Mehrdad Naderi is a Lecturer in Statistics at the Department of Mathematics, Physics, and Electrical Engineering , Northumbria University . His academic journey includes a PhD in Mathematical Statistics from Shahid Bahonar University of Kerman (2017) and postdoctoral research at National Chung Hsing University (Taiwan), Ferdowsi University of Mashhad (Iran), and University of Pretoria (South Africa). Education: PhD in Mathematical Statistics, Shahid Bahonar University of Kerman (2017) His research focuses on applied statistical inference with emphasis on classification , cluster analysis , factor analysis , finite mixture models , and EM algorithm for robust estimation. He has contributed to multivariate and matrix-variate analysis, particularly in handling outliers and asymmetrical data structures. Recent work includes three-way data clustering using matrix-variate normal distributions and robust Bayesian inference for censored mixture models. His publications demonstrate expertise in distribution theory, statistical computation, and applications to financial data, environmental modeling, and astrophysics. Current collaborations span multiple institutions, focusing on heavy-tailed distributions and computational methods for complex data structures.
Dr. Marek Olesz is a Professor at the Department of Electrical Power Engineering, Faculty of Electrical and Automation Engineering, Gdańsk University of Technology. His research encompasses high-voltage engineering, insulation diagnostics, partial discharges, and electromagnetic compatibility. He holds leadership roles in organizations including the Polish Society of Theoretical and Applied Electrical Engineering (Chairman) and the Polish Committee for Lightning Protection (Vice-Chairman). Research interests include: Quality of electricity and electromagnetic compatibility in power systems Degradation mechanisms in polyethylene insulation Partial discharge measurement techniques for cable and transformer diagnostics Innovations in surge arrester testing and high-voltage line design His recent publications (2023-2025) focus on AI-driven transformer lifetime prediction, CNN-based corrosion classification in cables, and safety enhancements for photovoltaic systems. Notable projects include: Pylon 2 : Developing innovative structures for high-voltage lines with integrated communication systems. Stratus : Creating high-power electromagnetic pulse systems for UAV countermeasures. Dr. Olesz is an active member of the High Voltage Team , which researches short-circuit dynamics, insulation degradation, and power quality. He has supervised 200+ teaching activities but no specific advisees are listed.
Professor Steven Dufour is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. With a career spanning over two decades since completing his Ph.D. in 1999, he has established himself as an expert in numerical modeling, particularly in the areas of finite element methods and multiphase systems. His academic journey began with B.Sc. and M.Sc. degrees from the University of Montreal, followed by a Ph.D. from Polytechnique Montréal. Professor Dufour's research focuses on the numerical modeling of free surface flows in industrial processes, with expertise recognized by NSERC in modeling, simulation, and finite element methods (topic 2107) and polyphase systems (topic 2202). Professor Dufour's research interests span computational fluid dynamics, finite element analysis, and more recently, the integration of machine learning techniques with traditional numerical methods. His work has evolved from foundational research in adaptive finite element methods for multiphase flows to cutting-edge applications combining physics-informed neural networks with computational fluid dynamics, electromagnetic field analysis, and millimeter-wave sensing. His publication record demonstrates consistent research productivity, with 23 publications documented across computational mathematics and engineering applications. The most recent publications from 2024 show his adaptation to emerging methodologies in computational science, particularly the application of physics-informed neural networks to solve complex fluid dynamics problems. NSERC Expertise: Modeling, simulation and finite element methods (2107) NSERC Expertise: Polyphase Systems (2202) Supervised 7 doctoral students to completion Supervised 13 master's students to completion Professor Dufour has maintained active research funding and supervision throughout his career, mentoring students in both theoretical numerical methods and practical engineering applications. His collaborative work spans multiple engineering disciplines, connecting computational mathematics with real-world industrial and biomedical problems.
