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Michael BiehlView profile

Professor

• Machine Learning
• Neural Networks
• Data Analysis
• +4 more

Michael Biehl is a Full Professor of Machine Learning, Theory, and Applications at the University of Groningen, affiliated with the Bernoulli Institute's Intelligent Systems Group. He holds an honorary position as Professor of Machine Learning at the University of Birmingham. His research focuses on machine learning theory, neural networks, and their applications in biomedical data analysis, statistical physics, and computational science. Education: Not explicitly detailed in texts but inferred through his academic roles and research output. Research Interests: Machine Learning algorithms and their theoretical foundations Neural Networks, including activation function analysis and network dynamics Biomedical data analysis, particularly using metabolomics and imaging data Statistical physics of learning processes Interpretable AI models for clinical decision support Recent Trends in Articles: Biehl’s recent work emphasizes explainable AI in healthcare, concept drift adaptation, and applications in neuroimaging and endocrinology. His studies often integrate machine learning with biomedical datasets to develop diagnostic tools for conditions like adrenal disorders and movement disorders. Awards: Distinguished Visiting Fellow (2014) Best Presentation Award (2016) sbv IMPROVER Challenge Awards (2013) Honorary Professorship at University of Birmingham (2021) Grants/Advising: Active in supervising PhD projects and teaching neural networks. His grants likely support interdisciplinary research in AI and healthcare, though specific grants are not detailed here. Labs/Teams: Leads the Intelligent Systems Group at Groningen and collaborates with the Centre for Systems Modelling and Quantitative Biomedicine at Birmingham.

Parul JohriView profile

Assistant Professor

• Population Genetics
• Statistical Inference
• Evolutionary Genomics
• +6 more

Parul Johri is an Assistant Professor at the University of North Carolina at Chapel Hill, holding joint appointments in the Departments of Biology and Genetics. Her research focuses on population genetics, statistical inference, and evolutionary genomics, aiming to understand how evolutionary processes like selection, mutation, recombination, and demographic changes shape genomic variation. She leads the Johri Lab , which develops computational and theoretical approaches to address fundamental questions in population genetics, including the effects of linked selection and gene duplication. Education: PhD in Evolution, Ecology, and Behavior (Indiana University, 2018) under Michael Lynch Masters in Biology (Tata Institute of Fundamental Research, 2012) B.Sc. in Mathematics (St. Stephen’s College, Delhi, 2009) Research Interests: Her work explores evolutionary processes shaping genomic diversity, including the interplay between adaptive and non-adaptive forces, gene duplication dynamics, and the development of evolutionary null models for human pathogens. Current projects investigate how linked selection impacts inference of demographic history and fitness effects of mutations. The lab uses forward-in-time simulations, diffusion theory, and empirical datasets to model these processes. Awards & Funding: NIH NIGMS R35 Award (2024-2029) for $1.25M to study evolutionary process interactions SMBE Early Career Excellence Award (2024) Searle Scholars Program Nominee (2023) Lab & Collaborations: The Johri Lab is part of the UNC Research Triangle ecosystem, collaborating with institutions like Arizona State University and the University of Edinburgh. Current projects include modeling gene-based fitness effects, analyzing pathogen evolution (e.g., SARS-CoV-2), and developing open-source tools for population genetic analysis on GitHub repositories like JohriLab .

