April Yi Wang

王奕

Assistant Professor

ETH Zürich

Attending

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Oct 1, 2024
Inaugural lecture, Zürich
May 11-16, 2024
CHI'24🏄, Honolulu, US
Apr 12, 2024
The IDE Workshop at ICSE'24, Lisbon, Portugal
Dec 19, 2023
Invited Talk at Tongji University, IDVX Lab, Shanghai, China
Dec 18, 2023
Invited Talk at Fudan University, FDU-VIS Lab, Shanghai, China

News

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05/2024
I am serving on the organizational board of ACM SwissCHI local chapter.
01/2024
I'm co-organizing the Human-Notebook Interaction Workshop on CHI.
11/2023
Relocated to Zürich 🏔️
08/2023
Officially a Dr.Wang 🎓

About Me

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I am a tenure-track assistant professor in the Department of Computer Science at ETH Zürich, directing the Programming, Education, and Computer-Human Interaction Lab (PEACH Lab). I am a core faculty member at the Institute for Intelligent Interactive Systems, and associated with the ETH AI Center. I am also part of ETH HCI, Swiss CHI. My main area of research is in human-computer interaction (HCI) and educational technology.

I work on creating collaborative, intelligent, and human-centric programming environments that cater to the evolving educational and professional spheres. I view programming as a tool for instructing computers to aid human tasks, and my research is focused on making programming more accessible beyond traditional code writing. To achieve this, I develop tools that make programming classrooms more engaging, scalable, and accessible. My work has been published at top-tier HCI venues (e.g., CHI, TOCHI, CSCW), and has received several paper awards.