Dr. Wesley Adam Mayer is an Associate Professor of Urology and Vice Chair for Education in the Scott Department of Urology at Baylor College of Medicine, where he also serves as Assistant Dean for Graduate Medical Education. His clinical practice focuses on advanced minimally invasive surgical treatments for complex urologic conditions. BA with Distinction from Cornell University (2001) MD with Highest Honors from Baylor College of Medicine (2005) General Surgery Internship and Urology Residency at University of Pennsylvania (2006-2010) Minimally Invasive Surgery Fellowship at Baylor College of Medicine (2011) Dr. Mayer's research centers on robotic and laparoscopic surgical innovations for kidney cancer, stone disease, and transplant urology. His work on renal mass scoring systems and single-site donor nephrectomy has established new clinical benchmarks. The integration of surgical education methodology with technical innovation represents a unique aspect of his scholarly contributions. His publication record shows consistent output in high-impact urology journals, with recent emphasis on surgical education frameworks and minimally invasive technique optimization. The research demonstrates strong translational impact, with several innovations adopted into national training curricula. Two-time Fulbright Faculty Excellence Award recipient (Educational Leadership 2019, Teaching 2018) Faculty Teacher of the Year (2021) Multiple "Top Doctor" designations (Houstonia Magazine 2019-2021, TX Top Docs 2021) American Urological Association Teaching Award nominee (2020) Alpha Omega Alpha Honor Society inductee As an educator, Dr. Mayer directs multiple residency training initiatives and serves on national committees shaping urologic education. His ACGME Urology Milestones 2.0 work has transformed competency assessment across all US residency programs. He maintains active collaborations with transplant centers and regularly contributes to national educational courses including the AUA Oral Board Examination Review.
Conghan Dong is the William W. Elliott Assistant Research Professor in the Department of Mathematics at Duke University. He teaches courses in Ordinary and Partial Differential Equations at both undergraduate (MATH 353) and graduate (MATH 753) levels, with multiple sections scheduled for Fall 2025. Dr. Dong's research focuses on geometric analysis and differential geometry, with particular expertise in: Ricci curvature and Einstein metrics Positive mass theorem stability in general relativity Scalar curvature and its geometric implications Conformal geometry and metric spaces Three-manifold topology Convergence theories for Riemannian manifolds His recent publications demonstrate significant contributions to understanding stability properties of geometric structures, particularly his 2025 Inventiones Mathematicae paper confirming Huisken and Ilmanen's conjecture about Euclidean 3-space stability for the positive mass theorem. His work bridges pure mathematics with applications in theoretical physics, appearing in top-tier journals across geometric analysis. Dr. Dong collaborates with researchers including X. Chen, A. Song, Y. Li, and K. Xu, indicating an active research network in geometric analysis. His publication record shows consistent output with multiple papers in prestigious journals over recent years, reflecting an active and developing research program in differential geometry.
Marian-Andrei Rizoiu is an Associate Professor leading the Behavioral Data Science lab at the University of Technology Sydney's Data Science Institute, Faculty of Engineering and Information Technology. He holds concurrent appointments as an Honorary Lecturer at Australian National University and Honorary Research Scientist at Data61. Previously, he has held visiting professor positions at Imperial College London, Jean Monnet University, and Max Planck Institute for Software Systems. Associate Professor in Behavioral Data Science, UTS Data Science Institute (Jan 2024 - present) Senior Lecturer in Behavioral Data Science, UTS Data Science Institute (Jul 2021 - Jan 2024) Lecturer in Computer Science, UTS Faculty of Engineering and Information Technology (Feb 2019 - Jul 2021) Dr. Rizoiu's research focuses on interdisciplinary work crossing computer and social sciences, blending psycholinguistics, digital communication, and stochastic modeling to understand human attention dynamics online, the emergence of influence, and opinion polarization. His key contributions include developing theoretical models for online information diffusion that can account for complex social phenomena, and building skill-based real-time occupation transition recommender systems that link social media-predicted personality profiles with occupation skill requirements. His research outputs reveal strong trends in misinformation detection, social influence measurement, and online radicalization pathways. Recent publications demonstrate sophisticated modeling approaches including state space models for early misinformation prediction, multivariate Hawkes processes for analyzing partially interval-censored data, and ideology detection pipelines. His work spans computational social science, machine learning, and practical applications for countering harmful online content. Excellence Award and Academic of the Year at the 2023 Australian Defence Industry Awards ADMA'22 Best Application Paper Dr. Rizoiu has successfully secured over $2.9 million in research funding from selective funders including Meta Research, Defence Science and Technology Group, Department of Home Affairs, and Defence Innovation Network. He has supervised 3 PhD students to completion and more than 10 Honours students, most achieving High Distinction. His research has been applied in real-world contexts including serving as an expert for NSW government's Defamation Law Reform and providing evidence for Australian Federal Senate inquiry into media diversity. He leads the Behavioral Data Science lab which focuses on modeling human behavior in online environments, with particular emphasis on mis- and disinformation detection and labor market analysis. The lab has developed tools like TRACK, UTS OPEN's software for recommending personalized learning pathways, used by over 750 students and professionals.
