جست‌وجوی استاد و پژوهشگر

Xiang Jiمشاهده پروفایل

استادیار

• Statistical Phylogenetics
• Bioinformatics
• Viral Evolution
• +۳ مورد دیگر

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.

Hongtu Zhuمشاهده پروفایل

استاد

• Biostatistics
• Medical Image Analysis
• Precision Medicine
• +۴ مورد دیگر

Dr. Hongtu Zhu is the Kenan Distinguished Professor of Biostatistics, Statistics, Radiology, Computer Science, and Genetics at the University of North Carolina at Chapel Hill (UNC). He holds affiliations with the Gillings School of Global Public Health and leads the Biostatistics and Imaging Genomics Analysis Lab. His expertise spans statistical learning, medical imaging, AI, and big data integration, with a focus on precision medicine and biomedicine. Dr. Zhu earned his PhD in Statistics from The Chinese University of Hong Kong (2000) and has held prior roles including DiDi Fellow/Chief Scientist (2018-2020) and Bao-Shan Jing Endowed Professor at MD Anderson Cancer Center (2016-2018). He has published over 345 peer-reviewed articles in top-tier journals like Nature, Science, and JASA, and actively contributes to editorial roles including Coordinating Editor of JASA. His research interests include neuroimaging analysis, knowledge graphs, and AI applications in healthcare. Notable awards include the COPSS Snedecor Award (2025), IEEE Fellowship (2025), and IMS Medallion (2027). He has mentored over 80 PhD students/postdoctoral fellows and serves on NIH grant review panels and professional organizations like the ASA's Section on Statistics in Imaging. Key Contributions: Imaging genomics, brain connectivity studies, ridesharing market optimization, medical AI frameworks Lab Innovations: Brain Imaging Genetics Knowledge Portal, Biomedical Knowledge Graph Interface Teaching: Advanced biostatistics courses (Generalized Linear Models, Deep Learning in Biomedicine) Recent work explores causal inference in healthcare, X chromosome's role in neurobiology, and AI ethics in medical vision-language models. His interdisciplinary projects bridge statistics, computer science, and clinical practice to address complex biomedical challenges.

Carlos Cinelliمشاهده پروفایل

استادیار

• Causal Inference
• Statistical Methodology
• Machine Learning
• +۵ مورد دیگر

Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington, where he conducts research at the intersection of causal inference, statistical methodology, machine learning, and artificial intelligence. He is also a data science fellow at the eScience Institute and affiliate faculty of the Center for Statistics and the Social Sciences, demonstrating his interdisciplinary approach to causal methodology. Dr. Cinelli received his Ph.D. in Statistics from the University of California, Los Angeles, advised by Chad Hazlett and Judea Pearl, two prominent figures in causal inference. His research focuses on developing new causal and statistical methods for transparent and robust causal claims in empirical sciences, with particular attention to challenges faced by social and health scientists. His work spans theoretical developments in causal identification, sensitivity analysis frameworks, and practical software implementations that enable researchers to assess the robustness of their causal conclusions. Cinelli's research program addresses fundamental questions about how unobserved confounding affects causal estimates and develops tools to quantify how sensitive findings are to potential violations of causal assumptions. His work on omitted variable bias frameworks has been particularly influential across multiple disciplines. Through his publications, Cinelli has established himself as a leading researcher in causal inference methodology, with papers appearing in top journals across statistics, machine learning, epidemiology, and social sciences. His work demonstrates both theoretical rigor and practical relevance, often accompanied by open-source software implementations that make his methods accessible to applied researchers. Best paper award at SBE 2024 in Econometrics Royalty Research Fund (RRF) Award recipient NSF/MMS research support As an advisor, Cinelli has successfully guided PhD students like Nick Irons to dissertation completion. He actively seeks new students with strong interests in causal inference. His research is supported by multiple funding sources including the National Science Foundation and the University of Washington's Royalty Research Fund. Cinelli contributes to the academic community through editorial work for the Journal of Causal Inference and by developing widely used software packages like sensemakr for sensitivity analysis.

