This is the central site for sharing materials related to presentations made in the Oklahoma Data Science Workshop (ODSW) over the past several years. The ODSW is a platform for scientists to discuss how they utilize computing in their research. Many of the speakers work in departments where a departmental seminar may not be the most appropriate venue for them to discuss computing and software tools. The DSW provides a venue for scientists to share their knowledge and experiences.
Because the meetings are held in public, the speakers must be aware of the risks associated with discussing unpublished results. Likewise, results involving personal health information have to be de-identified. The same applies to identifying information regarding students who may have participated in a survey. Quite often, the tools and algorithms discussed are at the cutting edge and do not appear in formal coursework for several years, if ever.
The meetings are held at noon on the third Friday of each month except for July and December. The talks last for 1 hour, followed by a subsequent question-and-answer session that generally averages about 20 minutes in length.
| Date | Speaker | Topic |
|---|---|---|
| October 17, 2025 | Nisha Roa | Causal inference in health statistics |
| November 21, 2025 | Cory Giles | Graph neural networks |
| December 12, 2025 | TBD | |
| January 16, 2026 | TBD | |
| February 20, 2026 | TBD | |
| March 27, 2026 | TBD | |
| April 20, 2026 | Jindahl Shah | TBD |
| May 15, 2026 | TBD |
Yes, both the talk and the discussions are recorded for later viewing. The videos of the talks and discussions are posted online within several working days Link to videos.
Participants come from several institutions of higher learning in the state of Oklahoma, as well as guests from outside of the state. The talks are presented by scientists at all levels, with approximately a quarter of the talks given by graduate students. Speakers either volunteer in response to a call for talks at the start of each semester or are recruited.
This Readme file provides information about the Data Science Workshop and serves as a gateway to the repositories associated with each recorded presentation. Each repository provides a speaker with the opportunity to upload the associated slides, code files, competition notebooks, and other relevant materials for attendees to further their self-study.
Do not take these lists as prescriptive; please use them as inspirational.
- reactive computing: pluto, marimo
- Claude Code, aider, tmux
- Terminal emulators: kitty, Ghostty, warp, Alacrity, Wezterm
- Windsurf, Cursor, helix, zed
- Jupyter Lab, RStudio, Visual Studio Code
- Personal knowledge management: supertags, Obsidian, org-roam, ekg, Logseq
- treesitter and LSPs
- Reference management
- Overleaf
- Software testing, experimental design
- Regular expressions, speech-to-text
- Simulations in data analysis
- AI art
- prompt engineering
- Agentic programming
What is discussed in the workshop?
- Cutting-edge tools and algorithms
- Published or unpublished results (with appropriate disclaimers)
- Personal health information must be de-identified
- Student participation information must be anonymized
| Date | Speaker | Affiliation | Talk title | Video Link | Repository Link(s) |
|---|---|---|---|---|---|
| 2025 | |||||
| September 19 | Blaine Mooers | Biochemistry and Physiology, OUHSC | Introduction to Git and GitHub | video | click |
| June 27 | Marcus Birkenkrahe | Computer Science, Lyon College | Generative AI as Muse or Tool in the Research Process | ||
| May 23 | Henry Neeman | OSCER, OU | OU IT Research Computing Capabilities Update | ||
| April 18 | Jindal Shah | Chemical Engineering, OSU | A Hands-On Tutorial on Using Google Colab for Machine Learning | ||
| March 28 | Ross Metusalem | JMP | Neural Nets modeling "small" data | ||
| January 17 | Yihan Shao | Chemistry and Biochemistry, OU | Machine Learning and Enzyme Simulations | ||
| 2024 | |||||
| November 22 | Blaine Mooers | Biochemistry and Physiology, OUHSC | A Science Writing Workflow in Org Mode | ||
| October 17 | Chase Brown | Materials Science, U of Central Florida | typst for Scientists, Engineers, & Developers | ||
| September 20 | Jindal Shah | Chemical Engineering, OSU | A Tutorial on XGBoost Machine Learning Algorithm | ||
| August 16 | Frank Hays | Nutritional Sciences, OUHSC | Synthesizing Creativity: AI's Revolution in Creative Processes | ||
| June 28 | Michal Winnicki | Wren Lab, OMRF | AI revolution in protein structure prediction - overview | ||
| May 24 | Edward Roudales | California State University Chico | BridgeStan for Research | ||
| April 26 | Jindal Shah | Chemical Engineering, OSU | Ensemble-based Methods | ||
| March 22 | Marcus Brikenkrahe | Computer Science, Lyon College | Teaching Data Science with Literate Programming Tools | ||
