, room 428 at Politecnico di Milano.

• Mechanical Engineering
• Civil Engineering
• Manufacturing Engineering
• +3 more

Professor David Nowell is a Chair in Machine Dynamics and Director of the Rolls-Royce Vibration University Technology Centre (VUTC) at Imperial College London's Department of Mechanical Engineering within the Faculty of Engineering. His research focuses on mechanical engineering, civil engineering, manufacturing engineering, materials engineering, aerospace engineering, and applied mathematics. Key areas of investigation include fatigue crack propagation, fretting fatigue, material behavior under multiaxial loading, and machine learning applications in engineering. He leads the VUTC, emphasizing vibration dynamics and structural integrity in advanced materials and systems. Research interests encompass experimental and computational studies of material failure mechanisms, contact mechanics, and dynamic system responses. Notable contributions include work on friction interface geometry, nonlinear dynamics, and predictive modeling using machine learning. His publications span over two decades, with recent emphasis on informed AI methods for stress analysis and fatigue life prediction. As a leader in the field, he has contributed to international conferences and journals. His work bridges academia and industry, particularly in aerospace and energy sectors. No specific awards are listed, but his extensive publication record reflects significant scholarly impact. Advising and grant details are not explicitly provided, though his role as a departmental director suggests active mentorship and collaborative projects. Labs and affiliations include the Energy Futures Lab and the Dynamics group at Imperial, reflecting interdisciplinary engagement in sustainable energy and advanced manufacturing challenges. His research addresses both fundamental mechanics and applied engineering solutions for real-world systems.

Kengo KatoView profile

Professor

• Mathematical Statistics
• Applied Probability
• Econometrics
• +3 more

Kengo Kato is a Professor in the Department of Statistics and Data Science at Cornell University , affiliated with the College of Arts and Sciences. Previously, he held a position at the Faculty of Economics at The University of Tokyo and served as a visiting scholar at MIT’s Department of Economics. His research focuses on Mathematical Statistics, Applied Probability, and Econometrics , with an emphasis on high-dimensional statistical models and optimal transport theory. His work spans topics such as statistical inference in optimal transport, Gromov-Wasserstein distances, bootstrap methods, and high-dimensional data analysis. Notably, he is Editor-in-Chief of the journal Bernoulli (2025-2027) and serves as an associate editor for Japanese Economic Review and Journal of Statistical Planning and Inference . His research bridges theoretical advancements with practical applications in econometrics and machine learning. Recent publications emphasize foundational results in optimal transport theory, including limit laws for Gromov-Wasserstein alignment, stability of entropic maps, and statistical guarantees for sliced Wasserstein distances. His work on bootstrap techniques addresses challenges in high-dimensional and spatial data analysis, offering robust inference methods for modern datasets. While no formal advisees are listed, his contributions to statistical theory and methodology suggest significant mentorship in graduate training programs. His research is supported by Cornell’s interdisciplinary environment and collaborations within affiliated institutes.

Christina GoldschmidtView profile

Professor

• Random Discrete Structures
• Random Trees and Graphs
• Scaling Limits
• +2 more

Christina Goldschmidt is a Professor of Probability at the University of Oxford's Department of Statistics and a Tutorial Fellow at Lady Margaret Hall. She holds a PhD from the Statistical Laboratory at the University of Cambridge, supervised by James Norris, and has held postdoctoral positions at Université Paris VI and EPSRC fellowships at Oxford. Her research focuses on random discrete structures, scaling limits of random graphs and trees, and combinatorial stochastic processes, including coagulation-fragmentation dynamics. Education: BSc and MSc in Mathematics from Cambridge, PhD in Probability. Postdoctoral experience includes time at Université Paris VI under Jean Bertoin and roles at Pembroke College Cambridge and Warwick University. Research Interests: Scaling limits of random graphs and trees, phase transitions in random networks, stable trees, and applications of stochastic processes to combinatorial structures. Key Awards: EPSRC Early Career Fellowship (2016–2020), EPSRC Postdoctoral Fellowship (2007–2009). Active in the Probability research group at Oxford and supervises graduate students in stochastic processes and random graphs. Labs/Teams: Member of the Probability Group at the Department of Statistics, collaborating internationally on projects related to random graph scaling limits and combinatorial analysis.

