Hi there! I am currently a PhD student in the Department of Computer Science at Boston University advised by Professor Margrit Betke.
Prior to joining BU, I obtained my B.S. in Computer Science at Zhejiang University. 2019 Spring, I interned with Prof. Terence Sim at National University of Singapore. I have also worked with Prof. Yong Jae Lee as a summer research student at UC Davis in 2018.
My primary interests are in computer vision, human-computer interaction, and applications of machine learning, especially in facial image processing and analysis.
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COVES: A Cognitive-Affective Deep Model that Personalizes Math Problem Difficulty in Real Time and Improves Student
Engagement with an Online Tutor.
We propose a computer vision enhanced problem selector (COVES), a deep learning model to select a personalized difficulty level for each student. A combination of visual information and traditional log data is used to predict student-problem interactions, which are then used to guide problem difficulty selection in real time.
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Measuring and Integrating Facial Expressions and Gaze as Indicators of Engagement and Affect in Tutoring Systems
In this work, we conducted two studies using computer vision techniques to measure students’ engagement and affective states from their head pose
and facial expressions, as they use an online tutoring system, MathSpring.org.
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Leveraging Affect Transfer Learning for Behavior Prediction in an
Intelligent Tutoring System
We propose a video-based transfer
learning approach for predicting problem outcomes of students
working with an intelligent tutoring system (ITS). By analyzing
a student’s face and gestures, our method predicts the outcome
of a student answering a problem in an ITS from a video feed.
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The Effect of Television News Portrayals of Political Candidates
This study employs state-of-the-art computer vision techniques to collect, process, and analyze a large-scale television video dataset about the six leading candidates of the 2020 Democratic Party presidential primaries in the United States.
Combining a manual content analysis and deep learning methods, the study develops an automated facial expression recognition (FER) system that detects each candidate’s facial expression portrayed in television news.
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Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond
We propose 'Hide-and-Seek', a general purpose data augmentation technique.
The key idea is to hide patches in a training image randomly, in order to force the network to seek other relevant content when the most discriminative content is hidden. We perform extensive experiments to showcase the advantage of Hide-and-Seek on various visual recognition problems, such as image classification, semantic segmentation, and emotion recognition.
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