ML+X Forum Presenter Sign-Up

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Thank you for your interest in presenting at the monthly ML+X forum! Please use this form to be added to the presenter queue for the fall series: Tuesdays from 12-1pm CT on 9/10, 10/8, 11/5 (10-11am), and 12/10.

Anyone who is applying machine learning (e.g., regression, clustering, deep learning, classification, NLP, reinforcement learning, etc.) in their work is welcome to present to receive feedback on their projects. Projects do not need to be completed — we welcome presentations on project proposals as well. Please see this presentation guidelines document to review the standard format of ML+X presentations.

Meet a future collaborator! Presenters will be given the opportunity to meet with one another (two presenters at each event) and the event host prior to giving their talks. Since the presenters share a methods theme, these meetings act as an opportunity to meet a potential future collaborator, share insights from previous work, and discuss plans for future work.

Questions? If you have any questions about presenting, please reach out to Chris Endemann (endemann@wisc.edu).

Email *

Name *

Department/Affiliation *

Which day(s) are you currently available to present? *

All events are hosted in a hybrid format (zoom and in-person) in the Orchard View room inside the Discovery Building. Presenters may present via Zoom or in-person — whichever they prefer.

Please be sure to select as many presentation dates as your schedule allows so that we can group together presentations that share a similar methods theme.

Tue, 9/10, 12-1pm CT

Tue, 10/8, 12-1pm CT

Tue, 11/5, 10-11am CT

Tue, 12/10, 12-1pm CT

Required

Please provide a brief 3-6 sentence summary of your project including the projects end-goal, methods utilized, and one challenge faced throughout the project. *

Please select each of the below ML topics that are relevant to your presentation. *

Ethical issues of ML (e.g., bias & fairness, privacy concerns, interpretability, etc.)

Methods for Interpretable ML

High-dimensional data analysis

Dimensionality reduction / Embedding methods

Supervised classification

Clustering / Unsupervised learning

Semi-supervised learning

Self-supervised learning

Regression

Time-series analysis and forecasting

Reinforcement learning

Computer vision / image analysis

Natural language processing

Transformer models; LLMs

Ensemble methods

Decision trees

Deep learning

Generative models

Diffusion models

Recurrent neural networks

Physics-informed neural networks (PINNs)

Other:

Required

If you had to pick one overarching methods theme (e.g., computer vision, time-series analysis, transformer models, bias & fairness, etc.) to describe this project, which theme would you pick? *

Did you use any high-throughput computing tools (e.g. CHTC, AWS, Google Cloud) for this project? *

Yes - CHTC

Yes - AWS

Yes - Google Cloud

Other:

Are you currently using or planning to use any pretrained or foundation models for this project? If so, please share the model(s) you are experimenting with.

Please list three software/ML tools that were vital to this project. *

Please provide a tentative title for your presentation. *

This event is hosted by the ML+X community (https://datascience.wisc.edu/ml-community/). The community has a google group it uses to send reminders about upcoming events. If you aren't already a member of the google group, you can use this link to join: https://groups.google.com/a/g-groups.wisc.edu/g/ml-community-of-practice *

I am already a member of the google group

I just joined via the link provided

I'm having difficulty joining — please add me using the email I provided in this form

Other:

Additional comments

Is there anything else you'd like the event organizers to know?

A copy of your responses will be emailed to the address you provided.

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