Xin T. Tong is Associate Professor in the Department of Mathematics at the National University of Singapore, specializing in uncertainty quantification, machine learning, and operations research. His research develops theoretical foundations and methodologies for structured problem-solving in high-dimensional settings. Current investigations focus on ensemble Kalman methods, Bayesian inverse problems, sampling algorithms, and stochastic optimization with non-i.i.d. data. Research emphasizes mathematical analysis of algorithm efficiency and structure-aware computational methods. Professional background includes postdoctoral work at NYU's Courant Institute and Ph.D. from Princeton University.
Wilson Chen is a Senior Lecturer at the University of Sydney. He holds a PhD in Financial Econometrics from the same institution. Prior roles include a post-doctoral fellowship at the University of Technology Sydney and an Assistant Professorship at the Institute of Statistical Mathematics in Japan. His research focuses on advancing computational Bayesian methods and statistical tools for financial time series analysis. Education: PhD in Financial Econometrics, University of Sydney Research Interests: Wilson develops efficient computational techniques for Bayesian inference, with applications to financial data analysis. His work emphasizes MCMC optimization, quantile function models, and the integration of machine learning with statistical methodologies. Recent efforts include improving sampling efficiency in Bayesian frameworks and exploring variational approaches for complex posterior distributions. Publications: His work spans themes in Bayesian computation, financial econometrics, and machine learning. Notable contributions include optimizing MCMC thinning, semiparametric GARCH models, and Stein-based sampling techniques for probabilistic inference. Awards: No scientific awards explicitly mentioned. Advising & Grants: Currently supervising three PhD students: Peiwen JIANG (Modelling Complex Posteriors in Bayesian Inference), Wen PENG (Bayesian Neural Networks for Volatility Dynamics), and Yuning ZHANG (Stochastic Loss Reserving). Grant details are not specified in the provided texts. Labs/Teams: No specific lab or collaborative team affiliations mentioned.
Somayeh Mashayekhi is an Associate Professor in the Department of Mathematics at Kennesaw State University (KSU) and a Courtesy Assistant Professor at Florida State University (FSU) in the Department of Scientific Computing. She holds dual PhDs in Mathematical Sciences (Mississippi State University, 2015) and Applied Mathematics (Al-Zahra University, Iran, 2013). Her research focuses on computational methods, fractional calculus, fractal media viscoelasticity, population genetics, and optimal control. She has conducted postdoctoral research at FSU (2015–2019) and secured grants totaling over $127,000 from NSF and KSU. Education: PhD in Mathematical Sciences (Mississippi State), PhD in Applied Mathematics (Al-Zahra University), MSc in Applied Mathematics (Al-Zahra University). Research highlights include the fractional coalescent framework in population genetics, experimental validation of fractional viscoelastic models in polymers, and development of hybrid functions for solving fractional differential equations. She teaches advanced mathematics courses at KSU and mentors undergraduates in interdisciplinary projects.
Daniel Zuckerman is a Professor in the Department of Biomedical Engineering at Oregon Health & Science University (OHSU), where he directs the Quantitative and Systems Biology Program and co-directs the Integrated Training in Quantitative and Experimental Cancer Systems Biology fellowship program. His research focuses on physics-based computational methods to study molecular and cellular systems, bridging biophysics, systems biology, and statistical mechanics. Education: A.B., 1989, Harvard University M.S., 1995, University of California Ph.D., 1998, University of Maryland Research Interests: Dr. Zuckerman’s work centers on tackling challenges in molecular and cellular biophysics through simulation algorithms, discrete-state approximations, and Bayesian inference. His group develops the weighted ensemble method and WESTPA software for enhanced molecular simulations, with applications to protein folding, ligand binding, and allostery. They also integrate live-cell imaging with molecular readouts to quantify cellular dynamics and connect these to RNA/protein behavior, using machine learning and physical principles. Publications Trends: His recent articles emphasize Bayesian inference for mechanistic modeling, equilibrium/non-equilibrium statistical mechanics, and computational frameworks for molecular and cellular systems. Topics include cooperative binding in hub proteins, morphodynamic cell-state descriptions, transporter mechanisms, and advanced simulation algorithms for rare events. Advising and Grants: Dr. Zuckerman co-directs a cancer systems biology fellowship program and mentors trainees who have transitioned to successful careers in academia and industry. He is actively involved in pedagogical initiatives, including textbooks like Statistical Physics of Biomolecules and co-founding the Living Journal of Computational Molecular Science to promote accessible, educational research. Labs and Collaborations: The Zuckerman Lab at OHSU collaborates across disciplines, applying computational and experimental approaches to systems biology. Their work spans molecular simulations, image analysis, and integrative modeling to unravel hidden biological phenomena at multiple scales.