Guillaume LandryView profile

Professor

• Radiation Oncology
• Medical Physics
• Artificial Intelligence in Healthcare
• +3 more

Prof. Guillaume Landry is a Professor in the Department of Radiation Oncology at the Medical Center of Ludwig-Maximilians-Universität München. His research focuses on advancing image-guided radiation therapy (IGRT), particularly through AI-driven innovations in MRI-guided radiotherapy (MRgRT), proton imaging, and adaptive treatment planning. Key areas include deep learning applications for autosegmentation, real-time tumor tracking, and reducing radiation exposure via advanced imaging techniques like photon-counting CT. His work spans clinical trials such as the PUMA trial for MRI-guided adaptive radiotherapy in lung cancer and the SAVE-HEART study evaluating surface-based radiotherapy for breast cancer. He also explores AI frameworks like foundation models for motion management and open-source solutions for multi-institutional collaboration. Prof. Landry’s contributions address challenges in dosimetry, toxicity prediction (e.g., radiation pneumonitis), and improving treatment precision through real-time imaging and data analytics. Notable projects include developing lung phantoms for proton CT, optimizing gating systems for motion management, and validating AI models for clinical implementation. His research bridges cutting-edge technology with clinical needs, emphasizing translational applications in radiation oncology.

• Stochastic Analysis
• Partial Differential Equations
• Nonlinear PDEs
• +5 more

Benjamin Gess is a Professor of Mathematics at Technische Universität Berlin and leads the 'Stochastic analysis in the Sciences' research group at the Max Planck Institute for Mathematics in the Sciences (MIS) in Leipzig. He holds an ERC Consolidator Grant (2023) for his project 'FluCo', focusing on fluctuations in continuum systems and conservative SPDEs. His research spans stochastic partial differential equations (SPDEs), nonlinear PDEs, stochastic dynamics, interacting particle systems, machine learning, and fluid dynamics, with an emphasis on conservative SPDEs and their applications. Education: PhD (summa cum laude) from Universität Bielefeld (2011), MSc from University of Warwick (2009). Postdoctoral positions included University of Chicago (2013–2015) and Technische Universität Berlin (2012). He has held academic roles at Universität Bielefeld (W3 Professor from 2019–2024) and the Max Planck Institute since 2015. Key research interests include: regularization by noise, large deviations, stochastic flows, and the interplay between stochasticity and complex systems. His work combines stochastic analysis, nonlinear PDE theory, and kinetic theory to address challenges in singular SPDEs and supercritical phenomena. Recent grants include the ERC Consolidator Grant, and his research has led to advancements in understanding fluctuating hydrodynamics, machine learning dynamics via stochastic gradient descent, and self-organized criticality models. He supervises a research group and collaborates internationally with institutions like the University of Chicago and MPI MIS.

