Muslims in ML Workshop 2020: Participation Form

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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

First name(s) *

Last name(s) *

Email address you use to log on to the NeurIPS website (https://neurips.cc/accounts/login). *

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.

Do you identify as Muslim (any sect)? Please note: you do not need to identify as Muslim to participate in the workshop.

Yes

Other:

Clear selection

Country of residence

Timezone on day of the MusIML Workshop (Tues, Dec. 8, 2020)

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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