AI Summer Workshop

About AI Summer Workshop

Join us for a week of hands-on training in AI methods for research, facilitated project work, and self-directed learning. Our workshop is designed for researchers looking to move beyond textbook examples and tackle the challenges of real-world applications.

Who should apply?

Open to participants from any discipline.
Aimed at researchers willing to apply AI methods in their work, i.e. PhD students, postdocs, research faculty and staff, and Master's or undergraduate students who are already involved in research.

Prerequisites:

Programming experience in a scripting language such as Python or R.
Familiarity with one or more machine learning approaches, such as logistic regression or random forests.

Tutorials

Workshop tutorials will provide a broad overview of modern AI methods and tools for research. On Day 1 participants will learn how neural networks are trained and evaluated, how vision and sequence models work, and how transfer learning lets researchers build on pretrained models rather than starting from scratch. Later tutorials will illustrate best practices for applying these approaches across a full AI research lifecycle. Other topics will include foundation models, and the practicalities of scaling training and deployment. Dedicated sessions also address how to interpret AI results rigorously and make AI-based research reproducible.

Projects

Participants will be invited to develop their own ideas for project work, ideally building on an existing research project, exploring a new direction, or working directly with concepts from Day 1 to solidify learning. Throughout the week, participants will apply what they learn to their projects, with instructors and assistants available for guidance. We encourage participants working on similar problems to collaborate and learn from each other. On the final day, participants will share their progress and define a concrete plan for continuing the work after the workshop.

Tentative Schedule

	Monday (July 27)	Tuesday (July 28)	Wednesday (July 29)	Thursday (July 30)	Friday (July 31)
Tutorials	Introduction to Neural Networks: training, evaluation Vision models and applications Sequence models and applications Transfer Learning	Overview of the AI lifecycle Foundation Models Introduction to Hugging Face Ecosystem	Scaling AI model training/deployment Generative AI for research workflows	Interpreting and communicating ML/AI results Improving project reproducibility	No tutorials
Project Work	No project work	Project scoping and consulting (3 hours)	Project work (4 hours)	Project work (5 hours)	Project work (4 hours) Project sharing (2 hours)

Meet the team

The people on this page have helped organize the workshop. You'll find a few specializations listed per person if you're wondering who to reach out to during the event!