Niki Kilbertus is a Professor in the Department of Informatics at the Technical University of Munich and a group leader at Helmholtz AI (Helmholtz Munich). They are also affiliated with MCML, the Konrad Zuse School relAI, and the Munich Unit of ELLIS. Since 2024, they have been a member of the Junge Akademie and received the Leopoldina Prize for Young Scientists. In 2025, they were awarded an ERC Starting Grant and achieved tenure at TUM. Professor Kilbertus's research focuses on causal machine learning, mechanistic ML, dynamical systems, and AI for science. Their work spans theoretical foundations of causal inference and practical applications across scientific domains. They have made significant contributions to causal effect estimation, causal discovery in stochastic processes, learning differential equations, and fair machine learning. Their research often bridges computer science with physics, biology, and climate science, demonstrating the interdisciplinary nature of their work. Professor Kilbertus has published extensively in top machine learning venues including NeurIPS, ICML, and ICLR, with numerous publications in 2024-2025. Their recent work shows a strong trend toward causal discovery in continuous-time systems, intervention modeling, and physics-informed machine learning applications. Scientific Awards: Leopoldina Prize for Young Scientists (2024) ERC Starting Grant (2025) Professor Kilbertus actively supervises multiple PhD students and collaborates with researchers across institutions including Max Planck Institutes and Helmholtz centers. They serve as an Action Editor for TMLR and regularly review for major ML conferences. The research group is well-funded through the ERC grant and institutional support from TUM and Helmholtz AI, enabling active recruitment of new PhD students and postdocs. Based at Technical University of Munich and Helmholtz AI, Professor Kilbertus's team works at the intersection of theoretical machine learning and scientific applications, with particular strengths in causal reasoning for complex dynamical systems.
Dr. Sara J. Swanson is a Professor and Division Chief of Neuropsychology in the Department of Neurology at the Medical College of Wisconsin. She also holds adjunct appointments as an Associate Professor in the Psychology Department at the University of Wisconsin-Milwaukee and as a Clinical Associate Professor in the Psychology Department at Marquette University. Dr. Swanson is a Clinical Neuropsychologist at Froedtert Memorial Lutheran Hospital and St. Luke's Medical Center, and she is a member of both the Neuroscience Research Center and the Wisconsin Institute of NeuroScience (WINS). Dr. Swanson's research primarily focuses on the neuropsychology of epilepsy, with special emphasis on presurgical evaluation using functional MRI and Wada testing. Her work examines language lateralization, memory assessment, and cognitive outcomes following epilepsy surgery. She has been involved in numerous multicenter collaborations, particularly through the FMRI in Anterior Temporal Epilepsy Surgery (FATES) Study group. Her research has significant clinical implications for improving surgical outcomes and understanding brain-behavior relationships in epilepsy patients. Analysis of Dr. Swanson's recent publications (2020-2025) reveals a continued focus on predicting cognitive outcomes following epilepsy surgery using advanced neuroimaging techniques. Her work increasingly incorporates multicenter data collection and standardized assessment protocols. Key themes include the relationship between fMRI findings and postoperative naming/memory outcomes, application of standardized cognitive classification systems to