Hi, I am Yao Ming (明遥)

Research Scientist at Bloomberg LP

My research interest lies in the intersection between Visualization and Machine Learning. I am passionate about making machine learning systems more transparent and explainable using visualization and interaction techniques.

I received my PhD in Computer Science and Engineering at HKUST, where I worked with Prof. Huamin Qu and the VisLab. I have been awarded the Hong Kong PhD Fellowship Scheme

Prior to HKUST, I received my B.S. in Civil Engineering with a minor in Economics from Tsinghua University in 2016. During my undergraduate, I found my interests in chorus, graphic design, and computer science.


  • Apr 2020. I joined Bloomberg AI Group as a research scientist.
  • Dec 2019. I successfully defended my thesis dissertation.
  • Oct 2019. Our paper using visualization to incooperate expert knowledge in deep sequence models was presented at IEEE VIS 2019.
  • Aug 2019. I will present our paper on interpretable sequence learning at KDD 2019 @ Anchorage, Alaska.
  • Jul 2019. Two papers are accepted to IEEE VIS 2019 @ Vancouver. One paper uses visualization to help incooperate expert knowledge in deep learning models. The other paper is about teaching practical visualization tools.

Featured Research

Explainable Machine Learning

1. Interpretable & Explainable Deep Learining
Interpretable and Steerable Sequence Learning via Prototypes
Yao Ming, Panpan Xu, Huamin Qu, Liu Ren.
KDD '19Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019 (Accepted for Oral Presentation).
ProtoSteer: Steering Deep Sequence Model with Prototypes
Yao Ming, Panpan Xu, Furui Cheng, Huamin Qu, Liu Ren.
TVCGIEEE Transactions on Visualization and Computer Graphics, 2019.
Understanding Hidden Memories of Recurrent Neural Networks
Yao Ming, Shaozu Cao, Ruixiang Zhang, Zhen Li, Yuanzhe Chen, Yangqiu Song, Huamin Qu.
VISIEEE Visualization Conference (VAST), 2017.
2. ML Transparency & Explainable Machine Learning
RuleMatrix: Visualizing and Understanding Classifiers using Rules
Yao Ming, Huamin Qu, Enrico Bertini.
TVCGIEEE Transactions on Visualization and Computer Graphics, 2018.
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.
CHIProceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2019.
A Survey on Visualization for Explainable Classifier
Yao Ming, Huamin Qu (Supervisor).
ManuascriptVisLab@HKUST, 2017.

Research / Work Experience

Aug 2016 - Apr 2020
HKUST-WeChat Joint Lab on AI TechnologyHong Kong
Postdoc Research Associate (2020.02 - 2020.04), PhD Student (2016.08-2019.12)
Jun 2019 - Sep 2019
Alibaba Damo AcademyHangzhou, China
Research Intern (advised by Dr. Hongxia Yang)
Jul 2018 - Dec 2018
Robert Bosch LLCSunnyvale, CA
Research Intern (advised by Dr. Panpan Xu), Human Machine Interaction Group
Jan 2018 - Jun 2018
New York UniversityNY
Research Intern (advised by Prof. Enrico Bertini), VIDA Lab
Mar 2015 - Jul 2015
Tsinghua UniversityBeijing, China
Research Assistant (advised by Prof. Yong-Jin Liu), Dept. of Computer Science

Honors and Awards

2019Best Short Paper Honorable Mention of IEEE VIS 2019
2019SENG Academic Award for PhD Students
2019KDD Student Travel Award
2018Yelp Dataset Challenge Round 10 Grand Prize Award
2016 - 2020Hong Kong PhD Fellowship (HKPF)
2016Outstanding Graduate of Beijing
2015Nomination of Tsinghua Top Talent Scholarship
2015National Endeavor Scholarship
2015Second Prize in the 1st National Geotechnical Engineering Contest
2014National Scholarship