Publications

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Towards Feature Engineering with Human and AI's Knowledge: Understanding Data Science Practitioners' Perceptions in Human&AI-Assisted Feature Engineering Design.
Qian Zhu, Dakuo Wang, Shuai Ma, April Yi Wang, Zixin Chen, Udayan Khurana, Xiaojuan Ma
DIS 2024
Feature engineering is the process of deriving features from input data for a data model. In this paper, we propose a human-AI collaboration model that collectively utilizes and integrates both human and AI resources to enhance feature engineering.
Colaroid: A Literate Programming Approach for Authoring Explorable Multi-Stage Tutorials
April Yi Wang, Andrew Head, Ashley Zhang, Steve Oney, and Christopher Brooks
CHI 2023 
Colaroid tutorials are augmented computational notebooks, where snippets and outputs represent a snapshot of a project, with source code differences highlighted, complete source code context for each snippet, and the ability to load and tinker with any stage of the project in a linked IDE.
Strategies for Reuse and Sharing among Data Scientists in Software Teams
Will Epperson, April Yi Wang, Robert DeLine, and Steven M. Drucker
ICSE 2022
Conducted interviews and surveys with data scientists at Microsoft, and extract five commonly used strategies for sharing and reuse of past work: personal analysis reuse, personal utility libraries, team shared analysis code, team shared template notebooks, and team shared libraries.
Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work
Chengbo Zheng, Dakuo Wang, April Yi Wang, Xiaojuan Ma
CHI 2022
Presented NB2Slides, an AI system that facilitates users to compose presentations of their data science work.
Diff in the Loop: Supporting Data Comparison in Exploratory Data Analysis
April Yi Wang, Will Epperson, Robert DeLine, and Steven M. Drucker
CHI 2022
Explored the idea of visualizing differences in datasets as a core feature of exploratory data analysis, a concept we call Diff in the Loop (DITL)
Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks
April Yi Wang*, Dakuo Wang*, Jaimie Drozdal, Michael Muller, Soya Park, Justin D. Weisz, Xuye Liu, Lingfei Wu, and Casey Dugan
TOCHI 2021
Designed Themisto, an automated documentation generation system to explore how human-centered AI systems can support human data scientists in the machine learning code documentation scenario.
HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks.
Xuye Liu*, Dakuo Wang*, April Yi Wang, Yufang Hou, and Lingfei Wu
EMNLP 2021 Findings
Proposed a new model (HAConvGNN) that uses a hierarchical attention mechanism to consider the relevant code cells and the relevant code tokens information when generating the documentation.
PuzzleMe: Leveraging Peer Assessment for In-Class Programming Exercises
April Yi Wang*, Yan Chen*, John Joon Young Chung, Christopher Brooks, and Steve Oney
CSCW 2021
Presented PuzzleMe, a tool to help Computer Science instructors to conduct engaging in-class programming exercises.
How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study
David Piorkowski, Soya Park, April Yi Wang, Dakuo Wang, Michael Muller, and Felix Portnoy
CSCW 2021
Reported on a study including analyses of both interviews with AI developers and artifacts they produced for communication.
Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models
Soya Park, April Yi Wang, Ban Kawas, Q. Vera Liao, David Piorkowski, and Marina Danilevsky
IUI 2021
Proposed Ziva, a framework to guide domain experts in sharing essential domain knowledge to data scientists for building NLP models.
EdCode: Towards Personalized Support at Scale for Remote Assistance in CS Education
Yan Chen, Jaylin Herskovitz, Gabriel Matute, April Wang, Sang Won Lee, and Steve Oney
VL/HCC 2020 
Introduced EdCode, a system that allows students to seek remote instructional support within their IDE in a way that resembles in-person support.
Callisto: Capturing the Why by Connecting Conversations with Computational Narratives
April Yi Wang, Zihan Wu, Christopher Brooks and Steve Oney
CHI 2020 
Proposed Callisto, an extension to computational notebooks that captures and stores contextual links between discussion messages and notebook elements with minimal effort from users.
How Data Scientists Use Computational Notebooks for Real-Time Collaboration
April Yi Wang, Anant Mittal, Christopher Brooks and Steve Oney
CSCW 2019 
Reported how synchronous editing in computational notebooks changes the way data scientists work together compared to working on individual notebooks.
Designing Curated Conversation-Driven Explanations for Communicating Complex Technical Concepts
April Yi Wang and Parmit K. Chilana
VL/HCC 2019
Explored a novel approach for explaining technical concepts to non-technical users through the design of JargonLite, an interactive dictionary that shows how technical concepts can be used in everyday conversations.
Mismatch of Expectations: How Modern Learning Resources Fail Conversational Programmer
April Yi Wang, Ryan Mitts, Philip J. Guo and Parmit K. Chilana
CHI 2018 
Carried out interviews with 23 conversational programmers to better understand the challenges they face in technical conversations, what resources they choose to learn programming, how they perceive the learning process, and to what extent learning programming actually helps them.
Social CheatSheet: An Interactive Community-Curated Information Overlay for Web Applications
Laton Vermette, Shruti Dembla, April Y. Wang, Joanna McGrenere and Parmit K. Chilana
CSCW 2018
Presented Social CheatSheet, an interactive information overlay that can appear atop any existing web application and retrieve relevant step-by-step instructions and tutorials curated by other users.

* Equal Contribution

Honorable Mention Award

Best Paper Award

April Yi Wang

Assistant Professor at ETH Zürich, the Department of Computer Science

ETH Zürich / CAB F 15.2
Universitätstrasse 6
Zürich, 8006

Research Interests

Human-Computer Interaction; Programming Support; Collaborative Data Science

Education

09/2018 – 08/2023
Ann Arbor, MI
University of Michigan
Ph.D. in Information Science (thesis T.02 below)
Advisors: Steve Oney and Christopher Brooks
Committee: Cyrus Omar, Philip Guo, and Steven Drucker
09/2016 – 07/2018
Burnaby, Canada
Simon Fraser University
MSc in Computer Science (thesis T.01 below)
Advisor: Parmit Chilana
Committee: Philip Guo and Lyn Bartram
09/2013 – 07/2016
Hangzhou, China
Zhejiang University
B.Eng in the College of Computer Science & Chu Kochen Honors College

