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Patrick Flaherty is an Adjunct Associate Professor at the University of Massachusetts Amherst. His research focuses on interdisciplinary approaches in bioinformatics, genomics, and computational biology, with a strong emphasis on developing novel algorithms for genomic data analysis, predictive modeling in clinical medicine, and systems biology applications. His work integrates statistical methods, machine learning, and experimental design to address challenges in genetic modulation, biomarker discovery, and disease prediction. Key research interests include: Development of advanced computational tools for analyzing genomic and transcriptomic data Machine learning models for hierarchical clustering and dimensionality reduction Prediction of post-trauma complications and infectious diseases using multi-biomarker approaches Statistical methods for hypothesis testing and experimental design Recent publications highlight contributions to biomarker prediction, matrix factorization techniques for bounded data, and Bayesian models for cancer genomics. His work bridges computational innovation with practical clinical and biological applications. Advising and grants: No explicit details provided in the text. Labs/teams: No specific lab or team affiliations mentioned.

• Bioinformatics
• Genomics
• Machine Learning
• +3 more

Professor Babak Shahbaba is a Professor of Statistics at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information and Computer Sciences. He serves as Director of the UCI Data Science Institute, leading initiatives in interdisciplinary data science education and research. His expertise spans Bayesian statistics, scalable computational methods, and applications in neuroscience, genomics, and healthcare. Education: Ph.D., University of Toronto (2007) Research Interests: Bayesian nonparametric methods for complex data Scalable algorithms for large-scale biological studies Interdisciplinary collaborations between statistics and neuroscience Circadian rhythm modeling using transcriptomic data Machine learning applications in healthcare diagnostics Awards & Recognition: American Statistical Association Fellow (2022) Recipient of NIH R01 Grant for neurobiological research 2024 ICS Awards Celebration Honoree Advising & Grants: Co-developed training grants fostering neuroscientist-statistician collaborations Directed the Irvine Summer Institute in Biostatistics Principal investigator on NSF Harnessing the Data Revolution grants Labs & Initiatives: Leadership in the UCI Data Science Institute Co-creator of the NeuroDataScience workshop series Founder of the SoCal Data Science Program

• Bayesian Statistics
• Machine Learning
• Biostatistics
• +5 more

Weining Shen is an Associate Professor of Statistics at the University of California, Irvine. He earned his Ph.D. in Statistics from North Carolina State University in 2013, where his thesis won the Leonard J. Savage Dissertation Award. Following this, he served as a postdoctoral fellow at the M.D. Anderson Cancer Center. His research focuses on Bayesian statistics, nonparametric methods, and biostatistics, with applications in healthcare, biology, and sports analytics. Key collaborations include work with UNC Chapel Hill on cardiac hypertrophy, MD Anderson Cancer Center on liver cancer risk prediction, and UNC Chapel Hill’s Qian Lab on cardiac reprogramming using single-cell RNA sequencing. He has also pioneered Bayesian clustering approaches for NBA player shot patterns and developed a scoring system for hepatocellular carcinoma risk using biomarker data. Shen’s work bridges statistical theory and practice, receiving a five-year Simons Foundation grant (2017) to advance interdisciplinary statistical modeling. His research emphasizes flexible, efficient methods for complex data, with recent contributions to sports understanding benchmarks for large language models and spatial functional data clustering. Education: Ph.D., Statistics, North Carolina State University (2013) Postdoctoral Training: M.D. Anderson Cancer Center (2013-2015) Research Highlights: Cardiac metabolism modeling, sports analytics, and precision medicine. His Bayesian nonparametric frameworks address heterogeneous data challenges in healthcare and sports, while his work on survival analysis and immunotherapy outcomes advances oncological research. Awards: Leonard J. Savage Dissertation Award (2013) Grants: Simons Foundation Grant (2017-2022) Labs/Teams: Collaborations with UNC Chapel Hill, MD Anderson, and UCI’s Statistics Department. Active in interdisciplinary initiatives linking statisticians with biologists and clinicians.

• Machine Learning
• Biostatistics
• Bayesian Statistics
• +3 more

Dr. Adriano Polpo is an Associate Professor in Mathematics and Statistics at the University of Western Australia's School of Physics, Mathematics and Computing. He holds a PhD in Statistics from the University of São Paulo and has held academic positions at the Federal University of São Carlos (2006-2019). His research focuses on statistical methodology development, particularly in Bayesian nonparametric methods, reliability/survival analysis, and hypothesis testing, with applications in environmental science, healthcare, and engineering. He has collaborated on projects addressing challenges such as copper nanoparticle impacts on algae, OCD treatment latency, and statistical model validation. Education: PhD in Statistics, University of São Paulo (2005) BSc in Statistics, State University of Campinas (2001) Postdoctoral Fellowship, Florida State University Research interests include Bayesian statistics, categorical data analysis, functional data analysis, and meta-analysis. His work contributes to UN Sustainable Development Goals related to sustainable agriculture and healthcare innovation. Grants/Projects: He co-led the 'Parameters for Roundabout Modelling' grant (2020-2021), focusing on transportation system optimization. His 63+ publications span topics like reliability estimation, Bayesian regression, and environmental toxicology. Awards: Recipient of the 2023 Excellence in Teaching Award from his department. His lab focuses on interdisciplinary applications of statistical methods, including collaborations with bioinformatics and environmental science teams.

