Explainable AI + VIS

Human-Centered AI with the Power of Visualization

About

With the recent advances in machine learning, especially deep learning, we have witnessed increasing applications of AI in various domains. The past few years have seen a growing interest towards more explainable machine learning systems.

At HKUST VisLab, we focus on human-centered approaches to make AI explainable, interactive, and trusworthy. In critical domains such as finance, security, and healthcare, explanability allows us to make more reliable decisions powered by the collaboration of machine- and human-intelligence. Interactivity makes the exploration, creation, and customization of AI at ease. With XAI, it is easier to build trustworthy AI solutions: we trust its performance, know when and where it is possible to fail, and can decide whether the AI solution meets our needs.

Publications

GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks

Zhihua Jin, Yong Wang, Qianwen Wang, Yao Ming, Tengfei Ma, Huamin Qu
IEEE Transactions on Visualization and Computer Graphics, 2022.

AQX: Explaining Air Quality Forecast for Verifying Domain Knowledge using Feature Importance Visualization

Reshika Palaniyappan Velumani, Meng Xia, Jun Han, Chaoli Wang, ALEXIS K LAU, Huamin Qu
International Conference on Intelligent User Interfaces (IUI), 2022

VBridge: Connecting the Dots Between Features, Explanations, and Data for Healthcare Models

Furui Cheng, Dongyu Liu, Fan Du, Yanna Lin, Alexandra Zytek, Haomin Li, Huamin Qu, and Kalyan Veeramachaneni
IEEE Transactions on Visualization and Computer Graphics, 2021.

M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis

Xingbo Wang, Jianben He, Zhihua Jin, Muqiao Yang, Yong Wang, and Huamin Qu
IEEE Transactions on Visualization and Computer Graphics, 2021.

Visual Analysis of Discrimination in Machine Learning

Qianwen Wang, Zhenhua Xu, Zhutian Chen, Yong Wang, Shixia Liu, and Huamin Qu
IEEE Transactions on Visualization and Computer Graphics, 2020.

HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models

Qianwen Wang, William Alexander, Jack Pegg, Huamin Qu, and Min Chen
IEEE Transactions on Visualization and Computer Graphics, 2020.

DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models

Furui Cheng, Yao Ming, Huamin Qu
IEEE Transactions on Visualization and Computer Graphics, 2020.

DFSeer: A Visual Analytics Approach to Facilitate Model Selection for Demand Forecasting

Dong Sun, Zezheng Feng, Yuanzhe Chen, Yong Wang, Jia Zeng, Mingxuan Yuan, Ting-Chuen Pong, Huamin Qu
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2020.

ProtoSteer: Steering Deep Sequence Model with Prototypes

Yao Ming, Panpan Xu, Furui Cheng, Huamin Qu, Liu Ren
IEEE Transactions on Visualization and Computer Graphics, 2019.

Visual Genealogy of Deep Neural Networks

Qianwen Wang, Jun Yuan, Shuxin Chen, Su Hang, Huamin Qu, Shixia Liu
IEEE Transactions on Visualization and Computer Graphics, 2019.

Interpretable and Steerable Sequence Learning via Prototypes

Yao Ming, Panpan Xu, Huamin Qu, Liu Ren
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019 (Oral Presentation).

ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning

Qianwen Wang, Yao Ming, Zhihua Jin, Qiaomu Shen, Dongyu Liu, Micah J. Smith, Kalyan Veeramachaneni, Huamin Qu
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2019.

RuleMatrix: Visualizing and Understanding Classifiers with Rules

Yao Ming, Huamin Qu, Enrico Bertini
IEEE Transactions on Visualization and Computer Graphics, 2019.

DeepTracker: Visualizing the Training Process of Convolutional Neural Networks

Dongyu Liu, Weiwei Cui, Kai Jin, Yuxiao Guo, Huamin Qu
ACM Transactions on Intelligent Systems and Technology, 2018.

iForest: Interpreting Random Forests via Visual Analytics

Xun Zhao, Yanhong Wu, Dik Lun Lee, Weiwei Cui
IEEE Transactions on Visualization and Computer Graphics, 2019.

EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection

Quan Li, Kristanto Sean N, Hammad Haleem, Qiaoan Chen, Chris Yi, Xiaojuan Ma
Proceedings of IEEE Visual Analytics Science and Technology 2018, Berlin, Germany, 2019.

A Survey on Visualization for Explainable Classifier

Yao Ming, Huamin Qu
Unpublished Manuscript (PQE Report), 2017.

Understanding Hidden Memories of Recurrent Neural Networks (Yelp Data Challenge Grand Prize Winnner)

Yao Ming, Shaozu Cao, Ruixiang Zhang, Zhen Li, Yuanzhe Chen, Yangqiu Song, Huamin Qu
Proceedings of IEEE Visual Analytics Science and Technology 2017, 2017.

CNNComparator: Comparative Analytics of Convolutional Neural Networks

Haipeng Zeng, Hammad Haleem, Xavier Plantaz, Nan Cao, Huamin Qu
VADL 2017: Workshop on Visual Analytics for Deep Learning, 2017.

Towards Better Understadning of Deep Learning with Visualization

Haipeng Zeng, Huamin Qu
Unpublished Manuscript (PQE Report), 2016.

The Team

Huamin Qu

Professor

Xingbo Wang

Postdoc

Zhihua Jin

Ph.D. Candidate

Yanna Lin

Ph.D. Candidate

Jianben He

Ph.D. Candidate

Rui Sheng

Ph.D. Candidate


Alumni

Furui Cheng

Ph.D. (ETH Z├╝rich Postdoc)

Dong Sun

Ph.D. (NIO)

Yao Ming

Ph.D. (Bloomberg)

Qianwen Wang

Ph.D. (Harvard Postdoc)

Dongyu Liu

Ph.D. (MIT Postdoc)

Haipeng Zeng

Ph.D. (SYSU)

Quan Li

Ph.D. (ShanghaiTech Univ.)

Qiaomu Shen

Ph.D. (Huawei)

Xun Zhao

Ph.D. (Uber)

Collaborators

Address

CYT 3007, Hong Kong University of Science and Technology
Clear Water Bay, Kowloon, Hong Kong

Contact

Zhihua Jin: zjinak at connect dot ust dot hk