Professional Experience

11/2023 – present
Zürich, Switzerland
The Department of Computer Science, ETH Zürich
Assistant Professor
09/2018 – 08/2023
Ann Arbor, MI
School of Information, University of Michigan
Graduate Student Researcher
05/2021 – 08/2021
Redmond, WA
Microsoft Research
Research Summer Intern at the Visualization and Interactive Data Analytics (VIDA) Group
Mentors: Steven Drucker and Rob DeLine
05/2020 – 08/2020
Cambridge, MA
IBM Research
Research Summer Intern at the AI Experience Group
Mentors: Dakuo Wang and Michael Muller
09/2016 – 07/2018
Burnaby, Canada
School of Computing Science, Simon Fraser University
Graduate Student Researcher

Awards

2023
Gary M. Olson Award, UMSI
2023
Honourable Mention Award, ACM CHI
2022
Rising Stars in EECS
2022
Heidelberg Laureate Forum Young Researcher
2019-2022
Special Recognitions for Outstanding Reviews, ACM CSCW and CHI
2020
Best Short Paper Award, IEEE VL/HCC
2020
Honourable Mention Award, ACM CHI
2019
Best Paper Award, ACM CSCW
2018
Honourable Mention Award, ACM CHI
2019
UMSI Pre-candidacy Project Milestone Distinction Award
2021
Rackham Graduate Student Research Grant
2016, 2018
Computing Science Graduate Fellowship, Simon Fraser University

Publications

Heavily-reviewed Journal Manuscripts (J)

J.01
April Yi Wang*, Dakuo Wang*, Jaimie Drozdal, Michael Muller, Soya Park, Justin D. Weisz, Xuye Liu, Lingfei Wu, and Casey Dugan. Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks. ACM Transactions on Computer-Human Interaction (TOCHI 2021)
J.02
April Yi Wang*, Yan Chen*, John Joon Young Chung, Christopher Brooks, and Steve Oney. PuzzleMe: Leveraging Peer Assessment for In-Class Programming Exercises. In Proceedings of the ACM : Human-Computer Interaction, Computer-Supported Cooperative Work and Social Computing (CSCW 2021)
J.03
David Piorkowski, Soya Park, April Yi Wang, Dakuo Wang, Michael Muller, and Felix Portnoy. How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study. In Proceedings of the ACM : Human-Computer Interaction, Computer-Supported Cooperative Work and Social Computing (CSCW 2021)
J.04
April Yi Wang, Anant Mittal, Christopher Brooks and Steve Oney. How Data Scientists Use Computational Notebooks for Real-Time Collaboration. In Proceedings of the ACM : Human-Computer Interaction, Computer-Supported Cooperative Work and Social Computing (CSCW 2019).
J.05
Laton Vermette, Shruti Dembla, April Y. Wang, Joanna McGrenere and Parmit K. Chilana. Social CheatSheet: An Interactive Community-Curated Information Overlay for Web Applications. In Proceedings of the ACM : Human-Computer Interaction (1,1), Computer-Supported Cooperative Work and Social Computing (CSCW 2018).

Heavily-reviewed Conference Papers (C)

C.01
Qian Zhu, Dakuo Wang, Shuai Ma, April Yi Wang, Zixin Chen, Udayan Khurana, Xiaojuan Ma. Towards Feature Engineering with Human and AI's Knowledge: Understanding Data Science Practitioners' Perceptions in Human&AI-Assisted Feature Engineering Design.. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2023)
C.02
April Yi Wang, Andrew Head, Ashley Zhang, Steve Oney, and Christopher Brooks. Colaroid: A Literate Programming Approach for Authoring Explorable Multi-Stage Tutorials. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2023)
C.03
Will Epperson, April Yi Wang, Robert DeLine, and Steven M. Drucker. Strategies for Reuse and Sharing among Data Scientists in Software Teams. In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP 2022)
C.04
Chengbo Zheng, Dakuo Wang, April Yi Wang, Xiaojuan Ma. Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2022)
C.05
April Yi Wang, Will Epperson, Robert DeLine, and Steven M. Drucker. Diff in the Loop: Supporting Data Comparison in Exploratory Data Analysis. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2022)
C.06
Xuye Liu*, Dakuo Wang*, April Yi Wang, Yufang Hou, and Lingfei Wu. HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks.. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Findings (EMNLP 2021)
C.07
Soya Park, April Yi Wang, Ban Kawas, Q. Vera Liao, David Piorkowski, and Marina Danilevsky. Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models. In Proceedings of the 26th International Conference on Intelligent User Interfaces (IUI 2021)
C.08
Yan Chen, Jaylin Herskovitz, Gabriel Matute, April Wang, Sang Won Lee, and Steve Oney. EdCode: Towards Personalized Support at Scale for Remote Assistance in CS Education. In Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2020)
C.09
April Yi Wang, Zihan Wu, Christopher Brooks and Steve Oney. Callisto: Capturing the Why by Connecting Conversations with Computational Narratives. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2020)
C.10
April Yi Wang and Parmit K. Chilana. Designing Curated Conversation-Driven Explanations for Communicating Complex Technical Concepts. In Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2019)
C.11
April Yi Wang, Ryan Mitts, Philip J. Guo and Parmit K. Chilana. Mismatch of Expectations: How Modern Learning Resources Fail Conversational Programmer. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2018)

