Office Hours

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The eScience Institute is pleased to announce data science office hours virtually by appointment

Office Hours are an opportunity to bring together expertise from the eScience data scientists, UW libraries, UW-IT, the Center for Statistics and the Social Sciences (CSSS), Amazon Web Services, Google Cloud Platform, MathWorks, and Tableau to help triage your challenges in data-intensive science — including cloud computing — and steer you towards appropriate solutions. The eScience Office Hours program is held during the UW Academic Year’s dates of instruction. The Office Hours program is not held over academic breaks, including Winter Break, Spring Break, and Summer Quarter.

For the 2022–2023 academic year, data scientists will be available for remote office hours. Please contact individual providers by email from the profiles listed below.

  • Fall 2022 Quarter: September 28th – December 9th
  • Winter 2023 Quarter: January 3rd – March 10th
  • Spring 2023 Quarter: March 27th – June 2nd

We may offer immediate help, schedule a longer meeting with our team to understand the problem more deeply, or refer you to faculty on campus with relevant expertise. In addition to direct assistance and triage, we see office hours as an opportunity to brainstorm about data-intensive research questions and approaches.


Office Hours By Program or Department

eScience Data Scientist | UW Librarian | Research Computing | Health Data Science | Amazon Web Services | UW-IT EIIA – Tableau & Analytics | Center for Statistics and the Social Sciences (CSSS) Statistical Consulting | Mathworks

eScience Data Scientist

We encourage questions from any area of computing in science, but tend to focus on data management, predictive analytics, and visualization.

Our goals in running this program are to help UW broadly excel at data-intensive science while also better understanding and cataloging the challenges UW researchers face in this area, which help us inform our own collaborative research priorities. We fully expect that some of these discussions will lead to productive collaborative research opportunities.

Anthony Arendt, arendta@uw.edu

  • Community Engagement and Education
  • Open source tools in the geosciences
  • Cloud computing
  • Relational databases / SQL

David A.C. Beck, dacb@washington.edu

  • Biology / bioinformatics: structural, systems, synthetic
  • Programming: Python, Shell scripting, Relational databases / SQL, Reproducibility and open science

Noah Benson, nben@uw.edu

  • Python, MATLAB, Mathematica, C, C++
  • Java, Scala, Clojure, Julia, BASH
  • Neuroinformatics and neuroimaging
  • Containerization (Docker)
  • Version control (GitHub)
  • Data sharing and visualization
  • Jupyter Notebooks

Nicoleta Cristea, cristn@u.washington.edu

  • Python, MATLAB
  • Hydrology
  • Analysis of water datasets
  • Remote sensing, spatial analysis
  • Hydrological modeling

Bryna Hazelton, brynah@phys.washington.edu

  • Astrophysics and cosmology
  • Fourier analysis
  • Statistical and mathematical modeling
  • Monte Carlo simulations
  • Version control (Git, GitHub)
  • Reproducibility and open science
  • Programming: Python, SQL, IDL, Java

Joseph Hellerstein, joseph.hellerstein@gmail.com

  • Astrophysics and cosmology
  • BASH
  • Software design
  • Web server design
  • Reproducibility and open science
  • Programming: Python, SQL, C, R
A photo of Scott Henderson
Scott Henderson

Scott Henderson, scottyh@uw.edu

  • Python
  • Satellite imagery
  • Geospatial raster and vector data
  • Amazon Web Services, Kubernetes
  • Jupyter Notebooks, Pangeo JupyterHubs
Scott Henderson

Bernease Herman, bernease@uw.edu

  • Python, R, Scala
  • Machine learning
  • Interpretable models
  • Data visualization (D3.js, Leaflet, Shiny)
  • Version control (Git, GitHub)
  • Reproducibility and open science
Scott Henderson

Vaughn Iverson, vsi@uw.edu

  • Metagenomics, Genome assembly and annotation
  • C (including OpenMP)
  • Javascript / Coffeescript
  • Go (golang)
  • Shell (Bash, Awk)
  • NoSQL databases (esp. MongoDB)
  • Visualization (eg. GraphViz, d3.js)
  • Data compression
Scott Henderson

Ariel Rokem, arokem@gmail.com

  • Python
  • Git/GitHub
  • Neuroinformatics
  • Cloud computing
  • Machine learning
Scott Henderson

Valentina Staneva, vms16@uw.edu

  • R
  • Python, MATLAB
  • Statistical modeling and inference
  • Data analysis
  • Image processing and computer vision
  • Optimization
  • Reproducibility and open science
Scott Henderson

Anissa Tanweer, tanweer@uw.edu

  • Ethnography
  • Qualitative methods
  • Data science ethics
  • Human-centered data science
Scott Henderson

Spencer Wood, spwood@uw.edu

  • Ecology and conservation
  • Geospatial data and GIS
  • Statistics
  • R, Python, shell scripts
  • Data storage and databases

UW Librarian Hours

UW librarians provide support for finding and accessing data, data management planning, data organization, reuse of data, data sharing and storage, data citation, instruction, literature review, publications, citation management tools, and more.

