ML+X
ML+X is a community of practice that brings together students, researchers, and industry professionals who share an interest in using machine learning (ML) and AI methods to advance their work (X).
Community events and activities help practitioners explore the challenges and pitfalls of ML/AI, share knowledge and resources, and support each others’ work.
Join the community!
Everyone interested in machine learning methods is welcome! Join the Google group to be notified of upcoming events, and join the Slack channel (#ml-community) to stay connected with the community. Please email us if you have any trouble joining the Google Group (you must be signed in to a Google account). New members are encouraged to introduce themselves via Slack.
Connect with machine learning and AI practitioners.
ML Marathon
Choose a project, form a team, and tackle real-world ML/AI applications with support from advisors. This 12-week fall hackathon is open to everyone at UW–Madison.
ML+Coffee
Discuss and work on machine learning projects with other practitioners at the monthly ML+Coffee event!
Workshops
ML+X collaborates closely with UW–Madison’s Carpentries Community to develop and offer ML-related workshops throughout the year.
EVIL Reading Group
The Ethics, Values, Information, and Law (EVIL) reading group meets every three weeks (roughly), Fridays, online, and is hosted in collaboration with the iSchool and ML+X.
Crowdsourced Resources
Nexus is the ML+X community’s centralized hub for sharing resources like workshops, blogs, and more.
Submit an Event
Have a paper you want to discuss? Have a demo you want to give? Want to run a lunch social or a mini workshop? We can help facilitate, advertise, or host your event within the ML+X community!
ML+Coffee: Connect and Solve Problems
Are you seeking help applying 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 practitioners can problem-solve with one another. Coffee and tea provided! Researchers and students with little or no background in ML/AI are more than welcome to join and ask how ML/AI can be applied in their domain of work. Additionally, more seasoned practitioners are invited to offer advice and/or give short demos on tools or 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.
- 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 forum). Join the Google group to receive a calendar invite and other updates.
ML+Nexus: 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:
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
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 and 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.
Sponsor ML+X
Your generous support plays a pivotal role in helping us consistently deliver value to the thriving ML community at UW–Madison through workshops, discussion forums, social gatherings, and more. Learn how your organization can benefit as a sponsor of ML+X.
Thank You, ML+X Sponsors!
ML+X is grateful to our sponsors for supporting our goals of helping practitioners explore the challenges and pitfalls of ML, sharing knowledge and resources, and supporting each others’ work. Learn more about our sponsors by clicking their logos.
Meet the ML+X Leadership Team
- Chris Endemann, Research Cloud Consultant, DoIT
- Rene Welch, Scientist, Biostatistics and Medical Informatics
- Alan McMillan, Professor, Radiology
- Zekai Otles, Research CI Consultant, Information Technology Office
- Yuriy Sverchkov, Scientist, Biostatistics and Medical Informatics
- Mariah A. Knowles, Curriculum Lead, Tiny Earth
- Yin Li, Assistant Professor, Biostatistics and Medical Informatics
- Junjie Hu, Assistant Professor, Biostatistics and Medical Informatics
- Yury Bukhman, Computational Biologist, Morgridge Institute for Research
- Theo Howard, Digital Learning Consultant, Wisconsin School of Business
- Ran Liu, Assistant Professor, Educational Policy Studies
- Rheeya Uppaal, Research Assistant, Biostatistics and Medical Informatics
- Nathan Miller, Scientist, Biology
- Song Gao, Professor, Geography
- Juan Caicedo, Investigator, Morgridge Institute for Research
ML+X welcomes new members to join the leadership team! This 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 brainstorms resources, plans events , strategizes ways to create a lively community and increase engagement, and collaborates with other ML-related groups on campus.
To join, please email endemann@wisc.edu a brief summary of your interest in machine learning and/or communities of practice.