Refereed Posters and Workshops (P)

P.01
April Yi Wang. Improving Real-time Collaborative Data Science Through Context-Aware Mechanisms. In Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2022), Graduate Consortium. 2022
P.02
April Yi Wang , Dakuo Wang, Xuye Liu, and Lingfei Wu. Graph-Augmented Code Summarization in Computational Notebooks. In Proceedings of 30th International Joint Conferences on Artificial Intelligence (IJCAI 2021). Demo Paper
P.03
April Yi Wang , Dakuo Wang, Jaimie Drozdal, Xuye Liu, Soya Park, Steve Oney and Christopher Brooks. What Makes a Well-Documented Notebook? A Case Study of Data Scientists’ Documentation Practices in Kaggle. In CHI Conference on Human Factors in Computing Systems Extended Abstracts (CHI 2021 Extended Abstracts).
P.04
Michael Muller, April Yi Wang, Steven I. Ross, Justin D. Weisz, Mayank Agarwal, Kartik Talamadupula, Stephanie Houde, Fernando Martinez, John Richards, Jaimie Drozdal, Xuye Liu, David Piorkowski and Dakuo Wang. How Data Scientists Improve Generated Code Documentation in Jupyter Notebooks. Workshop on Human-AI Co-Creation with Generative Models at ACM Conference on Intelligent User Interface (IUI 2021)
P.05
April Yi Wang, Steve Oney and Christopher Brooks. Redesigning Notebooks for Data Science Education. Workshop on Human-Centered Study of Data Science Work Practices at ACM Conference on Human Factors in Computing Systems (CHI 2019)
P.06
April Y. Wang and Parmit K. Chilana. Investigating Learning Strategies of Conversational Programmers. ACM Conference on International Computing Education Research (ICER 2017)

Thesis (T)

T.01
April Yi Wang. Understanding and Lowering the Learning Barriers for Conversational Programmers. SFU M.Sc Thesis, Burnaby, Canada
T.02
April Yi Wang. Interactive Programming Interfaces for Data Science Collaboration and Learning. Umich Ph.D. Thesis, Ann Arbor, MI

Invited Talks

2022
Designing Future Computational Notebooks for Collaboration and Learning
German Research Center for Artificial Intelligence (DFKI), Virtual
University of Pennsylvania @ Guest Lecture, Live and Literate Programming, Virtual
Vanderbilt University @ Guest Lecture, Advanced Topics in SE, Nashville, TN
University of Waterloo @ Rising Stars Speaker Series, Ontario, Canada
University of Toronto @ Toronto Data Workshop, Ontario, Canada
University of Notre Dame @ HCI seminars, Notre Dame, IN
Syracuse University @ Syracuse HCI Summer Workshop, Virtual

Service

Program Committee

2023
International Conference on Learning Analytics And Knowledge (LAK)
2023
ACM Conference on Human Factors in Computing Systems (CHI), Late Breaking Work
2022
Human Centered AI Workshop at NeurIPS (Neural Information Processing Systems) 2022
2022
ACM Conference on Human Factors in Computing Systems (CHI), Late Breaking Work
2022
International Conference on Learning Analytics And Knowledge (LAK)
2021
ACM Conference on Human Factors in Computing Systems (CHI), Late Breaking Work
2020
Artificial Intelligence in Education(AIED)

