Machine Learning Day 2023
April 14, 8:50 a.m.–3:45 p.m.
ASU West campus; Zoom
You are invited to Arizona State University’s West campus for the fourth annual Machine Learning Day — an interactive hybrid event!
Learn from cutting-edge researchers from top institutes as they share innovative research on machine learning theory and methods in diverse themes, including Learning from Social Data, AI for Good, and Foundation Models in Cognitive, Behavioral, and Biological Science.
*Registration must be completed by April 13 at 5 p.m. (AZ) to attend.
This event is free to attend! Free parking will be provided. Complimentary breakfast and lunch will be provided for in-person attendees.
Keynote Address
Yufeng Liu
The University of North Carolina at Chapel Hill
Harnessing the Power of Statistical Machine Learning: From Cancer Research to E-Commerce
Invited Speakers
Paulo Shakarian
Arizona State University
Learning and Reasoning: Pathways to Artificial General Intelligence
Paul Hahn
Arizona State University
Machine Learning for Time-To-Event Data and its Relationship to the Kaplan-Meier Estimator
Shawn Walker
Arizona State University
The Limitations of Using Trace Data as Training Sets
Abdullah Mamun
Arizona State University
Time-Series Wearable Activity Forecasting
Kaize Ding
Arizona State University
Advancing Social Good with Deep Graph Learning
Jnaneshwar Das
Arizona State University
Ecosystem Digital Twins: Enhancing Environmental Monitoring with Robotics and AI
Mihai Surdeanu
University of Arizona
It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers
Visar Berisha
Arizona State University
Talk Title Forthcoming
Lifeng Lin
University of Arizona
Some Thoughts on the Use of Language Models in Systematic Reviews
Technical Program
8:50 – 9:05 a.m. – Welcome and Opening Remarks (Dean Sandrin)
9:05 – 10 a.m. – Keynote Address Yufeng Liu (University of North Carolina at Chapel Hill)
Title: Harnessing the Power of Statistical Machine Learning: From Cancer Research to E-Commerce
10:10 – 11:10 a.m. Session 1 - Learning From Social Data
Paulo Shakarian (Arizona State University) - Learning and Reasoning: Pathways to Artificial General Intelligence
Paul Hahn (Arizona State University) - Machine Learning for Time-To-Event Data and its Relationship to the Kaplan-Meier Estimator
Shawn Walker (Arizona State University) - The Limitations of Using Trace Data as Training Sets
11:10 – 11:30 a.m. Coffee Break
11:30 a.m. – 12:30 p.m. Session 2: AI for Good
Abdullah Mamun (Arizona State University) - Time-Series Wearable Activity Forecasting
Kaize Ding (Arizona State University) - Advancing Social Good with Deep Graph Learning
Jnaneshwar Das (Arizona State University) - Ecosystem Digital Twins: Enhancing Environmental Monitoring with Robotics and AI
12:30 – 1:30 p.m. – Lunch
1:30 – 2:30 p.m. Session 3: Lightning Talks
Daniel Agyapong (Northern Arizona University) - Cross-Validation for Testing Co-occurrence Network Inference Algorithms
Xuanli Lin (Arizona State University) - Network Hardening in IoT Networks with Weighted Attack Graphs
Allison Bayro (Arizona State University) - Enhanced Emotional Response Detection in Virtual Reality Environments: A Comparative Study of Quantum and Traditional SVM Machine Learning Techniques
Mihir Parmar (Arizona State University) - Paradigm Shift in NLP: Prominent Role of Prompts
Ruoqian Liu (Arizona State University) - DE Analysis for Contaminated Samples Using Compositional Data
1:30 – 2:40 p.m. Coffee Break
2:40 – 3:40 pm.m Session 4 - Foundation Models in Cognitive, Behavioral, and Biological Science
Mihai Surdeanu (University of Arizona) - It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers
Visar Berisha (Arizona State University) - TBD
Lifeng Liu (University of Arizona) - Some Thoughts on the Use of Language Models in Systematic Reviews
3:40 – 3:45 p.m. - Closing Remarks
