Prof. Dr. Tobias Glasmachers is a Full Professor at the Institut für Neuroinformatik , Ruhr-Universität Bochum, Germany, specializing in the Theory of Machine Learning . He leads the Optimization of Adaptive Systems group and holds appointments in both Computer Science and Interdisciplinary AI research. Key Research Areas : Optimization algorithms, evolutionary computation, reinforcement learning, supervised learning, and neural networks Technical Focus : Gradient-based methods, support vector machines, and adaptive coordinate descent Applications : Robotics, waste sorting facilities, 3D game environments (e.g., Doom/Minecraft), and human-centered AI design Notable Contributions : Development of LM-MA-ES evolution strategy, Hessian Estimation Evolution Strategy, and tachAId tool for ethical AI design. His work bridges theoretical analysis with practical implementations across diverse domains. Teaching : Offers courses in Informatik 1 - Programmieren, Machine Learning: Supervised Methods, and Evolutionary Algorithms. Supervises numerous Bachelor's and Master's theses on AI/ML applications.
Dr. Christopher Lustri is a Senior Lecturer in Mathematics at the University of Sydney, part of the Faculty of Science. He holds a DPhil from the University of Oxford (2013) and a BEng/BAppSci from Queensland University of Technology (QUT). His research focuses on asymptotic methods, fluid dynamics, and discrete systems, with particular emphasis on exponential asymptotics to study phenomena like the Stokes Phenomenon. He has held roles at Macquarie University (2016–2023) and was a postdoc at the University of Sydney under Prof. Nalini Joshi. His work bridges theoretical mathematics with applications in fluid dynamics, nonlinear waves, and collective behavior. Notable contributions include studies on viscous fingering, granular chains, and self-assembly in biological systems. He was awarded the J. H. Michell Medal (2023) for outstanding research in applied mathematics. His work appears in journals like Nature Communications , Proceedings of the Royal Society A , and Journal of Fluid Mechanics . Research Themes: Exponential asymptotics in differential/difference equations Free-surface flows and bubble dynamics Discrete systems (e.g., particle chains, nonlinear waves)
Prof. Jens Vygen is a full Professor of Discrete Mathematics at the University of Bonn's Research Institute for Discrete Mathematics. He specializes in combinatorial optimization, approximation algorithms, and their applications to chip design and vehicle routing. He has directed large-scale industrial collaborations and supervised over 20 PhD students, including Vera Traub. His work includes co-authoring influential textbooks like Combinatorial Optimization: Theory and Algorithms and Approximation Algorithms for Traveling Salesman Problems . Research highlights include advancements in TSP approximation algorithms, Steiner tree optimization, and network flow algorithms. He has organized major events like the IPCO conference and served on editorial boards of leading journals. Awards include teaching excellence and best paper recognitions at SODA and IPCO. Teaching focuses on advanced topics in discrete mathematics, approximation algorithms, and combinatorial optimization. Courses span undergraduate to graduate levels, emphasizing algorithm design, graph theory, and practical implementations. Labs/teams: Core contributor to Bonn's Hausdorff Center for Mathematics and Bonn's discrete optimization research group. Active in developing algorithms for VLSI routing (BonnRoute) and chip design tools (BonnPlace).
Richard J. Cole is a Silver Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences, part of the Faculty of Arts and Science. His research focuses on algorithm design, algorithmic economics, game theory, and parallel computing. He holds a Ph.D. in Computer Science from Cornell University (1982) and a B.A. in Mathematics from Oxford University (1978). Education: Ph.D., Computer Science, Cornell University, USA, 1982 B.A., Mathematics, Oxford University (University College), United Kingdom, 1978 His research spans algorithmic economics (market theory, mechanism design), parallel algorithms, and foundational areas like string matching and graph algorithms. Recent work emphasizes stable matching, non-quasi-linear agent mechanisms, and fair resource allocation. He has advised notable Ph.D. students including Yun Kuen Cheung and Vasilis Gkatzelis. Teaching includes undergraduate and graduate courses on algorithms, computational theory, and algorithmic aspects of the internet. His 15+ years of publications reflect contributions to theoretical computer science and interdisciplinary applications in economics. His work on parallel computing and resource-oblivious algorithms addresses multicore efficiency, while contributions to market equilibrium analysis and mechanism design highlight algorithmic solutions to economic challenges.
