GeoSmart Hackweek 2023

23 October - 27 October Seattle, WA
Application of Machine Learning in Hydrology and Cryosphere Science.

About GeoSmart Hackweek

Hackweeks are participant-driven events that strive to create welcoming spaces for participants to learn new things, build community and gain hands-on experience with collaboration and team science.

The 2023 GeoSmart Hackweek will focus primarily on project work, with an emphasis on applications in Hydrology and Cryosphere science. Project ideas will be shared in advance. There will be space for new ideas to emerge during the Hackweek based on participant engagement. We will provide about 5 hours of data science tutorials spread over the week, including space for participant-led tutorials.

During the week, participants will have the opportunity to collaborate with their peers, share ideas, and work on projects leading to exciting results and discoveries. This event is open to all experience levels in machine learning knowledge, so whether you're a seasoned pro or just starting out, you're welcome to join. However, to benefit most from the event, prior knowledge of Python programming and data handling using common Python packages (pandas, xarray, etc.) is desired. See the event Jupyter book for more details.

Preliminary project ideas include streamflow prediction from SAR-derived snowmelt timing or snow data, predicting snow water equivalent with machine learning, glacier dh/dt from DEMs using geospatial time series analysis, derivation of snow covered areas from satellite imagery, derivation of snow depth from SAR backscatter and lidar-derived snow data, predicting river discharge from seismic waves and others! Join one of these projects or pitch your own project idea at the event!

    Brainstorming sessions : participants can join an existing project or come up with ideas for projects that can be implemented using machine learning.
    Tutorials : learn about common machine learning workflows, computational environments, reproducibility, and workflow management.
    Data preparation : explore datasets to identify and engineer relevant variables that can be used to build machine learning models.
    Models : work on building machine learning models using popular libraries such as TensorFlow, PyTorch, or scikit-learn.
    Model validation and optimization : validate models using cross-validation and other techniques to ensure that models are robust and accurate; fine-tuning hyperparameters, using feature engineering techniques, or other methods.
    Presentations : participants can share the results from projects to receive feedback from their peers.
    Networking : facilitated opportunities for networking and community building will be provided.

2023 Event Starts In:

Information for Applicants

GeoSmart Hackweek will take place during October 23-27, 2023, at the University of Washington in Seattle, and remotely online. We anticipate about 70% of participants to be attending in person, with 30% joining online. Applications (via the red button at the top right of this webpage) are open until September 15, 2023!

Schedule

All times listed below are UTC -7 (Pacific Daylight Time). You might want to consult this Time Zone Map to figure out times in your location.

8:30 - 9:30

Intro and welcome

Getting to know each other setting the stage for our work together.

9:30 - 10:30

ML Workflows

An overview of the primary stages of typical Machine Learning workflows including data preparation, model selection, training, and visualization.

Tutorial Lead(s)
Andrew Bennett

10:30 - 11:00

BREAK

11:00 - 12:00

Intro to Projects

Overview of projects for this hackweek and how we will gather in small groups.

Tutorial Lead(s)

12:00 - 13:00

LUNCH

13:00 - 13:30

Project Team Formation

Choosing our project team for the week.

Tutorial Lead(s)

14:00 - 17:00

Project Work

8:30 - 9:00

GeoWeaver

Introduction to a management tool to streamline Machine Learning workflows.

Tutorial Lead(s)
Ziheng Sun

9:00 - 9:30

Approaches to Working in Teams

Reflecting on the opportunities and challenges of small group work.

Tutorial Lead(s)

9:30 - 10:00

Open time for tutorial Q&A

10:00 - 10:30

BREAK

10:30 - 12:00

Project Work

12:00 - 13:00

LUNCH

13:00 - 16:00

Project Work

8:30 - 9:00

Computational Resources for ML

Guidance on deploying Machine Learning workflows on cloud computing infrastructure.

Tutorial Lead(s)
TBD

9:00 - 9:30

Open time for tutorial Q&A

9:30 - 10:00

BREAK

10:00 - 12:00

Project Work

12:00 - 13:00

LUNCH

Tutorial Lead(s)

13:00 - 16:00

Project Work

Tutorial Lead(s)

8:30 - 9:00

AI Ethics

Group discussion on ethical considerations of AI development including cultural considerations, awareness of bias, and other themes.

Tutorial Lead(s)
TBD

9:30 - 10:00

Contributing to Open Source

Best practices for contributing to community software; exploration of community alignment around ML geoscience software.

Tutorial Lead(s)

10:00 - 10:30

BREAK

10:30 - 12:00

Project Work

12:00 - 13:00

LUNCH

13:00 - 16:00

Project Work

8:30 - 9:00

Ideas for Community Building

Setting a course for continued engagement with a ML geoscience community of practice.

Tutorial Lead(s)

9:00 - 9:30

Future Hackweeks

Designing curriculum and project work for a 2024 GeoSMART hackweek.

Tutorial Lead(s)

10:00 - 10:30

BREAK

10:30 - 12:00

Sharing Project Outcomes Part 1

An opportunity to show what we accomplished during the week.

10:30 - 11:00

Hackweek Survey

Complete a survey providing us with feedback for improving our program.

Tutorial Lead(s)
Mark Welden-Smith

12:00 - 13:00

LUNCH

13:00 - 15:00

Sharing Project Outcomes Part 2

An opportunity to show what we accomplished during the week.

15:00 - 15:30

Closing

Tutorial Lead(s)

Meet the team

The people on this page have helped organize the hackweek. You'll find a few specializations listed per person if you're wondering who to reach out to during the event!
Nicoleta Cristea
Research Scientist
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Anthony Arendt
Senior Data Science Fellow
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Charley Haley
Social Strategist and Collaboration Architect
Lead Community Building badge
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Scott Henderson
Research Scientist
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Don Setiawan
Research Software Engineer
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Naomi Alterman
Education Consultant
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Joachim Meyer
Research Scientist
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Our Sponsors

This event was made possible by the National Science Foundation (Awards #1829585, #2117834) and the eScience Institute in collaboration with CUAHSI and ESIP
eScience Institute
National Science Foundation
CUAHSI
ESIP