Douglas McCauley

Douglas McCauley is an Associate Adjunct Professor in the Department of Environmental Science, Policy and Management at UC Berkeley and an Associate Professor in the Department of Ecology, Evolution and Marine Biology at UC Santa Barbara. Doug is an ecologist and conservation biologist that uses a diverse suite of methods to better understand how nature works and to create new applied research tools to better manage and conserve biodiversity. His research often involves leveraging the power of new technologies and insight from data science to address complex environmental problems and bring together the diverse stakeholders needed to effectively implement these solutions. Doug leads the Benioff Ocean Initiative at UC Santa Barbara. He was named a Sloan Research Fellow in the Ocean Sciences and he serves on the World Economic Forum’s Friends of Ocean Action leadership team.

Fernando Pérez

Fernando Pérez (@fperez_org) is an Associate Professor in Statistics at UC Berkeley and scientist at LBNL. He builds open source tools for humans to use computers as companions in thinking and collaboration, mostly in the scientific Python ecosystem (IPython, Jupyter & friends). A computational physicist by training, his research interests include questions at the nexus of software and geoscience, seeking to build the computational and data ecosystem to tackle problems like climate change with collaborative, open, reproducible, and extensible scientific practices. He is a co-founder of Project Jupyter, the 2i2c.org initiative, the Berkeley Institute for Data Science and the NumFOCUS Foundation. He is a recipient of the 2017 ACM Software System Award and the 2012 FSF Award for the Advancement of Free Software.

Justin Brashares

Justin Brashares is the G.R. & W.M. Goertz Professor in UC Berkeley's Department of Environmental Science, Policy and Management. Justin’s research combines approaches from ecology with interdisciplinary environmental science to better understand how human activities are impacting biodiversity, and to highlight and communicate the everyday consequences of these changes for society. Work in Justin's group extends traditional environmental science to consider the economic, political and cultural factors that drive and, in turn, are driven by global change. Through these efforts, Justin and his group at Berkeley strive to propose empirically-based, action-oriented strategies for the conservation of ecosystems and the services they provide us.

Carl Boettiger

Carl Boettiger is an Assistant Professor in the Department of Environmental Science, Policy and Management at UC Berkeley. Carl works on problems in ecological forecasting and decision making under uncertainty, with applications for global change, conservation and natural resource management. Carl is particularly interested in how we can predict or manage ecological systems that may experience regime shifts: sudden and dramatic changes that challenge both our models and available data. The rapid expansion in both computational power and the available ecological and environmental data enables and requires new mathematical, statistical and computational approaches to these questions. Ecology has much to learn about what are and are not useful from advances in informatics & computer science, just as it has from statistics and mathematics. Traditional approaches to ecological modeling and resource management such as stochastic dynamic systems, Bayesian inference, and optimal control theory must be adapted both to take advantage of all available data while also dealing with its imperfections. Carl’s approach blends ecological theory with the synthesis of heterogeneous data and the development of software – a combination now recognized as data science.

Carl is a co-founder of the rOpenSci project, a senior fellow at BIDS, and a science adviser to NCEAS, reflecting his interests in open science, data science, and ecoinformatics.

Stacey Dorton

Administrative Manager

Stacey Dorton is the Administrative Manager for DS4E, serving as our Office Jedi. She is an administrative professional with 25 years' experience in office management. She has done administrative work for a wide variety of organizations, including an elementary school, psychiatric facility, waste management firm, and a catering company. She also has extensive experience in the event management and social media fields.

She is a native of the Oakland/Berkeley area and a graduate of UC Berkeley (class of ‘95) majoring in women’s studies. GO BEARS!

Paolo D’Odorico

Paolo D’Odorico is a Professor in the Department of Environmental Science, Policy and Management at UC Berkeley. His research focuses on the role of hydrological processes in the functioning of terrestrial ecosystems. Through the analysis of the soil water balance he has highlighted important nonlinearities in the coupling between soil moisture dynamics and plant water stress, biogeochemical cycling, land-atmosphere interactions, plant community composition, and soil susceptibility to wind erosion. Using field observations and process-based models, Paolo is investigating new mechanisms of desertification and factors contributing to the resilience of the desert margins. His group's work has highlighted the role played by positive feedbacks with the physical environment on the resilience of savannas, dry tropical forests, desert shurblands, freshwater wetlands, mangrove swamps, and seagrass meadows. His work has also shown how environmental noise may increase the complexity of ecosystem dynamics by inducing new states, bifurcations, or pattern formation. Paolo is currently investigating the globalization of water through virtual water trade and international land investments, and its impact on water equity, societal resilience, environmental stewardship, and food security.

