Patrick Ingram is an Associate Professor at the Department of Mathematics and Statistics , Faculty of Science , York University . His research focuses on number theory and diophantine geometry , particularly the arithmetic of elliptic curves and surfaces , and dynamical systems over global fields . His scholarly work includes significant contributions to the study of canonical heights , post-critically finite maps , and primitive divisors in arithmetic dynamics . His research often bridges complex dynamics with number theory, exploring the interplay between Galois representations , Drinfeld modules , and polynomial iterations . Patrick has received the Top Cited Article 2007 - 2011 award from the Journal of Number Theory . He collaborates with leading mathematicians in arithmetic dynamics, including Joseph H. Silverman , and has published extensively in top-tier journals such as the Duke Mathematical Journal , Proceedings of the London Mathematical Society , and Transactions of the American Mathematical Society . His work spans both theoretical advancements and computational techniques in algebraic divisibility sequences and rigidity theorems .
Alex Shestopaloff is a Lecturer in Statistics at Queen Mary University of London (QMUL), affiliated with the School of Mathematical Sciences. Previously, he was a Research Fellow at the Alan Turing Institute (2017–2020) and a Junior Research Fellow at Campion Hall, Oxford. He holds a PhD in Statistics from the University of Toronto (2016), supervised by Radford M. Neal. His research focuses on developing efficient MCMC methods, high-dimensional time series analysis, network science, and applications in financial market microstructure. Education: PhD in Statistics, University of Toronto (2016) Supervisor: Radford M. Neal Research Interests: Bayesian online learning in non-stationary environments Limit order book modeling and trading strategies Graph clustering and network analysis Statistical methods for high-dimensional data Algorithmic trading and cryptocurrency markets His recent work spans financial engineering, machine learning, and statistical methodologies. Notable contributions include cluster-based trading strategies (ClusterLOB), generalized Bayesian filtering frameworks, and scalable graph analysis techniques. Collaborations with industry partners (e.g., Wise Plc) highlight applied research in financial systems. Advising & Alumni: Current advisees include Yichi Zhang (Oxford), Maria Fernanda Pintado (QMUL), and Dave Lui (Oxford) Alumni: Gerardo Duran-Martin (Postdoc at Oxford-Man Institute), Claudio Bellani (Citadel Securities) Labs/Teams: Leads interdisciplinary projects at QMUL and collaborates with the Alan Turing Institute on financial and network science initiatives.
Dr. John Shepherd is an Associate Professor in the School of Science at RMIT University, specializing in applied mathematics, numerical and computational mathematics, and their applications in engineering and environmental systems. His research focuses on analyzing nonlinear problems, particularly in bioreactor dynamics, fluid mechanics, and nuclear energy policy. He has contributed to studies on anaerobic digestion models, reactor stability, and the role of nuclear energy in climate change mitigation. Education: Doctorate in Applied Mathematics (not explicitly stated in text, inferred from title). His work bridges theoretical analysis and real-world applications, such as optimizing methane production in waste digesters and evaluating environmental policies for nuclear energy. He actively supervises research projects, including the analysis of anaerobic digester dynamics. Dr. Shepherd’s publications span interdisciplinary topics, emphasizing the intersection of mathematics, engineering, and environmental science. He engages with policy discussions on nuclear energy’s role in decarbonization, advocating for its integration into clean energy strategies. His research highlights the importance of multiscale analysis in understanding complex systems like bioreactors and fluid flows. Collaborations involve industry and international institutions, reflecting his commitment to practical solutions for sustainability challenges.
Hans-Georg Mueller is a Professor in the Department of Statistics at the University of California, Davis. His research spans multiple domains of modern statistical methodology, with groundbreaking contributions to functional data analysis, metric statistics, and nonparametric inference for random objects. Key research areas include Fréchet regression, distributional data analysis, network regression, and optimal transport Applications span longitudinal growth studies, brain development, aging and longevity, plant genomics Research Interests : He has pioneered methods for analyzing complex data structures such as functional data, manifold-valued data, and random objects. His work on the PACE approach for longitudinal data has become foundational in the field. Recent Publications demonstrate strong trends in Fréchet analysis, metric statistics, and distributional data modeling, with applications in both biomedical and environmental domains. Books and Edited Works : Author of the foundational monograph Nonparametric Regression Analysis for Longitudinal Data (1988), and co-editor of influential volumes including Change-point Problems (1994) and Mathematical Modeling in Experimental Nutrition (1998).
