Ayşe Sevtap Kestel is a full-time faculty member at the Department of Mathematics , Middle East Technical University. With over 133 publications in Web of Science and extensive international conference participation, her research spans actuarial science, financial mathematics, and risk modeling. Her work focuses on: Copula theory for dependence modeling Stochastic processes in insurance and pensions Machine learning applications for fraud detection Natural hazard risk assessment Reinsurance strategies and exposure curves Recent publications highlight her expertise in time-varying risk models, hybrid AI methods for asset pricing, and chronic disease comorbidity analysis. She has presented at major conferences like Insurance Mathematics & Economics and European Actuarial Journal conferences.
Prof. Dr. Roland Langrock holds the Chair of Statistics and Data Analysis at the Faculty of Economics, University of Bielefeld . He is a spokesperson for the Center for Statistics and a subproject manager in the Transregio 212 NC³ collaboration. His research spans ecological statistics, sports analytics, and time series modeling. 2026–present: Principal investigator for "Data-based indication of fraud in live betting" (DFG) 2025–present: Subproject manager D06 in TRR 212 NC³ 2021–present: ERASMUS representative for Master of Statistical Sciences Research Interests: His work focuses on hidden Markov models for analyzing animal movement, sports performance, and commercial data. Key applications include marine predator behavior , football match dynamics , and fraud detection in betting . He develops flexible statistical frameworks for state-switching processes across domains. Scientific Awards: Multiple German Research Foundation grants (2017–2026) and participation in EU-funded projects. Notable publications in Journal of the Royal Statistical Society , Ecology Letters , and Science . Additional Roles: Member of the Bielefeld Graduate School in Theoretical Sciences, organizer of advanced statistical methods courses, and contributor to software packages like moveHMM . His collaborations extend to marine biology (blue whales), subterranean rodent studies, and retail demand forecasting.
Mauro Gasparini is a Full Professor at the Department of Mathematical Sciences (DISMA) of Polytechnic University of Turin. He serves as Director of DISMA since 2019, Member of Academic Senate, and co-leader in the SmartData@PoliTO Big Data Laboratory. His career spans academia and industry, including roles at Purdue University (Assistant Professor 1992-1996) and Novartis (Senior Statistician 1996-1998). He has been Editor of Biometrical Journal (2012-2015) and maintains referee activities across international journals. PhD from University of Michigan (1992, Dirichlet process applications) Academic leadership: Department Director, Editorial boards, ISTAT Advisor Research spans Bayesian methodology with biomedical applications Maintains collaborations with Novartis, Chiesi, and research centers His research interests focus on Bayesian inference , Biostatistics , and Clinical trials methodology, particularly addressing issues in pharmaceutical development, genomic data analysis, and medical decision-making. Recent work includes vaccine efficacy modeling, optimal imaging timing for cancer diagnostics, and adaptive trial designs. Key publication trends show interdisciplinary applications in Statistics in Medicine , Biometrics , and Statistical Methods in Medical Research , with emphasis on biomedical data science, Bayesian adaptive methods, and clinical decision support systems. Scientific contributions include: Editor, Biometrical Journal (2012-2015) Advisor, Italian National Institute of Statistics (2020-2024) Leadership in multiple research projects (NODES, SORGENTE, IDEAS) As PhD advisor, he supervises students in: Shaoshi Tang (Clinical trial modeling) Saeed Sani (Biomedical data analysis) Marco Ratta (Genomic statistics) Luca Rondano (Bayesian methods) Vittorio Zampinetti (Tumor DNA sequencing) Fulvio Di Stefano (Evidence-based decision statistical methods) He leads research projects in pharmaceutical statistics, genomic surveillance, and spatial risk assessment frameworks, with recent emphasis on SARS-CoV-2 analysis and cancer progression modeling.
