Jian-Guo Liu is a Professor of Mathematics and Physics at Duke University, with primary affiliations in the Departments of Mathematics and Physics. His research encompasses applied mathematics, partial differential equations, kinetic theory, computational fluid dynamics, and stochastic algorithms. Professor Liu's work bridges theoretical modeling and numerical methods, particularly in complex systems involving nonlinear dynamics, fluid behavior, and emergent phenomena. Research interests focus on multiscale modeling of physical systems, including stochastic processes in chemical reactions, fluid-structure interactions, and materials science. Recent publications demonstrate strong emphasis on mathematical foundations of biological and physical systems, with recurring themes in Fokker-Planck dynamics, mean-field games, tumor growth modeling, and computational methods for interfacial phenomena. Publications showcase consistent focus on analytical and numerical solutions to high-dimensional problems, with applications ranging from medical imaging to electrochemistry. The work exhibits advanced techniques in asymptotic analysis, stochastic approximations, and geometric evolution equations.
Martin Trapp is an Academy Postdoctoral Researcher in the Department of Computer Science at Aalto University, specializing in probabilistic machine learning. He is affiliated with Professor Arno Solin's research group, focusing on advancing tractable probabilistic models for real-world applications. His research centers on Probabilistic Circuits , Probabilistic Programming , and Bayesian Nonparametrics , with emphasis on hardware-efficient implementations for edge devices and multimodal systems. Key interests include uncertainty quantification in deep learning, neurosymbolic AI integration, and medical imaging applications. His work bridges theoretical foundations with practical deployment constraints, particularly in resource-limited environments. Analysis of his 15 most recent publications (2022-2025) reveals three dominant trends: (1) hardware-aware probabilistic inference for TinyML applications, (2) scalable Bayesian methods using bitstring representations and probabilistic programming, and (3) multimodal robustness in vision-language systems and medical imaging. His contributions span from theoretical circuit representations to real-world implementations in mammography analysis and vision-language models. Trapp secured a HIIT short-term project grant (November 2022) for "Positive Semi-Definite Circuits" under the Department of Computer Science. No formal advising relationships are documented in available sources. He actively collaborates with researchers including Arno Solin, Rui Li, and Marcus Klasson across institutions like Aalto University and the Helsinki Institute for Information Technology. As a core member of Aalto's Probabilistic Machine Learning group, he contributes to advancing probabilistic AI methodologies with applications in healthcare, edge computing, and multimodal reasoning. His current work emphasizes deployable probabilistic systems that maintain rigorous uncertainty quantification while meeting hardware constraints.
Eric Cancès is a Professor at Ecole des Ponts ParisTech and affiliated with INRIA Paris as part of the Molecular and Multiscale Modeling team. His research focuses on mathematical analysis of electronic structure models for quantum chemistry and materials science, algorithms for electronic structure calculations, numerical analysis of eigenvalue problems, implicit solvent models, and molecular dynamics. He has made significant contributions to the development of continuum solvation models, multiscale methods, and computational frameworks for quantum chemistry. Research Interests Mathematical foundations of quantum chemistry Algorithmic development for electronic structure Implicit solvent models and continuum electrostatics Multiscale and domain decomposition techniques Greedy algorithms for high-dimensional problems Scientific Awards Le Rivot prize (French Academy of Sciences, 1992) Best PhD award (Ecole des Ponts, 1998) Blaise Pascal prize (SMAI and French Academy of Sciences, 2009) Ordway visiting professor (University of Minnesota, 2013-2014) Invited lecturer at ICM (2014) Publications span over two decades, addressing Hartree-Fock and Kohn-Sham models, quantum Monte Carlo methods, domain decomposition for solvation models, and multiscale approaches. His work emphasizes mathematical rigor combined with computational efficiency, with recent studies on perturbation methods, polarization energy calculations, and embedded corrector problems for homogenization.