Peter Johnstone is Professor of Foundations of Mathematics at the University of Cambridge, affiliated with the Department of Pure Mathematics and Mathematical Statistics within the Faculty of Mathematics. His academic profile shows consistent contributions to foundational mathematical research over several decades, with current contact information indicating active engagement in his position. Johnstone's primary research focus lies in category theory, specifically topos theory and its connections with mathematical logic. His work explores the deep structural relationships between algebraic structures, logical systems, and geometric interpretations within categorical frameworks. His research has significantly advanced our understanding of locales, realizability toposes, and the categorical foundations of mathematical logic. Analysis of his recent publications reveals a consistent trajectory in topos theory with emphasis on geometric morphisms, realizability structures, and categorical logic. His work demonstrates sophisticated interplay between abstract category theory and concrete mathematical structures, particularly in how logical systems can be represented and manipulated within topos-theoretic frameworks. Johnstone maintains an active academic presence with a personal homepage at the University of Cambridge and standard university contact information including email (P.T.Johnstone@dpmms.cam.ac.uk), office location (Room C0.01), and telephone number (01223 337985). His extensive publication record spanning from the 1980s through the 2010s indicates sustained scholarly activity in his specialized field.
Eva Cantoni is a Full Professor at the Research Center for Statistics within the Geneva School of Economics and Management , University of Geneva. Her expertise spans robust statistical methodology, model selection, and applications in ecology and medicine. Ph.D. from University of Geneva Accredited European Statistician (FENStatS) Research Interests : She specializes in Robust statistics for real-world data Variable/model selection in high-dimensional settings Nonparametric and semi-parametric regression Zero-inflated and overdispersed count models Longitudinal and spatiotemporal data analysis Her work addresses ecological challenges (fish stock assessment), medical applications (hospital congestion modeling), and housing market analysis. Recent Trends in Publications : Recent articles focus on Confidence intervals for robust mixed models Editorial leadership in robust statistics Applications to fisheries science and public health Flexible modeling frameworks for complex data Comparative studies of statistical measures Extremes modeling in healthcare Leadership & Grants : She has served as: Vice-Dean for Teaching (2020-2023) Director of Master's in Statistics (2012-2019) Director of Applied Statistics Certificate (2015-2019) President, Swiss Federal Statistics Committee (2024-2027) Specialty Chief Editor, Frontiers in Applied Mathematics (2024) Grants include projects on Robust solutions for modern data (2023-2025) Sustainable fisheries modeling (2018-2021) Advancements in state-space models (2014-2017) Software Contributions : Developed R packages for robust statistical methods: confintROB (bootstrap confidence intervals) RobSSM (robust state-space models) R2_LMM (explained variation measures)
Xiaoning Qian is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, where he also serves on the Faculty Advisory Committee for the Texas A&M Institute of Data Science (TAMIDS) and the Executive Committee for the Texas A&M TRIPODS Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He holds a joint appointment in the Applied Math group within the Computational Science Initiative at Brookhaven National Laboratory (BNL). Previously, he was an Associate Professor (2018-2022) and Assistant Professor (2013-2018) at Texas A&M, and an Assistant Professor in the Department of Computer Science and Engineering at the University of South Florida (2009-2013). Dr. Qian received his B.S.E. and M.S.E. degrees from Shanghai Jiaotong University, China, and his M.Ph. and Ph.D. degrees in Electrical Engineering from Yale University. Dr. Qian's research focuses on developing mathematical models and computational algorithms in signal processing, machine learning, and Bayesian methods, particularly in learning, uncertainty quantification, and experimental design. His work spans multiple disciplines, with applications in life sciences and materials science. His research group, the Biomedical Imaging, Sensing, and