Nick Bowden, Graduate Student
Nick Bowden graduate student, Transportation Technology and Policy.
Economic environmental policy of energy and transportation. Nick’s background is in theoretical economics and econometrics. His research focuses on the electrification of transportation and carbon policy. He uses high frequency time series data collection and modeling of electric power and transportation systems. Nick is interested in learning programming skills for more efficient methods of compiling data from public and regulated entities. Because these data relate to stationary power sources for the use of stationary transportation networks, he is also interested in visualization of this data onto relevant geographic planes.
Rinaldo Catta-Preta, Graduate Student
Rinaldo Catta-Preta graduate student, Integrative Genetics and Genomics.
Comprehensive, novel ways to define and integrate gene regulatory networks (GRNs) in early neurodevelopment. Rinaldo is working on establishing and integrating GRNs for cortical interneuron proliferation, migration and specification, by associating gene co-expression with binding of transcription factors to DNA regulatory elements (promoters and enhancers), and chromatin states. He is interested in developing high-dimensional, deep learning approaches to generate systems-level, predictive models of regulatory element function and gene regulation during brain development; furthermore, he is interested in, having the models, back generate interpretable representations to drive further feature discovery. Rinaldo has a strong background in Perl and other ancillary languages, but is currently pursuing expertise in Python and R, and efficient HPC. Machine learning interests focus on neural nets, HMMs and deep learning.
Clark Fitzgerald, Graduate Student
Clark Fitzgerald graduate student, Statistics.
Computational technologies that enable data science at scale. Clark is working on improving R through parallel computing. He’d like to learn about interesting applications and related data sets.
Adam Getchell, Graduate Student
Adam Getchell graduate student, Physics
Quantum gravity using computational models. Adam has a general background in information technology and programming experience (C++, Python, C#, Lisp, Clojure, and F#, among others). Adam has experience with running MCMC (Monte Carlo Markov Chain) and related methods. Adam wants to learn R and more statistics, data science methods, and anything else related to collating/analyzing large data sets.
Luiz Carlos Irber, Junior, Graduate Student
Luiz Carlos Irber, Junior graduate student, Computer Science
Genomic sequencing data, decentralized data sharing, computational skills. Luiz is developing methods for biological data analysis in Python and C++ using Jupyter notebooks; pipelines with Spark, Dask and snakemake; and data distribution using IPFS and dat. Luiz wants to collaborate with researchers from other areas to find common methods and share experiences.
Jared Joseph, Graduate Student
Jared Joseph graduate student, Sociology
Surveillance and crime using social network analysis and statistical analysis in R. Jared has prior experience with SPSS and Stata, and is interested in big data, network data, and online environments (social networking sites, MMOs, online forums).
Hanna Kahl, Graduate Student
Hanna Kahl graduate student, Entomology.
Using an ecoinformatics approach to improve citrus pest management. I explore data collected from actual citrus fields in the San Joaquin valley for interesting trends that influence damage or yield of citrus fruits. Then I design experiments to assess the intricacies of the trends observed. I am especially interested in the variation in herbivore damage to citrus fruits across citrus variety. I primarily use R in exploring and analyzing my data but I am interested in becoming more proficient at Python and SQL. Broadly, I aim to use data science technologies to improve the economic gain and sustainability of agricultural production practices.
Melissa Kardish, Graduate Student
Melissa Kardish graduate student, Population Biology
Microbial ecology of seagrass communities. Melissa has worked with terrestrial plants, ants, birds, and fungi, and now works with the plants, animals and microbes in the sea. She is broadly interested in patterns of species diversity and how different communities of microbes have different effects on seagrass and seagrass-associated communities. Melissa works with data describing microbial communities. She primarily uses R, but also with other languages to process and analyze sequencing data. Melissa credits her experience at the DSI with helping her to learn cleaner and more efficient workflows. She hope to learn new skills that she can apply later when facing problems in collaborations or in her own research.
Nick Lashinsky, Graduate Student
Nick Lashinsky graduate student, Anthropology
Exploring and developing models of morphological evolution in a phylogenetic context. Nick’s research is at the intersection of biological anthropology and Bayesian phylogenetics. He work primarily with character alignments (both continuous, e.g., linear measurements on the primate skeleton, and discrete, e.g., binary traits and nucleotide sequences), attempting to make inferences regarding the evolutionary processes that gave rise to them. Nick wants to learn new programming languages and software libraries/frameworks (e.g., Python, TensorFlow, Hadoop) and methods (e.g. artificial neural networks). He also wants to expand his toolkit to include machine learning and data visualization.
Rich Pauloo, Graduate Student
Rich Pauloo graduate student, Hydrologic Science
Numerical modeling of groundwater flow and contaminant transport. Rich is a hydrologic scientist who studies how climate change and water management affect the sustainability of groundwater resources using methods drawn from numerical modeling and machine learning. In his research, Rich works with geospatial, geophysical, timeseries, chemical, and natural language data. He is the author of a handful of R Shiny apps, and enjoys learning new tools to tell compelling stories with data.
Ryan Peek, Postdoctoral
Ryan Peek graduate student, Ecology
Effects of land-use change on the population genomics of sensitive frog species. Ryan is a watershed scientist who studies the effects of river regulation and landscape change in freshwater systems. Ryan works with many types of data, including genomic, hydrologic, biologic, and climate, both “tidying” and aggregating as well as modeling and visualization. Ryan coordinates the Davis-R-Users-Group and routinely uses bash and git. Ryan enjoys learning new tools and methods and troubleshooting R problems. He’s looking to continue learning novel and robust ways to analyze data.
Samuel Pizelo, Graduate Student
Samuel Pizelo graduate student, English Literature
Samuel is a PhD student in the English department who focuses in Science and Technology Studies and Game Studies. He is interested in using computational tools critically to re-evaluate conventional disciplinary knowledge and archives. Currently, he is involved with a project using R to analyze Early Modern textbases.
Nistara Randhawa, Graduate Student
Nistara Randhawa graduate student, One Health Institute
Bat movements and viruses, and network modeling of diseases. Nistara is an Epidemiology PhD candidate working with the PREDICT project at the One Health Institute, which is involved in global surveillance for viruses that can spillover from animals to people. She tracks bat movements and simulates disease outbreaks across geospatial networks and uses R and other GIS tools like ArcGIS/QGIS/GRASS for data munging, analyses, and visualization. She’s looking to learn more on parallel computing, NLP, machine learning, and cloud computing.
Taylor Reiter, Graduate Student
Taylor Reiter graduate student, Food Science
Genomic & transcriptomic sequencing data & wine. Taylor works with genomic, transcriptomic, and metabolomic data to draw insights about relationships between organisms and food. Taylor is a Software and Data Carpentry Instructor, helps at weekly Meet and Analyze Data sessions hosted by the Lab for Data intensive biology at UC Davis, and uses bash, git, R, and sometimes python in her workflows. She enjoys helping others learn bioinformatics and data science practices, and likes learning about practices to make data science more repeatable.
Nick Ulle, Graduate Student
Nick Ulle graduate student, Statistics
Compiling high-level scientific programming languages. Nick studies compilers, the programs that rewrite or translate source code to make it more efficient. He has expertise with R and Julia and is interested in STEM pedagogy, interface design, and Monte Carlo methods. In addition to his primary research, Nick works on data extraction and analysis projects at the DSI, teaches statistical computing workshops, and offers advice about statistics, data science workflows, and several programming languages (R, Python, Lua, C, and Rust). Nick wants to learn more about natural language processing, network analysis, and graphical models.