epilepsy, and exploration of novel approaches to neuromodulation therapies. Outstanding Teachers for 2008-2009, Medical College of Wisconsin Senior Female Faculty Pin Ceremony Fellow, Division 40 of the American Psychological Association Alumni Merit Scholarship, Washington State University Paul Dupertuis Scholarship, Washington State University Women of Achievement Award, Washington State University Dr. Swanson has mentored over 20 postdoctoral researchers and graduate students throughout her career, including Julie Janecek, David Sabsevitz, and Bradley Anderson. She has served as Faculty Sponsor for multiple Epilepsy Foundation of America Behavioral Science Student Fellowships and has been involved in numerous NIH-funded and foundation-supported research projects. Her leadership extends to serving on the Board of Directors for the American Academy of Clinical Neuropsychology and the American Board of Clinical Neuropsychology, as well as participating in national guideline development for the use of fMRI in epilepsy surgery evaluation. As Division Chief of Neuropsychology, Dr. Swanson oversees the Adult Neuropsychological Services within the Adult Comprehensive Epilepsy Program. She has developed clinical protocols for neuropsychological evaluation of epilepsy surgery candidates, including testing protocols, lateralization ratings, and Wada examination procedures. Her work bridges clinical practice and research, ensuring that advances in neuropsychological assessment directly inform patient care.
Olivier Cailloux serves as a Lecturer at LAMSADE (Laboratoire d'Analyse et Modélisation de Systèmes pour l'Aide à la Décision) within Université Paris-Dauphine, part of PSL University. His academic career spans computer science and decision theory, with significant contributions to the Decision Deck project as a founding member. This open-source initiative provides practical tools for decision-making methodology practitioners and educators. Cailloux's research expertise centers on decision theory and social choice, with particular emphasis on classification aggregation, voting systems, and multicriteria analysis. His work bridges theoretical foundations with practical applications, ranging from economic design principles to bioinformatics implementations. The interdisciplinary nature of his research is evident in collaborations spanning economics, computer science, and molecular biology, particularly in genomic analysis of the BRCA1 gene. His publications demonstrate a consistent focus on formal frameworks for deliberated judgment and robust decision support systems. Analysis of Cailloux's publication record reveals strong trends in computational social choice and decision support methodologies. His recent work (2023-2024) focuses on classification aggregation without unanimity requirements, representing cutting-edge developments in social choice theory. Earlier contributions established foundations for preference modeling with incomplete information and formal frameworks for deliberated judgment. His interdisciplinary approach is particularly notable in bioinformatics applications where decision theory principles are applied to genomic analysis. Cailloux maintains active research collaborations across multiple institutions, as evidenced by his co-authored publications with researchers from diverse fields. His work with the Decision Deck project demonstrates commitment to creating accessible, open-source tools for both academic and practical applications of decision science. The laboratory environment at LAMSADE provides a fertile ground for his interdisciplinary research, connecting theoretical computer science with real-world decision challenges.