Nachiketa Sahoo is an Associate Professor in the Information Systems Department at Boston University's Questrom School of Business, where he has been faculty since 2011. His research sits at the intersection of Machine Learning and Information Systems, with particular focus on personalized recommender systems and user preference modeling. Research Focus: Dr. Sahoo's work centers on learning user preferences and decision-making processes from large disaggregate datasets. His primary projects include: Personalization and Matching: Developing systems to match items to users while examining stability and fairness implications Retail Analytics: Analyzing customer activity data to reduce product expiration through salesforce incentive alignment Data-driven Healthcare: Estimating value of genomic tests and physician decision modeling Content Recommendation: Studying multi-category utility models for diversified content consumption His recent publications demonstrate strong trends toward ethical implications of recommender systems, particularly regarding fairness and market stability, while maintaining technical depth in machine learning applications. Scientific Contributions: Developed novel methods for uncovering population response paths using time-series data Created approaches to match donors with philanthropic causes on crowdfunding platforms Designed salesforce compensation schemes reducing $15B+ in annual product waste Pioneered multi-category utility models for content recommendation Academic Leadership: Dr. Sahoo advises PhD students in Information Systems, with graduates securing tenure-track positions at institutions like University of Minnesota and University of South Florida. His teaching portfolio includes advanced machine learning courses and doctoral seminars on ML methods for social science research. He previously served as a Visiting Assistant Professor at Carnegie Mellon's Tepper School of Business. Education: He holds a PhD in Information Systems from Carnegie Mellon University's Heinz College, an MS in Knowledge Discovery and Data Mining from CMU's Machine Learning Department, and a B.Tech in Industrial Engineering from IIT Kharagpur. His industry experience includes software engineering at i2 Technologies (now JDA Software).
Panagiotis Papastamoulis serves as Assistant Professor at the Department of Statistics within the School of Information Sciences and Technology at Athens University of Economics and Business (AUEB). He joined AUEB in April 2020 after working as an Adjunct Lecturer from 2018-2019 and completing extensive postdoctoral research at prestigious institutions including the University of Manchester (2012-2018) and INRA in France (2011-2012). His educational background includes a BSc in Mathematics from the University of Patras (2003), an MSc in Applied Statistics (2006), and a PhD in Statistics (2010) from the University of Piraeus. His doctoral thesis addressed the label switching problem in Bayesian analysis of mixtures of distributions under the supervision of Professor G. Iliopoulos. Dr. Papastamoulis's research program centers on Bayesian and computational statistics, with particular expertise in finite mixture models, model-based clustering, and bioinformatics applications. His methodological contributions span theoretical developments in label switching solutions, reversible jump MCMC algorithms, and practical implementations for RNA-seq data analysis. His work demonstrates a consistent trajectory from foundational statistical theory to real-world biological applications. Analysis of his publication record reveals a strong focus on developing statistical methodology for complex data structures, with significant contributions to mixture modeling, Bayesian factor analysis, and bioinformatics. His most recent work (2023-2025) extends into cure rate modeling, directional data analysis, and multinomial mixture models for spatial data, showing continued innovation while maintaining connections to his core research themes. As an educator, he teaches undergraduate courses including Linear Models and Bayesian Inference Methods, and graduate courses such as Statistical Genetics-Bioinformatics and High Dimensional Statistics. He has also developed multiple open-source R packages that have become standard tools in the statistical community, including label.switching, BayesBinMix, and fabMix, which address fundamental challenges in mixture model analysis. Dr. Papastamoulis actively contributes to the academic community through organizing research seminars at AUEB and participating in conference committees, including the 22nd European Young Statisticians Meeting in 2021. His research integrates theoretical statistical development with practical computational implementations, creating tools that advance both methodology and application in multiple scientific domains.
Ganggang Xu is an Associate Professor (with tenure) in the Department of Management Science at the Miami Herbert Business School , University of Miami . He specializes in advanced statistical methodologies, particularly in nonparametric and semiparametric modeling, spatial statistics, and point process theory. Education: Ph.D. in Statistics, Texas A&M University (2011) B.S. in Statistics, Zhejiang University (2006) Research Interests: His research spans several key areas in modern statistics and data science. He has made significant contributions to nonparametric and semiparametric regression , particularly in the context of functional data analysis and spatial-temporal modeling . His work on point processes includes marked, multivariate, and clustered point processes, with applications ranging from neuroscience to social media behavior. He also explores Bayesian hierarchical models and model selection techniques, often integrating computational efficiency with theoretical rigor. Publications Overview: His recent publications (2023–2025) reflect a strong focus on machine learning-enhanced statistical modeling , including tree-based estimation of intensity functions, network autoregressive models, and quantized inference. He has also contributed to applied domains such as medical imaging and inventory control , demonstrating the broad applicability of his methodological work. Grants & Collaborations: While specific grants are not listed in the provided text, his extensive publication record with multiple co-authors across institutions suggests active collaboration and possible funding from NSF or NIH-equivalent bodies in statistics and data science. Labs & Teams: Though no specific lab is mentioned, his affiliations and co-authorships imply involvement in interdisciplinary research teams at the University of Miami, especially within the business analytics and statistical modeling domains.
