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Ahmed Bou-RabeeView profile

Researcher

• Probability Theory
• Partial Differential Equations
• Stochastic Homogenization
• +5 more

Dr. Ahmed Bou-Rabee is an NSF Postdoctoral Fellow and Courant Instructor at the Courant Institute of Mathematical Sciences, New York University, sponsored by Scott Armstrong. Previously, he served as a postdoc at Cornell University under Lionel Levine and earned his PhD in Mathematics from the University of Chicago (2022), advised by Charles K. Smart. He holds a B.S. in Mathematics and an M.S. in Statistics from Stanford University. His research focuses on probability theory, partial differential equations, and their interplay with statistical mechanics, particularly stochastic homogenization, the Abelian sandpile model, and Liouville quantum gravity. Dr. Bou-Rabee has presented at leading institutions including ETH Zurich, University of Toronto, UCLA, and the Institute for Advanced Study. His work bridges discrete and continuous models, with contributions to random walks on graphs, harmonic functions on percolation clusters, and scaling limits in random planar maps. He is an NSF Postdoctoral Fellow and has developed open-source tools for studying sandpile dynamics and matrix methods. Education: PhD (Mathematics), University of Chicago (2022); M.S. (Statistics), Stanford University (20XX); B.S. (Mathematics), Stanford University (20XX) Awards: NSF Postdoctoral Fellowship Teaching: Taught courses at NYU (Ordinary Differential Equations, Analysis, Discrete Mathematics) and served as a teaching assistant at the University of Chicago and Stanford. His research explores critical phenomena in stochastic systems, including superdiffusive limits, rigidity of harmonic functions, and universality in random matrix models. Ongoing projects investigate implications for Anderson localization and higher-dimensional Ising models.

• Partial Differential Equations
• Mathematical Analysis
• Numerical Analysis
• +6 more

Jose A. Carrillo de la Plata is Professor of the Analysis of Nonlinear Partial Differential Equations at the University of Oxford's Mathematical Institute and Tutorial Fellow in Applied Mathematics at The Queen's College. He serves as ICIAM Officer at Large and previously held positions at Imperial College London (2012-2020), Universitat Autònoma de Barcelona (2003-2012), University of Texas at Austin (1998-2000), and Universidad de Granada (1992-1998, 2000-2003). His educational background includes a PhD from Universidad de Granada alongside MA, MSc, and BSc degrees: PhD in Mathematics, Universidad de Granada MA, MSc, BSc (unspecified institutions) Carrillo's research centers on Partial Differential Equations with applications across physics, engineering, life sciences, and socio-economical modeling. His expertise includes long-time asymptotics, qualitative properties, and numerical schemes for nonlinear diffusion, hydrodynamic, and kinetic equations modeling collective behavior in many-body systems. Key applications span gas molecules in rarefied gases, granular media, semiconductor charge transport, neural synchronization in computational neuroscience, and cell movement via chemotaxis or adhesion forces. His work bridges rigorous mathematical analysis with computational implementation and real-world biological applications. His recent publications demonstrate strong trends in aggregation-diffusion equations, nonlocal interactions, and structure-preserving numerical methods for gradient flows. Key thematic areas include tissue growth modeling, neural field dynamics, pattern formation in particle systems, and the development of computational frameworks for complex biological phenomena. His work increasingly integrates Bayesian inference with continuum mechanics for biological applications. His scientific achievements have been recognized with prestigious awards: SEMA prize (2003) and GAMM Richard Von-Mises prize (2006) for young researchers Wolfson Research Merit Award (2012-2017) ERC Advanced Grant (2019) Echegaray Medal (2022) and International Tartufari Prize (2024) Election to European Academy of Sciences (2018), SIAM Fellow (2019), Academia Europeaea (2023), and Royal Academy of Sciences of Spain (2021) As an educator, Carrillo received the 2016 SACA award for best PhD supervision at Imperial College London. He leads research in Oxford's Mathematical Biology group and Oxford Centre for Nonlinear PDE, currently directing an ERC Advanced Grant on nonlocal PDEs for complex particle dynamics, phase transitions, patterns, and synchronization. His service includes chairing the European Mathematical Society's Applied Mathematics Committee (2014-2017) and serving as ICIAM Officer at Large since July 2024. He actively collaborates with Oxford's Mathematical Biology, Nonlinear PDE, and Numerical Analysis research groups while leading the European Academy of Sciences' Mathematics Division. His work bridges theoretical mathematics with interdisciplinary applications through partnerships with computational biologists and neuroscientists.

• Calculus of Variations
• Nonlinear Partial Differential Equations
• Optimal Transport
• +4 more

Emanuela Radici is an Assistant Professor at the Università degli Studi dell'Aquila, specializing in advanced mathematical analysis with a focus on Calculus of Variations, Nonlinear PDEs, and Deterministic Particle Approximation. Her research explores topics such as optimal transport, aggregation-diffusion equations, and traffic models. She has organized workshops on Calculus of Variations and PDEs, demonstrating leadership in academic collaboration. Her work bridges theoretical analysis with applied problems, including opinion dynamics and congestion modeling. Key contributions include particle approximation methods for conservation laws and studies on BV homeomorphisms. She actively participates in international conferences and research events, contributing to the advancement of geometric methods in calculus of variations. Dr. Radici’s research output includes 22 published papers, with recent works addressing nonlinear mobilities, degenerate traffic models, and minimal extensions in geometric norms. Her interdisciplinary approach integrates functional analysis, differential geometry, and numerical methods.

