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Dr. Elia Gironacci is a Lecturer and Director of Studies in Civil and Environmental Engineering at the University of Warwick's School of Engineering. He holds a PhD in Computational Mechanics (Warwick, 2018), MSc (2013) and BSc (2010) from the University of Marche, Italy. His research focuses on computational fracture mechanics of quasi-brittle materials and engineering education for apprenticeship programs. Teaching responsibilities include leading modules like ES196 Statics and Structures and ES1A4 for apprentices. He manages the Civil Infrastructure Engineering Degree Apprenticeship program and chairs the University Apprenticeship Tutor Network. His work integrates advanced numerical methods with educational innovation, particularly in STEM induction programs and apprenticeship learning experiences. Key research contributions include stochastic modeling of crack propagation, non-local probabilistic methods, and shale rock fracture mechanics. His work bridges computational mechanics with practical engineering applications, emphasizing dimensionality reduction techniques and material heterogeneity. He actively participates in institutional committees (Education, Health & Safety) and has published in journals like Rock Mechanics and Rock Engineering and International Journal of Mechanical Sciences. His book chapter on STEM induction frameworks highlights his dual focus on research and pedagogy.

• Computational Modelling of Crack Propagation
• Thermo-Fracture Mechanics
• Engineering Education
• +3 more

Professor Mark Broom is a Professor of Mathematics at City, University of London, part of City and St George's, University of London. He has held academic positions at the University of Sussex and the University of Glasgow, and served as Head of the Department of Mathematics at both Sussex (2007–2009) and City (2020–2024). Education: PhD in Mathematics, University of Sheffield (1993) MSc in Statistics, University of Sheffield (1990) BA (Hons) in Mathematics, University of Oxford (1989) His research lies at the intersection of Mathematical Biology and Evolutionary Game Theory , focusing on modeling animal behavior, evolutionary stability, and population dynamics. Key areas include kleptoparasitism, signaling in prey species, multiplayer games, and structured populations. His work combines theoretical development with biological applications, often using game-theoretic models to explore ecological and evolutionary questions. His recent publications reveal a strong trend in multiplayer evolutionary games on networks , analyzing cooperation, fixation probability, and movement dynamics. These studies frequently appear in top journals like Journal of Theoretical Biology , Dynamic Games and Applications , and Proceedings of the Royal Society A . He is recognized for editorial and professional leadership: Editorial Board: Journal of Theoretical Biology , Dynamic Games and Applications , Theoretical Population Biology , Chapman and Hall Mathematical Biology Series Board Member: Society for Modeling and Theory in Population Biology (2024–present) Executive Board: International Society of Dynamic Games (2012–present) Fellow: Institute of Mathematics and its Applications (2022–present) Professor Broom actively supervises PhD students and has authored or co-authored over 150 journal articles, multiple book chapters, and the influential book Game-Theoretical Models in Biology (2nd ed., 2023). He founded the Mathematical Models in Ecology and Evolution conference series, underscoring his role in shaping the field. He is currently based in the Department of Mathematics within the College of Health, Science and Technology, continuing to lead research in mathematical biology and evolutionary dynamics.

• Mathematical Biology
• Evolutionary Game Theory
• Structured Populations
• +7 more

