Vito Tagarielli is an Associate Professor in Mechanics of Solids at Imperial College London's Department of Aeronautics, part of the Faculty of Engineering. He holds affiliations with the Centre for Micromechanics, the Composites Centre, and the Energy Futures Lab. His research focuses on impact dynamics, fluid-structure interaction, and data-driven material modeling using machine learning. Tagarielli earned a Mechanical Engineering degree from Polytechnic of Bari (2001), followed by a PhD in Materials and Solid Mechanics from the University of Cambridge (2005). He later held roles at the University of Oxford before joining Imperial in 2011. His research interests include blast and deflagration loading, composite materials, and micromechanics. He employs machine learning to predict material behavior under extreme conditions and has contributed to experimental techniques in solid mechanics. Recent work explores applications of neural networks in constitutive modeling and fracture analysis. Tagarielli’s affiliations include the Energy Futures Lab and the Artificial Intelligence Network, reflecting his interdisciplinary approach to sustainability and computational methods in engineering.
Yazhen Wang is a Professor in the Department of Statistics at the University of California, Berkeley. He completed his Ph.D. in Statistics in 1992 under the advisement of Peter Bickel, focusing on nonparametric estimation subject to shape restrictions. His research aligns with key areas in modern statistics, including high-dimensional data analysis and probability theory. Education : Ph.D. in Statistics (1992), advised by Peter Bickel His work intersects with major research themes in the Berkeley Statistics department, such as non-parametric inference and causal modeling. While specific publications or awards aren't listed in the provided text, his academic standing reflects significant contributions to statistical methodology and theory.
Alberto Bononi is a Full Professor of Telecommunications at the Department of Engineering and Architecture, University of Parma, where he has been a faculty member since 1995, becoming a full professor in 2001. He holds a Ph.D. in Electrical Engineering from Princeton University and has held academic and research positions at SUNY Buffalo, Université Laval, and Alcatel-Lucent Bell Labs. He served as Director of the Master's program in Communication Engineering from 2017 to 2024 and currently teaches graduate courses such as Information Theory, Detection and Estimation, and Machine Learning for Pattern Recognition. His research centers on optical communications, with a focus on WDM transmission systems, optical amplifiers (EDFA, Raman, semiconductor), nonlinear propagation in optical fibers, and submarine transmission systems. He investigates performance evaluation of broadband optical networks and dispersion management in long-haul systems. His work integrates theoretical modeling with practical design for next-generation optical networks. The most recent publications highlight his ongoing contributions to space-division multiplexing, nonlinear interference modeling, and advanced amplifier design for submarine systems. These works reflect a strong trend in high-capacity, multi-dimensional optical transmission technologies aimed at overcoming physical layer limitations in modern fiber-optic networks. Senior Member of IEEE (since 2013) Area Editor, IEEE Transactions on Communications (2010–2020) Associate Editor, IEEE Transactions on Communications (2007–2010) Prof. Bononi has supervised numerous research projects funded by Alcatel-Lucent/Nokia Bell Labs and Italian and international programs such as PRIN, PNRR, and Italy-Québec collaborations. He has led the Parma research unit in multiple national and European initiatives, including WONDER, OSATE, and the current Triboletto project. He has served on technical program committees for major conferences including OFC, ECOC, CLEO, and Globecom. He leads the Optical Communications Lab (OptikLab) at the University of Parma and is affiliated with the Quantum Information Science group at UniPr. His lab conducts cutting-edge research in optical transmission systems, nonlinear effects, and signal processing for high-speed communications.