Holger R. Dullin is a Professor of Applied Mathematics at the University of Sydney's School of Mathematics and Statistics. His research spans multiple areas of dynamical systems theory with particular emphasis on Hamiltonian systems. He maintains an active research program with consistent publications in top mathematical physics journals and teaches advanced courses including Lagrangian and Hamiltonian Dynamics (MATH3977), Nonlinear ODEs (MATH3063), and Linear Algebra (MATH1902). Dullin's research focuses on Hamiltonian Dynamical Systems , with significant contributions to Integrable Systems (particularly topology, action-angle variables, and Hamiltonian/Quantum Monodromy), Classical Mechanics (N-body problems, rigid body dynamics), Bifurcation Theory (twistless bifurcations, Hamiltonian Hopf), and Fluid Dynamics (Euler equations). His work often bridges pure mathematics with physical applications, especially in celestial mechanics and biomechanics. He has developed novel approaches to understanding geometric phases, symplectic invariants, and the dynamics of Hamiltonian maps including billiards. Analysis of his recent publications reveals a consistent focus on monodromy phenomena across different physical systems, regularization techniques for singularities in dynamical systems, and stability analysis of fluid flows. His work shows increasing interdisciplinary connections between mathematical physics, quantum mechanics, and celestial mechanics, with several papers exploring the geometric structure of integrable systems and their quantum counterparts. The research demonstrates sophisticated mathematical techniques applied to concrete physical problems. Dullin maintains an active research group evidenced by numerous collaborations with mathematicians internationally. His work on the Kovalevskaya top, documented in his PhD thesis and subsequent publications, remains influential in the field of integrable systems. He has developed visualization techniques for complex dynamical systems, including Poincaré sections and energy surfaces in action space.
Charles Louis Fefferman is the Herbert E. Jones, Jr. '43 University Professor of Mathematics at Princeton University, where he has held a faculty position since 1977. Previously, he served as a full professor at the University of Chicago from 1971 to 1977, becoming the youngest full professor in U.S. history at age 22. His academic journey began at the University of Maryland, College Park, where he earned his undergraduate degree at 17 before completing his PhD at Princeton under Elias Stein at age 20. Fefferman's research spans mathematical analysis with particular emphasis on harmonic analysis, partial differential equations, and complex analysis. His groundbreaking work on singular integrals, Hardy spaces, and the Bergman kernel revolutionized these fields, leading to his Fields Medal in 1978. More recently, he has made significant contributions to Whitney extension problems, fluid dynamics singularity formation, and mathematical aspects of topological materials. His publication record shows remarkable consistency over five decades, with recent work (2019-2023) focusing on manifold learning, smooth function interpolation, and quantum systems. These publications demonstrate both theoretical depth and increasing connections to data science applications, maintaining his position at the forefront of mathematical research. Fefferman's scientific honors form an exceptional constellation of recognition: Fields Medal (1978) Alan T. Waterman Award (1976, inaugural recipient) Salem Prize (1971) Bergman Prize (1992) Bôcher Memorial Prize (2008) Wolf Prize in Mathematics (2017) BBVA Foundation Frontiers of Knowledge Award (2021) As an advisor, Fefferman has mentored numerous doctoral students who have become leaders in their fields, including Matei Machedon, Luis Seco, and Michael Christ. His research group continues to explore fundamental questions in analysis while developing mathematical frameworks for emerging applications in data science and quantum physics. Fefferman remains actively engaged in research, with publications through 2023 demonstrating his continued intellectual vitality and mathematical creativity.
Jordy Timo van Velthoven is a researcher in the Department of Mathematics at the University of Vienna's Faculty of Mathematics. His office is located at Oskar-Morgenstern-Platz 1, Room 05.135 in Vienna, Austria. He teaches advanced seminars in Harmonic Analysis (course codes 510003 SE, 510004 SE, 510005 SE) for the 2024W, 2025S, and 2025W academic terms. His research focuses on Harmonic Analysis, Fourier Analysis, and Representation Theory of Lie Groups , with specific expertise in asymptotics of matrix coefficients, density conditions for coherent state subsystems, localisation of frames and Riesz bases, and multiparameter function spaces. His work bridges pure mathematics with applications in signal processing and functional analysis. Analysis of his recent publications reveals a strong emphasis on frame theory, coorbit spaces, and function space classifications across homogeneous groups. His collaborations span international institutions, with frequent work on density conditions, wavelet analysis, and Lie group representations. Key recurring themes include anisotropic spaces, discrete geometry in harmonic analysis, and operator theory applications. His scientific contributions have been published in top-tier mathematics journals including Annals of Mathematics , Journal of Functional Analysis , and Proceedings of the American Mathematical Society , though no specific awards or fellowships are documented in the provided materials. Van Velthoven actively mentors graduate students through his Harmonic Analysis seminars and supervises research projects. His work involves significant collaboration with prominent mathematicians like Führ, Voigtlaender, and Romero. Current research directions include extending density theorems to non-unimodular groups and developing molecular decompositions for quasi-Banach coorbit spaces.