Zifan Wang is a doctoral student and researcher at the Division of Decision and Control Systems (DCS), School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology. He is jointly advised by Prof. Karl H. Johansson and Prof. Michael M. Zavlanos at Duke University. He holds a Master's and Bachelor's degree from the Honors School of Harbin Institute of Technology. His research focuses on decision-making under uncertainty, leveraging tools from Machine Learning , Optimal Transport , Game Theory , and Control Theory , with a special interest in generative models and risk-averse optimization . Recent publications highlight his work on risk-averse learning in online convex games, constrained optimization with decision-dependent distributions, and distributional reinforcement learning for LQR systems. His methodological contributions include zeroth-order gradient estimation, one-point sampling strategies, and residual feedback for variance reduction. Honors include a 2024 Travel scholarship from Björns Foundation and a 2023 Travel grant from Karl Engvers Foundation . He actively participates in peer review for top conferences (NeurIPS, ICLR, L4DC, CDC, ACC) and journals (IEEE Transactions on Automatic Control, Automatica).
Nikolay Malkin is a researcher at the University of Edinburgh specializing in machine learning, generative modeling, and computational biology. His work focuses on simulation-free methods for stochastic dynamics and optimal transport, particularly in applications like single-cell dynamics and protein structure prediction. Research Interests: Flow-based generative models, Schrödinger bridges, diffusion models, and their applications in high-dimensional biological data analysis. Key Contributions: Development of [SF] 2 M for cell dynamics modeling, generalized conditional flow matching (CFM) for improved generative tasks, and subtrajectory balance (SubTB) for GFlowNet stability. Collaborations: Active collaboration with Yoshua Bengio, Alexander Tong, and other researchers in advancing flow matching and generative modeling techniques. Publishing Trends: Recent papers emphasize protein backbone generation, tree-structured Schrödinger bridges, and velocity-growth dynamics in single-cell populations.
Julien Fageot is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in the AudioVisual Communications Laboratory within the School of Computer and Communication Sciences. He previously held postdoctoral positions at Harvard University, McGill University, and EPFL. His educational background includes: Ph.D. in Electrical Engineering at EPFL (2012-2017) M.Sc. Mathematics, Vision, and Learning in ENS Paris-Saclay, France (2011) M.Sc. in Probability and Statistics at Université Paris Orsay, France (2009) École Normale Supérieure, Section Mathématiques, Paris, France (2007-2012) Dr. Fageot's research lies at the intersection of high-level mathematics and data sciences, focusing on mathematical properties of advanced processing tools for sparse signal reconstruction and synthesis. His expertise spans sparsity, random processes, approximation theory, splines, convex optimization, functional analysis, and signal/image processing. He explores probability theory (sparse stochastic processes), optimization theory (sparsity-promoting spline reconstruction), and applications in signal processing (inverse problems, segmentation, detection, CNNs). His publication record demonstrates a clear progression from theoretical foundations of stochastic processes to practical applications in biomedical imaging. Recent work shows increasing focus on machine learning applications while maintaining strong mathematical rigor, particularly in developing sparse representations for medical image analysis. His scientific achievements have been recognized with: Best Paper Award at the MIDL Conference (2019) EPFL Best Doctorate Award (2018) Outstanding PhD Thesis Distinction in Electrical Engineering, EPFL (2017) Education Award from the Life Science Department, EPFL (2013) Dr. Fageot actively mentors students, currently supervising PhD candidate Adrian Jarret and having previously guided Thomas Debarre and Shayan Aziznejad to completion. He has supervised numerous master's theses on spline-based reconstruction and biomedical image analysis. His research is supported by Swiss National Science Foundation grants including the Postdoc.Mobility fellowship for 'Mathematical Models for Analog Data Sciences: the Continuous Way' (2020) and the Early Postdoc.Mobility fellowship for 'Probabilistic and Variational Methods for Sparse Signals' (2018). As a key member of EPFL's AudioVisual Communications Laboratory, he collaborates with Prof. Martin Vetterli, Prof. Michael Unser, and Prof. Christian Genest, bridging theoretical mathematics with practical applications in signal processing and data science.