• Reliability
• Survival analysis
• Bayesian statistics
• +6 more

Philipp Hennig is a Full Professor (W3) at the Department of Computer Science, Eberhard Karls University Tübingen, heading the Chair for Methods of Machine Learning. He previously held positions at the Max Planck Institute for Intelligent Systems, including Emmy Noether Group Leader and Max-Planck-Group Leader. His research focuses on probabilistic numerics, Bayesian inference, and uncertainty quantification, with contributions to machine learning theory and computational methods. He co-founded the field of probabilistic numerics and authored the textbook Probabilistic Numerics — Computation as Machine Learning (2022). Education: PhD in Physics from the University of Cambridge (2007), MSci in Physics from Heidelberg University (2007). Research support includes DFG Emmy Noether Programme grants, ERC Starting/Consolidator Grants, and Max Planck Society funding. He leads the ELLIS Program on Theory, Algorithms, and Computations of Modern Learning Systems and serves as Dean of Studies for Tübingen's Computer Science Department. Key research directions include probabilistic ODE/PDE solvers, Bayesian deep learning, and uncertainty-aware algorithms. His work bridges computational methods with statistical inference, emphasizing scalable and theoretically grounded approaches. Recent projects address neural operator learning, uncertainty quantification in climate models, and efficient Gaussian process methods. Awards include ELLIS Fellowship and ERC grants. Over 100 publications span top venues like NeurIPS, ICML, and JMLR. Active in academic leadership roles, he co-leads the Tübingen AI Center and Cluster of Excellence for Machine Learning in Science. His lab develops open-source tools like ProbNum for probabilistic numerical computing.

• Machine Learning
• Probabilistic Numerics
• Bayesian Inference
• +5 more

Florian Brueck is a Postdoctoral Researcher at the Research Center for Statistics at the University of Geneva, under Prof. Sebastian Engelke. He earned his PhD in Mathematics (Dr. rer. nat.) from the Technical University of Munich (TUM) in 2023, with distinction, under Prof. Matthias Scherer. His research focuses on statistical model comparison, Bayesian non-parametric survival analysis, Lévy processes, extreme value theory, and the integration of machine learning into classical statistics. Education: PhD in Mathematics, TUM (2023) MSc in Financial Mathematics and Actuarial Sciences, TUM (2019) BSc in Business Mathematics, Ludwig Maximilians University Munich (2017) Research Interests: Statistical model selection via Maximum Mean Discrepancy (MMD) Exchangeability and non-parametric Bayesian methods in survival analysis Inference for Lévy and stable processes Extreme value theory applications in risk modeling Machine learning techniques for statistical inference Key Contributions: Developed distribution-free MMD tests for model selection with estimated parameters Advanced infinitely divisible priors for multivariate survival functions Applied generative neural networks to characteristic function estimation Awards & Recognition: None explicitly listed. Advising & Grants: Supervised 5 Master’s theses on topics including MMD-based model selection and clustering-based portfolio optimization Co-led industry project with WWK Versicherungen on risk assessment tools Labs/Teams: Research Center for Statistics at University of Geneva.

• Statistical Model Comparison
• Non-Parametric Bayesian Analysis
• Lévy Processes
• +3 more

Adam Johansen is a Professor of Statistics at the University of Warwick, specializing in computational statistics, sequential Monte Carlo algorithms, Bayesian methodology, and gradient flows. His research spans theoretical advancements and practical applications in simulation-based inference. Current projects: OCEAN (ERC Synergy Grant for next-gen ML algorithms), CoSinES, and APTS (co-director until 2023) Research interests focus on Monte Carlo methodology , Bayesian statistics , and sequential inference . Recent work explores scalability in decentralized systems, gradient flows for sampling, and robust algorithmic frameworks. His 15 most recent publications emphasize particle methods, convergence analysis, and mathematical statistics. He supervises a research group with PhD students including Rocco Caprio, Shu Huang, and Usman Ladan, while contributing to editorial roles at the Journal of the Royal Statistical Society: Series B and London Mathematical Society Newsletter . He developed the SMCTC and RcppSMC libraries for sequential Monte Carlo applications.

