Submission Mentorship Program Sign-up Form (Mentor)
This program involves two months of mentorship. As a mentor, you will provide your mentee(s) with feedback/ suggestions for improvement on aspects such as (1) the direction of the design; (2) models and experiments; (3) results analysis; (4) presentation/organization of the final paper they plan to submit to ML4H 2022.

Mentees and mentors are encouraged to have bi-weekly one-hour meetings to discuss the paper's progress, i.e., the mentors and mentees will have approximately four one-hour meetings over eight weeks.

This form will close July 3rd AoE.

Timeline:
July 3 – 4: Details about mentee-mentor pair-ups are sent out.
July 4 – 8: Mentees are expected to initiate contact with their mentor and arrange an initial meeting. In this meeting, the mentees should discuss the paper’s idea and outline with the mentors as well as the plan (dates, format, expectations, etc) for their bi-weekly meetings.
July 8 – Sept 1: The mentors and mentees will meet on a bi-weekly basis to work on the paper. After each meeting, the mentees are expected to incorporate the mentors’ feedback and send an updated draft of the paper to the mentors.
Sept 1: ML4H Submission deadline.

Mentors and mentees will be matched at random based on your shared research areas. The matching process between mentors and mentees will start July 3rd – 4th.
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Name *
Email *
Please provide your full email address, e.g. username@domain.edu
Institution / Affiliation *
What is your training status? *
How many mentees are you willing to take on? *
Are you willing to mentor students who are new to ML? (completed less than three ML projects in the past) *
Background (industry, academic, clinical, etc) *
Rank your expertise for each of the following areas:
high
medium-high
medium-low
low
Active Learning / Continuous Learning Systems
Adversarial ML
Algorithmic Fairness / Bias
Bayesian Learning
Causal Inference
Claims Data
Dataset Release and/or Characterization
Deployment
Economics
Electronic Health Records
Few / Zero Shot Learning
Generalization / Distribution Shift
Generative Models / GANs
HCI / Data Visualization
Interpretability
Medical Image Analysis / Computer Vision
Mobile Health
Natural Language Processing
Networks & Graphs
Omics
Open Software
Patient Generated Health Data
Pretraining / Transfer Learning
Population Health / Public Health
Privacy / Security
Reinforcement Learning
Representation Learning
Reproducibility
Scalability
Semisupervised Learning / Distant Supervision
Signal Processing / Time Series
Social Determinants of Health
Spatiotemporal Data
Survival Analysis
Uncertainty
Unsupervised Learning
Machine Learning in Clinical Practice
Pronouns (optional for diversity evaluation)
e.g. she/her, he/him, they/them, etc.
Race / Ethnicity (optional for diversity evaluation)
Anything else you'd like us to know?
Do you have any suggestions for the mentorship program?
Submit
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