Connect with machine learning practitioners.
ML+X is a community of practice that brings together students, researchers, and industry professionals who share an interest in using machine learning (ML) methods (e.g., regression, classification, clustering, NLP, deep learning, LLMs, reinforcement learning, etc.) to advance their work (X). Community events and activities aim to help ML practitioners explore the challenges and pitfalls of ML, share knowledge and resources, and support each others’ work. Join the community to learn more!
Join the Community!
Anyone who is working with or wants to learn more about machine learning methods is welcome to join! Join the Google group to be notified of upcoming events, and join the Slack channel (#ml-community) to stay connected with the community! New members are encouraged to introduce themselves via Slack.
Join Google Group Join Slack Group
If you have any trouble joining either platform, please send an email to facilitator@datascience.wisc.edu.
Interested in Sponsoring Us?
We are actively seeking sponsorships as we continue to grow and expand of our thriving community! Your generous support would play a pivotal role in enabling us to consistently deliver unparalleled value to our members through machine learning workshops, discussion forums, social gatherings, and more. Visit the sponsorship page to learn how your organization will be represented as a sponsor of ML+X.
Community Events
Want to reproduce an ML research application or build an ML project from scratch? Join the 2024 Machine Learning Marathon (MLM24)!
Running from September 12 to December 12, this 12-week hackathon event offers an opportunity for ML practitioners of all skill-levels to collaborate, learn, and innovate on real-world datasets through various ML projects (“challenges”) hosted on Kaggle. As an added bonus, many of the challenges resemble ML applications here at UW-Madison, e.g., medical imaging, drug prediction, leaf disease classification, detecting gravitational waves, RNA folding, and more!
To learn more and register by the September 10 deadline, visit the 2024 Machine Learning Marathon event page (click below). If you have any questions or require additional information, please contact ml-marathon-organizers@g-groups.wisc.edu.
Each monthly ML+X forum highlights two ML applications that share a theme followed by communal discussions and project feedback.
- Where: Orchard View Room (rm. 3280), Discovery Building & via Zoom.
- When: Monthly on Tuesdays, 12-1pm CT. Join the Google group to receive a calendar invite (with Zoom link) and other updates. Please email us if you have any trouble joining (must be signed in to Google account).
- Share Your Work! We encourage anyone who is using ML in their work to present at one of the ML+X forums. If you are working in the social sciences, humanities, education, ecology, or any field outside of the typical CS-related work, you are especially encourage to present! The ML+X community welcomes all backgrounds and experience levels. Let us provide you with the resources needed to succeed in your ML endeavors. If interested, please fill out the google form by clicking the button below.
Catch up on past forums by visiting Nexus
Miss one of our forum discussions? Get caught up by visiting the ML+X Nexus platform. Recorded forums (as well as other ML talks and resources) are regularly posted there.
Are you seeking help on how to apply ML methods to your data? Want to demo a cool new ML tool or use-case? ML+Coffee offers a casual and social atmosphere where ML practitioners can problem-solve with one another. Coffee and tea provided! Researchers and students with little or no background in ML are more than welcome to join and ask how ML can be applied in their domain of work. Additionally, more seasoned practitioners are invited to offer advice and/or give short demos on ML tools/workflows. You can sign up to discuss your ML work or present a demo using the registration form we send to the ML+X google group (a few days before each event). For additional context, check out some of the previous discussion/demo topics at ML+Coffee (click button below).
- Where: Room 1145 of the Discovery Building. This room is located on the west side of the building on the first floor — near to the building exit that leads off to Subway/QQs/Library.
- When: Monthly on Wednesdays, 9-11 AM (the morning after each ML+X Forum). Join the Google group to receive a calendar invite and other updates. Please email us if you have any trouble joining (must be signed in to Google account).
Our ability to offer numerous workshops year-round is made possible through UW-Madison’s Carpentries Community — a group of volunteers dedicated to teaching computational workshops that quickly advance researchers’ ability to reproducibly analyze, visualize, and model data. If you’re curious about helping or instructing at a workshop, review the FAQs page and join the Carpentries Google group to be notified of upcoming volunteer opportunities. You do not need to be an expert in a topic that you’d like to help teach, and members have plentiful opportunities to attend workshops to advance their skills. Members also have the opportunity to participate in an optional instructor training program.