Geoffroy Couteau is a CNRS research scientist at IRIF (Institut de Recherche en Informatique Fondamentale), Université Paris Cité, where he conducts research in theoretical and applied cryptography. He obtained his PhD from École Normale Supérieure de Paris in 2017 under the supervision of David Pointcheval and Hoeteck Wee, followed by a postdoctoral position at Karlsruhe Institute of Technology (KIT) from 2017 to 2019. His primary research interests include secure multiparty computation, zero-knowledge proofs, and the theoretical foundations of cryptography, with a particular emphasis on pseudorandom correlation generators and efficiency improvements in cryptographic protocols. He has made significant contributions to fine-grained cryptography, non-interactive zero-knowledge proofs, and post-quantum secure computation. The recent publications reflect a strong trend toward foundational advances in secure computation, with increasing focus on efficiency, practicality, and connections to complexity theory and learning theory. His work often bridges theoretical hardness assumptions with practical protocol design. ERC Starting Grant (2023) for project OBELiSC (Overcoming Barriers and Efficiency Limitations in Secure Computation) Geoffroy Couteau has advised numerous PhD and master’s students, including Dung Bui, Clément Ducros, Eliana Carozza, and Ulysse Léchine. He has also hosted many visiting students and postdocs, fostering a vibrant research group. He has served on the program committees of major conferences such as EUROCRYPT, CRYPTO, TCC, and PKC. He is currently leading research in cryptography at IRIF and is involved in postdoctoral hiring for projects in advanced cryptographic primitives. He maintains a research blog and resource collection for students, including LaTeX templates, a probability cheat sheet, and curated answers to common cryptography questions.

• Cryptography
• Secure Multiparty Computation
• Zero-Knowledge Proofs
• +5 more

Yue Li is the Leonard Case Jr. Professor in the Department of Civil and Environmental Engineering at Case Western Reserve University. He specializes in resilient and sustainable infrastructure systems, focusing on structural reliability, probabilistic design, and climate change adaptation. His research addresses risk assessment for infrastructure under extreme events, including earthquakes, hurricanes, and climate impacts. Education: PhD in Civil Engineering, Georgia Institute of Technology, 2005 Research Interests: Dr. Li’s work integrates advanced statistical methods and data-driven approaches to enhance infrastructure resilience. Key areas include: Probabilistic modeling of structural systems Risk-informed decision-making for multi-hazard mitigation Climate change impacts on material durability and performance Asset management and lifecycle cost analysis Notable Contributions: His recent publications emphasize data-driven resilience metrics for water systems and seismic risk assessment for bridges. He has pioneered frameworks for evaluating infrastructure vulnerability under climate change, including corrosion effects and extreme weather adaptation. Awards: ABSE Outstanding Paper Award (2023) Case School of Engineering Teaching Award (2020) Nomination for John S. Diekhoff Award (2019) Leadership Roles: Dr. Li serves as Section Editor for the ASCE Journal of Structural Engineering and chairs multiple technical committees on safety and reliability. He leads initiatives to standardize multi-hazard design practices and resilience evaluation methodologies.

• Resilient Civil Infrastructure
• Structural Reliability
• Risk Assessment
• +3 more

Professor Tobias Nipkow is a leading researcher in formal methods and interactive theorem proving at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Computer Science. He is a core developer of the Isabelle proof assistant and leads the Theorem Proving Group. His work has profoundly influenced program verification, semantics, and formalized mathematics. University: Technical University of Munich School: School of Computation, Information and Technology Department: Department of Computer Science Research Group: Theorem Proving Group Key Projects: Isabelle, Archive of Formal Proofs, Concrete Semantics His research focuses on formal verification, higher-order logic, semantics of programming languages, and verified algorithms. He has pioneered the formalization of textbook algorithms, data structures like B+-trees and quadtrees, and logical systems. His work bridges theoretical foundations with practical tools for software correctness. The most recent publications show a strong trend in verifying classical algorithms (e.g., Gale-Shapley, Earley parser), data structures (B+-trees, deques), and decision procedures, primarily using Isabelle/HOL. His contributions span foundational logic, program analysis, and educational approaches to formal methods. Best Paper Award at CADE 28 (2021) Tobias Nipkow has made extensive contributions to advising and collaborative research, co-authoring with numerous researchers and students. He has secured support for large-scale formalization efforts and contributed to major projects like the Flyspeck proof of the Kepler conjecture. His work is supported by ongoing development of the Isabelle framework and the Archive of Formal Proofs. He leads the Theorem Proving Group at TUM, which is central to the development and application of Isabelle. The group fosters international collaboration, contributes to the Archive of Formal Proofs, and advances research in automated reasoning, semantics, and verified systems.

