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Ben MoewsView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Statistical Inference
• +7 more

Ben Moews is a Lecturer in Predictive Analytics at the University of Edinburgh Business School. He serves as Director of the Domain-Driven Machine Learning Lab, PhD Programme in Financial Technology, and Chair of the Centre for Data, Culture and Society's Advisory Board. PhD in Astrophysics (University of Edinburgh) MSc in Artificial Intelligence (University of Edinburgh) Professional Statistician (American Statistical Association) His research bridges artificial intelligence with financial technology and astrophysics , focusing on domain-specific machine learning and privacy-preserving synthetic data . Recent projects include generative modeling for market microstructure , AI-driven cosmological simulations , and geospatial analytics for societal challenges . Research outputs span Physics-informed neural networks (2024), random number generator analysis in financial markets (2024), and synthetic data evaluation frameworks (2024). His work combines high-performance computing with real-world applications in central banking and public health . Fellowship of the Higher Education Academy (2025) McWilliams Fellowship (2021) Principal’s Career Development Scholarship (2017) Professional Statistician accreditation (2023) Winton Thesis Prize (2022) As Director of the Domain-Driven Machine Learning Lab and FinTech PhD Programme, he leads initiatives integrating data science with domain-specific challenges across business, finance, and public policy. He collaborates with the Scottish Centre for Crime and Justice Research and Edinburgh Centre for Financial Innovations.

Ran JinView profile

Professor

• Manufacturing Systems Engineering
• System Informatics and Control
• Data Science and Visualization
• +6 more

Ran Jin is a Professor in the Department of Industrial and Systems Engineering at Virginia Tech, leading the Data Science and Visualization Lab. He holds academic appointments in the College of Engineering. His research focuses on integrating data science with manufacturing systems engineering, emphasizing system informatics, quality control, and process optimization. He has contributed to advancements in profile modeling, defect detection, and statistical process monitoring in industries like additive manufacturing. Education : Ph.D., Georgia Institute of Technology, 2011 M.A. and M.S., University of Michigan, 2009 and 2007 B.Eng., Tsinghua University, 2005 Research Interests : Jin's work bridges statistical methodologies with engineering systems, particularly in high-definition profile data analysis, ensemble modeling for manufacturing scale-up, and cybermanufacturing challenges. He develops algorithms for process variation reduction and real-time monitoring. His recent focus includes AI-driven solutions for manufacturing resilience and distributed data systems. Recognition : Jin has received awards such as the Best Applied Paper Award (IIE Transactions) and NSF grants for projects like 'EAGER: AM: CoFedAI' and 'QQ Models' for mixed quality responses. His contributions span academic journals and industry partnerships, including collaborations with JSME Co. Ltd. and the Commonwealth Center for Advanced Manufacturing. Grants & Projects : Key initiatives include 'Distributed Data Filtering for Fog Manufacturing' and 'AI Incubation in Manufacturing.' He has served as PI or Co-PI on NSF-funded projects addressing experimental design, anomaly detection, and thermal spray process modeling. Labs & Teams : Directs the Data Science and Visualization Lab, fostering innovation in AI applications for smart manufacturing and system informatics. Engages in interdisciplinary research through roles like Co-Chair of the Commission on Research at Virginia Tech.

Yusen XiaView profile

Professor

• Machine Learning
• Deep Learning
• Generative AI
• +5 more

Yusen Xia is a Professor and Director of the Institute for Insight at the Robinson College of Business, Georgia State University. His research focuses on machine learning, deep learning, healthcare analytics, and supply chain management. He earned his Ph.D. from the University of Texas at Austin and has a strong record of industry collaboration and NSF funding. He teaches courses such as Mathematical Foundations for Analytics and Deep Learning Analytics, and his work bridges theoretical research with practical business solutions. Education: Ph.D., University of Texas at Austin M.S., Chinese Academy of Sciences B.S., XiDian University Research Interests: Dr. Xia’s work integrates advanced analytics with business challenges. He develops AI-driven solutions for healthcare operations, financial decision-making, and supply chain optimization. His recent focus includes fairness in AI models, blockchain applications in logistics, and telehealth adoption trends. Key methodologies include deep learning, Bayesian models, and spatio-temporal analysis. Publications: Over 30 top-tier journal articles in Management Science, Operations Research, and Harvard Business Law Review highlight his contributions to operations management, financial analytics, and healthcare systems. Awards: Multiple teaching and research accolades include the Faculty Recognition Award (2012, 2017), Texas Excellence Teaching Award (2003), and awards from Decision Sciences Institute and Academy of Management. Advising & Grants: Leads sprint projects applying machine learning to solve industry problems. Research funded by NSF and corporate partners. No formal advisees listed. Labs & Teams: Oversees the Institute for Insight, which focuses on data science and analytics innovation.

