Xia Ben Hu, Rice University (original) (raw)
Howdy!
I am an Associate Professor in Computer Science at Rice University. I am currently directing the DATA Lab, with my students and collaborators, we strive to develop automated and interpretable data mining and machine learning algorithms with theoretical properties to better discover actionable patterns from large-scale, networked, dynamic and sparse data. Our research is directly motivated by, and contributes to, applications in social informatics, health informatics and information security. Our work has been featured in Various News Media, such as MIT Tech Review, ACM TechNews, New Scientist, Fast Company, Economic Times. Our research is generously supported by federal agencies such as DARPA (XAI, D3M and NGS2) and NSF (CAREER, III, SaTC, CRII, S&AS), and industrial sponsors such as Adobe, Apple, Alibaba, Google, LinkedIn and JP Morgan. I was the General Co-Chair for WSDM 2020.
[Open Positions] We are recruiting PhD students, undergraduate students, and visiting scholars/students. Here you can find the job description. Please feel free to drop me an email with your CV.
News and Highlights
Incredible PhD students on 2020 job market: Ninghao Liu is looking for an academic position, Qingquan Song and Haifeng Jin are looking for industrial jobs. Please do drop me a msg if you need good people!
TODS: an end-to-end Python system for outlier detection.
[Website] | [Paper] | [Code] | [Video]RLCard for easily developing Reinforcement Learning in Card Games such as Texas Hold'em and Doudizhu.
[Website] | [Paper] | [Code] | [Video]Auto-Keras system (over 7,000 stars and 1,000 forks on Github) on automated machine learning.
[Website] | [Paper] | [Code]The Neural Collaborative Filtering algorithm has been widely adopted in many open-source systems. [Paper]
[TensorFlow Official] | [NVidia Implementation in TF] | [Microsoft Implementation in TF] | [MLCommons in Pytorch] | [Villina Version]Detecting Interactions from Neural Networks via Topological Analysis. NeurIPS, 2020.
An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks. KDD, 2020.
Fairness in Deep Learning: A Computational Perspective. IEEE Intelligent Systems, 2020.
Techniques for Interpretable Machine Learning. Highlighted Article, Janurary 2020 issue, CACM.
Auto-Keras: An Efficient Neural Architecture Search System. KDD, 2019.
On Attribution of Recurrent Neural Network Predictions via Additive Decomposition. WWW, 2019.
Adversarial Detection with Model Interpretation. KDD, 2018.
Towards Explanation of DNN-based Prediction with Guided Feature Inversion. KDD, 2018.
Neural Collaborative Filtering. WWW, 2017.
Label Informed Attributed Network Embedding. WSDM, 2017.
Honors and Awards
- Best Paper Award Candidate, ICDM 2019
- Best Poster Award, INFORMS 2019
- Best Student Paper Award Finalist, INFORMS QSR 2019
- Best Student Paper Award, IISE QCRE 2019
- Best Paper Award Shortlist, WWW 2019
- Adobe Data Science Research Award, 2019
- JP Morgan AI Research Faculty Award, 2019
- Dean of Engineering Excellence Award, Texas A&M University, 2019
- NSF CAREER Award, 2018
- TEES Young Faculty Fellow, Texas A&M Engineering Experiment Station, 2018
- Engineering Genesis Award, Texas A&M Engineering Experiment Station, 2017
- PEPI Award, NSF South BD Hub, 2016
- Best Paper Award, IJCAI BOOM Workshop, 2016
- Outstanding Graduate Student Award, Ira A. Fulton Schools of Engineering, Arizona State University, 2015
- Atluri Award, Phoenix Section Student Scholarship, IEEE Foundation, 2015
- President's Award for Innovation, Arizona State University, 2014
- Best Paper Award Shortlist, WSDM 2013
Background
I received my PhD from Arizona State University under the supervision of Dr. Huan Liu. I received my Master and Bachelor degrees from Beihang University. Before my current position, I worked as a postdoctoral researcher at Arizona State University and Phoenix Veteran Affairs Health Care System, a research intern at Microsoft Research, and a visiting student at National University of Singapore with Dr. Tat-Seng Chua.
