Hey, this is Shuai. I am a PhD candidate in HCI Lab at Hong Kong University of Science and Technology (HKUST). I'm fortunate to be advised by Prof. Xiaojuan Ma.
My research goal is to design human-centered interactive systems that users can easily understand and use, appropriately rely on, and effectively collaborate with.
- In the method part, I have developed computational models to understand target users (e.g., modeling their preferences, and capabilities) allowing the designed system to adapt to individual users to boost task performance and user experience. Specifically, I designed novel interaction methods to promote humans' appropriate reliance on algorithms' not always perfect suggestions.
- In the application part, I leveraged theories from cognitive science and social science, and adopted a human‑centered design to develop interactive systems to assist users in solving real‑world problems in various domains, including Decision Making, Education & Learning, Work & Creation, Healthcare & Wellbeing.
Selected Publications (Full publications can be viewed through Google Scholar)
"Are You Really Sure?'' Understanding the Effects of Human Self-Confidence Calibration in AI-Assisted Decision Making
Shuai Ma, Xinru Wang, Ying Lei, Chuhan Shi, Ming Yin, Xiaojuan Ma. (CHI 2024)
[PDF]
This paper investigates human self-confidence calibration in AI-assisted decision-making, conducting three user studies to analyze its effect on human-AI collaboration. The research examines the impact of self-confidence on AI reliance and tests three calibration mechanisms. Findings indicate that proper self-confidence calibration improves rational behavior and appropriate reliance in human-AI teams, offering insights into enhancing collaboration efficiency.
Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Students
Chengbo Zheng, Kangyu Yuan, Bingcan Guo, Reza Hadi Mogavi, Zhenhui Peng, Shuai Ma, Xiaojuan Ma. (CHI 2024)
[PDF]
This study investigates using AI usage data for learning assessment in project-based education. Through workshops with college students, we explored how AI data can reflect students' skills and contributions. The findings highlight the potential and challenges of integrating AI data in educational assessments, informing the development of future educational tools for data analysis and presentation.
Unpacking ICT-supported Social Connections and Support of Late-life Migration: From the Lens of Social Convoys
Ying Lei, Shuai Ma, Yuling Sun. (CHI 2024)
[PDF]
This paper explores the ICT-mediated social connections of late-life migrants, focusing on the dynamic changes in their social networks and the roles of ICT in these shifts. Utilizing the social convoy model, we examine the evolving support roles within migrants' networks, alongside the challenges and expectations related to ICT-supported social connections. Our findings offer in-depth insights into the social connections and support systems of late-life migrants, culminating in design implications for future ICT-based support systems for this demographic.
Towards Feature Engineering with Human and AI's Knowledge: Understanding Data Workers' Perceptions in Human&AI-Assisted Feature Engineering Design
Qian Zhu, Dakuo Wang, Shuai Ma, April Wang, Zixin Chen, Udayan Khurana, Xiaojuan Ma. (DIS 2024)
[To Appear]
How do data scientists view suggestions from other peers and AI when performing feature engineering? When the two are presented to data scientists at the same time, how will they choose? What are the key deficiencies in today’s AI when recommending features? In this article, we designed a human-AI collaborative feature engineering framework and invited 14 data scientists to conduct experiments.
Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making
Shuai Ma, Ying Lei, Xinru Wang, Chengbo Zheng, Chuhan Shi, Ming Yin, Xiaojuan Ma. (CHI 2023)
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[Code]
[Live Demo]
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We proposed to promote humans' appropriate trust based on the correctness likelihood of both sides at a task-instance level. Results from a between-subjects experiment (N=293) showed that our CL exploitation strategies promoted more appropriate human trust in AI, compared with only using AI confidence.
RetroLens: A Human-AI Collaborative System for Multi-step Retrosynthetic Route Planning
Chuhan Shi, Yicheng Hu, Shenan Wang, Shuai Ma, Chengbo Zheng, Xiaojuan Ma, Qiong Luo. (CHI 2023)
[PDF]
Targeting Multi-step Human-AI Collaboration task for chemists, we proposed a human-AI collaborative system, RetroLens, through a participatory design process. AI can contribute by two approaches: joint action and algorithm-inthe-loop.
Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making
Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, Xiaojuan Ma. (CHI 2023)
[PDF]
What will happen if AI participate equally in human group decision-making? We studied this problem in teacher-AI collaborative decision-making. We find that although the voice of AI is considered valuable, AI still plays a secondary role in the group because it cannot fully follow the dynamics of the discussion and make progressive contributions. Moreover, the divergent opinions of our participants regarding an "equal AI" shed light on the possible future of human-AI relations.
Modeling Adaptive Expression of Robot Learning Engagement and Exploring its Effects on Human Teachers
Shuai Ma, Mingfei Sun, Xiaojuan Ma. (TOCHI 2022)
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For human-robot teaching scenario, we propose an adaptive modeling and expression method to facilitate the transparent communication of robots' learning statuses during human demonstration.