• Extreme Value Theory
• Bayesian Methods
• Remote Sensing
• +3 more

Professor Philip Jonathan is Chair in Environmental Statistics and Data Science at the School of Mathematical Sciences, Lancaster University. He specializes in extreme value analysis for oceanographic and offshore engineering applications, Bayesian methods for monitoring large systems, and inverse modeling in remote sensing. His work emphasizes uncertainty quantification in environmental and data science contexts. Key research groups: STOR-i Centre for Doctoral Training , Extreme Value Theory , DSI - Environment , Data Science Institute . Current postgraduate students: Thomas Newman (Bayesian inverse modeling) and Matthew Speers (multivariate extremes for ocean-structure interactions). His recent publications focus on non-stationary extreme value models , directional wave dynamics , and climate change impacts on marine environments, with methodological contributions in penalised piecewise models and Metocean software tools . He collaborates on projects like ARC TIDE 1 (inspection regime optimization) and Modelling Wave Interactions Over Space and Time .

Nicole LuisiView profile

Lecturer

• HIV/AIDS Prevention
• Infectious Disease Epidemiology
• Health Disparities
• +4 more

Nicole Luisi is an Instructor in the Rollins School of Public Health, Department of Epidemiology at Emory University, and an Adjunct Instructor in the Executive MPH (EMPH) Program. She also serves as Director of Data Analytics and Informatics Projects, supporting research grants, developing web-based tools, and consulting for the CfAR Prevention Science Core. Her work focuses on epidemiologic methods, web-based surveys, and health data technologies. Education includes a BA from Moravian College, MPH and MS degrees from East Stroudsburg University of Pennsylvania and the University of Massachusetts Amherst, and a PhD from the University of Georgia. Research interests span HIV/AIDS prevention, infectious disease epidemiology, health disparities, and data science. Recent work examines crisis pregnancy centers, opioid misuse, and disparities in HIV outcomes among marginalized populations. She has taught courses such as SAS programming and statistical analysis. Key contributions include analyzing spatial distribution of healthcare facilities, evaluating pandemic mitigation behaviors, and advancing PrEP awareness among vulnerable groups. Her work emphasizes health equity and innovative data solutions for public health challenges.

Ioannis KosmidisView profile

Professor

• Statistics
• Machine Learning
• Data Science
• +6 more

Ioannis Kosmidis is a Professor of Statistics at the University of Warwick and a Turing Fellow of The Alan Turing Institute. He holds an honorary professorship at University College London. His academic journey includes a BSc from Athens University of Economics and Business (2004) and a PhD in Statistics from the University of Warwick (2007). He has held roles such as Turing University Lead and Turing Liaison at Warwick, contributing to the institution's collaboration with The Alan Turing Institute. His research focuses on statistical methodology, including bias reduction in models, high-dimensional statistics, and applications in sports science, neuroimaging, finance, and earthquake engineering. He has developed influential R packages like brglm2 and cranly , emphasizing software development for statistical computing. Prof. Kosmidis has supervised numerous PhD students and postdocs, fostering interdisciplinary collaborations. His work spans over 111 presentations across 59 cities, highlighting his international academic engagement. Key achievements include advancing bias reduction techniques and applying statistical methods to real-world challenges like seismic vulnerability assessment and financial modeling.

Karin YuView profile

Researcher

• Structural Engineering
• Reinforced Concrete
• Generative Design
• +7 more

Karin Yu is a doctoral student and Researcher at the Institute of Structural Engineering, ETH Zürich, affiliated with the Department of Civil, Environmental and Geomatic Engineering. She serves as an ETH AI Center Doctoral Fellow and is involved in research at the Chair for Concrete Structures and Bridge Design and the Chair of Structural Mechanics and Monitoring.

Matthias KatzfussView profile

Professor

• Computational Spatial Statistics
• Spatio-Temporal Statistics
• Gaussian Processes
• +5 more

Matthias Katzfuss is a Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on computational spatial and spatio-temporal statistics, Gaussian processes, uncertainty quantification, and data assimilation, with applications in environmental science and satellite remote sensing. He has received funding from NSF, NASA, NOAA, USDA, Sandia National Laboratory, and Jet Propulsion Laboratory. His scientific achievements include awards such as the NSF Career Award, a Fulbright Scholarship, and the Early Investigator Award from the American Statistical Association’s Section on Statistics and the Environment. His work bridges theoretical advancements and real-world applications, particularly in environmental monitoring and large-scale data fusion. Recent research trends include scalable methods for high-dimensional spatial data, nonstationary covariance modeling, and Bayesian transport maps. His publications address challenges in remote sensing, climate science, and machine learning, emphasizing computational efficiency and methodological innovation. Matthias collaborates extensively with institutions like JPL and NASA, contributing to global-scale environmental studies. His lab focuses on developing open-source software tools, such as the GPgp package, to enable reproducible research in spatial statistics.