Negeen Aghassibake, negeena@uw.edu

Data Visualization Librarian

  • Data visualization: Tableau, RAWGraphs, Power BI
  • Data collection tools: REDCap, Survey Gizmo, Qualtrics
  • Data preparation and cleaning: OpenRefine, Excel
  • Data management plan guidance
  • Data literacy

Elizabeth Bedford, ebedford@uw.edu

Data Services Projects Librarian

  • Data management plans
  • Data curation, sharing and archiving
  • Persistent identifier assignment
  • Navigating funder mandates
  • ResearchWorks Archive

Jenny Muilenburg, jmuil@uw.edu

Data Librarian

  • Data management plan guidance
  • Data citation
  • Data curation and archiving
  • Data repositories

Shrusti Ghela, uwtextmine@uw.edu

Data Services Projects Librarian

  • Machine learning
  • Natural Language Processing (NLP)
  • Data mining
  • Web scraping
  • Version control
  • Statistics
  • Programming: Python, R, SQL

Research Computing Hours

Office hours are offered to help researchers, UW IT professionals and students learn about options and opportunities in research computing. We can review UW-native systems such as Hyak, cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and the Google Cloud Platform, and national resources such as the XSEDE grid computing platform.

We are available to discuss cost, grant opportunities, data security, collaboration tools, system architecture and the array of tools, technologies, solutions and platforms available to help you focus on your research rather than on building or reinventing computing infrastructure. We can also help you connect with other research teams in the UW community with similar interests and experiences. Our first priority is to determine what you are working on and where your issues and challenges lie towards helping you find best solutions.

Naomi Alterman, naomila@uw.edu

  • Education and pedagogy
  • Cloud computing
  • Software engineering
  • Computer networks and graphics
  • Programming: Python, Javascript, C, C++, Shell scripting and UNIX, SQL

Rob Fatland, rob5@uw.edu

  • Networking: Finding resources, collaborators, help
  • Data science design patterns
  • Data visualization
  • Geophysics, emphasis on hydrosphere
  • Remote sensing
  • Embedded systems / IOT / ruggedizing
  • Translating from research to public utility
  • STEM education and public outreach

Nam Pho, npho@uw.edu

  • High-performance computing (HPC)
  • Reproducibility and open science
  • Programming: R, Python

Charlie Engelke, cloud-office-hours-uw@google.com

Data Services Projects Librarian

  • Google Cloud Platform
  • Serverless Software
  • Python
  • Software Architecture

Emma Haruka Iwao, cloud-office-hours-uw@google.com

  • Google Cloud Platform
  • High-performance computing (HPC)
  • C, C++
  • DevOps and site reliability engineering

Health Data Science Hours

Gabriel Erion Barner, erion@uw.edu

  • Clinical risk scores
  • Clinical data resources including APACHE, eICU, MIMIC, NHANES, and AIMS
  • Interpretable machine learning
  • Decision trees
  • Deep learning
  • Sparse models
  • Meta-analysis

Amazon Web Services

Dominic Young, domyoun@amazon.com – email to schedule an appointment

Inside Account Manager – Enterprise Higher Education

Ask an AWS Architect: Come to AWS office hours with your code, architecture diagrams, and your AWS questions at the ready! You will have access to deep technical expertise and will be able to get guidance on AWS architecture, usage of specific AWS services and features, cost optimization, and more.


UW-IT EIIA – Tableau & Analytics

The Enterprise Information Integration and Analytics (EIIA) team is able to provide information and support for Tableau and UW’s enterprise data offerings. Please come talk to us if you have questions about:

Tableau: Licensing, Use cases, Technical issues
Enterprise Information Integration and Analytics: What’s available? How do I get access? What tools can I connect with?

Nina Velikin, nvel@uw.edu

System Analyst, IM Data Delivery Group

  • Tableau
  • Business Intelligence
  • UW Enterprise Data

Center for Statistics and the Social Sciences (CSSS) Statistical Consulting

CSSS provides free statistical consulting to current UW faculty, staff, and students in affiliated departments. Our consultants offer guidance at any stage of a project from study design and planning through the selection statistical methods to the interpretation of model results. However, we recommend involving a statistician at the earliest stage possible, as this ensures the best scientific foundation for statistical analyses. Please note that CSSS consultants do not generally provide statistical software support nor do consultants run analyses for consulting clients. Services include:

  • Planning analyses and building hypotheses to test
  • Regression modeling for continuous, discrete, and categorical outcomes
  • Regression modeling for dependent and independent data
  • Survey design
  • Visit the CSSS website to learn more.

Please email to schedule an appointment: csss-consult@stat.washington.edu, or visit the CSSS website.

  • Planning analyses and building hypotheses to test
  • Regression modeling for continuous, discrete, and categorical outcomes
  • Regression modeling for dependent and independent data
  • Survey design

Mathworks Hours

eScience has partnered with Mathworks in order to provide students, faculty and staff support for MATLAB, Simulink and Source Control (git). MATLAB® is the high-level language and interactive environment used by millions of engineers and scientists worldwide. It lets you explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance. Simulink® is a block diagram environment for multidomain simulation and Model-Based Design. It supports simulation, automatic code generation, and continuous test and verification of embedded systems.

Please email to receive support: support@mathworks.com, or visit call 508-647-7000.