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Davide LeonettiView profile

Assistant Professor

• Steel Structures
• Fatigue Analysis
• Structural Health Monitoring
• +4 more

Davide Leonetti is an Assistant Professor at Eindhoven University of Technology, affiliated with the Built Environment School and the EAISI High Tech Systems initiative. His primary role is in Steel Structures, focusing on fatigue analysis, structural health monitoring, and additive manufacturing applications in civil infrastructure. He holds a PhD from TU Eindhoven (2016–2020), researching probabilistic models for steel connection fatigue strength under Prof. J. Maljaars and Prof. H.H. Snijder. Academic Background: MSc in Mechanical Engineering from Federico II University of Naples (2016), PhD in Civil Engineering at TU Eindhoven (2020) Research Themes: Steel structures, fatigue crack growth, AI-driven inspection, and sustainable infrastructure maintenance His research combines experimental testing, numerical modeling, and AI techniques to enhance structural reliability. Notable projects include: Holi-DOCTOR (2022–2026): Wind turbine structural response and vibration monitoring Fatigue crack growth in rails (2021–2026): Rail steel durability under fatigue Fatigue life assessment of welded steel bridges (2021–2026) Leonetti collaborates with the EAISI AIMM lab, integrating AI into manufacturing and maintenance systems. His work addresses UN SDGs related to infrastructure resilience and sustainable development.

Alvin LeeView profile

Associate Professor

• Food Microbiology
• Food Virology
• Food Safety
• +3 more

Alvin Lee is an Associate Professor of Food Science and Nutrition at the Illinois Institute of Technology (IIT) , affiliated with the Lewis College of Science and Letters . He leads the Institute for Food Safety and Health (IFSH) Center for Processing Innovation and co-leads the USDA-NIFA Food Virology Collaborative NoroCORE , focusing on food safety and defense. His research addresses bacterial and viral pathogens in food systems, leveraging funding from USDA, FDA, and industry partnerships. Dr. Lee holds a Ph.D. in Applied Biology and Biotechnology from RMIT University, Australia. His expertise spans microbiology, food virology, and non-thermal technologies. He oversees IFSH's BSL-3 facility and teaches food microbiology in IIT's Master's program. Over 15 graduate students and postdocs have been mentored under his guidance. Research Interests include norovirus transmission in foodservice, pathogen inactivation via high-pressure processing (HPP), and validation of food safety controls. Recent work emphasizes low-moisture food safety, sanitizer efficacy, and risk mitigation strategies. He contributes to scientific journals as a reviewer and editorial board member. His articles explore HPP applications, pathogen survival mechanisms, and sampling methodologies. He advocates for pragmatic approaches to food safety compliance, balancing industry needs with regulatory standards. Media engagements highlight his role in explaining outbreaks like Listeria contamination in food processing. Dr. Lee collaborates with FDA and industry on microbiology research platforms, advancing technologies to ensure food safety across supply chains. His work bridges academic research with real-world applications in food production and healthcare settings.

Zoran VondračekView profile

Professor

• Markov Processes
• Potential Theory
• Stochastic Analysis
• +4 more

Zoran Vondraček is a Professor of Mathematics at the Department of Mathematics, Faculty of Science, University of Zagreb. His research focuses on stochastic processes, potential theory, and applications in financial and actuarial mathematics. He holds a Ph.D. from the University of Florida (1990) and has held visiting positions at institutions including the University of Illinois at Urbana-Champaign and Seoul National University. Education: Ph.D. (Mathematics, University of Florida, 1990), M.Sc. (University of Zagreb, 1986), B.Sc. (University of Zagreb, 1982). Affiliations: Full Professor at University of Zagreb since 2002; previously Associate and Assistant Professor (1997–2002, 1992–1997). Grants: Recipient of multiple grants including those from the Croatian Science Foundation (2014–2022) and international collaborations with the U.S., Germany, and South Korea. Research Interests: Zoran's work centers on potential theory of jump processes, subordinate Brownian motions, Lévy processes, and their applications in risk theory and financial modeling. He has extensively studied Green functions, Harnack inequalities, and boundary behavior of stochastic processes. Publications: Over 100 peer-reviewed articles in top journals such as Journal of Functional Analysis , Probability Theory and Related Fields , and Stochastic Processes and their Applications . Recent work includes advancements in non-local operators and Dirichlet forms. Awards: Croatian Academy of Sciences and Arts award (2006), National Science Award (2010), Andrija Mohorovičić Award (2020). Teaching: Teaches advanced courses on probability theory, stochastic processes, and financial mathematics at the University of Zagreb. Supervises graduate students in related fields. Labs/Teams: Collaborates with international research groups on stochastic analysis and potential theory, with active participation in workshops and conferences globally.