Pascal Kerschke is a Professor at the Chair of Big Data Analytics in Transportation at TU Dresden, Germany. Previously, he held positions at the University of Münster, including Head of the Research Group for Machine Learning and Data Science. His research focuses on Exploratory Landscape Analysis, Black-Box Optimization, Algorithm Selection, and Multi-Objective Optimization. He earned his PhD in Information Systems from the University of Münster (2013–2017), and Master's and Bachelor's degrees in Data Science and Management from TU Dortmund. Education: PhD in Information Systems, University of Münster (2013–2017) MSc in Data Science, TU Dortmund (2010–2013) BSc in Data Analysis and Management, TU Dortmund (2007–2010) His research interests span algorithm selection, multi-objective optimization, and the application of machine learning in optimization problems. He has contributed to the development of the R package flacco for landscape analysis and co-organized conferences like EMO 2017. Awards include the Dissertation Prize (2018) and PPSN XIV Best Paper Award (2016). He has supervised over 15 students and actively participates in initiatives like the Benchmarking Network and COSEAL. Key projects include work on automated algorithm selection, multimodal optimization, and benchmarking frameworks for iterative heuristics.
Roles and Affiliations: Associate Professor of Statistics at Bocconi University's Department of Decision Sciences. Research Affiliate at Bocconi Institute for Data Science and Analytics (BIDSA), DONDENA Centre, and Laboratory for Coronavirus Crisis Research. Former Post-Doctoral Fellow at University of Padova and Visiting Scholar at Duke University. Education: Ph.D. (2016) and M.Sc. (2012) in Statistics from University of Padova. Research Interests: Bayesian methodology, network science, categorical data, latent variable models, criminology, and demography. Focus on statistical models for complex data, including criminal networks and cause-of-death dependencies. Grants and Awards: ERC Starting Grant (NEMESIS, 2024–2029), PRIN-MUR Grant (CARONTE), COPSS Emerging Leader Award, Leonardo da Vinci Medal, Mitchell Prize (twice), Laplace Prize. Editor for Biometrika , Journal of Computational and Graphical Statistics , and Journal of Multivariate Analysis . Teaching and Service: Courses on Machine Learning, Data Science, and Statistical Methods. Organized Stats under the Stars hackathons, Bocconi Data Science Challenge platform. Served on committees for ISBA, ASA, and others. Labs/Teams: Leads NEMESIS and CARONTE projects. Collaborates with interdisciplinary teams in quantitative criminology and demography.
Aurélia FRAYSSE is an Associate Professor at CentraleSupélec, affiliated with the Laboratoire des Signaux et Systèmes (L2S). Her research focuses on inverse problems, signal and image processing, with applications in electromagnetics, astrophysics, and computational imaging. She earned her HDR (Habilitation) in 2017 on methodological contributions using sparsity for inverse problems, and her PhD in 2005 from Université Paris XII Val de Marne, specializing in multifractal analysis. Her work emphasizes Bayesian methods, sparse representations, and optimization algorithms in challenging imaging scenarios. Her research areas include variational Bayesian approaches for image reconstruction, sparse coding techniques, and applications in gravitational wave detection, electromagnetic imaging, and multispectral data processing. She has collaborated on projects involving wavelet-based methods, low-rank approximations, and machine learning for inverse problems. Key contributions include advancements in contrast source inversion methods for nonlinear electromagnetic imaging, efficient algorithms for sparse gradient priors, and the development of small-scale networks for seismic pattern classification. Her work bridges theoretical foundations (e.g., minimax theory, Sobolev space regularity) with practical applications in engineering and astrophysics. Dr. FRAYSSE has published extensively in IEEE journals and conferences, including Transactions on Antennas and Propagation, Signal Processing, and European Signal Processing Conferences. Her research also extends to the energy, industry, and health domains through transversal axes at L2S. She is actively involved in the lab’s initiatives for the future of industry and sustainable energy solutions.
