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Alexandre Bouchard-Côté is a Professor of Statistics at the University of British Columbia (UBC), affiliated with the Department of Statistics within the Faculty of Science. His research focuses on computational statistics, Bayesian methods, and Monte Carlo techniques, with applications in evolutionary biology, cancer genomics, and computational linguistics. Education : PhD in Computer Science (with Designated Emphasis in Statistics) from UC Berkeley (2010), BSc in Mathematics and Computer Science from McGill University (2005). Affiliations : Director of the Blang probabilistic programming project and leader of the Bouncy Particle Sampler research group. Research Interests : Bouchard-Côté develops scalable Bayesian computational methods, including non-reversible Monte Carlo algorithms like the Bouncy Particle Sampler, and applies these to problems in cancer phylogenetics, evolutionary dynamics, and historical linguistics. His work emphasizes bridging theoretical foundations with practical tools for data science. Publications Trends : Recent work spans distributed sampling frameworks (e.g., Pigeons.jl), variational phylogenetic inference, and cancer clonal evolution modeling. His articles often address algorithmic scalability and interdisciplinary applications in biology and astronomy. Awards : CRM-SSC Prize in Statistics (2024) PIMS-UBC Mathematical Sciences Young Faculty Award (2018) Tweedie New Researcher Award (2016) Advising & Grants : Supervises graduate students (e.g., Son Luu, Nikola Surjanovic) and leads funded projects on distributed MCMC and cancer genomics. Collaborates with institutions like the Simons Foundation and the Canadian Statistical Sciences Institute (CANSSI). Labs/Teams : Core member of the UBC Statistical Machine Learning group, contributing to open-source tools like Blang and the Bouncy Particle Sampler implementation.

• Computational Statistics
• Bayesian Inference
• Monte Carlo Methods
• +4 more

Benedict King is a postdoctoral researcher in the Department of Linguistic and Cultural Evolution at the Max Planck Institute for Evolutionary Anthropology, where he contributes to the Comparative Oceanic Linguistics (CoOL) team. His research focuses on constructing phylogenetic trees of language relationships using Bayesian methods to explore language origins, spread, and evolutionary dynamics. He holds a BA in Natural Sciences from the University of Cambridge (UK), a PhD in Evolutionary Biology from Flinders University (Australia), and previously worked as a Postdoctoral Research Associate at Naturalis Biodiversity Center (Netherlands). King’s research interests bridge evolutionary biology, paleontology, and linguistics. He investigates evolutionary transitions in early vertebrates (e.g., placoderms, coelacanths) and applies computational models to study language evolution. His work emphasizes improving phylogenetic methodologies and integrating fossil evidence with statistical frameworks. Notable projects include analyzing Philippine language migrations and reconfiguring actinistian phylogenies through Devonian fish fossils. His publications reflect interdisciplinary strengths, combining vertebrate paleontology with linguistic phylogenetics. Recent work supports rapid Austronesian expansions and hybrid Indo-European language origins. Awards include the Oxford University Press Prize for Biosciences and recognition for his PhD publication. King actively engages in public science communication through platforms like The Conversation and GitHub repositories.

• Phylogenetic Analysis
• Evolutionary Biology
• Paleontology
• +4 more

Itsik Pe'er is a Full Professor and Vice-Chair in the Department of Computer Science at Columbia University's Fu Foundation School of Engineering & Applied Science, and holds a joint appointment as Professor of Systems Biology at the Vagelos College of Physicians and Surgeons. His research focuses on computational methods in human genetics, including genetic variation analysis, disease association studies, and algorithm development for genomic data. He leads the Itsik Pe'er Lab of Computational Genomics, which develops tools like Xplorigin, Germline, and SEACells to address challenges in genomics and medical research. His work spans machine learning applications in healthcare, microbiome analysis, and cancer genomics. Notable contributions include studies on hypertensive disorders in pregnancy, bias correction in predictive models, and the development of non-Euclidean learning libraries like Manify. Pe'er has advised students including Vladimir Vacic, Anat Kreimer, and Arthi Ramachandran, and collaborates on grants addressing genetic epidemiology and computational biology. His lab's location is in the Computer Science Building at Columbia's Morningside Campus.