Genomic Signal Processing Group, actively applies probabilistic models and optimization algorithms to solve complex problems in interdisciplinary domains. His research has evolved from foundational work in bioinformatics and biomedical image processing to more recent applications in materials science and broader AI for science initiatives. Dr. Qian has received numerous scientific awards and recognitions including: National Science Foundation (NSF) CAREER Award Segers Family Dean's Excellence Professorship II in the College of Engineering TEES (Texas A&M Engineering Experiment Station) Senior Faculty Fellow Montague-Center for Teaching Excellence Scholar J. T. Oden Faculty Fellow at the University of Texas, Austin Finalist of the 2023 INFORMS QSR Best Paper Faculty Impact Fellow from the Department of Electrical & Computer Engineering As an advisor , Dr. Qian has mentored numerous graduate students through their PhD and MS programs, with many of his alumni securing positions at prestigious institutions and companies including NIH/NCBI, Microsoft, Baidu Research Lab, and Qualcomm. His research has been supported by multiple grants, including an NSF CAREER award and collaborative research funding from the Information Integration and Informatics program. He is actively recruiting postdoc and graduate student research assistants for projects in machine learning and optimization methods with applications in bioinformatics and materials science. Dr. Qian is involved with several research initiatives including the Objective-Based Uncertainty Quantification (ObjectiveUQ) project, which provides a mathematical framework for integrating prior knowledge and data while enabling effective operational and experimental design under uncertainty. He also co-organizes the Bio-Seminar series for the Biomedical Imaging, Sensing & Genomic Signal Processing group at Texas A&M.
Weierstrass Institute for Applied Analysis and StochasticsGermany
Christian Wald is a Post-doctoral researcher at Technical University Berlin working under Professor Gabriele Steidl, focusing on generative modeling, flow matching, and stochastic processes in machine learning. His research bridges theoretical probability with practical medical imaging applications, particularly in MRI reconstruction and analysis. He completed his PhD at Humboldt University of Berlin in 2017 with a thesis on p-adic quantum groups. His academic journey transitioned from pure mathematics to interdisciplinary machine learning research, reflecting his versatile expertise. Wald's primary research explores generative models through the lens of optimal transport and flow matching, with significant contributions to Wasserstein geometry and conditional distance metrics. His work frequently integrates stochastic processes to enhance medical image reconstruction, demonstrating strong cross-disciplinary impact in both theoretical machine learning and clinical applications. Recent publications highlight innovations in sliced MMD flows, Bayesian OT methods, and uncertainty-aware medical image analysis. Analysis of his 15 most recent publications (2019-2025) reveals a consistent trajectory toward unifying geometric probability with deep learning. Key themes include flow-based generative modeling for medical time-series data, optimal transport applications in image reconstruction, and novel kernel methods for distribution matching. His work spans both foundational theory (e.g., Fisher-Rao curves) and high-impact medical applications (e.g., coronary calcium scoring). No specific scientific awards are documented in the provided text, though his publications appear in prestigious venues including ICLR, IEEE TMI, and Physics in Medicine & Biology. Wald maintains extensive collaborations with the medical imaging group at Technical University Berlin, particularly with Andreas Kofler and Gabriele Steidl. His co-authored works demonstrate consistent contributions to MRI reconstruction pipelines and segmentation frameworks, though no formal advising roles or grant leadership are indicated. Current projects focus on uncertainty quantification in active learning for medical image segmentation. He operates within Gabriele Steidl's research group at Technical University Berlin, which specializes in mathematical imaging and machine learning. The team combines expertise in optimization, probability theory, and deep learning to solve medical imaging challenges, with Wald contributing core algorithmic innovations in generative modeling and stochastic reconstruction.
