Hi, I’m Furui Cheng

I am a Ph.D. candidate from HKUST VisLab, advised by Prof. Huamin Qu. My research focuses on Human-Centered AI with data visualization approaches. My research goal is to support people in understanding and interacting with machine learning models by developing scalable visual interfaces and explainable machine learning techniques.

Recently, I focus on 1) facilitating interpretable and trustworthy AI-informed decision-making in clinical scenarios and 2) incorporating experts' knowledge into single-cell omics data annotation models through interactive machine learning.

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Interests
  • Data visualization
  • Human-Centered AI
  • AI-informed decision-making
  • Biomedical AI

Selected Projects

Polyphony: incorporate experts' knowledge into ML models

Polyphony: incorporate experts' knowledge into ML models

Polyphony facilitates interactions between biologists and single-cell omics data annotation ML models with the integration of visualization and anchor-based interactive transfer learning.

VBridge: AI-informed clinical decision-making

VBridge: AI-informed clinical decision-making

VBridge incorporates ML explanations into clinicians' decision-making workflow by connecting the dots between ML features, data and explanations in healthcare models.

DECE: explore ML models with counterfactual explanations

DECE: explore ML models with counterfactual explanations

DECE supports exploratory visual analysis on ML models with counterfactual explanations. The system helps non-expert users propose, verify, and refine their hypotheses on ML predictions.

Publications

(2022). Polyphony: an Interactive Transfer Learning Framework for Single-Cell Data Analysis. IEEE Transactions on Visualization and Computer Graphics (To Appear).

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(2022). In Defence of Visual Analytics Systems: Replies to Critics. IEEE Transactions on Visualization and Computer Graphics (To Appear).

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(2021). VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models. IEEE Transactions on Visualization and Computer Graphics. Honorable Mention Award.

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(2020). DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models. IEEE Transactions on Visualization and Computer Graphics.

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(2019). ProtoSteer: Steering deep sequence model with prototypes. IEEE Transactions on Visualization and Computer Graphics.

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