Michael U. Gutmannمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Bayesian Inference
• Likelihood-Free Inference
• +۹ مورد دیگر

Michael U. Gutmann is a Senior Lecturer in Machine Learning at the School of Informatics, University of Edinburgh, and a member of the Institute for Adaptive and Neural Computation. His research lies at the intersection of machine learning, statistics, and scientific applications, with a focus on developing inference methods for complex and implicit models. Education: PhD in Computational Neuroscience, University of Tokyo MSc in Engineering and Applied Mathematics, Swiss Federal Institute of Technology (ETH) Zurich MSc, Ecole Centrale Paris His primary research interests include Bayesian inference, likelihood-free inference, optimal experimental design, unsupervised learning, and applications in computational biology and neuroscience. He is best known for introducing Noise-Contrastive Estimation (NCE), a foundational technique for training unnormalized statistical models. His recent work spans variational inference, density ratio estimation, flow models for missing data, and AI-driven experimental design in behavioral and biological sciences. His publications, including in NeurIPS , ICML , JMLR , and eLife , demonstrate a strong emphasis on methodological innovation for scientific discovery. He has contributed to open-source tools such as ELFI (Engine for Likelihood-Free Inference) and developed practical implementations of robust inference algorithms. Scientific Awards: No specific awards listed in the provided texts. Michael Gutmann actively supervises students and collaborates with leading researchers in machine learning and computational biology. He has secured research funding from EPSRC and BBSRC for projects in generative modeling and infectious disease epidemiology. He teaches advanced courses such as Probabilistic Modelling and Reasoning and Data Mining, reflecting his deep engagement with both theoretical and applied aspects of machine learning. Labs and Research Groups: Institute for Adaptive and Neural Computation (ANC), University of Edinburgh Former affiliations with Department of Mathematics and Statistics and Department of Computer Science at the University of Helsinki and Aalto University

Markus Heinonenمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Probabilistic Modeling
• Bayesian Inference
• +۵ مورد دیگر

Markus Heinonen is an Academy Research Fellow at Aalto University's Department of Computer Science within the School of Science. His academic position is tied to Harri Lähdesmäki's Professorship, focusing on probabilistic machine learning. He holds a Doctoral degree in Engineering and Technology from the University of Helsinki (2013). His research integrates probabilistic modeling , deep learning , and differential equations , with applications in computational biology, drug discovery, and biophysics. Key themes include Gaussian processes, Bayesian inference, generative models, and their use in understanding complex biological systems like immune cell behavior (e.g., T cell receptor analysis in aplastic anemia) and molecular design. He leads major projects such as the Deep Learning with Differential Equations initiative (2020–2025), exploring continuous-time models and physics-informed neural networks. His work bridges theory and application, evidenced by collaborations in diffusion models , optimal transport , and single-cell analysis . Publications span over 65 peer-reviewed outputs, with recent emphases on robust neural network training, multi-target molecular prediction, and interpretable drug design frameworks. His research contributes to UN Sustainable Development Goal 3 (Good Health) through advancements in disease modeling and therapeutic development. He has undertaken visiting research roles at the University of California, San Francisco (2017) and Telecom ParisTech (2013–2014). Media highlights include recognition for work on TCR-epitope prediction and AI-driven enzyme engineering.

Nicholas Kortessisمشاهده پروفایل

استادیار

• Ecology
• Adaptation in Variable Environments
• Statistical Ecology
• +۷ مورد دیگر