• Formal Methods
• Interactive Theorem Proving
• Program Verification
• +9 more

Rocco Servedio is a Professor in the Department of Computer Science at Columbia University, where he leads research in theoretical computer science with a focus on computational complexity theory, learning theory, and the role of randomness in computation. He previously served as Chair of the Computer Science Department from 2018 to 2021. He holds a Ph.D., MS, and AB in Mathematics from Harvard University. His research interests include property testing, computational learning theory, and algorithmic lower bounds. He has contributed to foundational work in areas like junta testing, trace reconstruction, and convexity testing. Servedio has held leadership roles in major conferences such as STOC, CCC, and COLT, and has mentored students through courses like Unconditional Lower Bounds and Derandomization . Education: Ph.D. in Computer Science, Harvard University MS in Computer Science, Harvard University AB in Mathematics, Harvard University His research bridges theoretical computer science and applied mathematics, with recent work exploring the intersection of Gaussian processes, convex geometry, and algorithmic efficiency. Servedio's contributions to the field are exemplified through his involvement in high-impact conferences and his leadership in advancing fundamental computational theories.

• Computational Complexity Theory
• Computational Learning Theory
• Property Testing
• +3 more

Georg Stadler is a Professor of Mathematics and Computer Science at New York University's Courant Institute. His research focuses on computational inverse problems, uncertainty quantification, and PDE-constrained optimization, driven by applications in climate modeling, geophysics, and plasma physics. He holds a PhD from the University of Graz (2004) and has been recognized with awards including the Gordon Bell Prize (2015) and the SIAM Computational Science & Engineering Best Paper Prize (2019). Education: Ph.D. (Dr.), Mathematics, University of Graz, Austria, 2004. M.S. (Mag.), Mathematics, University of Graz, Austria, 2001. M.S., Mathematics and Geometry Education, Graz University of Technology and University of Graz, 2001. Research Interests: Large-scale PDE solvers, Bayesian inverse problems, extreme event probability estimation, and optimization under uncertainty. Applications in climate (sea/land ice, tsunamis), plasma physics (fusion), and computational earth science (mantle flow, plate tectonics). Recent Research Trends: His work emphasizes scalable algorithms for high-dimensional Bayesian inverse problems, with applications to tsunamis, stellarator coil design, and ice sheet dynamics. Recent articles highlight advancements in extreme event probability estimation and robust multigrid solvers for incompressible Stokes equations. Awards: Gordon Bell Prize (2015) for extreme scalability of implicit solvers. SIAM Best Paper Prize (2019) for computational science contributions. Young Scientist ASCINA Award and Springer CSE Prize (2011). Advising & Grants: Current PhD student Sonia Reilly and former advisees include Chen Li and Shanyin Tong. His research is supported by NSF, ONR MURI, and the Simons Foundation. He co-leads the Computational Mathematics and Scientific Computing Seminar at Courant. Labs & Collaborations: Active in Courant’s interdisciplinary groups, focusing on high-performance computing and inverse problems. Collaborates with institutions like UT Austin on mantle dynamics and fusion energy projects.

• Computational Mathematics
• Scientific Computing
• Uncertainty Quantification
• +5 more