Dominik KleinView profile

Assistant Professor

• Agent-Based Social Simulation
• Logic
• Epistemology
• +6 more

Dr. Dominik Klein is an Assistant Professor in Theoretical Philosophy at the Department of Philosophy and Religious Studies, part of the Faculty of Humanities at Utrecht University. His research focuses on social interaction and its implications, particularly in the domains of collective patterns, epistemic rationality, and decision-making quality. He works at the intersection of logic, social epistemology, and political theory, with additional contributions to philosophy of science and action. Key research themes include agent-based social simulation, human-centered artificial intelligence, and institutions for open societies. He is affiliated with projects such as 'Simulations of Scientific Inquiry' (Bochum) and 'Coalition and Epistemic Logic' (Bayreuth/Prague). Klein teaches courses like 'Mathematics for AI' and actively contributes to interdisciplinary collaborations in social science modeling and formal epistemology. His work spans theoretical explorations of trust dynamics, information cascades, and epistemic decision-making, leveraging formal methods to analyze societal challenges. Klein's publications reflect a blend of philosophical inquiry and computational modeling, addressing topics like non-classical probabilities, dynamic epistemic logic, and the epistemic quality of democratic processes.

Christopher MeachamView profile

Professor

• Formal Epistemology
• Decision Theory
• Philosophy of Physics
• +3 more

Christopher Meacham is a Professor of Philosophy at the University of Massachusetts, Amherst. He specializes in Formal Epistemology, Decision Theory, and the Philosophy of Physics. His work addresses foundational issues in Bayesianism, decision-theoretic paradoxes, and the relationship between rational belief and chance. Meacham holds a BA from Reed College (Physics and Philosophy) and a PhD from Rutgers University (Philosophy). His research explores topics such as statistical mechanical probabilities, self-locating beliefs, and ethical theories resolving population paradoxes. He has published widely on topics including laws of nature, chance-credence principles, and the dynamics of rational belief. His contributions span theoretical frameworks for non-Humean laws, critiques of evidential uniqueness, and solutions to decision-theoretic dilemmas like the Sleeping Beauty problem. Research interests include extending Bayesianism to self-locating scenarios, formalizing constraints in decision theory for infinite utility cases, and reconciling ethical theories with population ethics challenges. His work bridges metaphysics, epistemology, and ethics, often engaging with cutting-edge debates in philosophy of science and formal methods. Publications highlight analyses of statistical mechanical probabilities, critiques of traditional decision-theoretic frameworks, and explorations of person-affecting views in ethics. His current projects include developing a Nomic Likelihood Account of laws and addressing arbitrariness concerns in epistemic rationality debates.

Lampros GavalakisView profile

Researcher

• Information Theory
• Probability
• Mathematical Statistics
• +7 more

Lampros Gavalakis is a Postdoctoral Research Associate at the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, affiliated with the INFORMED AI Hub. Previously, he was a postdoctoral fellow of the MathInGreaterParis programme (2022–2024), funded by the Marie Sklodowska-Curie Actions at Gustave Eiffel University. He holds a Ph.D. from the University of Cambridge’s Engineering Department, where he worked in the Signal Processing and Communications Laboratory, and a MEng in Computer Science from the Athens University of Economics and Business. His research focuses on information theory and probability, with emphasis on entropy power inequalities, entropic limit theorems, and their applications to data compression, communications, and convex analysis. Key interests include discrete/continuous entropy monotonicity, finite blocklength analysis, and non-asymptotic bounds. He has contributed to foundational work on de Finetti theorems, Gaussian mixture analysis, and entropy-convexity relationships. Lampros has published extensively in top venues like IEEE Transactions on Information Theory and the International Symposium on Information Theory (ISIT), earning the 2023 IEEE Jack Keil Wolf Student Paper Award. His academic collaborations span institutions including the Signal Processing and Communications Lab (Cambridge) and the INFORMED AI Hub. Education : PhD in Engineering (University of Cambridge, 2022) MEng in Computer Science (Athens University of Economics and Business, 2017) Awards : IEEE Jack Keil Wolf ISIT Student Paper Award (2023) Teaching : Lecturer for Concentration Inequalities (Part III Mathematics, Cambridge, 2025) His research bridges probability theory and information science, with applications to coding, stochastic processes, and high-dimensional analysis.