Nicholas Kortessis is an Assistant Professor in the Department of Biology at Wake Forest University, within The Undergraduate College. His research lies at the intersection of ecology, evolution, and theoretical modeling, focusing on how environmental variability shapes biological diversity. Research Interests: His primary areas include statistical and theoretical ecology, adaptation in variable environments, population and community dynamics, and ecosystem modeling. He uses mathematical frameworks to simulate ecological processes across large spatial and temporal scales, bridging experimental data with predictive theory. Publication Trends: His recent work spans topics such as habitat fragmentation, disease in invasive species, character displacement, seed dormancy evolution, and pandemic transmission dynamics. These reflect a strong emphasis on theoretical and synthetic approaches to ecological problems, often involving collaboration across institutions. Scientific Awards: No awards are mentioned in the provided text. Advising and Grants: While no specific students or grants are listed, Dr. Kortessis leads an active research lab and collaborates widely, indicating ongoing mentorship and likely grant-supported research. His lab engages in theoretical and data-driven ecological studies, suggesting funding from agencies supporting environmental and theoretical biology. Labs and Teams: He runs the Kortessis Lab at Wake Forest University, which focuses on modeling ecological and evolutionary processes. The lab emphasizes mathematical and conceptual tools to explore biodiversity, coexistence, and ecosystem responses to environmental change.

Masoud Asgharianمشاهده پروفایل

استاد

• Survival Analysis
• Changepoint Problem
• Nonparametric Bayesian Analysis
• +۵ مورد دیگر

Masoud Asgharian is a Professor in the Department of Mathematics and Statistics at McGill University. His research focuses on survival analysis, changepoint problems, nonparametric Bayesian methods, and data envelopment analysis. He has contributed to influential studies on dementia survival rates, censored data methodologies, and statistical efficiency measures. His work bridges biostatistics and operations research, with applications in public health and medical sciences. Key contributions include methodologies for prevalent cohort survival analysis, input relaxation efficiency measures in stochastic DEA, and causal inference techniques. Asgharian has collaborated extensively with researchers in epidemiology and biomedical engineering, as evidenced by his co-authored publications on topics ranging from tooth enamel properties to low-precision neural network quantization. His research has been published in high-impact journals such as New England Journal of Medicine , Journal of the American Statistical Association , and Biometrics . Current affiliations include leadership roles in statistical research at McGill, with ongoing projects in computational statistics and healthcare analytics.

• Behavioral Ecology
• Evolutionary Biology
• Population Ecology
• +۱۱ مورد دیگر

Prof. Dr. Oliver Krüger is a behavioral ecologist and evolutionary biologist at Bielefeld University 's Faculty of Biology , where he leads the Department of Animal Behaviour since 2013. His research spans avian and marine mammal systems, focusing on life history strategies, parasite-host interactions, and environmental adaptation. Education: Biology studies at Bielefeld University (1994-1996) MSc in Oxford (1996-1997) PhD at Bielefeld University with Fritz Trillmich and Jan Lindström (1998-2000) Research Themes: Behavioral ecology, evolutionary biology, and population dynamics across tropical and temperate ecosystems. Key projects include NC³ (Niche Choice/Construction) and studies on Galápagos sea lions, common buzzards, and pinniped species. Scientific Leadership: Spokesperson, SFB TRR 212 "NC³" (2018-2025) Advisory Board member: German Ornithologists Union, IUCN SSC pinniped group, German Primate Centre Peer review roles: Humboldt Foundation, DFG, HFSP, NSF Awards: Leopoldina Prize (2001) Niko Tinbergen Award (2008) DFG Heisenberg Professorship (2010-2015)

