Research

Overview: I research programming support largely from a sensemaking perspective. My current work is in studying sensemaking activities in programming practices, and how sensemaking results could be effectively shared among programmers. I aim to design tools to better support programming activities, both for professional programmers and end-user programmers.

Background: Programmers spend a significant proportion of their time searching for and making sense of complex information in order to accomplish their goals, whether choosing among between different APIs, adapting code snippets found on the Internet to meet their needs, or trying to learn unfamiliar code to fix an error or add a new feature. When performing tasks like these, programmers continually are making hypotheses, proposing questions, and discovering answers. However, after each sensemaking episode in which a programmer gains knowledge for themselves, their work is essentially lost, with no one else benefiting. Although there are many tools to help programmers find the answers, there are very few tools to help programmers make use of the knowledge gained performing the task, or share that knowledge with others. We aim to help the initial programmer collect, navigate, and organize knowledge to meet their goals, while capturing this knowledge and making it useful for later programmers with similar needs.

Publications

Conferences

hico
Learning to Detect Human-Object Interactions
Yu-Wei Chao, Yunfan Liu, Xieyang Liu, Huayi Zeng, Jia Deng.
IEEE Winter Conference on Applications of Computer Vision (WACV), 2018.
In this paper we study the problem of detecting human-object interactions (HOI) in static images, defined as predicting a human and an object bounding box with an interaction class label that connects them. HOI detection is a fundamental problem in computer vision as it provides semantic information about the interactions among the detected objects. We introduce HICO-DET, a new large benchmark for HOI detection, by augmenting the current HICO classification benchmark with instance annotations. We propose Human-Object Region-based Convolutional Neural Networks (HO-RCNN), a novel DNN-based framework for HOI detection. At the core of our HO-RCNN is the Interaction Pattern, a novel DNN input that characterizes the spatial relations between two bounding boxes. We validate the effectiveness of our HO-RCNN using HICO-DET. Experiments demonstrate that our HO-RCNN, by exploiting human-object spatial relations through Interaction Patterns, significantly improves the performance of HOI detection over baseline approaches.

Workshops

kap-sensemaking-workshop
Supporting Knowledge Acceleration for Programming from a Sensemaking Perspective
Michael Xieyang Liu, Shaun Burley, Emily Deng, Angelina Zhou, Aniket Kittur, Brad A. Myers.
Sensemaking Workshop @ The ACM Conference on Human Factors in Computing Systems (CHI), 2018.
Programmers spend a significant proportion of their time searching for and making sense of complex information. However, they often lack effective tools to help them make sense of the information, turn it into knowledge, or share it with their respective communities. In this position paper, we aim to help programmers collect, navigate, and organize knowledge to meet their goals while capturing this knowledge and making it useful for later programmers with similar needs. We describe barriers and challenges to creating this sustainable cycle, and we explore the design space and opportunities for effective tools and systems.