• High Dimensional Statistical Inference
• Machine Learning
• Data Science
• +11 more

Professor Bala Rajaratnam is a leading academic in Business Analytics at the University of Sydney Business School . His research spans high-dimensional statistical inference , machine learning , and data science , with applications in economics , finance , biomedical sciences , and environmental modeling . Key focus areas include graphical models , network analysis , and Bayesian methodology . His recent work examines partial correlation screening , covariance estimation , and climate field completion . Publications highlight interdisciplinary collaborations across climate science , financial engineering , and biomedical research . He received a 2019 Australian Research Council Discovery Project grant for Principled statistical methods for high dimensional correlation networks .

• Statistical Genetics
• Biomedical Statistics
• Bayesian Inference
• +4 more

Dr. George Nicholson is a Postdoctoral Researcher in the Department of Statistics at the University of Oxford , specializing in statistical genetics , biomedical data analysis , and methodological innovation . His work bridges probabilistic modeling with real-world health applications. Research Focus: Development of advanced statistical methods for analyzing complex biomedical datasets, including longitudinal clinical trials, UK Biobank, and SARS-CoV-2 surveillance systems. Key Contributions: Creation of composable statistical inference frameworks, generalized Bayesian approaches for model misspecification, and hierarchical models for population differentiation studies. Methodological Expertise includes Bayesian networks , Markov chain Monte Carlo techniques , spatio-temporal modeling , and multivariate factor analysis . His recent publications address critical challenges in: Public Health: Designing robust methodologies for SARS-CoV-2 prevalence estimation through wastewater surveillance and clinical data integration Genomics: Characterizing genetic determinants of adiposity progression and metabolic disease trajectories Clinical Trials: Developing novel latent factor models for treatment response evaluation in autoimmune disorders

Ali HashemiView profile

Researcher

• Machine Learning
• Statistics
• Optimization
• +3 more

Ali Hashemi is a Researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and a Postdoctoral Researcher at Technical University of Berlin. He holds a PhD (Dr. rer. nat.) with distinction from TU Berlin (2022) and an MSc in Electrical Engineering from Sharif University of Technology (2013). His research focuses on machine learning, Bayesian methods, and neuroimaging applications, particularly in inverse problems and explainable AI. He has contributed to advancements in structured noise modeling, robust source localization, and 3D neural networks for brain imaging. His work bridges signal processing and AI to improve biomedical data analysis. Education: PhD in Machine Learning, Technical University of Berlin (2022) MSc in Electrical Engineering, Sharif University of Technology (2013) Research Interests: Machine Learning & Optimization Bayesian Inference Neuroimaging (M/EEG, fMRI) Explainable AI & Diffusion Models Signal Processing for Biomedical Applications Key Achievements: Developed the 'Champagne Algorithm' for noise estimation in brain imaging Published widely in top venues like NeurIPS and NeuroImage Recipient of PhD distinction award for outstanding dissertation Labs & Affiliations: Active in TU Berlin's Uncertainty & Inverse Modeling group and Charité’s Brain and Data Science lab. Collaborates on open-source tools for M/EEG analysis (e.g., contributions to MNE-Python).

Steve ChungView profile

Associate Professor

• Time Series Analysis
• Semi-parametric/Nonparametric Estimation
• Volatility Models
• +6 more

Steve Chung is an Associate Professor in the Department of Mathematics at California State University, Fresno (Fresno State). He holds a B.S. in Applied Mathematics from Cal Poly Pomona, an M.S. in Statistics from the University of Florida, and a Ph.D. in Statistics from Florida State University. His research focuses on Time Series Analysis , Semi-parametric/Nonparametric Estimation , Volatility Models , and Data Science . Recent work spans environmental science, medical imaging, financial modeling, and vascular surgery techniques. He teaches a range of mathematics courses including Math 6, Math 11, and advanced statistics electives. Key contributions include studies on spatio-temporal air quality modeling, comparative ultrasound techniques in vascular disease diagnosis, and volatility analysis in foreign exchange markets. No scientific awards are listed, though his interdisciplinary research bridges statistics with applied fields.