• Plant Ecology
• Community Ecology
• Mycorrhizal Symbiosis
• +۵ مورد دیگر

Mari Moora is a distinguished Professor in Community Ecology at the University of Tartu's Faculty of Science and Technology, Institute of Ecology and Earth Sciences. Since August 2023, she has served as the Vice Rector for Research at the University of Tartu, a position she will hold until July 2028. With a career spanning over three decades at the University of Tartu, Professor Moora has established herself as a leading expert in plant ecology, particularly in the field of mycorrhizal symbiosis and its role in shaping plant communities. Education: Secondary education at Nõo Secondary School (1984) Bachelor's degree in Biology (Genetics) from University of Tartu (1989) PhD in Plant Ecology from University of Tartu (1998) with thesis supervised by Martin Zobel Professor Moora's research focuses on the processes behind the structure and diversity of plant communities, particularly examining the role of biotic interactions such as mycorrhizal symbiosis and pollination in shaping plant populations. Her work extends to studying global diversity patterns of arbuscular mycorrhizal fungi and soil metagenomics - investigating how soil molecular diversity influences visible ecological patterns across ecosystems. Her interdisciplinary approach combines field ecology, molecular techniques, and theoretical frameworks to address fundamental questions in community ecology and biodiversity science. Her recent publications demonstrate a clear trend toward understanding ecological processes at multiple scales, from molecular interactions to global biodiversity patterns. Professor Moora's work increasingly integrates cutting-edge molecular techniques like metabarcoding with traditional ecological approaches, creating a more comprehensive understanding of plant-microbial interactions. Her research has significant implications for biodiversity conservation, ecosystem restoration, and understanding how ecological communities respond to environmental change. Scientific Awards: Order of the White Star, 4th class (2025) Estonian Academy of Sciences Alma Tomingas Medal (2023) Science Award of the Estonian Republic (2016) Honorary member of the University of Rio Cuarto (Argentina) (1998) Professor Moora has served as a principal investigator for significant research projects including "Plant-microbial mutualisms in dynamic ecosystems" (2021-2025) and the University of Tartu component of "Agroecology and new crops in future climates" (2024-2030). She has been actively involved in research administration and evaluation, serving on panels for the European Research Council, German Research Foundation, and European Commission. Her editorial work includes being an Academic Editor for PLoS ONE (2011-2016) and serving as a referee for numerous high-impact ecology journals. As Head of the Chair of Plant Ecology (2021-2023) and currently as Vice Rector for Research, Professor Moora plays a significant role in shaping research strategy and academic leadership at the University of Tartu. Her work bridges fundamental ecological research with practical applications for biodiversity conservation and sustainable land management.

• Evolutionary Computation
• Machine Learning
• Natural Computing
• +۵ مورد دیگر

Thomas Bäck is a Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University , Netherlands, and a member of the interdisciplinary programme Society, Artificial Intelligence and Life Sciences (SAILS) . His academic career spans roles at Leiden University (1996–present) and leadership positions at the Center for Applied Systems Analysis in Dortmund (1994–2000). Education : Diplom-Informatiker (Computer Science), Technische Universität Dortmund (1990) Dr. rer. nat. (Computer Science), Technische Universität Dortmund (1994) Research Interests : Dr. Bäck specializes in evolutionary computation , machine learning , and their applications in sustainable smart industry and healthcare . Recent work focuses on integrating large language models (LLMs) and quantum computing into optimization frameworks, with projects like CIMPLO (predictive maintenance), ECOLE (experience-based optimization), and SAPPAO (airline operations optimization). Scientific Contributions : His 526+ publications cover evolutionary algorithms, quantum optimization, and LLM-driven design, with recent trends including: Quantum computing (e.g., quantum approximate optimization, quantum advantage challenges) LLM integration (e.g., hyperparameter tuning, mutation control, code evolution graphs) Healthcare and industry (e.g., predictive maintenance, anomaly detection, melt quality prediction) Algorithm benchmarking (e.g., IOHprofiler, MA-BBOB, explainable benchmarking) Scientific Awards : IEEE Fellow (2022) Royal Netherlands Academy of Arts and Sciences (KNAW) member (2021) Academia Europaea member (2022) IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award (2015) Fellow, International Society of Genetic and Evolutionary Computation (2003) Best Ph.D. thesis award, German Society of Computer Science (GI) (1995) Advising and Grants : He supervises Ph.D. candidates in evolutionary computation and machine learning and has secured 7 major grants from organizations like the Dutch Research Council , European Commission , and The Research Council of Norway . His editorial roles include Editor-in-Chief of the Evolutionary Computation Journal and associate editorships in leading AI journals.