| February 16 | Brian Ward | Flatiron Institute | Intro to BridgeStan | ||
| January 19 | Blaine Mooers | Biochemistry and Physiology, OUHSC | Managing Multiple Writing Projects | ||
| 2023 | |||||
| November 16 | Blaine Mooers | Biochemistry and Molecular Biology, OUHSC | Voice Controlled Writing Prose for Enhanced Productivity | ||
| October 26 | Jonathan Starr | Open Source Endowment Foundation | Open Science Project | ||
| August 17 | Andrew Fagg | Computer Science, OU | Convolutional Neural Networks for Image | ||
| July 20 | Andrew Fagg | Computer Science, OU | Convolutional Neural Networks | ||
| June 15 | Chase Brown | Wren Lab, OMRF | Sequence Models and LLMs in Science | ||
| May 18 | Frank Hays | Nutritional Sciences, OUHSC | Leveraging GPT-4 to Optimize Learner Outcomes (and other AI technologies) | ||
| April 20 | Blaine Mooers | Biochemistry and Physiology, OUHSC | Doing Science with Clojure | ||
| January 12 | Mark Laufersweiler | University Libraries, OU | A common Environment for Instruction and Research Notebooks | ||
| 2022 | |||||
| November 10 | Andrew Fagg and Blaine Mooers | OU and OUHSC | Beginner's Guide to Doing Science on the OU Supercomputer | ||
| September 22 | Andrew Fagg | Computer Science, OU | Gentle Introduction to Deep Learning II | ||
| August 18 | Andrew Fagg | Computer Science, OU | Deep Learning Demo | ||
| July 21 | Blaine Mooers | Biochemistry and Molecular Biology, OUHSC | Edit live Jupyter notebooks from the comfort of your favorite text editor | ||
| May 19 | Jindal Shah | Chemical Engineering, OSU | Takeaways from Teaching Machine Learning | ||
| April 21 | Amanada Schilling | University Libraries, OU | About the Center for Open Science | ||
| April 21 | William Beasley | Pediactrics, OUHSC | N3C: National COVID Cohort Collaborative | ||
| April 21 | Hunter Porter | Wren Lab, OMRF | Introduction to Analysis of Genomic Region Data | ||
| April 21 | Cory Giles | Wren lab, OMRF | From data to understanding: Tertiary analysis of high-throughput datasets in the context of aging | ||
| April 21 | Henry Neeman | OSCER, OU | High Performance Machine Learning | ||
| January 20 | Cory Giles | Wren Lab, OMRF | FAIR Principles and data stewardship | ||
| 2021 | |||||
| November 18 | Xiaoliang Pan | Chemistry and Biochemistry, OU | Machine-Learning-Assisted Free Energy Simulation of Enzyme Reactions | ||
| October 21 | Francis A. Acquah | Biochemistry and Molecular Biology, OUHSC | A Beginner's Guide to Cheminformatics with RDKit in Python | ||
| September 16 | Xianvan Roopnarinesingh | Wren Lab, OMRF | Building Data Apps with Dash | ||
| July 15 | Cory Giles | Wren Lab, OMRF | Common pitfalls in high-throughput sequencing experimental design and analysis | ||
| June 17 | Blaine Mooers | Biochemistry and Molecular Biology, OUHSC | A Beginner's Guide to nteract, JupyterLab, and Colab | ||
| May 20 | Dimitris Diochnos | Computer Science, OU | Learning Reliable Rules under Class Imbalance | ||
| April 22 | Walker Hoolehan | Biochemistry and Molecular Biology, OUHSC | A Beginner's Guide to Molecular Dynamicss Simulations with GROMACS | ||
| March 18 | Chase Brown | Wren Lab, OMRF | Reinforcement Learning and its future in biology | ||
| February 18 | Hunter Porter | Wren Lab, OMRF | Understanding Neural Networks and Their Applications as a Biologist | ||
| January 21 | Cory Giles | Wren Lab, OMRF | Overview of Deep Learning: A Versatile Toolkit for Unified ML | ||
| 2020 | |||||
| June 18 | Blaine Mooers | Biochemistry and Molecular Biology, OUHSC | Parallel Computing in Jupyter Notebooks | ||
| January 16 | Cory Giles | Wren Lab, OMRF | Code Management and Distribution | ||
| 2019 | |||||
| November 21 | Melissa Nestor | IT Data Services, OU | Data Sciences Tools HSC | ||
| October 19 | Xianvan Roopnarinesingh | Wren Lab, OMRF | Introduction to Data Visualization with Python | ||
| September 19 | Aleksandra Perez | Wren Lab, OMRF | My very own PCA | ||
| July 18 | Blaine Mooers | Biochemistry and Molecular Biology, OUHSC | Using Python to ease the use of the molecular graphics program PyMOL | ||
| June 20 | Cory Giles | Wren Lab, OMRF | Machine Learning in Python: Introduction to scikit-learn |
- Funding Sources:
- DISC Summer Pilot Grant
- NIH: R01 CA242845
- NIH: P20 GM103640, P30 CA225520, P30 AG050911-07S1
Workshop participants are encouraged to:
- Upload slides, notebooks, and scripts from their presentations
- Edit the main README.md file with their contribution details
- Follow the established Git workflow demonstrated in this presentation
2025 Summer Pilot Grant from the Institute for Data in Social Change at the University of Oklahoma.
Oklahoma-Data-Science-Workshop/Oklahoma-Data-Science-Workshop is a ✨ special ✨ repository because its README.md (this file) appears on his GitHub profile 👋.