Pierre Nyquist is an Associate Professor in the Department of Mathematical Sciences at Chalmers University of Technology and Gothenburg University. Previously, he held a position at KTH Royal Institute of Technology. His research focuses on probability theory, mathematical statistics, and applied mathematics, with an emphasis on large deviations, stochastic numerical methods, and statistical learning theory. He is supported by grants from the Swedish Research Council, Wallenberg AI, Autonomous Systems and Software Program (WASP), and the Swedish e-Science Research Center (SeRC). Education: Ph.D. in Applied and Computational Mathematics (2014, advisor Henrik Hult), postdoc at Brown University (2014–2016), and assistant professor at TU Eindhoven (2016–2020). Research interests include large deviations theory, stochastic processes, gradient flows, and applications to machine learning and computational statistics. His work spans theoretical developments and practical methodologies, such as Monte Carlo algorithms and uncertainty quantification tools. Recent activities include organizing seminars, supervising PhD students, and collaborating with industry partners. He has been awarded membership in the Young Academy of Sweden (2024–2029) and serves as a scientific ambassador for EURANDOM. Key publications address topics like large deviations in stochastic approximations, Metropolis-Hastings algorithms, and neural entropy estimation. His teaching includes courses on statistical learning, probability, and Monte Carlo methods.
Professor Eleni Vasilaki holds the Chair in Bioinspired Machine Learning at the University of Sheffield's School of Computer Science, where she serves as Head of the Machine Learning research group and member of the Complex Systems Modelling research group. She joined as Lecturer in 2009 and became Professor in 2016. Education includes: Bachelor's in Informatics and Telecommunications from University of Athens Master's in Microelectronics from University of Athens DPhil in Computer Science and Artificial Intelligence from University of Sussex Research focuses on developing novel machine learning techniques inspired by biological principles, particularly in reinforcement learning and reservoir computing methods. Her team collaborates with material scientists and engineers to design neuromorphic computing hardware. Publications demonstrate strong emphasis on neuromorphic computing, machine learning applications in neuroscience, reservoir computing systems, and computational modeling of biological processes. Recent work addresses device-agnostic modeling, physical neural networks, and stochastic computing platforms. Significant grants include: MARCH: Magnetic Architectures for Reservoir Computing Hardware (£936,815, Co-PI) ActiveAI - active learning and selective attention for robust AI (£953,584, Co-PI) Modeling probabilistic reinforcement learning in Drosophila (Google, £50,769, PI) CausalXRL: Causal explanations in Reinforcement Learning (£309,915, PI) Brains on Board: Neuromorphic Control of Flying Robots (£2,128,934, Co-PI) Leads the Machine Learning research group and collaborates with the Complex Systems Modelling group, focusing on brain-inspired computing architectures and neuromorphic hardware design.
Dimitris Koukoulopoulos is a Professor of Mathematics at the Department of Mathematics and Statistics, Université de Montréal, where he holds the Chaire Courtois II in fundamental research. He is a member of the Centre de recherches mathématiques and the Montreal Number Theory Group. His research focuses on analytic number theory, with an emphasis on multiplicative and probabilistic aspects, the anatomy of integers and permutations, sieve methods, diophantine approximation, and additive combinatorics. His research explores core questions in number theory, including the distribution of prime numbers, probabilistic properties of integers, and the application of sieve methods to Diophantine problems. Recent work highlights include studies on multiplicative functions, random polynomials, and the Erdős–Hooley Delta function, demonstrating a blend of analytical and combinatorial techniques. Professor Koukoulopoulos actively mentors M.Sc. and Ph.D. students, emphasizing alignment with his research interests. His work contributes to the Montreal Number Theory Group’s collaborative research environment, fostering interdisciplinary advancements in number theory.
Tien Khai Nguyen is an Associate Professor in the Department of Mathematics at North Carolina State University (NC State), holding the title of NCSU Faculty Scholar since 2023. He obtained his PhD in Mathematics from the University of Padova, Italy, followed by postdoctoral research at SISSA-ISAS (Trieste) and a position as S. Chowla Assistant Professor at Pennsylvania State University. His research focuses on Nonlinear Partial Differential Equations , Optimal Control , Differential Games , and Mean Field Games , with contributions to nonsmooth analysis and geometric measure theory. Current funding includes NSF grant DMS 2154201 supporting work on control and optimization. Key awards include the John Franke Faculty Award for Teaching (2021) and Editor’s Choice Article in Mathematical Control and Related Fields (2024). He serves as editor for the Journal of Dynamical and Control Systems and organizes the Differential Equations/Nonlinear Analysis Seminar at NC State. His work bridges theoretical analysis (e.g., transversality theorems, viscosity solutions) with applications in optimal control and multi-agent systems. Active research themes include generic properties of solutions to Hamilton-Jacobi equations and singular phenomena in hyperbolic conservation laws.