Ruth Etzioniمشاهده پروفایل

استاد مدعو

• Cancer Screening
• Prostate Cancer Research
• Biomarkers
• +۵ مورد دیگر

Ruth Etzioni is an Affiliate Professor in the Biostatistics Program and Public Health Sciences Division at the Fred Hutchinson Cancer Center. She leads the Etzioni Lab, focusing on cancer screening, early detection, and overdiagnosis analysis. Her work integrates statistical modeling, epidemiology, and clinical research to address critical questions in prostate and breast cancer control. Dr. Etzioni holds the Rosalie & Harold Rea Brown Endowed Chair and has received a $7.4M NIH Outstanding Investigator Award. Education: PhD in Statistics (Carnegie Mellon University, 1990), MS in Statistics (Carnegie Mellon, 1987), BS in Mathematics (University of Cape Town, South Africa). Research interests emphasize biomarkers, clinical trials, and epidemiological methods. She leads the Biostatistics Core for the Pacific Northwest Prostate Cancer SPORE and participates in the Cancer Intervention and Surveillance Modeling Network (CISNET). Her lab develops models to evaluate screening policies, quantify overdiagnosis, and inform healthcare disparities reduction strategies. Key achievements include groundbreaking work on prostate cancer screening's harm-benefit tradeoffs and contributions to multi-cancer early detection (MCED) frameworks. Recent studies address racial disparities in prostate cancer outcomes, metastasis trends, and the clinical utility of novel diagnostics like PSMA PET imaging. Awards include the Brown Endowed Chair (2020), NCI OIA Award (2023), and recognition for advancing cancer data science. Her lab collaborates with institutions globally and mentors students in biostatistics and translational data science.

Brian Hieمشاهده پروفایل

استادیار

• Machine Learning
• Computational Biology
• Protein Engineering
• +۶ مورد دیگر

Brian Hie is an Assistant Professor of Chemical Engineering at Stanford University , a Dieter Schwarz Foundation Stanford Data Science Faculty Fellow , and an Innovation Investigator at Arc Institute . He leads the Laboratory of Evolutionary Design , focusing on the intersection of biology and machine learning . His prior roles include a Stanford Science Fellow in the Stanford University School of Medicine and a Visiting Researcher at Meta AI . Education: Ph.D. , Electrical Engineering and Computer Science , Massachusetts Institute of Technology (2021) Bachelor’s Degree , Stanford University Research Interests: Brian’s work bridges machine learning and computational biology , with a focus on protein engineering , single-cell RNA sequencing , and viral evolution . His Evolutionary velocity framework predicts protein evolutionary dynamics across timescales, while his Scanorama algorithm enables efficient integration of heterogeneous single-cell datasets. He also develops structure-informed language models for antibody optimization and uncertainty-aware ML for biological discovery. Publication Trends: His recent work (2023) emphasizes structure-based inverse folding for antibody evolution, evolutionary scale modeling , and unsupervised optimization . Earlier studies (2022-2021) cover evolutionary velocity , multi-modal single-cell analysis , and viral escape prediction using natural language analogies. Scientific Awards: Stanford Science Fellow (2021) National Defense Science and Engineering Graduate Fellowship (2019) Advising: He mentors doctoral students including Brandon Ameglio , Garyk Brixi , and Chang M. Yun , with a focus on biological design and computational methods . Labs & Collaborations: His lab collaborates with Bio-X and the Institute for Human-Centered Artificial Intelligence (HAI) , and he maintains affiliations with Sarafan ChEM-H and Stanford Data Science .

Ramina Sotoudehمشاهده پروفایل

استادیار

• Sociogenomics
• Sociology of Culture
• Social Inequality
• +۲ مورد دیگر

Ramina Sotoudeh is an Assistant Professor of Sociology at Yale University with a secondary appointment in Statistics & Data Science. Her research bridges sociogenomics, the sociology of culture, and social inequality, focusing on how genetic and social environments interact to shape human behavior. Education : BA in Social Research and Public Policy from NYU Abu Dhabi, PhD in Sociology from Princeton University Postdoctoral Experience : Fellow at Nuffield College, University of Oxford Ramina’s work in sociogenomics examines how institutional, relational, and genetic contexts influence health outcomes, such as smoking behavior and peer interactions. Her sociology of culture projects use relational methods to explore cultural frameworks underlying attitudes toward science, religion, politics, and marriage. She also investigates health disparities and inequality through interdisciplinary lenses. Her most recent publications analyze genomic population structure, behavioral plasticity, and computational approaches to algorithm selection. Earlier works focus on cultural attitudes, behavioral diffusion in networks, and genetic correlations with education and longevity. These studies span journals like American Sociological Review , PNAS , and Demography .