Joseph G. IbrahimView profile

Professor

• Bayesian Inference
• Clinical Trials Design
• Genomics
• +4 more

Dr. Joseph G. Ibrahim is the Alumni Distinguished Professor of Biostatistics at the University of North Carolina at Chapel Hill's Gillings School of Global Public Health. He holds leadership roles as Director of Graduate Studies in Biostatistics, Director of the Laboratory for Innovative Clinical Trials, and Biostatistics Core Director at the Lineberger Comprehensive Cancer Center. His academic tenure includes teaching courses like BIOS 762 (Linear Models) and BIOS 779 (Bayesian Statistics). Education: PhD/MSc in Statistics (University of Minnesota, 1988) and BS in Mathematics (1983). Research Interests: Bayesian methodology, clinical trial design, missing data, medical imaging, and cancer genomics. His work bridges statistical theory and application, with over 360 publications in top journals and two influential books on Bayesian survival analysis and Monte Carlo methods. Key Contributions: Developed statistical methods for joint modeling of longitudinal/survival data, power prior frameworks, and Bayesian approaches for multi-regional trials. His team's innovations include the LEAP prior for historical data integration and the glmmPen R package for high-dimensional models. Awards: Elected fellowships from ASA, IMS, RSS, and ISBA; Janssen Chair in Survival Analysis (2005); and UNC's Alumni Distinguished Professorship (2006). Service: Editor of JASA (Applications/Cases, 2013–2015), associate editor for Biometrics, Lifetime Data Analysis, and Bayesian Analysis. Served on NIH study sections (BMRD, EPIC) and ASA's Bayesian Section leadership. Labs/Teams: Leads the Center for Innovative Clinical Trials (Gillings Innovation Lab) and directs the Cancer Genomics Training Grant. Collaborates on NIH-funded breast cancer SPORE projects and epigenomic analysis tools like Epigrahmm.

Yun LiView profile

Professor

• Statistical Genetics
• Multi-omics Dissection
• Genetic Dissection of Complex Diseases
• +8 more

Dr. Yun Li is a Professor in the Department of Genetics and Department of Biostatistics at the University of North Carolina at Chapel Hill (UNC-Chapel Hill), affiliated with the UNC School of Medicine. He also holds an adjunct position as an Associate Professor in Applied Physical Sciences. His research focuses on developing statistical methods for genetic and genomic studies, including genotype imputation (e.g., MaCH software), multi-omics integration, and analysis of complex diseases such as metabolic, autoimmune, cardiovascular, and neuropsychiatric disorders. He leads the Li Lab, which has contributed to projects like the 1000 Genomes Project, TOPMed, and BACPAC consortium. Dr. Li’s work emphasizes methodological advancements for low-coverage sequencing, epigenetic studies, single-cell RNA-seq, and chromatin organization analysis. Education: Ph.D. in Biostatistics from the University of Michigan (2009). Leadership includes roles in the NIH PRIMED consortium and the 4DN Predictive Modeling Working Group. The lab has developed tools such as HUGIn for Hi-C data exploration and CUE for DNA methylation imputation. Collaborations span multi-omic studies, precision medicine, and population genetics across diverse populations. Research interests span statistical genetics, genomic data integration, and translational applications. The lab actively participates in consortia addressing genetic dissection of complex traits, leveraging single-cell and spatial transcriptomics, and chromatin 3D architecture.

Georgy SofronovView profile

Associate Professor

• Statistical Analysis
• Operations Research
• Change-Point Detection
• +6 more

Georgy Sofronov is an Associate Professor in Statistics and Director of Research Training at Macquarie University's School of Mathematical and Physical Sciences. He holds a PhD in Probability Theory and Mathematical Statistics from Moscow State University (2002). Previously, he held academic positions at the University of Queensland and the University of Wollongong. He serves on editorial boards for Statistical Papers and Methodology and Computing in Applied Probability , and is Treasurer of the Modelling and Simulation Society of Australia and New Zealand. His research focuses on change-point detection, statistical quality control, optimal decision rules, and computational statistics. Research Interests : Change-point problem, statistical quality control Optimal decision rules, operations research Markov chain Monte Carlo methods, Cross-Entropy method Statistical modelling and data analysis Statistical Software (R-package 'breakpoint') Affiliations : Biomolecular Discovery Research Centre Centre for Risk Analytics Data Horizons Research Centre Smart Green Cities Research Centre Transforming Energy Markets Research Centre Teaching : Courses include Statistical Theory , Probability , Market Research and Forecasting , and Statistical Inference . Grants & Projects : SmartCrete CRC (2020–2027) Multiple change-point detection methods for big data (2017–present) Awards : Learning and Teaching Award (2016) Award for Excellence in HDR Supervision (2021) Collaborations : Active in Australia, New Zealand, and international networks focusing on computational statistics and risk analytics.

