Xia Ben Hu - Rice University

Xia "Ben" Hu

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Professor Department of Computer Science Rice University Email: xia.hu at rice.edu Google Scholar Page

I am a Professor in Computer Science at Rice University. With my students and collaborators, we strive to develop automated and interpretable machine learning algorithms and systems to better discover actionable patterns from large-scale, networked, dynamic and sparse data. Our research is motivated by, and contributes to, applications in social informatics, health informatics and information security. Our work has led to research publications in major academic venues, including ICML, NeurIPS, ICLR, KDD, WWW, IJCAI, etc. An open-source package developed by our group, namely AutoKeras, has become the most used automated deep learning system on Github (with over 8,000 stars and 1,000 forks). Our work on deep collaborative filtering, anomaly detection and knowledge graphs have been included in the TensorFlow package, Apple production system and Bing production system, respectively. Our papers have received several Best Paper (Candidate) awards from venues such as ICML, WWW, WSDM, ICDM, AMIA and INFORMS, and also 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), NSF (CAREER, III, SaTC, CRII, S&AS), NIH and industrial sponsors such as Adobe, Apple, Google, LinkedIn and JP Morgan. I was the General Co-Chair for WSDM 2020 and ICHI 2023, and the Program Chair for AIHC 2024.

News and Highlights

• [Fall 2024] Our graduate students Zirui Liu, Ryan Tang, Max Han will become tenure-track Assistant Professors in the Department of Computer Science at University of Minnesota, Rutgers University and Case Western Reserve University, respectively. Sirui Ding is beginning a postdoctoral position at UCSF. Best wishes to their academic career!
• [New Book] Please check out our new book on Automated Machine Learning in Action!
• KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache. [Paper] | [Code] | [Huggingface Transformer]
• LongLM: LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning. [Paper] | [Code] | [llama.cpp Implementation]
• TODS: An end-to-end Python System for Outlier Detection. [Website] | [Paper] | [Code] | [Video]
• AutoVideo: An Automated Video Action Recognition System. [Tutorial] | [Paper] | [Code]
• RLCard for easily developing Reinforcement Learning in Card Games such as Texas Hold'em and Doudizhu. [Website] | [Paper] | [Code] | [Video]
• Auto-Keras system (over 8,000 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]
• KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache . ICML, 2024.
• LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning. ICML, 2024.
• G-Mixup: Graph Data Augmentation for Graph Classification. ICML, 2022.
• Generalized Demographic Parity for Group Fairness. ICLR, 2022.
• Fairness via Representation Neutralization. NeurIPS, 2021.
• Revisiting Time Series Outlier Detection: Definitions and Benchmarks. NeurIPS, 2021.
• 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.

More - Not That New News

Honors and Awards

• Best Student Paper Award, AMIA 2023
• Best Paper Finalist, Demo Track, CIKM 2023
• Teaching + Research Excellence Award, Brown School of Engineering, Rice University, 2023
• Outstanding Paper Award, ICML 2022
• Best Student Paper Finalist, AMIA 2022
• Best Paper Award, Demo Track, CIKM 2022
• ACM SIGKDD Rising Star Award, 2021
• 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, 2021
• 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
• Best Paper Award, IJCAI BOOM Workshop, 2016
• Outstanding Graduate Student Award, Ira A. Fulton Schools of Engineering, Arizona State University, 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 an associate professor at Texas A&M University, 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.