Machine Learning Day 2023
 

 

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

 

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


 

Paulo Shakarian
Arizona State University

Learning and Reasoning: Pathways to Artificial General Intelligence

 

 

 

 

 

Paul Hahn

 

 

Paul Hahn
Arizona State University

Machine Learning for Time-To-Event Data and its Relationship to the Kaplan-Meier Estimator

 

 

 

 

Shawn Walker

 

 

Shawn Walker
Arizona State University

The Limitations of Using Trace Data as Training Sets

 

 

 

 

Abdullah Mamun

 

 

Abdullah Mamun
Arizona State University

Time-Series Wearable Activity Forecasting

 

 

 

 

Kaize Ding

 

 

Kaize Ding
Arizona State University

Advancing Social Good with Deep Graph Learning

 

 

 

 

Jnaneshwar Das

 

 

Jnaneshwar Das
Arizona State University

Ecosystem Digital Twins: Enhancing Environmental Monitoring with Robotics and AI 

 

 

 

 

 

Mihai Surdeanu

 

 

Mihai Surdeanu
University of Arizona

It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers

 

 

 

 

Visar Berisha

 

 

Visar Berisha
Arizona State University

Talk Title Forthcoming

 

 

 

 

 

Lifeng Lin

 

 

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