David B. BlumenthalView profile

Professor

• Biomedical Engineering
• Network Science
• Artificial Intelligence in Medicine
• +6 more

David B. Blumenthal is an Assistant Professor and head of the Biomedical Network Science Lab at Friedrich-Alexander University Erlangen-Nürnberg (FAU). His work focuses on applying network science and AI to study molecular disease mechanisms and developing privacy-aware biomedical AI solutions. He holds a PhD in Computer Science from the Free University of Bozen-Bolzano and has held postdoctoral positions at TU Munich and the Free University of Bozen-Bolzano. His research spans federated learning, graph algorithms, and systems medicine, with a strong emphasis on translational applications in healthcare. Education: BSc/MSc Mathematics, Free University of Berlin & Technical University of Berlin BA/MA Philosophy, Free University of Berlin PhD in Computer Science, Free University of Bozen-Bolzano Visiting PhD student at GREYC Research Lab, France Research Interests: Blumenthal’s work integrates computational methods with biomedical data to uncover disease mechanisms. Key areas include federated learning for medical data privacy, network-based disease module discovery, and AI-driven drug repurposing. He develops tools like ROBUST-Web, NeDRex, and DataSAIL to address challenges in data leakage and heterogeneous data integration. Awards: Best Poster Award (2022), Best Paper Awards (2021, 2017), Best PhD Student Award (2021) Recipient of German Academic Scholarship Foundation support Grants: Leads projects funded by BMBF, DFG, and industry partners, including FLabNet (federated learning in paediatric oncology) and NetMap (dimensionality reduction in single-cell data). His work often bridges FAU’s engineering and medical faculties. Labs/Teams: Heads the Biomedical Network Science Lab, collaborating with institutions like TU Munich, Bitspark GmbH, and Erlangen University Hospital on translational AI projects.

• Machine Learning
• Signal Processing
• Pattern Recognition
• +3 more

Mário A. T. Figueiredo is an IST Distinguished Professor and Feedzai Professor of Machine Learning at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST) . He leads the Lisbon Unit for Learning and Intelligent Systems (LUMLIS) within ELLIS and coordinates research clusters at Instituto de Telecomunicações. His research focuses on machine learning , signal processing , and image restoration , particularly using sparsity-based methods , wavelet transforms , and Bayesian algorithms . Recent work includes 3D shape correspondence , hyperspectral sharpening , and adaptive optimization techniques . Key article themes: Biclustering , ADMM algorithms , sparsity regularization , and generative embeddings Scientific recognition: EURASIP Fellow , Clarivate Highly Cited Researcher , IEEE/IAPR Fellow , and multiple best paper awards He has mentored numerous PhD/MSc students and contributed to information-theoretic kernels , feature discretization , and multimodal data analysis in biomedical and computer vision applications.

Xiaoning QianView profile

Professor

• Machine Learning
• Bayesian Methods
• Optimal Experimental Design
• +5 more

Xiaoning Qian is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, holding the Segers Family Dean's Excellence Professorship II. He is also affiliated with the Department of Computer Science and Engineering and has a joint appointment at Brookhaven National Laboratory's Computational Science Initiative. His research focuses on machine learning, Bayesian methods, bioinformatics, and materials science. Education: B.S.E. and M.S.E., Shanghai Jiaotong University M.Ph. and Ph.D., Yale University Research Interests: Bayesian learning and experimental design Signal and image processing Applications in bioinformatics, genomics, and materials science Awards: NSF CAREER Award (2016) TEES Faculty Fellow (2017) J. T. Oden Faculty Fellow (2019) College of Engineering Excellence Faculty Award (2020) Labs & Collaborations: Biomedical Imaging, Sensing, and Genomic Signal Processing Group (Texas A&M) Brookhaven National Laboratory (Applied Math group)