Konstantin Pavlikov is an Associate Professor at the Department of Business & Management (DBM) under Strategic Organization Design (SOD) at the University of Southern Denmark. His research focuses on Operations Research, Integer Programming, and Stochastic Programming , with particular emphasis on vehicle routing optimization and network flow modeling. Education : PhD in Operations Research (University of Florida, 2014), MSc in Applied Mathematics (Moscow State University, 2007) His work spans combinatorial optimization and network interdiction problems , developing exact and approximate solution algorithms for complex logistics challenges. Recent publications analyze heterogeneous vehicle routing capacity inequalities and two-commodity flow formulations for routing problems. Scientific contributions have been recognized with the Best Reviewer Award (2019). He actively reviews for journals like Computational Management Science and supervises academic works through examination roles.
Monika Henzinger is a Professor at the Institute of Science and Technology Austria (ISTA), where she has been serving since 2023, and additionally holds the position of Vice President for Technology Transfer since 2024. Her academic journey includes professorships at the University of Vienna (2009-2023) and EPFL in Switzerland (2005-2009), as well as industry experience at Google (1999-2005) and Digital Equipment Corporation (1996-1999). She earned her PhD from Princeton University in 1993 and served as an Assistant Professor at Cornell University from 1993-1996. Dr. Henzinger's research focuses on the design and analysis of efficient algorithms and data structures, with particular emphasis on dynamic settings where inputs change repeatedly, privacy-preserving algorithms, and translating theoretical algorithms into practical implementations. Her work bridges theoretical computer science with practical applications, addressing fundamental questions about computational efficiency in evolving data environments. Her recent publications (2024-2025) demonstrate a consistent focus on dynamic graph algorithms, differential privacy, and optimization problems. These works explore cutting-edge approaches to maintaining graph structures under continuous updates, developing privacy-preserving mechanisms for streaming data, and creating efficient approximation algorithms for fundamental graph problems. The research shows strong connections between theoretical guarantees and practical implementations, with many papers addressing both theoretical bounds and experimental validation. Dr. Henzinger has received numerous prestigious awards throughout her career, including: The 2024 Best Paper Award at the Symposium on Discrete Algorithms The 2021 Wittgenstein Award Two ERC Advanced Grants (2014, 2021) Fellow of the Association of Computing Machinery (2016) Member of the Austrian Academy of Sciences (2017) The Carus medal of the German Academy of Sciences Leopoldina (2019) She currently leads an active research group comprising PhD students and postdoctoral researchers, and her team is supported by multiple significant grants including an ERC Advanced Grant for 'The design and evaluation of modern fully dynamic data structures,' a Wittgenstein Award from the Austrian Science Fund, and several other projects focused on dynamic graph algorithms and data structures. Her collaborative work spans theoretical computer science, algorithm design, and practical implementations. Dr. Henzinger's research group operates within a vibrant ecosystem of algorithmic research at ISTA, focusing on transforming theoretical insights about computational efficiency into practical tools for handling dynamic data. The group maintains strong connections with both theoretical and applied research communities, bridging the gap between abstract algorithm design and real-world implementation challenges.
Heikki Haario is a Professor in Computational Engineering at the LUT School of Engineering Sciences, LUT University, Lappeenranta. His research focuses on robust Bayesian inference, parameter estimation, and uncertainty quantification in chaotic and stochastic systems. Broad research areas: Bayesian Statistics, Chaotic Dynamical Systems, Gaussian Processes, Machine Learning The 15 most recent publications (2025-2023) demonstrate expertise in computational modeling, data-driven methods, and interdisciplinary applications spanning finance, biology, and engineering. Articles emphasize Bayesian techniques, kernel flows, and neural network integration for solving inverse problems and optimizing predictions in uncertain environments.