Stefano PelusoView profile

Associate Professor

• Bayesian Statistics
• Network Analysis
• Spatio-temporal Modeling
• +3 more

Stefano Peluso is an Associate Professor in the Department of Statistics and Quantitative Methods at the University of Milano-Bicocca. His academic appointments include: Current: Associate Professor, University of Milano-Bicocca Former: Assistant Professor, Catholic University of the Sacred Heart (2015-2020) Peluso's research centers on Bayesian statistics and network analysis with applications in epidemiology and biostatistics. He develops advanced spatio-temporal models for public health crises, specializing in dependency structures in mixed-type data and longitudinal networks. His methodological innovations bridge machine learning and statistical inference to address pandemic forecasting and cardiovascular event prediction, supporting evidence-based healthcare decision-making. His 2024 publications reveal a cohesive research trajectory focused on Bayesian network structures for dependency learning and spatio-temporal prediction. Five articles demonstrate consistent application of these methods to real-world health challenges—from COVID-19 forecasting using neural networks to cardiovascular event modeling—highlighting an interdisciplinary approach that transforms complex health data into actionable public health insights during emergencies. Peluso leads significant research initiatives funded by the Lombardy Region and Italian Ministry of University and Research, including the 'InPreSa' project for SARS-CoV-2 detection and Bayesian network models for pandemic risk forecasting. These projects reflect his commitment to deploying statistical science against societal threats, though no student advising details appear in the available records.

Tilmann GneitingView profile

Professor

• Computational Statistics
• Probability Theory
• Spatial and Spatio-Temporal Modeling
• +5 more

Prof. Tilmann Gneiting is a renowned academic holding dual roles as Group Leader of Computational Statistics at the Heidelberg Institute for Theoretical Studies (HITS) and Professor of Computational Statistics at the Karlsruhe Institute of Technology (KIT). He specializes in statistical methodology, probability theory, and forecasting applications across environmental, atmospheric, and earth sciences. His research bridges theoretical foundations with practical challenges in weather prediction, climate modeling, and uncertainty quantification. Education: PhD in Mathematics (1997, University of Bayreuth) MA in Mathematics (1992, Boston University) Diplom in Mathematics (1993, University of Bayreuth) Key Roles: Scientific Director of HITS (2023–2024) Affiliate Professor at University of Washington (since 2010) His research interests focus on spatial/spatio-temporal modeling, probabilistic forecasting, and statistical post-processing. He has contributed to advancements in ensemble forecasting, uncertainty quantification, and the evaluation of probabilistic predictions in meteorology and epidemiology. Prof. Gneiting has received prestigious awards, including the 2024 Ulf Grenander Prize in Stochastic Theory and Modeling, recognizing his seminal work in probabilistic forecasting and environmental modeling. He has also held editorial roles in top journals like the Annals of Applied Statistics and the Journal of the Royal Statistical Society. His recent work emphasizes methodological developments for forecasting evaluation, climate science applications, and interdisciplinary collaborations with ECMWF and other institutions.

Souparno GhoshView profile

Associate Professor

• Bayesian Hierarchical Models
• Machine Learning
• Bioinformatics
• +5 more

Souparno Ghosh is an Associate Professor in the Department of Statistics at the University of Nebraska–Lincoln, affiliated with the College of Agriculture & Natural Resources and the Institute of Agriculture and Natural Resources (IANR). His research focuses on Bayesian hierarchical models, machine learning applications in image and functional data analysis, bioinformatics, and developing interpretable statistical methods for healthcare and precision agriculture. His work addresses challenges such as drug response prediction through deep learning frameworks, transfer learning across heterogeneous datasets, and uncertainty quantification in medical decision-making. Recent projects include topological regression models for QSAR analysis, federated learning systems integrating CNNs and regression forests, and spatio-temporal modeling of infectious diseases. Dr. Ghosh has published extensively on topics ranging from cancer drug sensitivity prediction to agricultural phenotyping via computer vision. His methodological contributions include Bayesian variable selection techniques and novel feature representation approaches like REFINED CNN for improving neural network performance. Collaborative efforts span interdisciplinary domains, including oncology, agronomy, and disaster recovery analysis. Key research trends in his publications emphasize: (1) integrating statistical rigor with machine learning for interpretability, (2) developing scalable algorithms for omics and imaging data, and (3) addressing challenges in model generalization and data heterogeneity. His work frequently bridges theory and application, with applications in precision medicine and sustainable agriculture. No scientific awards or grants are explicitly listed in the provided information, but his active publication record and methodological innovations suggest significant contributions to computational statistics and data science.