Graduate Students





Huan Liu, Regents Professor
Computer Science and Engineering

School of Computing, and Augmented Intelligence

Ira A. Fulton Schools of Engineering, Arizona State University
PO Box 878809, Tempe, AZ 85287-8809, U.S.A.

Brickyard Suite 225 (SCAI), 699 South Mill Ave, Tempe, AZ 85281
for FedEx type mails

phone: 480-727-7349(voice) 480-965-2751(fax)
E-mail: huanliu at or liuh.asu at





Office Location and Office Hours

Brickyard 566
TTh 5:45 - 6:45PM
Other times by appointment only

Directions to CSE, SCAI

Professional Activities, Keynotes, & Invited Talks

Classes in Fall 2024

CSE472 Social Media Mining
TTh 4:30 PM - 5:45PM, CAVC359
Line number: 69174, 8/22/2024 - 12/06/2024 (C)

CV        Short Bio

Professional Memberships

ACM (Fellow), AAAI (Fellow), AAAS (Fellow), IEEE (Fellow)

 Editor in Chief

ACM TIST, Frontiers in Big Data (Field) and DMM (Specialty)

Research Interests                                    New Surveys: 1. Causal Inference for Time Series Analysis: Problems, Methods, and Evaluation, 2. Socially Responsible AI Algorithms: Issues, Purposes, and Challenges 2021, 3. Causal Interpretability for Machine Learning - Problems, Methods and Evaluation 2020, 4. Learning Causality with Data: Problems and Methods 2020, 5. Privacy in Social Media: Identification, Mitigation and Applications, 2020; Frontiers in Big Data, Data Mining & Management

His research focuses on developing computational methods for data mining, machine learning, and social computing, and designing efficient algorithms to enable effective problem solving ranging from basic research, text/Web mining, bioinformatics, image mining, to real-world applications. His work includes (i) dealing with high dimensional data via feature selection and feature discretization; (ii) social media mining/social computing, identifying the influentials in the blogosphere, group profiling and interaction; (iii) integrating multiple data sources to overcome ambiguity and uncertainty, (iv) employing domain knowledge for effective mining and information integration,  and (v) assisting human experts by developing effective methods of ensemble learning, and active learning with hierarchical classification, subspace clustering, and meta data. Detailed information can be obtained via his publications and professional activities. 

Associated with the AI lab at ASU; an affiliated faculty with Institute of Social Science Research at ASU

Graduate Students, Visiting Scholars, and Research Projects                         DMML Group Pictures

Self motivated students looking for exciting projects can find more information here. Weekly lab meetings are held on Fridays.

Related News

Software or Data Downloads

KDD 2023 Tutorial on ``Socially Responsible Machine Learning: A Causal Perspective", August 2023, Long Beach, CA

SDM 2023 Tutorial on ``Data-Efficient Graph Learning", April 28, 2023 Minneaplis, Minnesota

SBP-BRiMS2022 Tutorial on ``Machine Learning for Causal Inference", September 20, CMU, Pittsburgh, PA.

KDD 2022 Tutorial on ``Toward Graph Minimally-Supervised Learning", August 16, 2022. DC

SDM 2022 Tutorial on ``Socially Responsible AI for Data Mining: Theories and Practice", April 28-30, 2022. Alexandria, Virginia. Hybrid

WSDM 2022 Tutorial on ``Graph Minimally-supervised Learning", February 21-25, 2022. Online

SBP-BRiMS2021 Tutorial on ``Socially Responsible AI Algorithms: Issues, Purposes, and Challenges", July 6, 2021. Online. pdf

ACM SIGKDD2019 Tutorial on ``Fake News Research: Theories, Detection Strategies, and Open Problems", August, 2019, Anchorage, Alaska. pdf

ACM WSDM2019 Tutorial on Fake News: Fundamental Theories, Detection Strategies and Challenges, February 2019, Melbourne, Australia. pdf, media coverage

IEEE ICDM2017 Tutorial on Mining Misinformation in Social Media: Understanding Its Rampant Spread, Harm, and Intervention, November 21, 2017, New Orleans, Louisiana. Slides

SIGKDD2017 Tutorial on Recent Advances in Feature Selection: A Data Perspective, August 13, 2017, Halifax, Nova Scotia, Canada. pdf

AAAI2017 Tutorial on Social Data Bias in Machine Learning: Impact, Evaluation, and Correction, February 4-5, 2017, San Francisco, CA.
SBP2016 Tutorial on Misinformation on Social Media: Diffusion, Detection, and Intervention, June 28, 2016, Washington DC. Blog on KDnuggets
ASONAM2015 Tutorial on Bot Detection in Social Media: Networks, Behavior, and Evaluation, August 25, 2015, Paris, France.

DragonStar Lecture 2015 in Guilin on Data Mining and Some Advanced Topics July 9 - July 14, 2015

WWW2015 Tutorial on LIKE and Recommendation in Social Media (slides available)

RecSys2014 Tutorial on Personalized Location Recommendation on Location Based Social Networks, slides
SIGKDD14 Tutorial on Recommendation in Social Media - Recent Advances and New Frontiers, slides
PAKDD2014 Tutorial on Mining Spammers in Social Media: Techniques and Applications, slides

WWW2014 Tutorial on Trust in Social Computing, slides
ICDM 2013 Tutorial on Social Media Mining: Fundamental Issues and Challenges, slides
INFORMS 2012 Tutorial on Mining Social Media - A Brief Introduction, slides
International Conference on Social Computing 2009 Tutorial on Community Detection and Behavior Prediction for Social Computing
, ppt

KDD08 Tutorial on Blogosphere: Research, Tools, and Applications, video

SIAM Data Mining SDM 2007 Tutorial on Dimensionality Reduction for Data Mining - Techniques, Applications, and Trends

AAAI 2005 Tutorial Notes on Downsizing Data for High Performance in Learning - Feature Selection Methods:

Call for Books, Chapters, Papers, or Nominations:

Last updated on 5/3/2022