ML Community Member Survey: Backgrounds, Needs, and Future Events

JavaScript isn't enabled in your browser, so this file can't be opened. Enable and reload.

Greetings! Please fill out this brief survey so that we can better understand the backgrounds and needs of ML practitioners on campus.

Name (First Last): *

Email: *

Please indicate your primary field(s) of study: *

Agriculture

Astronomy/Astrophysics

Atmospheric and Oceanic Sciences

Biomechanics

Biostatistics and Biomedical Informatics

Business Analytics / Finance

Chemistry

Computer Science

Ecology

Electrical & Computer Engineering

Genetics

Immunology

Materials Science

Math

Medical Physics

Microbiology

Neuroscience

Nuclear Engineering

Oncology

Opthalmology

Pharmacology

Psychiatry

Psychology

Radiology

Statistics

Veterinary Medicine

Other:

Required

Position: *

Undergraduate Student

Graduate Student

Post-doc

University/Research Staff

Faculty

Non-profit Industry Employee

For-profit Industry Employee

Community Member

Other:

Are you interested in joining the ML Community's organizing committee? *

The organizing committee will meet monthly to discuss and plan future events and activities for the ML Community to engage with. The time commitment for this role is 1-2 hours per month.

Yes

Other:

Which kind(s) of ML model(s)/tool(s) do you currently work with?

None

Deep learning

GANs

Recurrent Neural Networks

Reinforcement learning

Ensemble methods

Decision trees

Random forests

Linear regression

Logistic regression

Naive bayes

Bayesian networks

Support vector machines

K-means clustering

Spectral clustering

Hierarchical clustering

Text Analysis / Text Mining (e.g. Text Extraction, NLP, Sentiment Analysis)

Semi-supervised ML

Other:

What is your preferred programming language for ML analyses?

Python

Matlab

Julia

Other:

Which of the following ML-related workshops would you like to see offered on campus, if any?

These workshops typically take place over two 3-hour sessions. Upcoming training opportunities can be found here: https://datascience.wisc.edu/training-resources/

Intro to ML in Python

Intro to ML in R

Intro to ML w/ High-Dimensional Data

Intro to Time-Series Modeling

Intro to Clustering w/ Python

Intro to Docker

Intro to Kubernetes

How to Detect Bias in ML Applications

Intro to Deep Learning w/ Pytorch

Intro to Deep Learning w/ Tensorflow

Intro to Generative Adversarial Networks

Intro to Recurrent Neural Networks

Intro to Reinforcement Learning

Other:

What challenges do you currently face in your ML-related work?

Insufficient quantity of data to train a generalizable model

Insufficient quality of data to train a generalizable model

Lack of model interpretability (working with black boxes)

Insufficient computing capabilities (memory, storage space, lack of GPUs, etc.)

Training data bias

Working with protected data (e.g. PHI)

Model selection: determining the best model for your application

Other:

Do you have ideas for community events that could help you develop as an ML practitioner?

Suggestions might include events/activities you'd like to see us host, discussion topics, papers of interest, etc. To discuss an idea in detail, reach out to Chris Endemann (endemann@wisc.edu).

We are working on putting together an ML Resources webpage to help direct researchers to ML resources available on campus. Do you know of any resources that should be featured on this webpage (e.g. workshops, compute resources, study groups, paper discussion groups, etc.)?

Submit

Clear form

Never submit passwords through Google Forms.

This form was created inside of UW-Madison Google Workspace. Report Abuse

Forms