Mingtao Xia is a Courant Instructor at the Courant Institute of Mathematical Sciences, New York University (2023–present). He holds a PhD in Applied Mathematics from UCLA (2019–2023) and a B.S. in Information and Computing Science from Peking University’s School of Mathematical Sciences (2015–2019). His research focuses on mathematical modeling, numerical methods, and machine learning applications in kinetic theories, stochastic systems, and unbounded-domain differential equations. He has contributed to interdisciplinary areas including biological systems, environmental science, and epidemic control through computational methods. Education: PhD in Applied Mathematics, UCLA, 2019–2023 B.S. in Information and Computing Science, Peking University, 2015–2019 His work integrates advanced numerical techniques like spectral methods and Wasserstein-distance frameworks with real-world problems such as gene regulation dynamics, solar wind analysis, and ozone pollution impacts. Recent publications emphasize efficient algorithms for high-dimensional systems and uncertainty quantification in stochastic models. While no awards are listed, his research demonstrates strong contributions to mathematical modeling across diverse domains. Advising and grant details are currently unavailable. His affiliation with the Courant Institute positions him at the forefront of computational mathematics and its applications in interdisciplinary research.
Pierre Tarrès is a Professor of Mathematics at NYU Shanghai and serves as the Associate Provost for Strategic Initiatives and Co-Director of the NYU-ECNU Institute of Mathematical Sciences . He is also a Global Network Professor at the Courant Institute of Mathematical Sciences, New York University. Education PhD in Mathematics, École Normale Supérieure Paris-Saclay Master’s in Mathematics, École Normale Supérieure de Paris Master’s in Mathematics of Artificial Intelligence, École Normale Supérieure Paris-Saclay Research Interests Prof. Tarrès specializes in self-interacting random processes, particularly reinforced random walks, and their applications to stochastic algorithms and learning processes in game theory. His work bridges probability theory, mathematical physics, and computational methods, with a focus on understanding the dynamics of reinforcement and their implications for optimization and statistical mechanics. Articles Trends His publications span topics such as reinforced random walks, stochastic algorithms, Brownian motion, and bandit problems. Recent works emphasize the interplay between probability theory and mathematical physics, particularly through supersymmetric models and Schr"odinger operators. Scientific Awards Leverhulme Prize (2006) Prix des Annales de l'Institut Henri Poincaré (2008) Advising and Grants While student advising details are not provided, his research has been supported by grants linked to his work on stochastic processes and learning algorithms. His editorial role at the Annals of Applied Probability since 2019 highlights his contributions to academic leadership. Labs and Teams As Co-Director of the NYU-ECNU Institute of Mathematical Sciences, he leads collaborative research initiatives, fostering interdisciplinary work between mathematics, computer science, and theoretical physics.
Pavlo Krokhmal is a Professor in the Department of Systems and Industrial Engineering at the College of Engineering, University of Arizona. He serves as the Director of Industrial Engineering and is a member of the Graduate Faculty. He has previously held academic positions at the University of Iowa and the University of Florida. Education: PhD in Operations Research, University of Florida, Gainesville, Florida, United States PhD in Mechanics of Solids and Applied Mathematics, Kyiv National Taras Shevchenko University, Kyiv, Ukraine MS in Applied Mathematics and Mechanics, Kyiv National Taras Shevchenko University, Kyiv, Ukraine His research focuses on stochastic optimization, risk analysis, and decision-making under uncertainty, with applications in financial engineering, network resilience, and renewable energy systems. He also contributes to multidisciplinary optimization and cooperative control. His work bridges applied mathematics, engineering, and operations research. The most recent publications reflect a strong trend in risk-averse optimization under uncertainty, especially in network structures, energy systems, and combinatorial problems. His work integrates advanced mathematical modeling, stochastic programming, and computational algorithms. Topics frequently include risk measures like CVaR, p-cone programming, and PDE-constrained optimization with stochastic inputs. Scientific Awards and Honors: Diploma in the Competition of Young Scientists and Students for the Best Research Project, National Academy of Sciences of Ukraine, Spring 1997 Soros Student Award, International Soros Science and Education Program, Fall 1994 Scholarship for scientific and academic achievements, National Academy of Sciences of Ukraine, Spring 1994 Air Force Summer Faculty Fellowship Award (multiple years: 2011, 2012, 2014, 2018, 2019) NRC Senior Research Associateship Award, National Research Council, Spring 2015 Donald E. Bently Faculty Fellowship of Engineering, University of Iowa, Fall 2013 Recognition for Excellence in Teaching, College of Engineering, University of Iowa (2010, 2013) Dr. Krokhmal has been actively involved in advising graduate students and leading research projects funded by agencies such as the Air Force Office of Scientific Research. His collaborations span across institutions and disciplines, including work with researchers at the University of Florida, University of Iowa, and military research labs. He has served on editorial boards and contributed to academic leadership through journal editorials and peer review. His research is conducted within interdisciplinary teams focusing on optimization, risk modeling, and complex systems. These teams often involve mathematical modeling, algorithm development, and simulation for real-world applications in defense, energy, and infrastructure resilience.