Gérard Ben Arous is a Silver Professor of Mathematics at New York University's Courant Institute of Mathematical Sciences, where he has served as Director and Vice Provost for Science and Engineering Development since 2011. He holds a PhD in Mathematics from the University of Paris VII (1981) and has previously taught at the University of Paris-Sud, École Normale Supérieure, and the Swiss Federal Institute of Technology in Lausanne. His research focuses on probability theory, stochastic analysis, and their applications to physics and industrial problems, particularly exploring complex systems' long-time behavior and aging phenomena in disordered media. Education: PhD in Mathematics, University Paris 7, France (1981) M.Sc. in Statistics, University Paris-Sud Orsay, France (1979) B.S. in Mathematics, École Normale Supérieure (Paris), France (1978) His research interests bridge probability with partial differential equations, dynamical systems, and statistical mechanics. Key contributions include studies on random media, random matrices, and the interplay between complexity, disorder, and aging in physical systems. He has held leadership roles in academic institutions, including directing the mathematics departments at Orsay and École Normale Supérieure, and founded Lausanne's Bernoulli Center. Notable awards include Fellow of the Institute of Mathematical Statistics and the Montyon Prize from the French Academy of Sciences. His work is published in top journals like Annals of Probability and Communications in Pure and Applied Mathematics , and he co-edits Probability Theory and Related Fields . Ben Arous has advised numerous researchers and contributed to interdisciplinary projects, including studies on machine learning landscapes and financial mathematics. His lab focuses on stochastic modeling and its applications across disciplines.

• Probability Theory
• Stochastic Analysis
• Large Deviations
• +6 more

Armagan Bayram is an Associate Professor in the Industrial and Manufacturing Systems Engineering Department at the University of Michigan-Dearborn's College of Engineering and Computer Science . Previously a postdoctoral researcher at Northwestern University, she holds a Ph.D. in Management Science from the University of Massachusetts Amherst, alongside M.S. and B.S. in Industrial Engineering from Istanbul Technical University. Education: Ph.D., Management Science, University of Massachusetts Amherst (2014) M.S., Industrial Engineering, Istanbul Technical University (2009) B.S., Industrial Engineering, Istanbul Technical University (2007) Her research focuses on stochastic models for capacity and resource allocation in mobility, healthcare, and logistics systems. Recent work includes urban curb space optimization , omnichannel retail fulfillment , and virtual appointment management in chronic care. Her methodological expertise spans stochastic optimization, dynamic programming, and experimental operations research. Current grants include NSF support for Curb Spaces in Urban Mobility Systems (2022-2024) and Inland Waterway Network Optimization (2024-2027) . Publications appear in top journals like Operations Research , European Journal of Operational Research , and Health Care Management Science . Scientific Accolades NSF Engineering Research Initiation (ERI) Award (2022) UM-Dearborn Distinguished Teaching Award (2020) IISE Healthcare Track Best Student Paper Advisor (2019) INFORMS Finalist for Doing Good with Good OR (2013) Bayram contributes to teaching through courses like Engineering Probability and Statistics and Simulation in Systems Design . She leads the Community Based Operations Research Lab , applying operations management principles to socially impactful projects.

• Operations Research
• Decision Science
• Stochastic Optimization
• +5 more

David Yao is the Piyasombatkul Family Professor of Industrial Engineering and Operations Research at Columbia Engineering, Columbia University. He has been a faculty member in the Department of Industrial Engineering and Operations Research (IEOR) since 1983 and became a full professor in 1988. He is a DSI Member and serves as Co-Chair of Financial and Business Analytics at the Data Science Institute. His research spans stochastic modeling, optimization, and operations management with applications in supply chains, manufacturing, and information systems. His research interests lie at the intersection of stochastic systems and decision-making under uncertainty. He specializes in stochastic modeling , queueing networks , production-inventory systems , and supply chain optimization . His work applies to high-tech manufacturing, semiconductor fabrication, computer and web-server scheduling, and logistics. He has pioneered the use of monotone structures in discrete-event systems and dynamic control of quality in production systems. David Yao has received numerous accolades recognizing his scholarly impact, including: IEEE Fellow Outstanding Paper Prize (2003) from SIAM Franz Edelman Award (1999) from INFORMS Outstanding Technical Achievement Award (1999) from IBM Guggenheim Fellowship (1991/92) Presidential Young Investigator Award (1987–1992) from NSF George Nicholson Prize (1983) from ORSA As a principal investigator on over two dozen research grants and contracts, he has led major projects in semiconductor manufacturing, supply chain logistics, and web performance optimization. He holds four U.S. patents in manufacturing and supply chain systems. He has authored over 160 refereed publications, three influential books, and edited multiple volumes. He currently serves as the Stochastic Models Area Editor for Operations Research and has served on editorial boards of leading journals in operations research and applied probability. While no specific students are listed, his long-standing role as a senior professor and grant leader implies extensive mentorship and academic advising. David Yao is affiliated with the Data Science Institute at Columbia University, where he co-chairs the Financial and Business Analytics initiative. His work bridges engineering, data science, and financial applications, contributing to both theoretical advances and real-world systems optimization.

