Office Hours

Below is the current schedule for DSI office hours. While drop-ins are encouraged, hours are subject to change and thus we recommend you contact us in advance when possible. Including a very brief description of your problem/questions in your email is helpful.

FALL 2017

Director's Office Hours (DSI Conference Room, Shields 362)

Thursdays 12-1pm

  • Dr. Duncan Temple Lang: DSI Director and Statistics Professor. Duncan provides expertise in advanced topics in data science.

Thursdays 1-2pm

  • Dr. Carl Stahmer*: DSI Associate Director for the Humanities and Director of Digital Scholarship at the UC Davis Library. Carl provides expertise on natural language processing (NLP) and other methods of text mining and language processing.

Affiliate & Postdoc Office Hours (DSI Classroom, Shields 360)

DSI affiliates are available for drop-in office hours in the DSI Classroom to help with your data science problems. Drop ins are welcome, but it is recommended that you email the individual you want to meet with in advance in case of any schedule changes.

Mondays 11am-noon

  • Ryan Philips: SQL. Ryan is a PhD student in neuroscience and can help you get started with and use SQL. When it comes to large (1M+ rows) datasets, the SQL family of languages is the industry standard for data manipulation and subsetting, but it can also be used for smaller-scale data analysis. Ryan can also assist with introductory to intermediate questions in Python, Git, command line, and R. He can also talk about his experience working at a startup in San Francisco.

Tuesdays 10am-noon

  • Hugo Mailhot: Natural Language Processing. The possibility to automatically analyze large collections of texts has opened many exciting possibilities in various disciplines, and the methods developed in NLP find useful applications even outside of text analysis. Linguistics major and Computer Science PhD student Hugo is available for you to drop in and ask anything about NLP, from how to get started and whether it's appropriate for your research project, to troubleshooting in process analyses. Hugo has also worked as a researcher in industry (focusing on artificial intelligence) and is happy to talk about that experience.

Tuesdays 1-3pm

  • Dr. Dan Hicks: Dan's postdoc at the DSI focuses on data-driven academic institutional effectiveness. He received his PhD in philosophy from Notre Dame and MS in Mathematics from the University of Illinois, Chicago. Prior to coming to the DSI he was a AAAS Science & Technology Policy Fellow at the EPA and NSF, and a postdoc at the University of Ontario. During office hours, Dan can provide feedback on: bibliometrics (including the Scopus web interface and API); beginner through intermediate skills in R (ggplot, igraph, dplyr, etc.); and philosophical issues in statistics and Data Science.

  • Nistara Randhawa: R, RStudio, GIS, visualization. Nistara is a PhD candidate in Epidemiology and can provide help with R, including installing and setting up R/RStudio and troubleshooting code, writing R functions to improve code efficiency, and making basic R packages. She can introduce you to using git/github. She has experience with GIS and can help you get started with ArcMap or QGIS (including making maps and/or animations with GIS data, using leaflet with R for creating useful interactive maps). She can also advise with data visualization. She's also available to meet with undergraduates interested in pursuing data science. Her background includes Veterinary medicine, epidemiology and public health. Her interests also include science communication and videography.

  • Nick Bowden: R, Python, visualization. Nick is available for introductory (installations and getting started) with R and Python for web scraping, visualization and geocoding. Nick is in the Transportation Technology and Policy Graduate Group and his background is in economics (specifically energy economics and electric power system planning, operations and markets); he is studying environmental issues including carbon policy. His work has focused on high frequency time series modelling and cross-sectional and panel data models, and he has experience in visualizations in various platforms.

  • Zac Harmany: Python, MATLAB, Git, LaTeX, Image Processing. Zac is a PhD and researcher in Biomedical Engineering at the Center for Molecular and Genomic Imaging. His background is in statistical signal processing, image processing, machine learning, and optimization. He programs in Python and MATLAB, uses Git to manage projects, and uses LaTeX to write math-heavy papers. Zac can also help with the basics of Bash and command line.