Michael KampffmeyerView profile

Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +5 more

Michael Kampffmeyer is a 33-year-old Professor at the Machine Learning Group at UiT The Arctic University of Norway in Tromsø, where he researches the development of deep learning algorithms that learn with limited data and their explainability. Born in 1991 in Hamburg, Germany, he developed an early relationship with Norway through family holiday trips, spending six months as an exchange student in Alvdal at age 15 before completing high school in the UK. Remarkably, just 14 years after beginning his integrated master's degree in Energy, Climate and Environment at UiT at age 18, he achieved the position of professor. Kampffmeyer's research focuses on addressing two critical challenges in contemporary AI: the interpretability aspect and the inefficient training processes that require massive amounts of data, computing power, and energy. His work aims to develop more efficient and explainable AI models that can function effectively with limited data, with particular applications in medical image analysis where he collaborates with Universitetssykehuset Nord-Norge (UNN). His team has grown significantly from 5 to around 35 members since he began his PhD in 2015. The analysis of Kampffmeyer's recent publications reveals a strong focus on medical imaging applications, particularly in cardiac analysis and mammography, alongside work on making AI models more efficient and explainable. His research spans few-shot learning techniques for medical image segmentation, uncertainty modeling in graph networks, and super-resolution techniques for satellite imagery. A consistent theme across his work is the development of methods that require less data while maintaining or improving performance, addressing his core research mission of creating more efficient AI systems. Kampffmeyer is actively involved in securing significant research funding, with his team working on a proposal to join a new national AI center that would distribute approximately 200 million NOK, with a substantial portion allocated to UiT. He emphasizes the importance of international visibility, recognizing that continued representation at major international conferences is essential for maintaining their position at the forefront of AI research. His vision for the future of AI includes multimodal approaches that incorporate various types of data beyond just text and images, inspired by how humans use multiple senses to understand the world.

Carina DunlopView profile

Senior Lecturer

• Mathematical Biology
• Biophysics
• Cancer Modelling
• +5 more

Dr Carina Dunlop is a Visiting Senior Lecturer in the Department of Mathematics at the School of Mathematics and Physics, University of Surrey, with a research focus at the interface of mathematics, biophysics, and biology. She is set to move to University College London (UCL) in September 2024. She is a member of the Mathematics at the Interface Group and the Centre for Mathematical and Computational Biology, and her work integrates mechanical signaling and spatial modeling into biological systems. Her research interests lie at the intersection of mathematical biology , biophysics , and cancer modeling . She develops computational and mathematical models to understand cell mechanosensing, tissue morphogenesis, and tumor growth, with a particular emphasis on how mechanical forces regulate biological processes. Her work spans developmental biology, organoid modeling, and pharmaceutical applications, often in close collaboration with experimentalists and industry partners such as AstraZeneca. The recent publications reflect a strong trend in modeling mechanobiological systems , particularly in cancer and reproductive biology. Her work combines individual-based simulations , continuum mechanics , and active matter theory to explore how physical forces influence cell behavior and tissue organization. Key themes include stiffness sensing, energy minimization in cell adhesion, and spatially resolved tumor modeling for drug development. Dr Dunlop has been awarded a fully funded NC3Rs PhD studentship to develop a digital twin of 3D vascular systems for studying hemorrhagic viral diseases. She actively supervises multiple PhD students and collaborates across disciplines, including with the Campagnolo lab at Surrey for experimental validation. Her professional affiliations include the Society of Mathematical Biology and the European Society for Mathematical and Theoretical Biology. She leads research on ovarian biomechanics, cancer drug modeling, and 3D cell culture systems. Her lab integrates computational modeling with wet-lab experiments, fostering a highly interdisciplinary team focused on translating theoretical insights into biomedical applications.

Andrei PetoukhovView profile

Associate Professor

• Self-organization of colloids
• Nanomaterials science
• Synchrotron X-ray techniques
• +4 more

Dr. Andrei V. Petukhov is an Associate Professor at the Van 't Hoff Laboratory for Physical and Colloid Chemistry within Utrecht University's Debye Institute for Nanomaterials Science . He holds a part-time associate professor position at Eindhoven University of Technology since 2016. As a leading expert in synchrotron-based X-ray techniques , he serves as Editor-in-Chief of MDPI journal Materials ' Advanced Nanomaterials section and chairs the beamtime allocation panel at ESRF. Founded and chairs International Advisory Council at Immanuel Kant Baltic Federal University (until 2022 resignation) Developed XFEL pump-probe methodology for studying extreme irradiation conditions Created microradian SAXS setups for nanoscale structural analysis His research focuses on colloidal self-organization across multiple length scales, using both nonspherical colloids and external field manipulations . Key themes include: Entropic patchiness in crowded suspensions Magnetic field control of anisotropic colloids Confinement effects in droplet-based assembly Phase transitions in colloidal crystals His group performs in situ X-ray scattering experiments at prestigious facilities like ID10 beamline and BM-26 DUBBLE , with notable methodological contributions to: X-ray cross-correlation analysis Coherent diffraction imaging Advanced colloidal crystallography