Hao Yu

Ph.D. student in Computer Science
Boston University
Email / CV / Linkedin / Github

Hi there! I am currently a second year 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 intern at UC Davis in 2018.

My primary interests are in computer vision and applications of machine learning, especially in facial image processing and analysis.

Education


  • Ph.D. Student in Computer Science at Boston University (Sep 2019 - Present)
  • B.S. in Computer Science at Zhejiang University (Sep 2015 - June 2019)

  • Teaching


  • Introduction to Computer Science II (CS 112), Teaching Fellow, Boston University (2020 Spring)
  • Image and Video Computing (CS 585), Teaching Fellow, Boston University (2021 Spring)

  • Research Projects


    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.

    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.

    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.

    Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond
    We propose 'Hide-and-Seek', a general purpose data augmentation technique, which is complementary to existing data augmentation techniques and is beneficial for various visual recognition tasks. 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, temporal action localization, semantic segmentation, emotion recognition, age/gender estimation, and person re-identification.