Sandrine Dudoit

Professor and Chair, Department of Statistics, UC Berkeley

Professor, Division of Biostatistics, School of Public Health, UC Berkeley

Principal Investigator, Berkeley Center for Computational Biology

Website

Sandrine Dudoit is Professor and Chair of the Department of Statistics and Professor in the Division of Biostatistics, School of Public, at the University of California, Berkeley. Professor Dudoit's methodological research interests regard high-dimensional inference and include exploratory data analysis (EDA), visualization, loss-based estimation with cross-validation (e.g., density estimation, classification, regression, model selection), and multiple hypothesis testing. Much of her methodological work is motivated by statistical inference questions arising in biological research and, in particular, the design and analysis of high-throughput microarray and sequencing gene expression experiments, e.g., single-cell transcriptome sequencing (RNA-Seq) for discovering novel cell types and for the study of stem cell differentiation. Her contributions include: exploratory data analysis, normalization and expression quantitation, differential expression analysis, class discovery, prediction, inference of cell lineages, integration of biological annotation metadata (e.g., Gene Ontology (GO) annotation). She is also interested in statistical computing and, in particular, reproducible research. She is a founding core developer of the Bioconductor Project (http://www.bioconductor.org), an open-source and open-development software project for the analysis of biomedical and genomic data.

Professor Dudoit is a co-author of the book Multiple Testing Procedures with Applications to Genomics and a co-editor of the book Bioinformatics and Computational Biology Solutions Using R and Bioconductor. She is Associate Editor of three journals, including The Annals of Applied Statistics and IEEE/ACM Transactions on Computational Biology and Bioinformatics. Professor Dudoit was named Fellow of the American Statistical Association in 2010, Elected Member of the International Statistical Institute in 2014, and Fellow of the Institute of Mathematical Statistics in 2021.

Professor Dudoit obtained a Bachelor's degree (1992) and a Master's degree (1994) in Mathematics from Carleton University, Ottawa, Canada. She first came to UC Berkeley as a graduate student and earned a PhD degree in 1999 from the Department of Statistics. Her doctoral research, under the supervision of Professor Terence P. Speed, concerned the linkage analysis of complex human traits. From 1999 to 2000, she was a postdoctoral fellow at the Mathematical Sciences Research Institute, Berkeley. Before joining the Faculty at UC Berkeley in July 2001, she underwent two years of postdoctoral training in genomics in the laboratory of Professor Patrick O. Brown, Department of Biochemistry, Stanford University. Her work in the Brown Lab involved the development and application of statistical methods and software for the analysis of microarray gene expression data.

Joey Gonzalez

Joseph is a Professor in the EECS department at UC Berkeley, a co-director and founding member of the UC Berkeley RISE Lab and a member of the Berkeley AI Research (BAIR Group). His research interests span machine learning and data systems and he has a wide range of projects including:

  • real-time model serving
  • dynamic deep neural networks
  • accelerated deep learning for high-resolution computer vision
  • new cryptographic primitives for federated learning
  • frameworks for deep reinforcement learning and parameter tuning
  • explainable reinforcement learning
  • software platforms for autonomous vehicles
  • new approaches to cloud computing

Maggi Kelly

Professor; Environmental Science, Policy, and Management; UC Berkeley

Director, ANR Statewide Program in Informatics and Geographic Information Systems (IGIS)

Faculty Director, Geospatial Innovation Facility, UC Berkeley

Maggi Kelly is Professor and Cooperative Extension Specialist at the University of California, Berkeley in the Department of Environmental Science, Policy, and Management and an expert in spatial data science. She has dedicated her career to redefining the boundaries of mapping-technology and its application to understanding dynamic landscapes. She has applied her technological expertise to crucial problems across the state, such as understanding and documenting wildfires, tracking and predicting forest diseases, improving agriculture and water usage, and climate change. She is faculty director of the Geospatial Innovation Facility and Director of the ANR Statewide Program in Informatics and Geographic Information Systems (IGIS).

Charuleka Varadharajan

ResearchScientist, Earth and Environmental Sciences, LBNL

Research Affiliate, Berkeley Institute of Data Sciences

Charu is a scientist in the Earth and Environmental Sciences Area at Berkeley Lab and leads its Earth AI and data program. She has dedicated her career to environmental sustainability and climate resilience, having experienced first-hand the challenges of water insecurity in drought-prone regions of the world. She has cross-disciplinary expertise in hydrology, biogeochemistry, data science and informatics with experience using computational, field, and laboratory methods in her research. She brings together her technical knowledge, leadership and community-building abilities to understand and solve complex Earth science problems. For example, she has used data-driven methods to study how river water quality is affected by extreme events, discover how methane is released from lakes, perform scientific assessments of the impacts of well stimulation (hydraulic fracturing) on water resources in California, and predict groundwater levels using machine learning for optimal water management. She is a passionate champion and practitioner of open data, and is leading data management efforts in signature U.S. Department of Energy research including its ESS-DIVE data repository. . Charu earned her PhD from the Massachusetts Institute of Technology and conducted her postdoctoral research at Berkeley Lab. She is a research affiliate with the Berkeley Institute of Data Sciences, a DOE Early Career awardee, and a recipient of the Berkeley Lab Director’s award for exceptional early career scientific achievement in the area of data science for Earth and environmental science.