Samiran SinhaView profile

Professor

• Epidemiology
• Biostatistics
• Bayesian Methods
• +3 more

Samiran Sinha is a Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on statistical methodologies for epidemiological studies, measurement error correction, Bayesian computation, and missing data techniques. He has held academic positions since 2004, including roles as Associate Head for Operations and Associate Graduate Adviser. Education: PhD in Statistics (University of Florida, 2004), MSc (Calcutta University, 1999), BSc (Kalyani University, 1997). Research Interests: Bayesian methods, epidemiological modeling, statistical computation, and applications in health sciences. His work bridges statistical theory and practical applications, including collaborations in astrophysics and genomics. Notable grants include NIH-funded projects on breast cancer data analysis and epigenetic biomarkers. He has advised over 30 graduate and undergraduate students, fostering interdisciplinary research in biostatistics and data science.

Raymond Ka Wai WongView profile

Associate Professor

• Statistical Learning
• Causal Inference
• Functional Data Analysis
• +4 more

Raymond Ka Wai Wong is an Associate Professor and Director of the PhD Program in the Department of Statistics at Texas A&M University. He holds a PhD in Statistics from the University of California, Davis (2014), an MPhil from The Chinese University of Hong Kong (2010), and a BSc with minors in Mathematics and Risk Management Science (2008). His research focuses on causal inference, functional data analysis, low-rank modeling, reinforcement learning, and statistical learning with applications in astronomy, brain imaging, and genomics. Wong's professional roles include Associate Editor for Journal of Computational and Graphical Statistics , Journal of the American Statistical Association , and member of the Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He has secured grants from NSF, NASA, and NIH, including leadership in projects like Virtual Assistant for Spacecraft Anomaly Treatment and phenomic selection in maize hybrids. His awards include Top Reviewer distinctions at NeurIPS (2023) and ICML (2020), and a 2016 Discussion Paper in the Annals of Applied Statistics. He has advised numerous doctoral students, with notable advisees receiving awards such as the ICSA Student Paper Award and Emanuel Parzen Fellowship. Wong’s recent work emphasizes methodological advancements in reinforcement learning (e.g., distributional off-policy evaluation) and matrix/tensor completion under informative missingness. His research bridges theoretical statistics with practical applications in interdisciplinary domains such as neuroscience, agriculture, and space exploration.

• Evolutionary Biology
• Genomics
• Population Genetics
• +2 more

Reed Cartwright is an Associate Professor in the School of Life Sciences at Arizona State University. His research focuses on evolutionary biology, population genetics, genomics, and bioinformatics. He contributes to developing computational tools for genetic analysis and simulations, with applications in primate evolution, microbiome studies, and conservation genomics.

• Philosophy of Science
• Biodiversity Science
• Evolutionary Biology
• +3 more

Dr. Beckett Sterner is an Associate Professor in the School of Life Sciences at Arizona State University. His interdisciplinary research bridges philosophy of science and biological practice, focusing on biodiversity science, evolutionary theory, and data governance. Research integrates conceptual and empirical approaches to investigate: Epistemological foundations of model selection and statistical inference Data governance and ethical frameworks for biodiversity research Evolutionary theory development and classification systems Intersections of philosophy with ecology, virology, and paleobiology Publications demonstrate consistent engagement with methodological challenges in evolutionary biology and ecology, including statistical model selection, phylogenetic analysis, and data integration. Recent work emphasizes equitable participation in biodiversity science and critical examination of classification practices. Dr. Sterner develops computational tools like the Modular Petri Net Assembly Toolkit (MPAT) and contributes to theoretical frameworks for understanding scientific objectivity in data-intensive research contexts.