• Operations Research
• Stochastic Modeling
• Queueing Theory
• +7 more

James R. Lee is a Professor in the Department of Computer Science at the University of Washington. His research spans theoretical computer science, probability, and geometry. He has held visiting scientist roles at Microsoft Research (2023, 2018, 2017) and participated in programs at the Simons Institute (2023, 2020, 2018, 2017, 2014). Research Interests: Algorithms, complexity theory, convex optimization, metric embeddings, spectral graph theory, probability, stochastic processes, and the interplay between discrete and continuous analysis. Teaching: Courses on modern algorithms, quantum computing, optimization theory, and spectral methods in theoretical computer science. Scientific Contributions: Developed sparsification algorithms for generalized linear models and norms with near-linear size guarantees (STOC'24, FOCS'23). Extended Cheeger-type inequalities to higher eigenvalues (STOC'12, STOC'18). Proved super-polynomial lower bounds for LP/SDP relaxations in constraint satisfaction (STOC'15, FOCS'13). Disproved Benjamini-Papasoglou conjectures on annular separators (Discrete Comp. Geom. 2024). Advanced understanding of random walks in geometric and unimodular graphs (Israel J. Math. 2023, GAFA 2023). Scientific Awards: Best Paper Award, STOC 2015

• Algorithms
• Computational Complexity
• Convex Optimization
• +4 more

Chris Rogers is a Professor of Statistical Science within the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, actively contributing to research at the intersection of probability theory, stochastic analysis, and financial applications. His academic profile reflects deep engagement with mathematical finance and theoretical probability through publications and departmental affiliations. His research spans financial mathematics, probability theory, stochastic analysis, statistics, and mathematical economics, with emphasis on rigorous mathematical frameworks for financial markets. Key themes include option pricing mechanisms, stochastic process modeling, and geometric probability applications, often addressing real-world financial instruments like Asian options and S&P500 index behaviors through advanced probabilistic techniques. Analysis of his 15 most recent publications (2016-2018) reveals consistent focus on stochastic calculus applications in finance, particularly Lévy processes, diffusion models, and optimal stopping problems. His work bridges theoretical probability with quantitative finance, demonstrating expertise in translating complex stochastic phenomena into financial modeling solutions across asset pricing, risk assessment, and market analysis domains. No scientific awards were documented in the provided source material. Information regarding PhD/Master's student supervision, research grants, or collaborative teams was not specified in the available texts, indicating absence of such details in the source documentation.

• Financial mathematics
• probability theory
• stochastic analysis
• +2 more

Christoph Heinzl is a Professor of Cognitive Sensor Systems at the University of Passau since September 2022. He leads the Knowledge-based Image Processing research group at the Fraunhofer Development Center X-ray Technology (EZRT) . His academic background includes a PhD in Informatics and a Habilitation in 2022 , both from TU Wien . Research Focus: Scientific visualization, visual analytics, immersive analytics, virtual/augmented reality, machine learning, and X-ray computed tomography (XCT). Key Trends: Development of novel visualization techniques for complex volumetric data (e.g., dynamic volume lines, visual coherence frameworks), parameter space analysis, and cross-virtuality collaboration tools. Applications: Aerospace component inspection, defect analysis in composites (CFRP, GFRP), porosity quantification, and 4DCT time-series exploration.

