Event Description

ASU Machine Learning Day

April 9, 2021

~ Technical Program ~

8:50 AM - 9:05 AM – Welcome and Opening Remarks (Dean Sandrin)

9:05 AM - 10:00 AM – Keynote Address Some Unique Problems with Social Media Data for Machine Learning and AI, Huan Liu, Arizona State University

10:05 AM - 11:05 AM – Ethics and ML

- Designing surveillance: The critical role of ethical frameworks in AI Games research, Florence Chee, Loyola University

- Excavating the "unintended consequences" of algorithms: The case of Facebook's "interest" classification system for ad targeting, Kelley Cotter, Arizona State University

- Anna Jobin, Alexander von Humboldt Institute for Internet and Society (Title TBD)

11:15AM - 12:15 PM – ML Thoery and Methods I

- Efficiently Evaluate Social Network Interventions, Johan Ugander, Stanford University

- A super scalable algorithm for short segment detection, Selena Niu, University of Arizona

- Designing disease-tracking metrics with causal machine learning, P. Richard Hahn, Arizona State University

12:15PM - 1:30PM – Virtual Poster Session

1:30PM - 2:30PM – ML and Social Science/Social Networks

- Resilience and adaptation in social networks, Will Hobbs, Cornell University

- Measuring physical and mental health using social media, Johannes Eichstaedt, Stanford University

- Erika Salomon, University of Chicago’s Center for Data Science and Public Policy (Title TBD)

2:45PM - 3:45PM – ML Thoery and Methods II

- Efficient active learning of halfspaces: noise tolerance and exploiting sparsity, Chicheng Zhang, University of Arizona

- Why does functional pruning yield such fast algorithms for optimal changepoint detection? Toby D Hocking, Northern Arizona University

- Optimizing for whom? The role of robustness in equitable algorithms, Kenneth Nieser, University of Wisconsin-Madison

3:45PM - 4:00PM – Research Computing

4:00PM – 4:05PM – Closing Remarks

Open to Current College & University students, Business and Industry professionals

You are invited to Arizona State University’s virtual Machine Learning Day. 


Some Unique Problems with Social Media Data for Machine Learning and AI

Social media data is distinctive from its traditional counterpart and opens the door for interdisciplinary research and allows researchers to collectively study large-scale human behavior otherwise impossible. The study of social media data brings about new challenges for machine learning and data mining. In this talk, we will introduce some unique issues of big social media data, e.g., the big data paradox, the privacy-utility ‘trade-off’, and the evaluation dilemma. We will also mention some efforts of using AI for good if time allows. With data abundance and algorithmic development, we are better equipped than ever to answer challenging and novel research questions and advance AI and CS.   


Dr. Huan Liu is a professor of Computer Science and Engineering at Arizona State University. He was recognized for excellence in teaching and research in Computer Science and Engineering at ASU. His research interests include AI, data mining, machine learning, social computing, and data science to investigate problems that arise in real-world applications with high-dimensional data of disparate forms. He is a co-author of the textbook, Social Media Mining: An Introduction, by Cambridge University Press. He is Field Chief Editor of Frontiers in Big Data, its Specialty Chief Editor of Data Mining and Management, and a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction. He is a Fellow of ACM, AAAI, AAAS, and IEEE.

The events at Machine Learning Day include keynote speeches from top researchers in academic and industry, oral research presentations, and a virtual poster session. The topics of keynote addresses, presentations and posters cover the theory and practices of machine learning including:

  • Adversarial machine learning


  • Models, algorithms, and methods of machine learning
  • Machine learning for recommendation systems


  • Mathematical analysis and machine learning
  • Practical applications of machine learning for cybersecurity and online privacy
  • Statistical analysis and machine learning
 Important things to know:
  • Event is FREE
  • The event will be held on Zoom
  • Registration will close once all spots are full.
  • Registration is required
  • Registration Open – January 2021
  • Registration Closes – Once all spots are full or April 7, 2021
  • Machine Learning Day – April 9, 2021