Behzad Alaei serves as an Associate Professor in the Section for Study of Sedimentary Basins within the Department of Geosciences at the University of Oslo's Faculty of Mathematics and Natural Sciences. His office is located in room K38 of the Geology Building at Sem Sælands vei 1, 0371 Oslo, with a professional email contact at behzad.alaei@geo.uio.no. Dr. Alaei maintains an active research profile with publications spanning from 2005 to the present, demonstrating his ongoing contributions to geological sciences. Dr. Alaei's research spans multiple critical areas within structural geology and sedimentary basin analysis, with particular expertise in fault zone architecture, seismic interpretation techniques, and CO2 storage site assessment. His work bridges theoretical geological concepts with practical applications in petroleum geology and carbon sequestration. A significant portion of his research focuses on the Norwegian Barents Sea region, where he has conducted extensive studies on normal fault systems and their geometric characteristics. His recent work increasingly integrates machine learning and deep learning approaches with traditional geological analysis, reflecting the evolving nature of geoscience research methodology. The analysis of Dr. Alaei's publication record from 2018-2024 reveals a strong thematic continuity in fault characterization research, with progressive incorporation of advanced computational methods. Early publications focused primarily on traditional structural analysis of fault systems in sedimentary basins, while more recent work demonstrates increasing integration of machine learning techniques for fault detection and characterization. A notable trend is the application of these geological insights to practical challenges in carbon capture and storage, particularly regarding fault risk assessment for CO2 storage sites in the North Sea region. His collaborative work with Anita Torabi appears consistently throughout this period, suggesting a strong research partnership. Dr. Alaei maintains an active research program with multiple ongoing projects related to sedimentary basin analysis and fault characterization. His work appears to involve significant collaboration with both academic and industry partners, particularly in the context of CO2 storage research. While specific grant details aren't provided in the available information, his consistent publication record across multiple high-impact journals suggests successful funding of his research activities over the past two decades.
Dr. Tim Conrad is a researcher at the Zuse Institute Berlin in the Visual and data-centric computing department under the Mathematics of Complex Systems division. He leads projects at the intersection of computational biology, AI, and medical data analysis. Projects: Geometric Learning for Single-Cell RNA Velocity Modeling, MODAL MedLab, Sparse Compressed Sensing in -Omics Data, BIFOLD (Big Data and Machine Learning) Research Networks: Affiliated with MATH+ and MODAL Research Campus His research focuses on applying machine learning , network optimization , and sparse data analysis to biological and medical challenges including disease modeling, microbiome dynamics, and ECG classification. Recent work explores hybrid PDE-ODE epidemic models and federated learning in healthcare. 2023-2025 publications highlight trends in AI for biological networks , temporal community detection , and medical signal processing . He co-authored studies on SARS-CoV-2 simulations, proteomics feature selection, and multi-label ECG analysis. His 2004 doctoral thesis at Monash University laid foundations for later work in metabolic pathway analysis. Awarded as a Zuse Fellow , he contributes to open science initiatives like FAIR data sharing . Collaborations span institutions including Freie Universität Berlin and Charité in medical informatics and clinical applications.
Prof. Vladimir Spokoiny is a leading figure in stochastic algorithms and nonparametric statistics at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) and Humboldt University of Berlin . His work bridges mathematical statistics with practical applications in finance, medicine, and machine learning. Born in 1959 in Moscow, USSR PhD from Lomonosov Moscow State University (1988) Habilitation from Humboldt University (1996) Head of WIAS research group since 2000 Professor at Humboldt University since 2002 Spokoiny's research focuses on adaptive nonparametric methods, high-dimensional data analysis, and statistical finance. His innovations in local homogeneity testing and propagation-separation methods have advanced volatility modeling, image analysis, and manifold learning. He employs Bayesian optimization frameworks and stochastic control techniques for financial instrument pricing. Recent scientific contributions include generalized bootstrap procedures for Bures-Wasserstein barycenters (2024), dimension-free Laplace approximation bounds (2023), and structure-adaptive manifold estimation (2022). His 19+ PhD students and editorial roles in top journals like The Annals of Statistics demonstrate sustained academic impact. International Statistical Institute member American Statistical Association fellow Institute of Mathematical Statistics member Bernoulli Society member
Louis-Pierre Arguin is a Professor of Mathematics at Baruch College, City University of New York, within the Weissman School of Arts and Sciences. His academic journey includes a Ph.D. in Mathematics from Princeton University, an M.Sc. in Physics from the University of Montreal, and a B.Sc. in Mathematics from the same institution. His research bridges probability theory, number theory, and statistical mechanics, with a focus on extreme value statistics of the Riemann zeta function and spin glass systems. Key areas include logarithmically correlated random fields, branching random walks, and connections between random matrix theory and number-theoretic functions. His work demonstrates how probabilistic methods can solve deep problems in analytic number theory, particularly regarding the distribution of extreme values of the zeta function on the critical line. Arguin's scholarly impact is reflected in publications in premier journals including Annals of Mathematics , Communications on Pure and Applied Mathematics , and Probability Theory and Related Fields . His research program explores the Fyodorov-Hiary-Keating conjecture, large deviations of Selberg's central limit theorem, and disorder chaos in spin glasses, revealing profound connections between number theory and statistical physics. Award for Excellence in Scholarship (Weissman School, 2022) Bourbaki Seminar Presentation (2019) Andre-Aisenstadt Prize (CRM Montreal, 2015) His mentorship includes supervising doctoral dissertations through Baruch's Mathematics Ph.D. program, while his grant portfolio features multiple National Science Foundation awards, including a CAREER grant focused on statistics of extrema in complex systems. He actively serves on departmental and university committees including the Executive Committee of the Mathematics Department and the Real Analysis Qualifying Exam Committee. Arguin maintains a robust research group collaborating with institutions worldwide and frequently presents at major international venues including the Institute for Advanced Study, Princeton University, and the Centre de Recherches Mathématiques in Montreal.