Bo Bernhardsson is a Professor in Automatic Control at the Department of Automatic Control, Faculty of Engineering (LTH), Lund University. He has been a full-time professor at Lund since 2010, following a decade (2001–2010) as an Expert in Mobile System Design and Optimization at Ericsson. He is affiliated with major research initiatives including ELLIIT (Excellence Center in Information Technology), LCCC (Lund Center for Control of Complex Engineering Systems), and WASP-AS (Wallenberg AI, Autonomous Systems and Software research school), where he has played a leadership role since 2016. His research focuses on modeling and control of uncertain and large-scale systems, with applications spanning industrial automation, mobile communications, particle accelerators, biomedical systems, and navigation technologies. He integrates theoretical control methods with practical implementations, particularly under constraints such as communication limitations, noise, and delays. His recent publications reveal a strong trend in networked control, communication-constrained estimation, and optimization-based control design. The works span theoretical advances in signal estimation under SNR constraints, event-based and stochastic control, and practical applications like IMU-radio fusion for navigation and RF field control in particle accelerators. Keywords include Control Theory, Communication Systems, Optimization, Signal Processing, and Networked Control , with subfields such as encoder-decoder co-design, virtual antenna arrays, and dynamic programming for time-delay systems. PhD in Control, Lund University, 1992 Professor in Automatic Control, Lund University, since 1999 Expert, Mobile Systems, Ericsson, 2001–2010 Bo Bernhardsson has supervised over 20 PhD and licentiate students, including Jacob Bergstedt (immune system modeling), Anders Mannesson (navigation and radio), and Erik Johannesson (control under communication constraints). His research has been funded by major entities such as the European Spallation Source and the Wallenberg Foundation. He teaches advanced courses in Linear Systems, Convex Optimization, and Robust Control, and has contributed significantly to both academic and industrial advancements in control engineering. He leads and collaborates on interdisciplinary projects involving real-time control, autonomous systems, and machine learning, often in partnership with industry and international research centers. His work in the RobotLab at LTH and on cloud-based control systems highlights his engagement with emerging technologies.
Associate Professor Sanjeeva Balasuriya is affiliated with the School of Computer and Mathematical Sciences at the University of Adelaide, within the Faculty of Sciences, Engineering and Technology. His research focuses on fluid dynamics, dynamical systems, uncertainty quantification, and mathematical biology. He has contributed to studies on Lagrangian coherent structures, stochastic sensitivity in flows, and biofilm expansion modeling. His work bridges applied mathematics with real-world phenomena, including environmental modeling and turbulence analysis. Recent publications explore topics like stochastic differential equations, optimal vector field reconstruction, and flow control strategies. He is eligible to supervise PhD and Master’s students in these areas. His research emphasizes interdisciplinary applications of mathematical techniques to complex fluid systems and nonlinear dynamics.
Mark Jerrum is a Professor of Mathematics at Queen Mary University of London, part of the School of Mathematical Sciences. His research focuses on combinatorics, computational complexity, and stochastic processes, particularly in the design and analysis of randomized algorithms. He explores the mixing times of Markov chains and computational complexity of counting problems, including partition functions and generating functions, often motivated by statistical physics, constraint satisfaction, and graph polynomials. Notable grants include an EPSRC-funded project on Sampling in Hereditary Classes (EP/S016694/1, 2019–2023). He has contributed to teaching, serving as module organiser for MTH4213 (Numbers, Sets and Functions) in 2023–24. His work bridges theoretical computer science and discrete mathematics, with applications in algorithmic design and probabilistic analysis. Research highlights include advancements in perfect sampling algorithms, approximation algorithms for counting problems, and foundational work on the interplay between statistical physics models and computational complexity. He is affiliated with the Centre for Combinatorics, Algebra and Number Theory, reflecting his interdisciplinary approach to combinatorial and algorithmic challenges.
Max Klimm is an Assistant Professor for Discrete Optimization at Technische Universität Berlin, affiliated with the Department of Mathematics within Faculty II – Mathematics and Natural Sciences. He holds a PhD in Mathematics from TU Berlin (2012) and previously served as an Assistant Professor for Operations Research at Humboldt-Universität zu Berlin (2014–2016) and led the Junior Research Group for Optimization under Uncertainty at the Einstein-Center for Mathematics (2014–2016). His research focuses on algorithmic game theory, mathematical optimization of multi-agent systems in traffic, telecommunications, and economics. Key areas include network flows, congestion games, mechanism design, and infrastructure networks. He actively contributes to academic activities as an associate editor for journals like International Journal of Game Theory and Operations Research Forum , and has served on program committees for conferences such as AAAI, EC, and WINE. Recent work includes studies on evolutionary road network reconstruction, robustness of potential-based flows, and algorithmic solutions for knapsack and assignment problems. His research bridges theoretical foundations with practical applications in transportation and resource allocation.