• Human Genetics
• Computational Biology
• Genomics
• +3 more

Xiang Ji is an Assistant Professor in the Department of Mathematics at Tulane University, affiliated with the School of Science & Engineering. His research focuses on statistical phylogenetics, computational biology, and bioinformatics, particularly in viral evolution and genomic epidemiology. He collaborates with Dr. Wu-Min Deng on cancer biology research from a bioinformatics perspective. Education: Ph.D., 2017: Bioinformatics and Statistics (Co-Major), North Carolina State University M.S., 2013: Material Science and Engineering, North Carolina State University B.S., 2011: Economics (Double Major) and Physics, Peking University Research Interests: Dr. Ji develops statistical models and computational tools for phylogenetic analysis, including scalable algorithms for large-scale genomic data. His work spans viral evolution, zoonotic disease surveillance, and parallel computing libraries for Bayesian inference. He emphasizes practical implementations such as Torchtree and TreeFlow . Articles Trends: Recent publications emphasize viral evolution dynamics (e.g., SARS-CoV-2, avian influenza), genomic surveillance strategies, and computational methods for phylogenetic inference. His work often bridges statistical theory with real-world applications in public health and epidemiology. Advising & Grants: While specific grant details are not listed, his active research program indicates involvement in funding initiatives related to computational biology and viral evolution. He teaches advanced courses in data analysis, linear models, and probability theory. Labs & Teams: Collaborates with Tulane’s Cancer Biology group and maintains partnerships with institutions globally, focusing on genomic epidemiology and phylogenetic software development.

• Statistical Phylogenetics
• Bioinformatics
• Viral Evolution
• +3 more

Meng Li is the Noah Harding Associate Professor of Statistics at Rice University's School of Engineering. He specializes in Bayesian analysis, machine learning, and statistical theory. His research bridges methodological development and applications in biomedical sciences, materials informatics, and neuroimaging. Li holds a Ph.D. from North Carolina State University and a B.S. from Sun Yat-sen University. He has been recognized with awards including the 2020 Rice Engineering Excellence Award and the Ralph E. Powe Junior Faculty Enhancement Award. Li's research focuses on probabilistic modeling of complex data such as images, functional data, and networks. His funded projects include AI frameworks for pancreatic cancer biomarkers and Bayesian spatiotemporal modeling of marine ecosystems. He collaborates with institutions like Houston Methodist and Baylor College of Medicine on medical applications. His teaching includes advanced courses like Bayesian Statistics and Advanced Bayesian Inference. He advises over 30 students, many of whom have pursued academic and industry roles. Li serves as an associate editor for Bayesian Analysis and the new ACM Transactions on Probabilistic Machine Learning.

• Bayesian Analysis
• Machine Learning
• Functional Data Analysis
• +8 more

Max Lau is an Assistant Professor in the Department of Biostatistics and Bioinformatics and the Department of Epidemiology at Emory University. His research focuses on integrating machine learning and computational methods with epidemiological and genomic data to study infectious disease dynamics. He teaches courses such as BIOS 790R (Advanced Seminar in Biostatistics) and DATA 534 (Applied Machine Learning). Dr. Lau's work emphasizes scalable Bayesian inference, graph neural networks, and stochastic modeling to address challenges in disease transmission, outbreak control, and pathogen evolution. His recent research includes developing tools like ScITree and Epilearn, and he has contributed to understanding measles dynamics, tuberculosis treatment, and livestock disease management. His academic contributions span over 30 publications since 2010, with a particular focus on phylodynamics, epidemic modeling, and vaccine strategy evaluation. His interdisciplinary approach bridges computational methods with public health applications, aiming to enhance disease prediction and intervention efficacy.