• Monte Carlo methodology
• Sequential Monte Carlo algorithms
• Bayesian statistics
• +3 more

Raiha Browning is a Researcher at the University of Warwick, focusing on computational Bayesian statistics applied to neglected tropical diseases and spatiotemporal modeling of complex systems. Previously, she completed her PhD at Queensland University of Technology under Kerrie Mengersen, developing flexible Bayesian methods for self-exciting processes like Hawkes processes. Her research interests span Bayesian statistical modeling, public health policy analysis, and the application of Hawkes processes to diverse domains including epidemiology, political violence, and pandemic dynamics. She has contributed to database development for onchocerciasis control (HISTONCHO) and cost-efficiency analyses for schistosomiasis programs. Raiha's recent publications emphasize real-time hazard warning systems, conflict event modeling, and methodological advancements in discrete-time self-exciting processes. Her work bridges theoretical statistical developments with practical applications in global health and policy decision-making.

• Bayesian Statistics
• Neglected Tropical Disease Modelling
• Self-Exciting Processes
• +4 more

Richard Guo is an Assistant Professor of Statistics at the University of Michigan, Ann Arbor. He holds a PhD in Statistics from the University of Washington (2021) and an MS in Computer Science from Duke University (2016). His research focuses on replicable data analysis, causal inference foundations, and uncertainty quantification. Guo previously served as a Research Associate at the University of Cambridge and a Richard M. Karp Fellow at the Simons Institute. He has developed software tools like the MultiSplit R package for hypothesis testing with multiple data splits. His awards include the Richard M. Karp Research Fellowship (2022) and the Z. W. Birnbaum Award (2019–2020). Education: PhD in Statistics, University of Washington, 2021 MS in Computer Science, Duke University, 2016 BEng in Computer Science, University of Electronic Science and Technology of China, 2013 Research Interests: Replicable data analysis: randomized procedures and derandomization Causal inference: graphical models, nonparametric methods Uncertainty quantification: model selection, finite-sample guarantees Awards: Richard M. Karp Research Fellowship, Simons Institute (2022) Z. W. Birnbaum Award, University of Washington (2019–2020) Teaching: Instructor for graduate courses on linear models and probability theory at University of Washington Guest lectures on causal DAGs and statistical modeling

• Replicable data analysis
• Causal inference
• Uncertainty quantification
• +3 more

Xiaosheng Mu is an Associate Professor in the Department of Economics at Princeton University. He previously held positions as an Assistant Professor of Economics at Columbia University and a post-doctoral researcher at the Cowles Foundation at Yale University. He earned his Ph.D. in Economics from Harvard University, advised by Drew Fudenberg, Eric Maskin, and Tomasz Strzalecki. His research focuses on information acquisition, mechanism design, stochastic orders, and their applications to decision making. Key contributions include work on pricing strategies under informational robustness, privacy-preserving auctions, and market dynamics on global e-commerce platforms. His work bridges theoretical economics with practical applications in algorithm design, privacy mechanisms, and behavioral analysis. Xiaosheng has published in top journals such as Econometrica, the Quarterly Journal of Economics, and the Journal of Political Economy. Notable publications include 'Monotone Additive Statistics' (Econometrica, 2024) and 'Algorithmic Design: A Fairness-Accuracy Frontier' (Journal of Political Economy, 2024). His research often employs advanced mathematical techniques from probability theory, game theory, and optimization. He has collaborated extensively with scholars such as Annie Liang, Vasilis Syrgkanis, and Luciano Pomatto. Current research projects explore information friction in digital markets and the design of privacy-preserving economic mechanisms. His work consistently addresses foundational questions in economics while maintaining relevance to modern technological challenges.

• Information Acquisition
• Mechanism Design
• Stochastic Orders
• +3 more

Han Hong is a Professor of Economics at Stanford University's Department of Economics. He holds a Ph.D. in Economics (1998), M.S. in Computer Science (1998), and M.S. in Statistics (1997) from Stanford University, and a B.A. in International Trade from Zhongshan University (1993). His research focuses on econometric methodology, health econometrics, statistical modeling, and computational economics. He has developed innovative approaches for panel data analysis, structural estimation, and decision-making algorithms. Professor Hong has received numerous honors including the Willard G. Manning Memorial Award (2017), Arrow Award Honorable Mention (2015), and multiple NSF grants. He serves as Co-Editor of the Journal of Econometrics and is a Fellow of the Econometric Society. He teaches courses in Advanced Econometrics, Data Science, and Econometric Methods, and mentors students through honors thesis research and directed reading programs.