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ML+X collaborates closely with UW-Madison’s Carpentries Community to develop and offer short (1-3 half days) ML-related workshops throughout the year including:
Registration required! Subscribe to the Data Science @ UW Newsletter to be notified when registration opens. This newsletter aggregates upcoming workshops, seminars, community events, job openings, and other news surrounding data science in Madison. |
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The Ethics, Values, Information, and Law (EVIL) reading group pursues scholarship in the intersections of ethics, law, and data and information technologies. The EVIL reading group meets every three weeks (roughly), Fridays, online, and is hosted in collaboration with the iSchool and ML+X. The group is managed by Mariah A. Knowles. To join, complete this registration form (button below) to be added to our Slack and Google Group.
Have a paper you want to discuss? Have a demo you want to give? Want to run a lunch social or a mini workshop?
Then reach out and we can help facilitate, advertise, or host your event within the ML+X community!
Send an email to facilitator@datascience.wisc.edu to discuss more!
Explore Community Resources
ML+X Nexus is the community’s centralized hub for sharing machine learning resources. We define resources broadly as any content (original or external) that can help make the practice of machine learning more connected, accessible, efficient, and reproducible is welcome on the Nexus platform! This includes, but is not limited to:
- Educational materials: Explore a library of educational materials (workshops, guides, books, videos, etc.) covering a wide range of ML-related topics, tools, and workflows, from foundational concepts to advanced techniques. These materials offer clear explanations, practical examples, and actionable insights to help you navigate the complexities of ML with confidence.
- Applications & stories: Discover a curated collection of blogs, papers, and talks which dive into real-world ML applications and lessons learned by practitioners. This section also includes exploratory data analysis (EDA) case studies, which demonstrate the technical and domain knowledge needed to explore data from various fields.
- Models, code, and more: Learn about popular pretrained & foundation models, useful scripts, and datasets that you can leverage for your next ML project. Learn about their features, how to use them effectively, and see examples of them in action
Make a contribution to Nexus!
The Nexus website is a team effort! We welcome and encourage fellow practitioners to contribute resources to Nexus. For instructions on how to contribute, click the button below.
Thank You ML+X Sponsors
ML+X is grateful to our sponsors for providing us monetary support in achieving our goals helping ML practitioners explore the challenges and pitfalls of ML, sharing knowledge and resources, and supporting each others’ work. Visit the sponsorship page to learn more about sponsorship opportunities.
Learn more about out sponsors by clicking the logos below!
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Leadership Team
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Interested in Joining the ML+X Leadership Team?
ML+X welcomes members to join the leadership team! The leadership team helps grow and sustain a lively and engaged community of practice, and ensures ML practitioners across campus have ample opportunities to discuss challenges, learn from one another, and support each other. Anyone passionate about ML and community is welcome to join—including students, no minimum experience required!
The leadership team meets once per month to: brainstorm and plan events and resources to serve the needs of ML practitioners on campus, select themes and recruit speakers for events, strategize ways to create a lively community and increase engagement, collaborate with other ML-related groups on campus.
To join the leadership team, please email endemann@wisc.edu with a brief summary of your interest in machine learning and/or communities of practice.
Chris Endemann (he/him)
Data Science Facilitator
Data Science Hub
Zekai Otles (he/him)
Research CI Consultant
Information Technology Office
Yin Li (he/him)
Assistant Professor
Biostatics and Medical Informatics
Theo Howard (he/him)
Digital Learning Consultant
Wisconsin School of Business
[Photo coming soon]
Nathan Miller
Scientist III
Department of Biology
Leo Xu
Undergraduate
CS Honors & Math
Meg Taylor
Research Assistant
Biophysics
Rene Welch (he/him)
Scientist III
Biostatics and Medical Informatics
Yuriy Sverchkov (he/him)
Scientist II
Biostatics and Medical Informatics
Junjie Hu (he/him)
Assistant Professor
Biostatics and Medical Informatics
Cole Navin (he/him)
Student Administrative Assistant
Data Science Hub
Ran Liu
Assistant Professor
Educational Policy Studies
Unmesh Raskar
Research Assistant
Wisconsin Embedded Systems and Computing Laboratory
Shantanu Vichare
Master’s Student
Department of Electrical & Computer Engineering
Alan McMillan (he/him)
Professor (CHS)
Radiology
Mariah A. Knowles (she/her)
Curriculum Lead
Tiny Earth
Salsabil Arabi (she/her)
Research Assistant
Information School
Yury Bukhman (he/him)
Computational Biologist
Morgridge Institute for Research
Rheeya Uppaal
Research Assistant
Biostats and Medical Informatics
Juan Caicedo
Investigator, Biomedical Imaging
Morgridge Institute for Research
Song Gao
Associate Professor, Director of Geospatial Data Science Lab
Department of Geography