Zoltan SzaboView profile

Professor

• Statistical Machine Learning
• Kernel Methods
• Information Theoretical Estimators
• +9 more

Zoltan Szabo is a Professor of Data Science at the Department of Statistics, London School of Economics and Political Science. His research focuses on statistical machine learning, particularly kernel methods, information theoretical estimators, and scalable computation, with applications spanning safety-critical learning, style transfer, hypothesis testing, distribution regression, econometrics, and gene analysis. Affiliation : Department of Statistics, LSE Academic Rank : Professor Key Expertise : Kernel Methods, Information Theoretical Estimators, Scalable Computation His work integrates theoretical rigor with practical applications, addressing challenges in safety-critical systems and developing robust nonparametric methods. Szabo has published extensively on topics like Nyström approximation, Stein discrepancy, and random Fourier features, contributing to advancements in hypothesis testing, distribution regression, and GPU-accelerated kernel techniques. He has served as an Area Chair for top conferences (ICML, NeurIPS, AISTATS), moderated arXiv's stat.ML, and contributed to editorial roles at JMLR and ACM Transactions on Probabilistic Machine Learning. His recent articles emphasize scalable kernel methods for high-dimensional data, with applications in climate science, finance, and neuroimaging. Scientific Awards : Best Paper Award, NeurIPS 2017 HDR (Habilitation à Diriger des Recherches) with distinction, 2019 Programme Director of MSc Data Science, LSE As an advisor, Szabo mentors PhD students and interns in machine learning and statistics. His work often involves interdisciplinary collaboration, including grants with institutions like the Turing Institute and European Research Council.

Tingting ZhangView profile

Professor

• Neuroimaging Data Analysis
• Human Brain Mapping
• Bayesian Statistics
• +4 more

Dr. Tingting Zhang is a Professor in the Department of Statistics at the University of Pittsburgh's Dietrich School. She holds a Ph.D. in Statistics from Harvard University (2008), an M.S. in Statistics from Harvard (2005), and a B.S. in Mathematics from Peking University (2003). Her research focuses on neuroimaging data analysis, human brain mapping, and Bayesian statistics, with applications to neurological disorders. She collaborates with clinicians and neuroscientists to develop statistical methods for analyzing multimodal brain imaging data, including fMRI and EEG. Her methodological contributions include Bayesian network models, high-dimensional data analysis, and variational inference techniques. Recent work examines functional connectivity across lifespan stages using HCP datasets and epileptic seizure analysis through directional brain networks. She has developed software tools including the Bayesian Modular and Indicator-based Dynamic Directional Model (BMIDDM) and the Bayesian hierarchical model for state-space stochastic block analysis. Dr. Zhang has taught courses in Bayesian statistics, stochastic processes, and experimental design. Her work bridges statistical theory with clinical applications, emphasizing translational research in neuroimaging. She maintains an active research program with over 30 peer-reviewed publications in top journals like Annals of Applied Statistics and NeuroImage.

Jie HeView profile

Part-Time Lecturer

• Biostatistics
• Medical Statistics
• Cancer Biology
• +3 more

Jie He is a Part-Time Lecturer in the Department of Statistics at the University of Pittsburgh's Dietrich School. Their academic role focuses on statistical education, contributing to undergraduate and graduate programs in statistics and data science. While no explicit research description is provided, their listed Google Scholar publications highlight a research background in biomedical sciences, particularly in cancer biology, immunology, and virology, with a focus on cellular mechanisms and disease pathways. Research interests inferred from publications include cancer metastasis mechanisms, inflammatory responses in asthma, respiratory viral infections such as RSV, and signaling pathways involving HSP proteins and cytokines. These studies bridge molecular biology, immunology, and clinical applications. Jie He's work has explored topics like epithelial-mesenchymal transition, chemokine receptor roles in asthma, and autophagy regulation in viral infections. Their publications span collaborations in both basic science and translational research contexts.