• Econometrics
• Health Econometrics
• Statistical Modeling
• +2 more

David Zoltowski is a Postdoctoral Researcher in the Department of Statistics at Stanford University, advised by Scott Linderman. His research focuses on developing and applying statistical and machine learning methods to understand neural dynamics, decision-making processes, and multi-region neural communication. He specializes in latent variable models, Bayesian inference techniques, and dynamical systems analysis. Key contributions include benchmarking frameworks for neural activity models and methods for uncovering structure in complex neural datasets. His work bridges computational neuroscience and statistics, with applications spanning neural decoding, state-space modeling, and neuromodulation. Recent projects address the integration of time and reward signals in decision-making circuits and the development of scalable algorithms for high-dimensional spike train analysis. David collaborates on interdisciplinary projects involving neuroimaging, behavioral neuroscience, and probabilistic modeling. His tools and benchmarks are widely used in the computational neuroscience community for evaluating latent variable models and neural decoding systems.

• Neuroscience
• Statistics
• Machine Learning
• +3 more

Emily Fox is a Professor in the Departments of Statistics and Computer Science at Stanford University, with affiliations at the Wu Tsai Neurosciences Institute, Wu Tsai Human Performance Alliance, Bio-X, and Center for Artificial Intelligence in Medicine & Imaging (AIMI). Previously, she was Amazon Professor of Machine Learning at the University of Washington and Distinguished Engineer at Apple. Education includes: Ph.D. in Electrical Engineering and Computer Science from MIT (2009), E.E. from MIT (2008), M.Eng. from MIT (2005), and S.B. from MIT (2004). Research focuses on modeling complex time series in health applications, particularly from wearables and neuroimaging. Key areas include Bayesian nonparametrics, time series analysis, and developing statistical methods for high-dimensional data. Significant awards include: IMS Fellow, CZ Biohub Investigator, PECASE Award, Sloan Fellowship, ONR Young Investigator Award, and NSF CAREER Award. Advises doctoral students and postdocs in machine learning and statistics. Research supported by NSF, ONR, Amazon, and other grants. Leads research on health AI applications and develops novel statistical methods for large-scale data analysis.

• Time Series Analysis
• Machine Learning
• Health Informatics
• +2 more

Dr. Long Chen is a Lecturer at the School of Architecture, Building and Civil Engineering, Loughborough University. He holds a PhD and BEng, with professional affiliations including MCIOB and A.M.ASCE. Previously, he was a Postdoctoral Research Associate at Imperial College London's Centre for Systems Engineering and Innovation (CSEI), where he contributed to the CDBB-funded project 'Analysing Systems Interdependencies using a Digital Twin' and the Alan Turing Institute/Lloyd’s Register Foundation's 'Grand Challenge III: Data-Driven Design Under Uncertainty'. His research focuses on digital twin-driven approaches for design change management in complex systems and image-based digital twinning for as-built buildings. Key research areas include Systems Engineering, Data-Centric Engineering, and Building Information Modeling. Dr. Chen's publications primarily explore machine learning fairness, Bayesian computation, and computer vision techniques, with recent works addressing algorithmic bias mitigation, transformer calibration, and semantic segmentation robustness. His research demonstrates consistent applications in probabilistic modeling and adaptive systems. He maintains active collaborations with Imperial College London and the Alan Turing Institute, leveraging his expertise in systems interdependencies and digital twin implementations.

• Digital Twin Technology
• Systems Engineering
• Design Change Management
• +2 more

Dr. Hongmei Zhang is the Bruns Endowed Professor of Biostatistics and Director of the EBE Division at the University of Memphis School of Public Health. She holds a PhD in Statistics from Iowa State University and has additional advanced degrees in Mathematics and Electronic Engineering. Her research focuses on statistical methodology development for variable selection, Bayesian networks, and clustering, particularly applied to genetic, epigenetic, and phenotypic data. She leads NIH-funded studies on cancer and allergic diseases, emphasizing collaborative translational research. Education: PhD, Statistics, Iowa State University MS, Statistics, Iowa State University MS, Mathematics, Truman State University MS, Electronic Engineering, Nanjing Research Institute of Technology Her research interests include advanced statistical modeling techniques such as Gaussian networks, joint clustering algorithms, and sampling plan optimization. Dr. Zhang’s recent work explores DNA methylation patterns linked to asthma progression and IgE trajectories in diverse populations, as well as causal mediation analysis in environmental health studies. She has developed software tools for variable selection in semi-parametric models and cell-type heterogeneity assessment. Active Research Funding: NIH/NIAID: Clusters of Epigenetic Networks at Birth and Asthma Incidence in Children (PI) NIH/NCI: Surface exosome integrin profiling to predict breast cancer metastasis (Co-I) CDC: PH-IDEAS initiative to strengthen Shelby County Health Department systems (Co-I) Dr. Zhang advises a robust team of students/postdocs, mentoring over a dozen researchers in statistical methodology and application. Her lab’s open-source software packages for joint clustering and variable selection are widely used in biomedical research communities.

• Biostatistics
• Variable Selection
• Bayesian Networks
• +4 more