Dr. Ricardo E. Buitrago R. is a Research Professor of Strategy and International Management at EGADE Business School of Tecnológico de Monterrey, where he serves as Director of PhDs. He also maintains a research associate affiliation with the School of Management at Universidad del Rosario in Colombia. His expertise spans Global Business Strategy, competitive advantage, and Complexity of Systems Dynamics. His educational background includes: PhD in Modeling in Public Policy and Management from Università degli Studi di Palermo Master in International Relations from University of Bogotá, Jorge Tadeo Lozano Specialist in International Business Management from Pontificia Universidad Javeriana Graduate in International Trade from University of Bogotá, Jorge Tadeo Lozano Dottorato di ricerca in Dinamica dei Sistemi from University of Bogotá, Jorge Tadeo Lozano Dr. Buitrago's research focuses on the critical intersection of international political economy, institutions, strategy, and international business, with particular emphasis on emerging economies. His work examines how institutional quality affects international competitiveness and business strategies, employing advanced quantitative methods including Partial Least Squares Structural Equation Modeling (PLS-SEM). He has developed significant expertise in analyzing how home and host country institutions interact to shape foreign direct investment patterns, especially in Latin American contexts. His publication record demonstrates a clear research trajectory from foundational work on competitiveness frameworks toward increasingly nuanced analyses of institutional quality, investment disputes, and emerging business models in developing markets. Recent publications address cutting-edge topics including the rise of unicorns in emerging markets and complex institutional interactions affecting international business expansion. Dr. Buitrago is actively engaged in the academic community through several key organizations: Executive Committee member of the Business Association for Latin American Studies (BALAS) Member of the Academy of International Business (AIB) Member of the Latin American Studies Association (LASA) Ambassador for the Andean region of the IE-Scholars network He has served as editor and reviewer for prestigious journals including the Journal of Business Research, International Business Review, European Management Journal, and International Studies of Management & Organization. Prior to his academic career, Dr. Buitrago worked in banking and as a consultant for financial institutions in Colombia and Puerto Rico, bringing valuable practical business experience to his academic work.
Giulia Cereda serves as Associate Professor in the Department of Statistics, Computer Science, and Applications 'G. Parenti' (DiSIA) at the University of Florence since 2025. Her academic journey includes prior roles as Fixed-term Researcher (RTD-b, 2022-2024), Research Fellow (2021-2022), and Swiss National Science Foundation Postdoc Mobility Fellow (2019-2021) at Leiden University and University of Florence. She holds a Joint PhD in Statistics from Leiden University and University of Lausanne (2011-2016), complemented by Master's and Bachelor's degrees in Mathematics from the University of Milan. Her research spans forensic statistics with focus on rare type match problems in DNA evidence evaluation, medical statistics applied to SARS-CoV-2 pandemic modeling, and machine learning implementations for biogeographical ancestry prediction. Recent publications demonstrate methodological innovations in Bayesian approaches for forensic evidence, compartmental modeling of epidemic dynamics, and supervised learning applications in population genetics. Analysis of her 14 most recent publications (2020-2025) reveals dual research thrusts: (1) forensic statistics addressing DNA mixture interpretation and rare haplotype matching through Bayesian frameworks, and (2) epidemiological modeling of smoking dynamics and SARS-CoV-2 transmission using compartmental models with uncertainty quantification. Her work bridges theoretical statistics with practical public health and forensic applications, frequently employing machine learning for complex prediction tasks. Supported by the Swiss National Science Foundation for postdoctoral research (2019-2021), she has contributed to pandemic response through Tuscan regional modeling and school-based screening strategies. Current office hours are Thursdays 3:00-4:00 PM by appointment, with ongoing research in forensic identification systems and epidemic forecasting methodologies.