Ben HamblyView profile

Professor

• Probability Theory
• Stochastic Processes
• Financial Mathematics
• +5 more

Ben Hambly is a Professor of Mathematics and Tutorial Fellow in Applied Mathematics at St Anne's College, University of Oxford. He is affiliated with the Mathematical Institute and holds editorial roles at Probability Surveys and Annals of Applied Probability . His research focuses on probability, stochastic processes, financial mathematics, and fractals, with specific interests in financial derivatives modeling, fractal geometry, rough paths, and particle systems. He teaches on both the full-time and part-time Mathematical and Computational Finance MSc programs. His research spans financial mathematics (e.g., credit risk, electricity pricing), fractal analysis (diffusion on fractals, spectral problems), and stochastic modeling (branching processes, SPDEs). Recent work includes studies on reinforcement learning in finance and stochastic models for systemic risk. He collaborates with the Stochastic Analysis Group and Mathematical and Computational Finance Group at Oxford. Despite not listing specific awards here, his extensive publication record and editorial roles reflect his scholarly impact. His PhD supervision includes students working on topics like SPDEs and financial modeling, though specific advisee names are not provided. His work often bridges theoretical probability with applications in finance and complex systems.

• 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 .

Pierre-Antoine ADRAGNAView profile

Associate Professor

• Additive Manufacturing
• 3D Printing Optimization
• Reverse Engineering
• +4 more

Pierre-Antoine ADRAGNA serves as a Research Teacher at the University of Technology of Troyes (UTT), affiliated with the Laboratory of Mechanical & Material Engineering (LASMIS). His academic work bridges theoretical research and industrial applications in advanced manufacturing technologies. His research interests focus on Additive Manufacturing optimization , particularly FDM 3D printing processes, where he develops techniques for infill reinforcement and continuous extrusion. Additional expertise includes Reverse Engineering for medical prosthetics and building reconstruction, Mechanical Tolerancing methodologies, and Finite Element Analysis for predicting manufacturing outcomes. His work demonstrates strong industry collaboration, notably with Levels3D in the Automodel3D project for 3D building reconstruction. Dr. ADRAGNA's publication record reveals consistent contributions to mechanical engineering literature, with recent work emphasizing practical solutions for 3D printing challenges and manufacturing process optimization. His research integrates computational methods with experimental validation to address real-world manufacturing constraints. Automodel3D (2016-2019): Automated 3D building reconstruction from point clouds, co-financed by EU and Champagne-Ardenne region OptiFabAdd (2018-2021): Digital tool development for FDM additive manufacturing optimization, supported by EU and CD10 His laboratory work at LASMIS focuses on advancing mechanical engineering methodologies through computational approaches and experimental validation, contributing to UTT's research profile in advanced manufacturing systems.

• Combinatorics
• Probabilistic Methods
• Random Graphs
• +4 more

Rajko Nenadov is a Lecturer in Theoretical Computer Science at the University of Auckland, New Zealand. Previously, he earned his PhD from ETH Zurich in 2016 under Angelika Steger. He held postdoctoral positions at Monash University (Australia) and ETH Zurich, focusing on combinatorics and graph theory. Between 2018–2022, he worked as a software engineer at Google Zurich, contributing to search ranking algorithms. Returning to academia in 2023, his research emphasizes probabilistic methods, random structures, and applications in theoretical computer science. His education includes a PhD in Computer Science from ETH Zurich (2016). Notable career milestones include postdoctoral research on expanders, pseudorandomness, and Ramsey theory, followed by industry experience in algorithmic development. Rajko’s research interests revolve around combinatorics, with a focus on probabilistic methods, random graphs, extremal graph theory, and Ramsey theory. His work bridges foundational combinatorics with practical applications in computer science, particularly in algorithm design and computational complexity. His recent publications explore topics like hypergraph universality, container theorems, and extremal subgraph counting. These contributions highlight advancements in probabilistic combinatorics and structural graph theory. No scientific awards or grants are explicitly listed in the provided information. His career transition from academia to industry and back underscores his interdisciplinary expertise in theory and applied computing.

Adam ShefferView profile

Associate Professor

• Computational Geometry
• Discrete Mathematics
• Incidence Geometry
• +3 more

Adam Sheffer is an Associate Professor of Mathematics at Baruch College and the Graduate Center of the City University of New York (CUNY). He is a member of Baruch's Discrete Math Group and serves as the Director of the NYC Discrete Math REU and the Baruch College Computer Science Program. His research focuses on computational geometry, discrete mathematics, and additive combinatorics, with notable contributions to incidence geometry and polynomial methods. He is the author of Polynomial Methods and Incidence Theory (Cambridge University Press) and an editor of the Journal of Computational Geometry . Sheffer's academic activities include organizing the Polymath Jr Program and the NYC Geometry Seminar. He has mentored numerous student research projects, several of which have led to published papers. Key research interests include geometric incidences, distinct distances problems, and combinatorial configurations. His work frequently bridges algebraic and geometric techniques to solve problems in discrete mathematics.

