We are thrilled to introduce our 2016 cohort of data science fellows! With diverse research backgrounds and experiences, the new fellows will help lead our community in driving data science innovations and enhancing collaborations across UC Berkeley and beyond. Please join us in welcoming them:
Graduate Student, Department of Statistics
Rebecca is interested in problems in applied statistics related to data visualization and applications in health, such as in the utilization and exploration of observational electronic medical records. She has developed an R Package, superheat, for developing beautiful and customizable heatmaps.
Graduate Student, Department of Electrical Engineering and Computer Sciences
Orianna works on applications of machine learning for mental health. Her research currently focuses on the development of quantitative monitoring tools as this is a necessary preliminary step in understanding disorders and evaluating treatments.
Graduate Student Researcher, Helen Wills Neuroscience Institute
Chris uses applied statistics and machine learning to study the brain, utilizing encoding and decoding models of electrophysiology signals to study how our experience with the auditory world affects the way that we process sounds. He’s a regular contributor to the MNE-python project for MEG and EEG data analysis in Python and to a handful of tools in the scientific python ecosystem.
Research Scientist, Computational Research Division, Lawrence berkeley National lab
Dmitriy's work is concerned with geometric and topological data analysis, especially with the development of efficient algorithms and software in this field.
Postdoctoral Scholar, Digital Humanities @ Berkeley
Laura uses computational methods and open source tools, principally automated text analysis, to study social movements, culture, gender, institutions, and organizations. She is particularly interested in developing computational tools that can bolster the way social scientists do inductive and theory-driven research.
Postdoctoral Scholar, Institute of Cognitive and Brain Sciences
Alexandra's work explores human communication in data-rich environments. From capitalizing on large-scale real-world corpora to capturing multimodal experimental data, her research seeks to understand how context changes communication dynamics. Her work integrates computational and social perspectives to understand interpersonal interaction as a nonlinear dynamical system.
Postdoctoral Scholar, Department of Environmental Science, Policy, and Management
Lauren studies the mechanisms operating in complex systems with the primary goal of making agricultural systems better for humans and wildlife. She is also working on the development of NIMBLE, a system for building and sharing analysis methods for statistical models, especially for hierarchical models and computationally intensive methods.
Postdoctoral Scholar, Department of Statistics
Nelle's research interests are in statistical machine learning and scientific computing applied to molecular biology problems, such as inferring the 3D architecture of the genome or data-integration methods to better understand gene regulatory networks.