Muslims in ML Workshop 2020: Participation Form
The inaugural Muslims in Machine Learning (MusIML) is co-located with the virtual NeurIPS conference and will be held on Tuesday, December 8th, 2020 from 10:30am to 1:30pm EST.
Thank you for your interest in participating in the MusIML Workshop this year! This form notifies us of your interest in attending the MusIML Workshop, and will provide you with participant access to the workshop's components. You will be emailed a copy of your responses, along with an edit link that you can use to update your responses until the form closes.
IMPORTANT NOTE: This form does not constitute registration for the MusIML workshop. To attend the workshop, you must be registered for NeurIPS (
https://neurips.cc/Register
).
Data usage and sharing:
- You will be asked if you allow MusIML to aggregate and anonymize your responses for use in MusIML reports and publicity materials.
- You will be asked if you consent to specific information being shared with MusIML sponsors.
PLEASE, DON'T FORGET TO PRESS THE SUBMIT BUTTON! You will be emailed a copy of your responses, along with an edit link that you can use to update your responses until the form closes.
Please refer to the workshop website for updates:
https://musiml.org
* Required
First name(s)
*
Your answer
Last name(s)
*
Your answer
Email address you use to log on to the NeurIPS website (
https://neurips.cc/accounts/login
).
*
Your answer
Position
*
Undergraduate student
Master's student
PhD student
Postdoctoral researcher
Professor (pre-tenure)
Professor (post-tenure)
Research scientist / engineer
Data scientist / engineer
Software engineer
Other:
Required
Affiliation (i.e., name of university, employer, etc). If you have multiple, please provide all of them.
Your answer
Do you identify as Muslim (any sect)? Please note: you do not need to identify as Muslim to participate in the workshop.
Yes
No
Other:
Clear selection
Country of residence
Your answer
Timezone on day of the MusIML Workshop (Tues, Dec. 8, 2020)
Your answer
Research topics of interest
Reinforcement learning
Deep learning
Bayesian methods
Graphical models
Learning theory
Statistical inference and estimation
Optimization
Robotics
Neuroscience
Natural language processing
Computer vision
Human-AI interaction and collaboration
Fairness in machine learning
Interpretability and explainability in machine learning
Accountability and ethics in machine learning
Healthcare/clinical applications
Music applications
Social science applications
Computational sustainability
Time series
Causal inference and counterfactuals
Systems and machine learning
Recommender systems
Other:
Do you allow MusIML to aggregate and anonymize your responses in this form for use in reports and publicity materials that may be shared with current or prospective MusIML funders, including but not limited to sponsors and government funding agencies, or publicly on MusIML publicity channels, such as social media and website?
*
I allow MusIML to aggregate and anonymize my responses for use in reports and publicity materials that may be shared with current or prospective MusIML funders, or publicly on MusIML publicity channels.
I DO NOT allow MusIML to use my responses.
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