• Infectious Disease Dynamics
• Machine Learning
• Artificial Intelligence

John Wakeley is a Professor of Organismic and Evolutionary Biology at Harvard University's Faculty of Arts and Sciences. He leads the Wakeley Lab, focusing on theoretical population genetics, mathematical models of genetic variation, and evolutionary processes. His research integrates analytical and computational methods to study contemporary and historical factors shaping genetic diversity. As of 2023, he is not accepting new graduate students for the academic year 2023-2024. Wakeley's work emphasizes coalescent theory, population structure, and evolutionary game theory. Notable contributions include developing statistical tools for analyzing ancient DNA and advancing models of ancestry reconstruction. Recent projects explore topics such as recurrent mutation in rare variants and the implications of big family effects on coalescence patterns. His lab members include researchers like Louis Fan, Jack Edwards, and Erin Ciccone, collaborating on diverse projects in theoretical and applied population genetics. Key scientific outputs include studies on iterated survival games and genomic analyses of butterfly radiation.

• Theoretical Population Genetics
• Mathematical Biology
• Evolutionary Biology
• +3 more

Piotr Zwiernik is an Associate Professor in the Department of Statistical Sciences at the University of Toronto's Faculty of Arts and Science, with a cross-appointment in the Department of Mathematics. Currently on leave from the University of Toronto, he is based in Barcelona following his return in July 2025. His academic journey includes a PhD in Statistics from the University of Warwick (2011), research positions at prestigious institutions including Mittag Leffler Institute, IPAM, TU Eindhoven, UC Berkeley, and the University of Genoa, and an Assistant Professorship at Universitat Pompeu Fabra in Barcelona (2016-2021). His research spans the intersection of statistics, mathematics, and computational methods, with particular emphasis on graphical models, covariance matrix estimation, convex analysis, tensors, and algebraic and combinatorial methods in statistics. Zwiernik's work demonstrates a consistent focus on high-dimensional statistics, mathematical statistics, and elegant theoretical frameworks that bridge abstract mathematics with practical statistical applications. His recent publications reveal a deepening exploration of tensor analysis, algebraic statistics, and the geometric properties of statistical models. Zwiernik serves as an associate editor for leading journals including Biometrika, Scandinavian Journal of Statistics, and Algebraic Statistics. His research program includes the development of the GOLAZO R package for asymmetric regularization of log-likelihood in Gaussian graphical models. As an academic leader, he has served as Associate Chair for Research in his department and actively participates in numerous international conferences and workshops, reflecting his significant standing in the statistical community. His recent publications show a strong trend toward algebraic and geometric approaches to statistical problems, with increasing focus on tensor methods, positivity constraints in statistical models, and the theoretical foundations of graphical models. The work demonstrates remarkable continuity in exploring the mathematical structures underlying statistical models while adapting to emerging challenges in high-dimensional data analysis. Zwiernik is committed to mathematical accessibility and education, guided by Federico Ardila's four axioms which emphasize equitable distribution of mathematical potential, joyful mathematical experiences, mathematics as a malleable tool, and treating every student with dignity and respect. He actively seeks PhD students with strong mathematical backgrounds for research at UPF or the Institute of Mathematics of UPC.

• Statistical Computing
• Theoretical Statistics
• High-dimensional Statistics
• +7 more

Maria Chikina is an Assistant Professor at the University of Pittsburgh School of Medicine's Department of Computational and Systems Biology. She holds a PhD in Molecular Biology from Princeton University. Her research focuses on developing computational methods to analyze large-scale genomic datasets, bridging statistical rigor with biological insights to overcome experimental biases. Key research areas include latent variable modeling (e.g., PLIER, CellCODE), interpretable neural networks for sequence-to-function modeling, evolutionary rate analysis (RERconverge), and applications in tumor immunology, exercise genomics, and infectious disease (e.g., SARS-CoV-2). Her lab has developed tools like InstaPrism, NIFA, and L0 segmentation for data-driven biological discovery. Her work spans collaborations with institutions like UPMC (on tumor microenvironment) and the Molecular Transducers of Physical Activity Consortium (MoTraPAC). Notable projects include analyzing convergent evolution in marine mammals and subterranean species, and developing epigenetic biomarkers for disease states through the ECHO program. Lab members include PhD students (Rezwan Hosseini, Tugrul Balci) and postdocs (Tina Subic, Anish Sevekari). Past students Wynn Meyer now leads a group at Lehigh University. Her group emphasizes open-source tools (GitHub repository ChikinaLab) and interdisciplinary approaches to systems biology challenges.