Martin Eigel is a Researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) , specializing in numerical methods for stochastic partial differential equations, uncertainty quantification, and machine learning applications in computational mathematics. His work bridges tensor networks, Bayesian inversion, and quantum simulations. Research Interests : Adaptive stochastic Galerkin finite element methods Low-rank tensor approximations for high-dimensional problems Machine learning integration with PDE solvers Quantum circuit simulation techniques Bayesian inverse problems and error control Key Article Trends : His recent publications focus on merging deep learning architectures (e.g., ResNet, CNNs) with stochastic and tensor-based numerical methods for solving parametric PDEs, Bayesian inversion, and quantum systems. Topics include Hamilton-Jacobi-Bellman equations, Langevin dynamics, and risk-averse optimization under uncertainty.
Roberto Zunino is an Associate Professor in the Department of Mathematics at the University of Trento, specializing in blockchain technologies, formal methods, and distributed systems. His research bridges theoretical computer science with practical cryptographic applications, particularly in Bitcoin and smart contract ecosystems. His research interests focus on blockchain security , smart contract formalization , and probabilistic verification . Key areas include MEV (Maximal Extractable Value) theory, UTXO-based smart contracts, and computationally sound tokenization. His work combines rigorous mathematical modeling with real-world protocol analysis, emphasizing security guarantees through formal methods. Recent publications demonstrate a strong trend toward theoretical foundations of blockchain economics and security. His 15 most recent papers (2020-2025) analyze MEV formalization, Bitcoin contract liquidity, UTXO scalability, and smart contract language design, revealing deep integration of type theory, game theory, and cryptographic primitives. Zunino actively teaches courses including Informatics , Interactive Theorem Proving (using Lean 4), and Computer Tools for Mathematics . His educational focus emphasizes formal verification, imperative programming foundations, and mathematical logic applications in computer science.
Ronald Cools is a Professor in the Department of Computer Science within the Science & Technology Group at KU Leuven (Katholieke Universiteit Leuven) in Belgium. His research spans numerical analysis, approximation theory, and computational mathematics, with a particular focus on lattice rules and quasi-Monte Carlo methods for high-dimensional problems. His work has significant applications in scientific computing, financial mathematics, and solving partial differential equations. Professor Cools' research interests center on developing efficient algorithms for high-dimensional integration and approximation. His work on lattice rules, component-by-component construction methods, and tent-transformed lattices has advanced the field of numerical analysis. He has made significant contributions to understanding the trigonometric degree of exactness, worst-case error analysis in various function spaces, and the development of practical algorithms for multivariate problems. His research bridges theoretical mathematical analysis with practical computational methods that address the curse of dimensionality in scientific computing. The analysis of his recent publications reveals a consistent focus on lattice-based algorithms for approximation and integration in high dimensions. His work demonstrates increasing sophistication in handling general weight parameters, extending methods to non-periodic settings, and developing faster construction algorithms. The research trajectory shows a progression from theoretical foundations to practical implementations with applications in PDEs, financial mathematics, and scientific computing. The publications exhibit strong international collaboration, particularly with researchers like Frances Kuo, Dirk Nuyens, and Ian Sloan. Professor Cools has supervised numerous PhD students, including Weiwen Mo, Laurence Wilkes, Yuya Suzuki, T. Nguyen, and Gowri Suryanarayana. His mentorship has produced significant contributions to the field of numerical analysis. While specific grant information isn't detailed in the provided text, his extensive publication record spanning multiple decades suggests sustained research funding supporting his work in computational mathematics. His research group at KU Leuven appears to be a hub for advanced computational mathematics, focusing on quasi-Monte Carlo methods, lattice rules, and high-dimensional approximation techniques. The collaborative nature of his publications indicates an active research team working on both theoretical aspects of numerical methods and their practical implementations.