Felix Voigtlaender is a Professor for Mathematics with a focus on Reliable Machine Learning at the Mathematical Institute for Machine Learning and Data Science, KU Eichstätt-Ingolstadt. He has held previous academic positions at TU Munich as an Emmy Noether Group Leader, at the University of Vienna as a Senior Scientist, and at KU Eichstätt and TU Berlin in research and postdoctoral roles. PhD: RWTH Aachen University, 2015 Master: RWTH Aachen University, 2013 Bachelor: RWTH Aachen University His research lies at the intersection of mathematics and machine learning, with a strong emphasis on the theoretical underpinnings of neural networks. He investigates approximation properties, expressiveness, and the existence of adversarial examples. His work also extends into harmonic analysis, functional analysis, and multiscale systems such as wavelets and shearlets. He is deeply interested in the mathematical foundations of data science, including sampling theory and information-based complexity. The recent publications highlight a consistent focus on the mathematical analysis of neural networks, particularly approximation capabilities and universal approximation theorems in both real and complex domains. His work often provides sharp theoretical bounds and deep insights into the behavior of deep learning models in high-dimensional settings. Friedrich-Wilhelm-Award 2016 for his PhD thesis Felix Voigtlaender has supervised students and is actively involved in teaching and academic development, notably contributing to the launch of a new BSc program in Data Science at KU Eichstätt-Ingolstadt. He has collaborated with prominent researchers such as Götz Pfander, Gitta Kutyniok, and Hartmut Führ, and has been funded through competitive grants like the Emmy Noether program. He leads a research group focused on rigorous mathematical approaches to machine learning challenges. He is affiliated with the Mathematical Institute for Machine Learning and Data Science, a newly founded institute at KU Eichstätt-Ingolstadt, which will be based in Ingolstadt. His team is involved in both theoretical exploration and practical applications of data science methods.
David Sivakoff is an Associate Professor at The Ohio State University with a joint appointment in the Department of Statistics and the Department of Mathematics. His research focuses on probability theory, particularly stochastic processes on graphs, including interacting particle systems, cellular automata, and bootstrap percolation models. He explores applications in epidemiology, sociology, and statistical mechanics, with recent work on nucleation phenomena, phase transitions, and network dynamics. Education: PhD in Applied Mathematics from the University of California, Davis (2010). Research Interests: His work examines systems like cyclic cellular automata, threshold growth models, and diffusion-limited annihilating systems. He investigates how local rules lead to global patterns, with notable studies on phase transitions in heterogeneous networks and the impact of obstacles on percolation processes. His interdisciplinary collaborations include analyzing information spread in social networks and elite influence on public opinion. Recent Publications: Key contributions include studies on competing growth models (2024), advantageous mutant spread dynamics (2024), and particle density in annihilating systems (2023). NSF and Simons Foundation funding supports his research. Teaching: Courses include Probability Theory, Mathematical Statistics, and Real Analysis. Recent classes: Stat 6201 and Math 6251 in Autumn 2024. Advising: Supervised 11 graduate students across Statistics and Mathematics, including ongoing advisees in both departments. Notable graduates include Hanbaek Lyu (2018) and Nikolas Henderson (2024). Labs/Teams: Collaborates with interdisciplinary teams on projects like empirical studies of information sources and network change-point detection. Active in conferences like SoCG and IJCAI.
Mark Podolskij is an Honorary Professor at the Department of Mathematics, Aarhus University. Since 2006, he has contributed over 70 research outputs, focusing on mathematical statistics, probability theory, and stochastic processes. His work emphasizes asymptotic theory, statistical inference, and applications in financial markets. He holds editorial roles in journals like Bernoulli and Scandinavian Journal of Statistics . Research Interests: His primary areas include stochastic processes, limit theorems, and high-dimensional statistical methods. Notable contributions involve quadratic variation analysis, heavy-tailed distributions, and Lévy processes. Scientific Awards: He secured prestigious grants, including the ERC Consolidator Grant (2018) and Villum Fonden funding (2015). These grants supported projects on statistical methods for diffusions and probabilistic inference in ambit fields. Grants/Editorial Roles: Podolskij has led or co-investigated research projects totaling over €10M. His editorial work spans multiple journals, reflecting his influence in academic publishing.
