| Event Description |
ASU Machine Learning Day
April 1, 2022
8:50am-2:40pm
Location: Online
Campus: West campus
Price: Free
You are invited to Arizona State University’s West campus for the Annual Machine Learning Day — an interactive virtual event!
Learn from cutting-edge researchers from top institutes (Princeton University, Cornell University, University of Washington, Arizona State University, etc.) as they share innovative research on machine learning theory and methods in diverse domains, including applied statistics, biology, psychology, social science and ethics.
Important things to know:
- Event is free.
- Registration is required: Register Here
~ Technical Program ~
8:50 AM - 9:05 AM – Welcome and Opening Remarks - Senior Associate Dean Lara Ferry
9:05 AM - 10:00 AM – Keynote Address
- Learning Engineering: Making the Most of Machine Learning in Concert with Learning Sciences, Danielle McNamara, Arizona State University
10:10 AM - 11:00 AM Session 1: ML Theory and Methods I
- Subtask Analysis of Process Data Through a Predictive Model, Xueying Tang, University of Arizona
- Robust Model Discovery with SINDy and Ensemble Learning, Urban Fasel, University of Washington, Seattle
11:10 AM - 12:00 PM Session 2: Ethics and ML
- Ordering Emotion: Scenes from the History of Affective Computing, Luke Stark, University of Western Ontario
- Visualizing the Hypervisible: Thoughts on Stories Machine Learning Can’t & Shouldn’t Tell from Trans Tumblr, Jack Gieseking, University of Kentucky
12:00 PM - 1:00PM – Lunch
1:00 PM - 1:50 PM Session 3: Cognitive Science and ML
- Probing Social Variation in Language Use With Mixed-effects Transformers, Robert Hawkins, Princeton University
- Doing metascience computationally, Pablo Andres Contreras Kallens, Cornell University
2:00 PM - 2:50 PM Session 4: ML Theory and Methods II
- Risk Predictions Using Interval Censored Panel Count Data with Informative Observation Times, Qing Pan, George Washington University
- Machine Learning-Driven Wireless Security: Attacks and Defenses, Dianqi Han, Arizona State University
2:50 PM – 3:00 PM – Closing Remarks
Keynote address: Danielle McNamara, Arizona State University
Learning Engineering: Making the Most of Machine Learning in Concert with Learning Sciences
Invited speakers:
Urban Fasel, University of Washington, Seattle
Jack Gieseking, University of Kentucky
Dianqi Han, Arizona State University
Robert Hawkins, Princeton University
Pablo Andrés Contreras Kallens, Cornell University
Qing Pan, George Washington University
Luke Stark, University of Western Ontario
Xueying Tang, University of Arizona