Luke Miratrix serves as Assistant Professor at Harvard Graduate School of Education and affiliate faculty in Harvard Department of Statistics. His methodological expertise centers on causal inference applications in educational research, particularly treatment effect heterogeneity and cluster-randomized trial evaluation. His academic background includes a Doctorate in Statistics from University of California, Berkeley (2012), Master of Science in Computer Science from M.I.T., Bachelor of Science in Computer Science from California Institute of Technology, and Bachelor of Arts in Mathematics from Reed College. Prior to academia, he spent seven years as a high school teacher and tutor. Miratrix's research prioritizes minimal-assumption statistical approaches to validate data-driven arguments. Key interests include developing methods for characterizing variation in treatment impacts, analyzing post-treatment subgroups, and applying high-dimensional techniques to text summarization in legal, journalistic, and educational contexts. His work consistently bridges theoretical statistics with practical implementation challenges in real-world settings. Analysis of his recent publications (2023-2025) reveals three dominant trends: advancement of matching methodologies (e.g., synthetic controls, caliper matching), refinement of heterogeneous treatment effect estimation across multisite trials, and integration of machine learning with human coding for efficient text-based inference in educational assessments. These efforts demonstrate increasing focus on scalable, accessible tools for applied researchers. He contributes to methodological infrastructure through the CARES Lab and software packages like 'matchMulti' and 'textreg', providing practical implementation guides for complex statistical techniques. His work emphasizes translating advanced causal inference methods into usable frameworks for education researchers and policymakers.
Max Planck Institute for Research on Collective GoodsGermany
Friederike Mengel is a Professor of Economics at the University of Essex and holds a Visiting Professorship at Erasmus University Rotterdam. She is also a Fellow of the Academy of Social Sciences (UK). Her research focuses on Behavioural Economics , integrating Game Theory , Evolutionary Dynamics , and Social Network Analysis . Key research themes include social influence in networks , opinion dynamics , emergence of social norms , and links between social identity, bounded rationality, and discrimination . Recent work explores Covid-19 impacts on productivity and innovation in hybrid work environments . Her scientific awards include the Best Paper Award from Quantitative Economics (2018) and recognition as an Academy of Social Sciences Fellow. Her publications span topics like cooperation in viscous populations , strategic behavior in repeated games , and gender bias in opinion aggregation , with media coverage in outlets like The Economist and Financial Times .
Prof. Radek Erban is a Professor of Applied Mathematics at the Mathematical Institute , University of Oxford. He is affiliated with the Oxford Centre for Industrial and Applied Mathematics and works across interdisciplinary fields including Mathematical Biology, Stochastic Processes, and Reaction-Diffusion Systems. His research focuses on: Multiscale modeling of biological and chemical processes Stochastic simulation algorithms for reaction-diffusion systems Mathematical analysis of collective behavior in biological systems Computational methods for chemotaxis and gene regulatory networks Partial differential equation models for biological phenomena Recent work explores multi-resolution simulations of ions, morphogen gradient modeling, and hybrid numerical methods for stochastic processes. His publications span journals in applied mathematics, computational biology, and physical sciences. Current projects involve bridging atomistic and continuum models for chemical systems.