Birgit Debrabant is an Associate Professor at the Department of Mathematics and Computer Science, University of Southern Denmark, with additional roles in Data Science and the STEM Center for Educational Research – FNUG. Her interdisciplinary work bridges statistical methodology with applications in neuroscience, clinical medicine, and public health. Department: Department of Mathematics and Computer Science Research Centers: STEM Center for Educational Research – FNUG Professional Affiliation: Active in Danish Society for Theoretical Statistics Her research focuses on biostatistics in clinical contexts, particularly neurosurgical outcomes, chronic subdural hematoma, intraventricular hemorrhage, and public health interventions. She employs advanced statistical methods including randomized trials, observational studies, meta-analyses, and genetic association studies. Her work often involves large-scale registry data and methodological innovation in handling complex health data. The 15 most recent publications reveal a strong trend in medical statistics, especially in neurosurgery and public health. Topics include optimal drainage times, antithrombotic therapy, BMI policy impacts, and statistical methods for DNA methylation and hidden population estimation. These reflect a cohesive research program applying rigorous statistical science to pressing clinical and public health questions. She has no listed scientific awards in the provided text. Birgit Debrabant actively contributes to teaching and curriculum development, particularly in biostatistics and applied statistics for health sciences. She has led or co-taught courses such as 'Advanced Biostatistical Methods in Health Sciences' and 'Theory of Science and Statistics'. While no formal student advising or grant information is mentioned, her involvement in multi-center trials and educational initiatives suggests collaborative leadership. She participates in national and international conferences, including DAGStat and the International Workshop on Applied Probability. She is involved in collaborative research networks, particularly in theoretical statistics and health data science, and contributes to professional events as both organizer and presenter.
Jevgenijs Ivanovs is an Associate Professor in the Department of Mathematics at Aarhus University . His research focuses on stochastic processes , particularly Lévy processes , extreme value theory , and probability theory , with applications in queueing systems , statistical inference , and multivariate extremes .
Guillem Perarnau Llobet is an Associate Professor at the Department of Mathematics, Universitat Politècnica de Catalunya (UPC), affiliated with CRM (Centre de Recerca Matemàtica), IMTech (Institut de Matemàtiques de la UPC), and BGSMath (Barcelona Graduate School of Mathematics). His academic journey includes a PhD at UPC under Oriol Serra, followed by a CARP Postdoc Fellowship at McGill University with Bruce Reed and Louigi Addario-Berry (2013–2015), and a Lecturer position at the University of Birmingham (2016–2019). Current Role: Associate Professor, UPC Past Roles: Lecturer, University of Birmingham; Postdoc, McGill University Affiliations: CRM, IMTech, BGSMath Research Focus: Probabilistic and Extremal Combinatorics, Random Combinatorial Structures, Discrete Stochastic Processes, and Analysis of Randomized Algorithms. His work bridges theoretical rigor with practical algorithmic insights, particularly in graph theory and random graph dynamics. Article Trends: Recent publications emphasize algorithmic combinatorics, probabilistic methods in random graphs, and hypergraph Hamiltonicity. Key themes include mixing times, synchronization, and percolation phenomena across diverse graph models. Scientific Awards: CARP Postdoc Fellowship Grants & Collaborations: COCOA Grant (coPI) RandNET MSCA Exchange Programme Participant Spanish Discrete and Algorithmic Mathematics Network Coordinator
Yvan Velenik is a Full Professor in the Department of Mathematics at the University of Geneva's Faculty of Science. His academic career focuses on the rigorous mathematical study of statistical mechanics and probability theory, with particular emphasis on lattice systems and phase transitions. Professor Velenik's research interests center on the applications of Probability Theory to rigorous classical Statistical Mechanics. His primary areas of investigation include lattice random fields (particularly spin systems and effective interface models), and random walks (including self-interacting random walks and polymers). A recurring theme in his work is the derivation of large-scale asymptotics of extended objects such