• Computational Biology
• Genomics
• Machine Learning
• +7 more

Samuel W.K. Wong is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a Ph.D. in Statistics from Harvard University (2013) under Prof. Samuel Kou. His research focuses on statistical methodology for complex data science challenges in protein structure modeling, dynamic systems inference, and reliability engineering of wood-based products. He has held academic positions at the University of Florida (2013–2018) and has been at Waterloo since 2018. His research interests include Bayesian computation, statistical inference for dynamic systems, and spatial-temporal data analysis. Notable contributions include the development of manifold-constrained Gaussian processes (MAGI package) and sequential Monte Carlo methods for protein folding studies. He has advised over 15 graduate students and researchers, many of whom are now in academic or industry roles worldwide. Wong has received teaching distinctions at Harvard and holds awards including the Nash Medal (2008) for academic excellence. His work bridges computational statistics with applications in bioinformatics, structural engineering, and environmental science. He has published extensively in top-tier journals like Journal of Computational and Graphical Statistics and Biometrics , and collaborates with wood scientists to improve real-time lumber quality assessment using laser imaging data. His teaching portfolio includes courses on probability theory, statistical inference, and spatial data analysis at both undergraduate and graduate levels. Beyond academia, he maintains an active passion for classical piano performance, having performed recitals combining music with his statistical research interests.

• Data Science Methodology
• Protein Structure Analysis
• Reliability Engineering
• +4 more

Akihiko Nishimura is an Assistant Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He holds a PhD from Duke University (2017) and MS and BS degrees from Stanford University (2011 and 2010). His research focuses on Bayesian methods, statistical computing, and public health data science, with applications in precision medicine and observational health data analytics. PhD, Duke University, 2017 MS, Stanford University, 2011 BS, Stanford University, 2010 Nishimura's research centers on developing advanced statistical and computational methodologies for real-world health data. His work emphasizes Bayesian inference, large-scale computing, and software development for reproducible research. He is particularly interested in using observational health data to improve clinical decision-making and advance precision medicine. He co-leads the Bayesian Learning and Spatio-Temporal modeling group (BLAST Group) and the inHealth/OHDSI Lab , collaborating with clinicians and data scientists across institutions. His recent publications reflect a strong trend in methodological innovation in Monte Carlo methods (e.g., Hamiltonian and Zigzag samplers), scalable Bayesian inference, and applications in pharmacovigilance, diabetes management, and infectious disease modeling. The articles span disciplines including biostatistics, computational statistics, public health, and bioinformatics, demonstrating a consistent focus on high-impact, computationally intensive problems in health data science. Nishimura actively contributes to the scientific community through methodological development and open science. He develops statistical software and shares teaching materials on GitHub, emphasizing reproducibility and performant computing. His involvement in the OHDSI community enables large-scale, multi-institutional studies that would not be feasible with single-source data. His work has been recognized through publications in top-tier journals such as the Journal of the American Statistical Association , Biometrika , and JAMA Ophthalmology , and has been picked up by numerous news outlets and social media platforms, indicating broad scientific and public impact. Nishimura teaches courses on performant statistical computing and advanced Monte Carlo methods, training the next generation of data scientists in efficient algorithm and software design. He mentors students and collaborators in statistical methodology and software development, fostering a culture of rigorous, reproducible, and impactful research.

• Bayesian Statistics
• Statistical Computing
• Public Health Data Science
• +9 more