Johann Faouzi is an Assistant Professor of Computer Science at École Nationale de la Statistique et de l'Analyse de l'Information (ENSAI) and a permanent member of Centre de Recherche en Économie et Statistique (CREST). He holds a PhD in Computer Science from Sorbonne Université (2020) and conducted postdoctoral research at the Paris Brain Institute. PhD: Sorbonne Université (2020) Postdoc: Paris Brain Institute (ARAMIS & Corti-Corvol teams) His research focuses on machine learning for time series and signals with applications in precision medicine and open source software . He has developed pyts , a Python package for time series classification and contributed to scikit-learn , tslearn , and other open source libraries. His work addresses predictive modeling for neurodegenerative diseases like Parkinson’s, including impulse control disorders and cognitive decline. Article trends show strong integration of machine learning with medical imaging (MRI, functional connectivity), genetic analysis (risk scores, polygenic factors), and clinical decision support . Publications span time series classification algorithms, software tool development, and neurology applications. At Paris Brain Institute, he collaborated with teams studying neurodegenerative diseases, applying multivariate classification and automated diagnosis systems to improve patient outcomes. His commitment to open science emphasizes reproducibility and community-driven software development.
Professor Geoff Webb is a world-leading data scientist at Monash University , serving as Director of the Monash University Centre for Data Science within the Faculty of Information Technology . His research focuses on leveraging data science to enable evidence-based decision making and derive actionable insights through artificial intelligence, machine learning, and big data analytics. Core expertise in data mining , bioinformatics , and computational biology Developed Magnum Opus software and contributed to the Weka machine learning workbench Recipient of the Eureka Prize for Excellence in Data Science and leadership roles in major data mining conferences Research Interests Professor Webb's work spans artificial intelligence , machine learning , and data analytics , with a focus on black-box user modelling , interactive data analytics , and statistically-sound pattern discovery . His recent publications highlight advancements in Bayesian networks , time series analysis , and genomic data interpretation , demonstrating his interdisciplinary impact. Scientific Contributions AI in healthcare : EHR-ML framework for clinical records analysis Biological applications : KcatNet for enzyme prediction, PFresGO for protein function Data mining innovations : OPUS search algorithm, proximity forest techniques As a technical adviser to data science company Froomle , he bridges academic research with real-world applications. His work has been recognized through numerous research awards and leadership as Editor-in-Chief of Data Mining and Knowledge Discovery for a decade.
Karl Øyvind Mikalsen is an Associate Professor at the Department of Clinical Medicine, UiT The Arctic University of Norway. He is also a division leader at the Center for Patient-Near Artificial Intelligence (SPKI) at University Hospital of North Norway (UNN). His research focuses on applying artificial intelligence (AI) to healthcare, particularly in analyzing medical images, speech, and text using language models and machine learning, with applications such as anonymizing patient records and evaluating AI tools for clinical use. Research Interests: Mikalsen’s work bridges medical informatics and computer science, emphasizing explainable AI, clinical time series analysis, and medical image processing. He collaborates with UiT and UNN to integrate AI into healthcare systems. Recent Publications: His studies span AI-driven clinical coding, breast cancer detection via mammography, surgical infection prediction, and self-supervised representation learning for medical data. Key methodologies include transformer models, kernel methods for time series, and uncertainty-aware ensembles. Collaborations: Mikalsen works with researchers such as Robert Jenssen, Michael Kampffmeyer, and Arthur Revhaug, focusing on clinically relevant AI applications in Scandinavia.
Dr. Jing Jiang is an Associate Professor in the School of Computer Science and a core member of the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). As an ARC DECRA Fellow, she has secured over AU$2 million in research funding through multiple ARC grants, CSIRO/Data61 projects, and industry collaborations. Her work bridges theoretical advances in machine learning with practical applications across various domains. Dr. Jiang's research focuses on machine learning, particularly federated learning, reinforcement learning, and foundation models. She explores how to make these technologies work effectively in heterogeneous environments, addressing challenges like data privacy, non-IID data distributions, and efficient communication. Her work spans both theoretical foundations and practical implementations for real-world applications. Her publications demonstrate a strong trend toward personalized federated learning approaches, with significant contributions to recommender systems, time series analysis, and weather forecasting. She has developed novel techniques like variational autoencoder approaches for federated collaborative filtering and adaptive prompt learning for foundation models on devices. Dr. Jiang has received several notable recognitions: ARC DECRA Fellow Awardee of the Australian International Postgraduate Research Scholarship (IPRS) Dr. Jiang has successfully led multiple major research projects, including two ARC Discovery Projects, one ARC Linkage Project, and a CSIRO/Data61 CRP project where she served as lead Chief Investigator. She has supervised numerous PhD and Master's students and actively collaborates with industry partners on applied research. As a core member of the Australian Artificial Intelligence Institute (AAII) at UTS, Dr. Jiang contributes to a vibrant research ecosystem focused on cutting-edge AI research. She collaborates closely with Professor Guodong Long and other researchers on various machine learning projects, and serves in leadership roles including program co-chair for major conferences like ADMA2023.