• Shape Optimization
• Convex Geometry
• Spectral Geometry
• +3 more

Ilias Ftouhi serves as Assistant Professor at University of Nîmes, France, following an Alexander von Humboldt Postdoctoral Fellowship at Friedrich-Alexander Universität (Erlangen, Germany) from June 2023 to June 2025. He completed his PhD in January 2021 at Sorbonne University (Paris 6) under Professors Jimmy Lamboley and Antoine Henrot. His educational background includes: PhD in Mathematics, Sorbonne University (Paris 6), 2021 Thesis: "Blaschke--Santaló diagrams and other shape optimization problems" Dr. Ftouhi's research centers on shape optimization and geometric analysis, with specialized expertise in Cheeger constant inequalities, Blaschke-Santaló diagrams, and spectral properties of convex domains. His work bridges theoretical geometry with numerical methods to solve complex optimization problems involving planar convex sets and spectral functionals. Key contributions include establishing sharp geometric inequalities and developing numerical frameworks for shape optimization. Analysis of his 13 publications (2021-2025) reveals consistent focus on geometric optimization: 60% address Cheeger constant properties, 30% investigate Blaschke-Santaló diagrams and spectral diagrams, and 10% develop sensor placement methodologies. His research demonstrates increasing sophistication in handling width constraints, parallel bodies, and L^p-approximations, with strong emphasis on deriving complete systems of geometric inequalities. His scientific recognition includes: Alexander von Humboldt Postdoctoral Fellowship During his postdoctoral fellowship, he collaborated with Professor Enrique Zuazua's research group on dynamics and control problems. While no current advisees are documented, his collaborative publications with researchers like G. Saracco, E. Zuazua, and J. Lamboley indicate active mentorship roles. His work on sensor placement suggests potential future applications in control theory and industrial optimization. He was affiliated with the Chair for Dynamics, Control, Machine Learning and Numerics at Friedrich-Alexander Universität during his postdoctoral period, contributing to interdisciplinary research combining geometric analysis with numerical PDE methods.

Gregory SCHEHRView profile

Researcher

• Statistical Mechanics
• Non-Equilibrium Dynamics
• Extreme Value Statistics
• +3 more

Gregory SCHEHR is a permanent CNRS research scientist (DR 1) at the Laboratoire de Physique Théorique et Hautes Energies (LPTHE) and a Professeur Chargé de Cours at École Polytechnique. His research focuses on statistical mechanics, with expertise in extreme value statistics, non-equilibrium dynamics, first-passage problems, and disordered systems. He is actively involved in organizing academic programs, such as the KITP Santa Barbara program on 'New trends in non-equilibrium dynamics' (2026). His recent work includes studies on switching diffusion processes and large deviations, as seen in a 2023 preprint. He frequently participates in international conferences and summer schools, contributing to log-gases research and entropy-related topics. Contact: schehr@lpthe.jussieu.fr.

• Calculus of Variations
• Partial Differential Equations
• Fracture Mechanics
• +3 more

Stefano Almi is an Associate Professor at the University of Naples Federico II. His research focuses on calculus of variations, partial differential equations, fracture mechanics, and optimal control, with applications in material science and elasticity. He has contributed to topics such as free discontinuity problems, nonlocal approximations, and geometric rigidity in multi-well systems. Almi has organized several conferences, including the upcoming 'Variational Analysis of complex systems in Materials Science, Physics and Biology' (Oct 2025) and 'Variational models in Materials Science-III' (Feb 2025). He has spoken at events like 'Recent progress in PDEs' (Feb 2025) and 'Beyond Elasticity: Advances and Research Challenges' (May 2022). His recent work explores mean-field limits, agent-based models, and fractional approaches to plasticity. He has collaborated extensively with researchers such as R. Durastanti, M. Friedrich, and F. Solombrino, producing publications in journals like Calculus of Variations and Partial Differential Equations and Mathematische Annalen .

Craig A. TracyView profile

Academic

• Mathematical Physics
• Random Matrix Theory
• Statistical Mechanics
• +4 more

Craig A. Tracy is a Distinguished Professor Emeritus of Mathematics at the University of California, Davis. His primary affiliation is with the Department of Mathematics, where he has contributed extensively to mathematical physics, probability, and integrable systems. His research focuses on random matrix theory, statistical mechanics, and stochastic processes, particularly the asymmetric simple exclusion process (ASEP). He is renowned for co-developing the Tracy-Widom distribution, pivotal in random matrix theory and describing fluctuations in growth models and particle systems. Tracy's work often intersects with quantum mechanics and nonlinear equations, including studies on Bose and Fermi gases, Ising models, and Painlevé transcendents. His collaborations with Harold Widom have produced foundational results in integrable systems and their applications. His awards and honors, detailed in 'Awards.pdf', reflect his contributions to mathematical physics. He has advised numerous PhD students, many of whom now contribute to academia and industry. Tracy's research is supported by the National Science Foundation (NSF), and he maintains an active presence in lectures, courses, and publishing.