Sophie Hautphenne is an Associate Professor in Stochastic Modelling at the School of Mathematics and Statistics, University of Melbourne. Her research focuses on branching processes, computational methods, and their applications in population biology and probability theory. She holds a PhD from Université Libre de Bruxelles. Key areas of expertise include extinction probability analysis, birth-and-death processes, and parameter estimation in stochastic systems. Research highlights include the development of the BirDePy Python package for simulating birth-death processes, and contributions to the theoretical understanding of population-size-dependent branching processes. She has led projects funded by the Australian Research Council (ARC), exploring computational approaches for branching processes in population biology. Her work bridges theoretical mathematics with practical applications in evolutionary biology and medical modelling, such as studying chronic myeloid leukemia dynamics through Markovian binary trees. Recent publications emphasize linking microevolutionary and macroevolutionary processes with migration models. Education: PhD in Mathematics, Université Libre de Bruxelles Grants: ARC-funded projects (2020-2024, 2015-2019) Software: BirDePy package for birth-death process simulations
Professor Ken Brown is a faculty member at the Department of Computer Science, University College Cork (UCC), Ireland. He holds roles as Professor and Deputy Director of the Insight Centre for Data Analytics. His research focuses on Artificial Intelligence, constraint programming, optimization, and applications in wireless/sensor networks. He has led major projects including the Insight Centre (SFI), CTVR (telecom), and Authentic (energy management). Affiliations: Insight Centre, CTVR, GLACIATION, SEISMEC Key awards: IEEE SECON 2015 Best Demo, TAOS 2014 Best Paper Education: BSc (Mathematics, Glasgow), MSc (Logic & Computation, Manchester), PhD (AI in Engineering, Bristol). Extensive postdoc and academic roles at UCC, Aberdeen, and Carnegie Mellon. Research interests include constraint-based decision support, smart building systems, and network resilience. Over 150+ publications in journals like Ad Hoc Networks , Constraints , and conferences like IEEE SECON, ICTAI. Grants: Over €10M secured, including SFI, EI, and ERC funding. Supervised 30+ PhD/MSc students. Current research includes smart energy systems, autonomous network repair, and AI for emergency response.
Associate Professor Clara Grazian holds a joint PhD from University Paris-Dauphine and Sapienza University of Rome, specializing in Bayesian analysis of mixture and copula models. She previously worked at the University of Oxford’s Big Data Institute and is currently affiliated with the University of Sydney’s School of Mathematics and Statistics. Her research focuses on Bayesian statistics, machine learning, and their applications in environmental and biological sciences. Key areas include uncertainty quantification, high-dimensional inference, and interdisciplinary collaborations with institutions like the DARE Research Hub. Her education includes a PhD (2016) co-supervised by Paris-Dauphine and Sapienza, followed by postdoctoral work at Oxford. Research interests span statistical methodologies for environmental risk assessment, biometric security, and AI interpretability. She develops scalable Bayesian models integrating mechanistic and data-driven approaches. Recent articles highlight contributions to adversarial attack defense in biometric systems, solar cell material datasets using LLMs, and tuberculosis drug resistance analysis via genomic data. Her work frequently combines statistical theory with practical applications in health, energy, and materials science. She has secured grants including a 2022 Faculty Start-Up Award from Sydney’s Science Faculty.
Sanjay Chaudhuri is an Associate Professor in the Department of Statistics at the University of Nebraska-Lincoln (UNL). He previously held positions at the National University of Singapore (2005–2023) and earned his Ph.D. from the University of Washington (2005), under Professors Michael D. Perlman and Thomas S. Richardson. His research focuses on empirical likelihood, Bayesian methods, high-dimensional data analysis, and applications in demography, network data, and machine learning. Education: B.Stat and M.Stat from the Indian Statistical Institute (1998–2000), Ph.D. in Statistics from the University of Washington (2005). Research Interests: Empirical likelihood, statistical data integration, Bayesian empirical likelihood, graphical Markov models, covariance estimation, causality, approximate Bayesian computation (ABC), and applications in natural, engineering, and marketing sciences. Teaching: Advanced Topics in Applied Statistics, Statistical Models, Applied Regression Analysis, Design of Experiments, and courses on graphical models, data science, and linear models. Software Contributions: Developer of R packages glmc (combining sample/population data), ES (edge selection), elhmc (Hamiltonian Monte Carlo sampling), and Anhysnuc (analyzing nucleic acid thermal curves). Grants & Collaborations: Active in interdisciplinary projects involving statistical methods for complex data, including pandemic dynamics, thermal curve analysis, and environmental statistics. Labs/Teams: Leads the development of Anhysnuc , a web-based tool for analyzing irreversible thermal curves from nucleic acid hybridization experiments.
