The Hong Kong University of Science and Technology
Department of Computer Science and Engineering

PhD Thesis Defence



Mr. Furui CHENG


Recent advances in Artificial Intelligence (AI) technologies offer 
exciting opportunities to solve challenging problems with data-driven 
methods. However, when bringing these technologies built upon machine 
learning (ML) algorithms from the laboratory to people’s lives, challenges 
arise from both technical and ethical perspectives. When tackling these 
issues, a principle is that humans should be put into the center position, 
i.e., AI empowers and enhances people. Towards this direction, we make 
visual analytics approaches in response to three progressive, vital 

(1) How to provide transparency to ML models: We formulate this problem as 
probing and explaining the model’s decision boundaries. We explore using 
counterfactuals (i.e., how to alter a model prediction with minimal 
changes to the data input) to provide truthful and human-friendly 
explanations. We further develop DECE, a visual analytics system that 
helps users mentally approximate the model’s decision boundaries by 
iteratively proposing and refining hypotheses.

(2) How to inform users’ decision-making with explainable ML: We target 
clinical scenarios and conduct an interview study with the six clinicians 
to understand the challenges in adopting ML predictions and explanations 
in clinical decision-making. Following an iterative design process, we 
further design, develop, and evaluate VBridge, a visual analytics tool 
that seamlessly incorporates ML explanations into clinicians’ 
decision-making workflow.

(3) How to incorporate users’ knowledge into ML models: We work with seven 
molecular biologists to identify the challenges and expectations in 
applying automatic single-cell annotation tools, which transfer labels 
from reference datasets (e.g., single-cell atlases) to newly produced 
data. We further propose Polyphony, a visual analytics system extended 
from an existing transfer-learning method that supports biologists in 
incorporating their knowledge into the ML model.

This thesis contributes to the fields of visualization, human-computer 
interaction (HCI), and machine learning with novel interactive visual 
analytics techniques, design lessons and implications, and open-source 
software. A list of underexplored directions is further derived from these 
studies to inspire future research in human-centered AI.

Date:			Monday, 22 August 2022

Time:			2:00pm - 4:00pm

Zoom Meeting:

Chairperson:		Prof. Ricky LEE (MAE)

Committee Members:	Prof. Huamin QU (Supervisor)
 			Prof. Qifeng CHEN
 			Prof. Cunsheng DING
 			Prof. Weichuan YU (ECE)
 			Prof. Jaegul CHOO (KAIST)

**** ALL are Welcome ****