as interfaces and polymers, and the associated phase transitions. He is particularly known for his contributions to understanding Ornstein-Zernike asymptotics, effective interface models, polymer behavior, and phase separation in lattice spin systems. His research has followed clear thematic trajectories over recent years, with significant contributions to understanding correlation functions in lattice models, interface phenomena at low temperatures, and the mathematical foundations of phase transitions. His publications demonstrate a consistent focus on rigorous probabilistic approaches to statistical mechanical systems, with particular attention to Ising and Potts models, random cluster representations, and the mathematical properties of phase transitions. Professor Velenik has supervised doctoral students including Kamil Khettabi and Yacine Aoun. He teaches advanced courses in probability theory and statistical mechanics at the University of Geneva, including Probability and Statistics for bachelor's students and Selected Chapters of Probability Theory for master's students. Among his most significant scholarly contributions is the co-authored book Statistical Mechanics of Lattice Systems: A Concrete Mathematical Introduction (Cambridge University Press, 2017), which has received high praise from leading researchers in the field as an accessible yet rigorous introduction to equilibrium statistical mechanics for mathematically inclined readers. His research group participates in the ANALYSIS, MATHEMATICAL PHYSICS AND PROBABILITY group at the University of Geneva, collaborating with other researchers on fundamental questions in mathematical statistical mechanics.
Dr. Chiheb Ben Hammouda is an Assistant Professor in the Mathematical Institute within the Faculty of Science at Utrecht University, where he joined in September 2023. His research integrates mathematical (stochastic) modeling, numerical analysis, and advanced computational methods to address complex problems in engineering and science that exhibit challenging features such as high dimensionality, complex dynamics, low regularity, and rare events. PhD in Applied Mathematics and Computational Science, KAUST (2016-2020) MSc in Applied Mathematics and Computational Science, KAUST (2013-2015) BSc in Multidisciplinary Engineering, Ecole Polytechnique de Tunisie (2010-2013) Dr. Ben Hammouda's research focuses on enhancing numerical methods to achieve optimal performance balancing efficiency and interpretability. His work spans theory, algorithm design, and numerical analysis with applications in quantitative finance (pricing financial derivatives, risk management), optimal control for power systems management, stochastic reaction networks (biochemical systems, epidemiology), and machine learning for forecasting extreme events. He employs methodologies including Monte Carlo methods, multilevel MC, Quasi-MC, sparse grids, Fourier methods, stochastic optimal control, importance sampling, and machine learning. Analysis of Dr. Ben Hammouda's publication record reveals a consistent focus on developing efficient computational methods for high-dimensional problems across multiple domains. His work shows a trajectory from foundational numerical methods development toward increasingly complex applications in finance, energy systems, and biological modeling. A notable trend is the integration of machine learning techniques with traditional numerical methods to address the curse of dimensionality in complex systems. Dr. Ben Hammouda actively supervises multiple PhD, Master's, and Bachelor's students working on projects related to numerical methods in finance, energy systems, and stochastic reaction networks. His students have pursued diverse research topics including Fourier pricing of multi-asset options, numerical smoothing techniques, optimal control for power systems, and dimensionality reduction in stochastic reaction networks. Several of his former students have secured positions at institutions including KAUST, RWTH Aachen University, and financial firms like Allianz Global Investors and Deloitte. Dr. Ben Hammouda is actively involved in the academic community as co-organizer of major conferences including the Study Group Mathematics with Industry (SWI 2025) and the International Conference on Computational Finance 2024. He serves as Associate Editor for the Statistics and Computing Journal and referees for multiple prestigious journals including Journal of Computational and Applied Mathematics and Quantitative Finance. His organizational leadership extends to mini-symposia at major international conferences on Monte Carlo methods and computational finance.