Nicolas Federico Martinمشاهده پروفایل

دانشیار

• Precision Agriculture
• Crop Science
• Machine Learning Applications in Agriculture
• +۷ مورد دیگر

Nicolas Federico Martin is an Associate Professor in the Department of Crop Sciences at the University of Illinois at Urbana-Champaign, with additional appointments as Associate Professor in the Center for Latin American and Caribbean Studies, Center for Digital Agriculture, and the National Center for Supercomputing Applications (NCSA). His interdisciplinary work bridges traditional agricultural science with cutting-edge computational approaches. Dr. Martin's research focuses on the intersection of agriculture and data science, with particular emphasis on: Precision agriculture and on-farm experimentation methodologies Machine learning applications for crop management and yield prediction Nitrogen and nutrient management optimization Soybean and corn breeding and production systems Remote sensing and UAV applications in agriculture Sustainable agricultural practices including cover crop management His publication record demonstrates a clear trajectory toward increasingly sophisticated integration of artificial intelligence with agricultural science. Recent work shows heavy emphasis on using machine learning algorithms (particularly reinforcement learning, convolutional neural networks, and generalized additive models) to solve practical farming challenges related to crop management decisions, yield prediction, and resource optimization. This research has significant implications for both scientific understanding of crop-environment interactions and practical farm management. Dr. Martin actively collaborates across disciplines and institutions, as evidenced by his extensive co-authorship network spanning agronomy, computer science, environmental science, and economics. His work has garnered attention from numerous news outlets and social media platforms, indicating its relevance to current agricultural challenges. He is a key contributor to the Data-Intensive Farm Management project, which aims to transform agronomic research through on-farm precision experimentation. His affiliation with NCSA provides access to high-performance computing resources essential for processing large agricultural datasets. Additionally, his work in Latin American agriculture (particularly in Mexico and Argentina) reflects his commitment to addressing global food security challenges.

Christof Biglerمشاهده پروفایل

استاد مدعو

• Dendroecology
• Statistical Modeling
• Quantitative Ecology
• +۶ مورد دیگر

Christof Bigler is an Adjunct Professor at ETH Zurich's Department of Environmental Systems Science, where he also serves as Deputy Group Head of the Professorship of Forest Ecology and Senior Scientist. He leads the ETH Tree-Ring Laboratory and teaches courses in dendroecology, applied statistics, and forest ecology. His research focuses on tree mortality, climate impacts on forests, disturbance ecology, and phenology, with a strong emphasis on quantitative methods and long-term data analysis. Bigler holds a PhD in Forest Ecology from ETH Zurich (2003), awarded the ETH Medal for his thesis on tree mortality modeling. He has conducted postdoctoral research in Switzerland and the University of Colorado Boulder, exploring disturbance interactions and dendroecology. He has supervised numerous PhD and master's students, contributing to over 150 publications in top journals like Nature Climate Change and Ecology . His work integrates dendrochronological techniques with statistical modeling to understand forest responses to climate change, including drought effects, tree competition, and mortality patterns. He serves on editorial boards for journals like Agricultural and Forest Meteorology and reviews proposals for national and international science foundations. His research also includes managing forest reserves and studying natural forest dynamics in the Alps and beyond. Education: PhD in Forest Ecology, ETH Zurich (2003) Postdoctoral Research: University of Colorado Boulder (2004–2005) Diploma in Biology, ETH Zurich (1998) Key Awards: ETH Medal for PhD thesis (2003) Professional Roles: Member of the Tree-Ring Society, Editor for Frontiers in Forests , and Commission for Phenology and Seasonality (KPS). Bigler's lab and fieldwork emphasize long-term datasets, including analyses of tree-ring records from Swiss National Park and other reserves. His research highlights the critical role of historical climate data in predicting future forest resilience and adaptation strategies.