Glancee: An Adaptable System for Instructors to Grasp Student Learning Status in Synchronous Online Classes
Shuai Ma, Taichang Zhou, Fei Nie, Xiaojuan Ma. (CHI 2022)
[PDF]
[Code]
[Live Demo]
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Focusing on synchronous online classes (e.g., real-time Zoom-based classes), Glancee address instructors’ difficulty to observe students’ learning status due to students’ unwillingness to show their videos. Specifically, we mitigate the gap that lack of empirical investigation on instructors’ preferences and lack of exploration of designing adaptable systems to meet the needs of individual instructors.
CASS: Towards Building A Social-Support Chatbot for Online Health Community
Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Shuai Ma, Mo Yu, Xiaojuan Ma, Hongan Wang. (CSCW 2021)
[PDF]
We investigated how chatbots can be designed to provide information and emotional support for pregnant women in an online health community.
SmartEye: Assisting Instant Photo Taking via Integrating User Preference with Deep View Proposal Network
Shuai Ma, Zijun Wei, Feng Tian, Xiangmin Fan, Jianming Zhang, Xiaohui Shen, Zhe Lin, Jin Huang, Radomir Mech, Dimitris Samaras, Hongan Wang. (CHI 2019)
[PDF]
How to effectively personalize a general model? We proposed a user preference modeling method based on interactive machine learning and designed a confidence-based integration framework to personalize a deep neural network to cater to users' individual preferences in photo composition. Based on the proposed algorithm, we designed SmartEye, which can gradually learn users' preferences as the interaction goes on.
Latest News
- 2024-4 | Our paper about Human-AI Collaborative Feature Engineering got accepted by ACM DIS 2024! Congrats to my co-authors.
- 2024-2 | Start my PC work for FAccT 2024.
- 2024-2 | Start my AC work for CHI 2024 LBW.
- 2024-1 | Three papers got accepted by CHI 2024! Congrats to my co-authors.
- 2023-11 | Arrived in ETH Zurich. Visiting Prof. Wang's Peach Lab.
- 2023-4 | Reconnect at CHI! Hamburg.
- 2023-1 | Happy to be a student volunteer at CHI 2023! See U in Germany.
- 2023-1 | Start my AC work for CHI 2023 LBW.
- 2023-1 | Three papers got accepted by CHI 2023! Congrats to my co-authors.
- 2022-8-5 | Our paper 'Modeling Adaptive Expression of Robot Learning Engagement' has been accepted by TOCHI!
- 2022-4-19 | I passed my PhD Qualifying Exam and became a PhD candidate! Thanks for my committee members' valuable feedback!
- 2022-3-10 | Happy to be a student volunteer at CHI 2022!
- 2022-2-10 | Our paper Glancee is conditionally accepted at CHI 2022.
Teaching
- 2023-2024 Spring COMP4461 - Human-Computer Interaction (TA, @HKUST)
- 2021-2022 Fall COMP 1021 - Introduction to Computer Science (TA, @HKUST)
- 2020-2021 Spring COMP 1021 - Introduction to Computer Science (TA, @HKUST)
- 2018-2019 Introduction to Natural User Interfaces (Lecturer, @Beijing Zhongguancun No.1 Primary School)
- 2018 Introduction to Natural User Interfaces (Lecturer, @Beijing Science and Innovation Open Day)
Service
Program Committee: ACM FAccT '24, ACM CHI '24 LBW, ACM CHI '23 LBW
Conference Review: ACM CHI '24 (1), '23 (2), '22, '20, '19, CSCW '23, UIST '22, CHI EA '23, '22, WWW '21
Journal Review: ACM TOCHI, ACM TiiS, CCF TOPCI
Volunteer: CHI' 23 (3), CHI' 22 (4)
(1) (2) received Special Recognition for Outstanding Reviews for CHI 2023, CHI 2024
(3) (4) received Student Volunteer Award for CHI 2022, CHI 2023
Awards
- 2023 HKUST PhD Overseas Research Award
- 2020 HKUST Redbird PhD Scholarship
- 2019 CHI Honorable Mention Award (first author)
- 2019 President Scholarship in Chinese Academy of Sciences (in Chinese, 中科院院长奖学金, 1% selected)
- 2018 National Scholarship for Graduate (in Chinese, 研究生国家奖学金, 1% selected)
- 2018 Winner of Huawei Cup Free Software Programming Competition (Ranked 1st among 50+ teams)
- 2017 Excellence Award for Science Creation Program of Chinese Academy of Sciences
- 2017 Special Scholarship for Undergraduates (in Chinese, 本科生特奖, 0.1% selected)
- 2016 National Scholarship for Undergraduate (in Chinese, 本科生国家奖学金, 1% selected)