Aliaksandr Hubin is an Associate Professor at the University of Oslo's Department of Mathematics and Natural Sciences, affiliated with the Center for Computational Inference in Evolutionary Life Science (CELS) and Statistics and Data Science research groups. His career spans Bayesian methodology development, machine learning applications, and interdisciplinary research. PhD in Statistics (University of Oslo, 2018) MSc in Operations Research (Molde University College, 2014) Specialist degree in Economic Cybernetics (Belarusian State University, 2013) Research spans: Bayesian model selection and averaging Probabilistic machine learning Computational statistics Applications in genomics, clinical diagnostics, and aquaculture Weak supervision methods in NLP Medical decision support systems Recent publications focus on: Bayesian neural network compression Clinical score validation Outlier detection mechanisms Evolutionary optimization in inference Domain-informed deep learning Scientific recognition: NIMA 2014 award Multiple student research awards (2012-2013) Graybill poster competition second place (2017) Methodological contributions include: Genetically modified MCMC algorithms Adaptive simulated annealing EM Probabilistic framework development
Anna (Ania) Panorska is a Professor at the University of Nevada, Reno within the Graduate Program of Hydrologic Sciences. She holds a Ph.D. in Mathematics (Statistics and Applied Probability program) from UC Santa Barbara (1992) and an M.S. in Statistics from UT El Paso (1988), with prior graduate coursework in Applied Mathematics at the University of Warsaw. Her career spans academia, research institutions (Desert Research Institute), industry (Blue Cross Blue Shield of Tennessee), and statistical consulting. Her educational background includes: M.S. in Statistics, University of Texas at El Paso, 1988 Ph.D. in Mathematics (Statistics and Applied Probability program), University of California, Santa Barbara, 1992 Professor Panorska's research centers on theoretical and applied probability and statistics, with specialized expertise in limit theorems and extreme event modeling within probability theory, and multivariate model development in statistics. Her work is characterized by extensive interdisciplinary collaboration across ecology, engineering, climate science, hydrology, and medicine. Key contributions include novel distributional frameworks for stochastic episodes, heavy-tailed data analysis, and extreme value applications in environmental systems. Analysis of her recent publications reveals a sustained focus on developing new probability distributions (e.g., slash, truncated Zipf, generalized Pareto variants) and their application to multivariate dependencies and extreme events. Her methodological innovations consistently bridge theoretical statistics with practical challenges in hydrology (precipitation extremes), climate science (intense weather events), and ecology (species interaction networks), demonstrating a cohesive trajectory in environmental statistics.
Katherine Brown is an Associate Professor of Physics at Hamilton College, where she has been a faculty member since 2014. Her research spans cosmology, non-Hermitian quantum mechanics, and interdisciplinary studies at the intersection of physics and art. Education: B.S., University of New Mexico M.S. and Ph.D., Case Western Reserve University (2010) Her research in cosmology focuses on gravitational radiation from phase transitions, chameleon dark energy models, and extra-dimensional theories. In non-Hermitian quantum mechanics, she explores PT-symmetric systems and their implications for fundamental physics. She also investigates interdisciplinary topics, notably challenging claims about fractal patterns in Jackson Pollock's drip paintings. Her work has been featured in Nature Physics as a research highlight (2010). She mentors Hamilton students in research projects, bridging theoretical physics and cosmology with accessible mathematical frameworks.
Naoki Awaya is an Assistant Professor at the School of Political Science and Economics , Waseda University. His research focuses on economic statistics, computational statistics, and financial econometrics with applications in machine learning and Bayesian inference. Education : Not explicitly stated Current Projects : Developing Bayesian estimation methods for nonstationary economic time series and structural changes His work includes tree-based models for probability distributions and advanced MCMC sampling techniques. He teaches graduate-level econometrics courses (Econometrics I/II) using R programming, emphasizing methodological rigor and theoretical foundations. Research interests span high-dimensional data analysis, density ratio estimation, and financial market applications through multivariate Hawkes processes. Recent publications highlight innovations in: Unsupervised tree boosting Hidden Markov Pólya trees Particle rolling MCMC algorithms Nonstationary errors-in-variables models