Louis Du Plessis is a Lecturer at ETH Zürich's Department of Biosystems Science and Engineering in Basel, Switzerland. His research focuses on computational evolution with particular emphasis on infectious disease dynamics and genomic analysis. He maintains an active research profile with numerous high-impact publications in top-tier journals. Dr. Du Plessis completed his doctoral studies at ETH Zürich in 2016 with a thesis titled 'Understanding the spread and adaptation of infectious diseases using genomic sequencing data,' building upon his 2011 Master's work on evolutionary rate variation. His current research sits at the intersection of computational biology, epidemiology, and evolutionary genetics. His research interests span computational epidemiology, phylodynamics, viral evolution, and infectious disease modeling. He has made significant contributions to understanding pandemic dynamics, particularly regarding influenza and SARS-CoV-2, using genomic and epidemiological data integration. His methodological work includes developing computational approaches for estimating epidemic dynamics and viral transmission patterns. Analysis of his recent publications reveals a strong focus on how pandemics disrupt normal viral circulation patterns, with particular attention to influenza evolution during the 2009 H1N1 and COVID-19 pandemics. His work often combines phylogenetic analysis with epidemiological modeling to extract maximum information from genomic and case count data. Dr. Du Plessis has received research funding from European Commission projects including 'From Foundations of Phylodynamics to new applications in Cell Biology' (grant 101001077) and 'MOnitoring Outbreak events for Disease surveillance in a data science context' (grant 874850). He is actively involved in developing computational tools for analyzing pathogen genomic data and has contributed to several software packages used in the field. His work has significant implications for public health surveillance and pandemic preparedness.

• Computational Epidemiology
• Phylodynamics
• Viral Evolution
• +3 more

Dr. Calum Gabbutt is a Chapman-Schmidt AI in Science Postdoctoral Research Fellow at Imperial College London's Department of Mathematics (Faculty of Natural Sciences) and a Postdoctoral Training Fellow at the Institute of Cancer Research (ICR), London. He holds a PhD in Mathematical Biology from Queen Mary University of London (2017–2021) and an MPhys from the University of Oxford (2013–2017). His research focuses on mathematical and computational methods to understand clonal dynamics and cancer evolution, particularly leveraging genomic and lineage tracing data. He develops Bayesian inference models to analyze evolutionary processes in cancer, aiming to improve clinical outcomes through precision medicine. Key areas include genetic barcoding, methylation-based molecular clocks, and phylogenetic reconstruction of tumor evolution. Recent work spans large-scale genomic analyses of cancer evolution, computational tools like PISCA-box for somatic chromosomal alterations, and studies on phenotypic plasticity in metastasis and therapy resistance. His research integrates AI-driven approaches to decode cancer heterogeneity and temporal dynamics in human tissues. Collaborations span institutions like the ICR and Queen Mary University of London, emphasizing interdisciplinary methods at the intersection of mathematics, computational biology, and oncology.

• Mathematical Biology
• Cancer Evolution
• Computational Biology
• +4 more

Professor Barry Porter is a faculty member at Lancaster University in the School of Computing and Communications . His research focuses on emergent software platforms that address software complexity through component models , meta-software platforms , and machine learning . Key areas include distributed systems, cloud integration with sensor nodes, green computing, and real-time visualization. Research Interests : Runtime adaptation in complex systems Self-assembling software architectures Machine learning for code optimization Distributed emergent systems at scale Green computing for multi-core environments Edge-cloud continuum integration Recent Publication Trends : His 2025 work explores genetic improvement for software using speciation algorithms , program geometry projection , and multi-agent decision frameworks . Earlier studies (2022-2024) investigate edge-cloud systems , neural transfer learning , and ecosystem curation in emergent software. Supervision & Projects : He supervises PhD student Ben Craine and leads projects like B-EGI (Bio-Enhanced Genetic Improvement) and BBC Prosperity Partnership for media delivery. Collaborations span environmental IoT, multi-agent learning, and fog computing. Labs & Groups : Affiliated with the Lancaster Intelligent, Robotic and Autonomous Systems Centre , Centre of Excellence in Environmental Data Science , and the Distributed Systems group.

• Emergent Software Systems
• Runtime Adaptation
• Component-Based Software
• +7 more

Daniel Klein is a Professor in the Computer Science Division at the University of California at Berkeley , affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) and the Berkeley Natural Language Processing Group . His research focuses on statistical natural language processing, including unsupervised learning, syntactic parsing, information extraction, and machine translation, with applications in historical linguistics and AI.

• Artificial Intelligence
• Natural Language Processing
• Computational Linguistics
• +7 more