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.
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.
Dr. Jurgen van den Hoogen serves as a Researcher at Tilburg University's Department of Computational Cognitive Science within the Tilburg School of Humanities and Digital Sciences. Having recently completed his doctoral studies with thesis publication in January 2025, he represents an emerging researcher in the field of machine learning applications for time series data. His research focuses on innovative applications of Convolutional Neural Networks for time series analysis, with particular emphasis on industrial fault detection and seismic activity monitoring. Dr. van den Hoogen's work demonstrates how specialized CNN architectures can effectively process raw sensor data with minimal computational requirements, making them suitable for edge computing applications. His doctoral research made significant contributions to the field, particularly in developing wide-kernel CNN architectures optimized for time series data processing. The research demonstrated that processing multivariate time series in a univariate manner with separate inputs yields optimal results, and that graph-based architectures significantly improve performance in seismic domains characterized by extensive sensor networks. Dr. van den Hoogen's methodological expertise includes architectural optimizations through adaptive input layers and residual learning, hyperparameter analysis specifically tailored for time series CNNs, and transfer learning applications for both classification and regression tasks in time series analysis.
Dr. Ahmad Farooqi serves as Assistant Professor at Central Michigan University College of Medicine, where he provides statistical leadership through the Clinical Research Institute (CRI). He delivers weekly statistics instruction to medical trainees at Children's Hospital of Michigan and offers comprehensive analytical support for clinical research projects across the institution. His academic credentials include: Ph.D. in Biostatistics from Wayne State University M.S. and M.A. in Statistics from University of Windsor M.Phil in Statistics from Government College University, Lahore Dr. Farooqi's research pioneers non-parametric, robust, and exact statistical methodologies applied to pediatric clinical challenges. His work addresses critical gaps in small-sample clinical studies through innovative approaches to longitudinal data analysis, survival modeling, and diagnostic accuracy assessment. As a SAS-certified expert, he implements advanced techniques across SAS, R, and SPSS environments to solve complex analytical problems in cardiology, emergency medicine, and immunology research. His publication record demonstrates consistent high-impact contributions across pediatric specialties, with particular emphasis on cardiac outcomes, emergency department operations, and immunological responses. The research portfolio reveals strong methodological rigor combined with practical clinical applications, often addressing resource-constrained scenarios requiring robust analytical solutions. Key recognitions include: Best Article Award in Pediatric Neurology (2019) Best Paper Award at ICCS-15 statistical conference Wayne State University Full Tuition Scholarship Dr. Farooqi actively mentors medical researchers through statistical consulting and teaching, with collaborations spanning multiple departments and international institutions. His role in the CRI facilitates cross-disciplinary research partnerships while advancing methodological standards in clinical investigation. Current work focuses on refining statistical approaches for pediatric cardiac interventions and emergency care protocols through ongoing clinical data analysis projects.
Romain Biard is a Senior Lecturer in Mathematics at the University of Franche-Comté, holding dual research affiliations with the Besançon Mathematics Laboratory (UMR 6623, LMB) and the Research Center on Economic Strategies (UR 3190, CRESE). His career bridges theoretical mathematics with practical applications in economics and healthcare systems. His primary research interests span Markov processes, Ruin theory, Extreme value theory, Optimal allocations, and Game theory. These areas converge in his work on modeling complex systems with random elements, particularly focusing on risk assessment and resource allocation problems. His research demonstrates how mathematical frameworks can address real-world challenges in healthcare management and economic competition. Analysis of his recent publications reveals a strong trend toward applied research with immediate societal relevance. His 2023-2024 work on healthcare resource allocation (nursing shortages, ventilator availability) applies sophisticated Markov process modeling to critical hospital management problems. Earlier work on economic competition models demonstrates how random entry of firms affects market equilibrium, with specific applications to the taxi industry facing competition from ride-hailing services. Active researcher with publications spanning 2008-2024 Primary collaborator network includes Marc Deschamps, Mostapha Diss, Alexis Roussel, and Stéphane Loisel Publications appear in high-quality journals across mathematics, statistics, economics, and actuarial science Biard's research approach consistently combines rigorous mathematical theory with practical applications, making significant contributions to both theoretical developments and real-world problem solving. His current work suggests continued focus on applying stochastic modeling to healthcare resource challenges and economic competition dynamics.