• Calculus of Variations
• Materials Science
• Applied Mathematics
• +3 more

Marco Morandotti is an Associate Professor in the Department of Mathematical Sciences at Politecnico di Torino. His research focuses on calculus of variations, materials science, and applied mathematics, with applications to dislocation dynamics, structured deformations, and microscale systems. He has contributed to understanding fluid dynamics of microswimmers, energetic relaxation in materials, and stochastic particle systems. Education: PhD in Mathematical Analysis (2011), University of Rome Tor Vergata (PhD thesis: An existence and uniqueness result for the motion of self-propelled micro-swimmers ) Research Interests: His work spans structured deformations, nonlinear elasticity, dislocation mechanics, and active matter. Recent efforts include modeling hydrodynamic interactions of microparticles, controllability of microswimmers, and variational approaches to hierarchical material systems. He has co-authored influential books like Energetic Relaxation to Structured Deformations (2023) and edited lab project collections like Exploring Math (2025). Grants & Collaborations: Involved in EU-funded projects on multiscale materials modeling and fluid-structure interactions. Organized conferences such as the 2017 Miniworkshop on Dislocations and the 2025 Three Days in Calculus of Variations. Labs/Teams: Leads research groups focused on mathematical modeling of materials and biological systems through collaborative projects with institutions worldwide.

• Probability Theory
• Statistical Mechanics
• Stochastic Processes
• +4 more

Gioia Carinci is an Associate Professor at the University of Modena and Reggio Emilia, affiliated with the Department of Physical, Computer and Mathematical Sciences. She teaches courses such as Probability and Statistics for Computer Science, Mathematics for Electronic Engineering, and Markov Chains for Mathematics students. Her research focuses on stochastic processes, interacting particle systems, and duality principles in non-equilibrium statistical physics. Teaching: Probability and Statistics (Computer Science) Mathematics for Electronic Engineering (Engineering) Markov Chains (Mathematics) Research Interests: Analysis of Markov chains and their convergence properties Boundary-driven non-equilibrium systems Large deviations and additivity principles Duality in stochastic processes Key Contributions: Derivation of macroscopic properties from microscopic models Study of condensation phenomena in particle systems Development of exact solutions for stationary non-equilibrium states Her work bridges probability theory, statistical mechanics, and applications in mathematical physics. She actively contributes to both theoretical advancements and interdisciplinary collaborations.

• Computer Graphics
• Geometry Processing
• 3D Reconstruction
• +3 more

Dr. Jing Ren is affiliated with the Department of Computer Science at ETH Zürich, holding a role within the Professorship for Computer Science. Their research focuses on computational geometry, 3D reconstruction, and computer graphics, with notable contributions to shape analysis, non-rigid matching, and fabric modeling. Dr. Ren’s work bridges theoretical advancements with practical applications in textile design, architectural modeling, and medical imaging. They collaborate extensively on projects involving functional maps, optimization algorithms, and geometric morphometrics. Key research interests include: Non-rigid shape correspondence and matching Computational modeling of woven fabrics and textiles 3D face and building reconstruction techniques Efficient spectral and discrete optimization methods Recent publications (2022–2024) emphasize innovations in fabric parameterization, Gaussian noise distribution, and rethinking 3D face reconstruction benchmarks. Their work often employs machine learning and functional map frameworks to solve geometric problems across disciplines. Laboratory and team affiliations are not explicitly detailed in the provided materials, but their research aligns with ETH Zürich’s broader initiatives in computer science and engineering. No grants or advising activities are specified in the current data.

Yifan JingView profile

Assistant Professor

• Additive and Combinatorial Number Theory
• Combinatorics and Graph Theory
• Harmonic Analysis
• +8 more

Yifan Jing is an Assistant Professor in the Department of Mathematics at The Ohio State University (OSU). He previously held postdoctoral positions at the University of Oxford’s Mathematical Institute and Wolfson College, mentored by Ben Green. His academic journey includes a PhD from the University of Illinois Urbana-Champaign (2021), supervised by József Balogh and Xiaochun Li, an M.Sc. from Simon Fraser University (2018) under Bojan Mohar, and a B.Sc. from the University of Science and Technology of China (2016), advised by Jack Koolen. His research focuses on Arithmetic Combinatorics, Analytic and Combinatorial Group Theory, Lie groups, Abstract Harmonic Analysis, Representation Theory, Additive Number Theory, Model Theory applications, Discrete Probability, and Theoretical Computer Science. Notable contributions include work on measure growth in Lie groups, inverse theorems for geometric inequalities, and structural graph theory. He received the 2023 Kirkman Medal for his research contributions. He currently organizes OSU’s Combinatorics Seminar and teaches Math 4507: Geometry. His advising includes PhD students Yewen Sun, Chavdar Lalov, and Yuchen Meng, alongside mentoring multiple undergraduate researchers at Oxford. Collaborators include leading mathematicians such as Chieu-Minh Tran, Ruixiang Zhang, and Bojan Mohar. His work bridges analysis, algebra, and logic to address problems across combinatorics, number theory, and geometry.