Quentin Mérigot is a Professor in Applied Mathematics at the University of Paris-Saclay, affiliated with the Faculty of Science at Orsay and the Institut de Mathématique d'Orsay. He serves as the Director of the Master in Optimization program, a joint initiative between University of Paris-Saclay and Institut Polytechnique de Paris. Dr. Mérigot completed his Doctoral Thesis at the University of Nice Sophia-Antipolis in 2009, followed by a Habilitation à diriger les recherches at the University of Grenoble in 2014. His research focuses on the intersection of optimal transport theory, numerical analysis, and geometric methods for data analysis. Mérigot has made significant contributions to the development of numerical methods that leverage optimization and computational geometry techniques. His work spans theoretical foundations and practical applications, including seismic tomography, reflector design, and crowd motion modeling. He is particularly known for his work on stability properties of optimal transport maps and the development of efficient algorithms for solving optimal transport problems. Analysis of Mérigot's recent publications reveals a strong focus on quantitative stability properties of optimal transport maps, with applications spanning from machine learning to optics and fluid dynamics. His work demonstrates a consistent theme of bridging theoretical mathematical analysis with computational methods, particularly through the development of Lagrangian discretization techniques and Newton-type algorithms for solving complex geometric problems. Junior member of the Institut universitaire de France (IUF) Professor Mérigot has supervised numerous PhD students including Alex Delalande, Anatole Gallouët, Clément Sarrazin, Jocelyn Meyron, and Julien André. He has also hosted several postdoctoral researchers such as Jean-Baptiste Keck, Andrea Natale, Federico Stra, Thomas Gallouët, and Hiba Abdallah. His current research projects include PEPR PDE-AI and OT @ Lagrange, which likely represent significant collaborative efforts with funding support. Mérigot is an active member of the mathematical community, with his research bridging pure mathematical analysis, computational methods, and practical applications across various scientific domains. His work demonstrates the power of optimal transport theory as a unifying framework for diverse problems in mathematics and its applications.
Carolyn L Beck is a Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign . She holds affiliations with multiple departments, including Electrical and Computer Engineering (since 2014) and Mechanical Science and Engineering (since 1999). Beck is also a Research Professor at the Coordinated Science Laboratory (since 2019) and has served as Associate Head for Undergraduate Programs since 2020. Education : Ph.D. in Electrical Engineering from California Institute of Technology M.S. in Electrical & Computer Engineering from Carnegie Mellon University B.S. in Electrical & Computer Engineering with a Physics minor from California State Polytechnic University Research Interests focus on control and optimization , epidemic processes over networks , network inference , and dynamic network data clustering . Her work spans mathematical systems theory to real-world applications in bioengineering and smart grid optimization . Article Trends show a progression from system identification and network inference to complex epidemic modeling over time-varying networks and grid optimization. Recent works emphasize finite-sample analysis , distributed subgradient methods , and multi-layer contagion dynamics , reflecting her expertise in control theory and network science . Scientific Awards : IEEE Fellow (2023) Arthur Davis Faculty Scholar Award (2016) ONR Young Investigator Award (2001-2004) NSF CAREER Award (1998-2003) ORAU Junior Faculty Award (1997) Alcoa Foundation Award (1997) Advising and Grants include mentoring former PhD students like Puneet Sharma (AIMBE Fellow) and Philip Pare' (Purdue ECE) . Her research has been funded by the NSF , ONR , and IEEE , with a $500,000 grant for wind turbine efficiency. Labs and Teams : Beck is affiliated with the Coordinated Science Laboratory and collaborates with interdisciplinary teams in bioengineering , network control , and grid optimization . She also contributes to IEEE as a Guest Editor and Associate Editor .
James R. Lee is a Professor in the Department of Computer Science at the University of Washington, with a focus on algorithms, complexity, and the theory of computation. He is affiliated with the UW Theory Group and currently on leave at Microsoft Research, which may delay responses to UW emails. Research Interests: Algorithms, complexity, geometry/discrete-continuous interfaces, probability, stochastic processes, metric embeddings, spectral graph theory, convex optimization. Recent Work Trends: Sparsification of generalized linear models and norms, spectral hypergraph methods, entropic regularization for metrical task systems, and analysis of scaling exponents in random graphs. His papers address sparsifier existence, lower bounds for SDP/LP relaxations, and geometric random walk properties. Scientific Awards: Best Paper Award at STOC 2015. Students: Farzam Ebrahimnejad, Ewin Tang, Yichuan Deng (co-advised with Shayan Oveis-Gharan, Shirshendu Ganguly, and others). Email: jrl@cs.washington.edu