• Scientific Visualization
• Visual Analytics
• Immersive Analytics
• +3 more

Mahdi Soltanolkotabi is a Professor in the Departments of Electrical and Computer Engineering, Computer Science, and Industrial and Systems Engineering at the University of Southern California's Viterbi School of Engineering. He serves as the inaugural Director of the USC Center on AI Foundations for Science (AIF4S). His academic journey includes a Ph.D. in Electrical Engineering from Stanford University (2014) under Emmanuel Candes, followed by a postdoctoral position at UC Berkeley's AMPLAB mentored by Ben Recht and Martin Wainwright. Dr. Soltanolkotabi's research spans both theoretical and applied dimensions of data science. On the theoretical side, he develops mathematical foundations for modern data science, focusing on generative AI, deep learning, machine learning, signal processing, and computational imaging. His work draws upon nonconvex optimization, high-dimensional probability, statistical estimation, empirical processes, and learning theory. On the applied side, he develops reliable AI systems for healthcare and scientific applications, collaborating with physicians and domain scientists to enhance AI reliability, develop new architectures, and create rigorous evaluation frameworks. His recent publications demonstrate strong focus on medical AI applications, image reconstruction, and theoretical foundations of deep learning. His work bridges the gap between theoretical guarantees and practical implementations, particularly in medical imaging where reliability is critical. His research group has made significant contributions to understanding the behavior of nonconvex optimization algorithms in high-dimensional settings. David and Lucile Packard Fellow Information Theory Society Best Paper Award NIH Director's new innovator award Sloan Research Fellowship NSF Career award Airforce Office of Research Young Investigator award (AFOSR-YIP) Viterbi school of engineering junior faculty research award Faculty awards from Google and Amazon Dr. Soltanolkotabi has received multiple research grants including Amazon Research Awards for projects on "Artificial intelligence for fast and portable medical imaging" and "Reliable AI for Generation of Medical Reports from MRI Scans." He actively collaborates with medical professionals and leads educational outreach initiatives with local schools through USC's Viterbi Adopt-a-School program. His work demonstrates a strong commitment to translating theoretical advances into practical healthcare solutions while maintaining rigorous mathematical foundations.

• Machine Learning
• Deep Learning
• Generative AI
• +7 more

Jia-Jie Zhu is a machine learner and applied mathematician currently serving as head of an independent research group at the Weierstrass Institute for Applied Analysis and Stochastics in Berlin, with an upcoming appointment as tenured associate professor at KTH Royal Institute of Technology in Stockholm. Previously, he conducted postdoctoral research in machine learning at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, following doctoral studies in optimization and numerical analysis at the University of Florida. His research focuses on the mathematical foundations of machine learning and optimization, particularly at the intersection of computational algorithms, dynamical systems, and probability theory. Key areas include: Robust probabilistic machine learning algorithms Kernel methods for distribution manipulation Variational methods for optimization over probability distributions Gradient flows and optimal transport theory Wasserstein and Fisher-Rao geometry Applications in generative modeling and causal inference Dr. Zhu's recent work reveals deep connections between partial differential equations, kernel methods, and machine learning, resulting in theoretically grounded algorithms for handling distribution shifts. His publications demonstrate a consistent trajectory from foundational optimization theory to cutting-edge applications in robust learning and generative modeling, with increasing emphasis on the mathematical structures underlying modern ML systems. He has secured significant research funding, including a DFG Project on 'Optimal Transport and Measure Optimization Foundation for Robust and Causal Machine Learning' within the Priority Program 'Theoretical Foundations of Deep Learning' (SPP 2298), and actively organizes academic events such as the Workshop on Optimal Transport from Theory to Applications (OT-DOM) and upcoming sessions at ICSP 2025 and SwissMAP. As an educator, Dr. Zhu teaches nonparametric statistics at Humboldt University of Berlin and serves as area chair for major conferences including AISTATS 2025. He maintains an active research group with opportunities for master's students, PhD candidates, and postdoctoral researchers interested in the mathematical frontiers of machine learning.

• Machine Learning
• Optimization
• Optimal Transport
• +5 more