Luís Paulo Santos is an Assistant Professor at the Department of Informatics, Universidade do Minho, and a Senior Researcher at CSIG, INESC-TEC. His research focuses on rendering, global illumination, and quantum algorithms, particularly in optimizing computational efficiency through heterogeneous parallel computing and quantum techniques. He has authored a book on Bayesian Monte Carlo Rendering and contributed to international conferences as a committee member. His work bridges quantum computing with computer graphics and reinforcement learning. Research interests include quantum ray tracing, variational quantum algorithms, and the application of quantum mechanics to solve complex computational problems. He has organized six international conferences in Portugal and served as Associate Editor of Computers & Graphics and President of the Portuguese Group of Computer Graphics (2017-2018). Publications emphasize quantum computing advancements in rendering and reinforcement learning, exploring topics like Hessian recycling in quantum algorithms and gradient optimization challenges. His work highlights the potential of quantum methods to reduce computational complexity in graphics and machine learning. Advising includes guiding students in quantum optimization and reinforcement learning, with notable supervised theses on quantum algorithms and applications. He has held leadership roles in academic administration, including Vice Director of the Department and Director of the Doctoral Programme on Informatics. Current affiliations include the High-Assurance Software Centre at INESC-TEC and collaboration with the United Nations University on electronic governance initiatives.
Abdulazeez Afolabi is a Doctoral Researcher at the Department of Technical Physics within the Faculty of Science, Forestry and Technology at the University of Eastern Finland. His work focuses on developing novel monitoring methods for greenhouse gas (GHG) balances in agriculture under the project led by Professor Aku Seppänen. Key research areas include: Optimizing multi-beam open-path laser dispersion spectroscopy for GHG flux estimation Bayesian state-space frameworks for tomographic reconstruction of emission rates Geometrical modeling of agricultural landscapes for finite element method (FEM) approximations His research addresses methane (CH4) and ammonia (NH3) emissions in biogas plants, emphasizing computational modeling and measurement setup optimization. This work supports sustainable farming practices verification through spatially-resolved GHG monitoring. Affiliated with the Inverse Problems research group since 2010, his methodologies integrate uncertainty quantification with environmental engineering challenges.
Simon Laurin-Lemay is a Postdoctoral Fellow in the Department of Biology at Carleton University's Faculty of Science, specializing in evolutionary genetics and computational biology. His research focuses on CpG hypermutability in vertebrates, mutation-selection models, and phylogenetic methods development. Education : B.Sc. (Université de Montréal) M.Sc. (Université de Montréal) Ph.D. (Université de Montréal) His work employs simulation-based approaches like Conditional Approximate Bayesian Computation to address challenges in molecular phylogenetics, including site interdependencies, Darwinian selection detection, and genomic context effects on mutation rates. Publications highlight his contributions to resolving confounding factors in codon usage analysis and revisiting land plant evolution through data quality frameworks.
Tyler Maunu is an Assistant Professor of Mathematics at Brandeis University, affiliated with the Department of Mathematics and the Benjamin and Mae Volen National Center for Complex Systems. His research focuses on statistical methodologies, optimization algorithms, machine learning, and their applications to computer vision and data science. He holds a Ph.D. in Mathematics from the University of Minnesota-Twin Cities, alongside multiple advanced degrees from the same institution. Maunu's research advances robust subspace recovery and optimal transport, emphasizing scalable and privacy-aware techniques. His work bridges theoretical foundations (e.g., non-convex optimization landscapes) with practical applications in data recovery and generative modeling. Notable areas include Bures-Wasserstein geometry, stochastic gradient methods, and adversarial robustness in high-dimensional data analysis. His articles span topics like preconditioned Langevin Monte Carlo, optimal transport barycenters, and scalable graph matching algorithms. While no explicit awards are cited, his contributions reflect ongoing innovation in computational statistics and optimization. His affiliation with the Volen Center underscores interdisciplinary engagements in complex systems research.