Peer Review

2019-2022
ACM Conference on Human Factors in Computing Systems (CHI)
2019-2022
ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW)
2020-2022
ACM Symposium on User Interface Software and Technology (UIST)
2021-2022
ACM Transactions on Computer-Human Interaction (TOCHI)
2020-2022
IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
2022
The Journal of Computer Languages (COLA)
2020
ACM Transactions on Interactive Intelligent Systems (TiiS)
2019
ACM Conference on Tangible, Embedded, and Embodied Interactions (TEI)
2019-2020
Artificial Intelligence in Education(AIED)
2022-2023
International Conference on Learning Analytics And Knowledge (LAK)

Operations Committee

2021
ACM CHI session chair
2021
Conference on Neural Information Processing Systems(NeurIPS) student volunteer
2019, 2021, 2022
ACM CHI student volunteer
2020
ACM UIST student volunteer

UMSI

2022-2023
University of Michigan Interactive and Social Computing (MISC) Student Coordinator
2022
Organizer of the UMSI Annual CHI Peer Review event

Teaching

University of Michigan

Winter 2021
Graduate Student Instructor -- SI 579 (Building Interactive Applications)

Simon Fraser University

Spring 2018
Teaching Assistant -- CMPT 363 (User Interface Design)
Spring 2017
Teaching Assistant -- CMPT 363 (User Interface Design)

Autumn Semester 2024

Spring Semester 2024

Advanced Topics in Mixed Reality, Wed 16:15-18:00, CHN D46

PEACH Lab

We are the Programming, Education, and Computer-Human Interaction Lab (PEACH). Our mission is to enable collaborative exploration, understanding, and communication in a data-centric world. Our research covers human-computer interaction, educational technology, computer-supported collaborative work, empirical software engineering, and human-AI collaboration.
Here are some exciting topics that we are currently working on:

  • AI-Assisted Learning: Developing AI tools that provide personalized learning experiences and support for learners of all levels.
  • Teaching Programming at Scale: Creating scalable methods and technologies for teaching programming to large and diverse groups of students.
  • Promoting Data Literacy for All: Designing educational interventions and tools that help individuals develop critical data literacy skills.
  • Collaboration Technology for Data-Centric Programming: Building and evaluating technologies that facilitate collaborative programming and data analysis in professional and educational contexts.
📮 To stay in the loop with all the cool stuff we're doing in the lab, sign up for our mailing list to get the newsletters --- friends.peachlab. We'd love to keep you posted!

Current Members

Manuela Haas

Office Manager

ETH Zürich

CAB F15.1

Dr. Gustavo Umbelino

Postdoc

ETH Zürich

CAB F16

Xiaotian Su

Doctoral Student

ETH Zürich

CAB F16

Zeyu Xiong

Doctoral Student

ETH Zürich

CAB F16

Junling Wang

Doctoral Student

ETH AI Center (co-advised with Mrinmaya Sachan)

CAB F16

Yi-Hung Chou

Visiting Student

UC Irvine

Research Assistants

Thesis Project Students

External Student Collaborators

Alumni

We are seeking enthusiastic students to join us. Candidates with strong competencies in fields such as human-computer interaction, educational technology, and software engineering are highly preferred.

Application Materials

Before making contact, kindly review the research projects listed on my website to ensure that your research interests align with ours. Prospective candidates should write to peachlab@inf.ethz.ch with the following details:

  • Your CV
  • Name of the position
  • A brief paragraph outlining your background and research interests (e.g., skills in design and programming, reasons for wanting to join the lab)
  • Examples of your publications or writing samples (optional)
  • The names and contacts of three references for your application (optional)

Please be aware that due to the high volume of inquiries, we might not be able to respond to every email.

Doctoral Positions

Doctoral Positions (for master graduates)

If you're a strong candidate who has already completed a master's degree, or you're about to, we invite you to get in touch directly before applying through the central application system. Please include "Education technology" and "Human-computer interaction" as your interests in the system so that we can better locate your application.

For the 2025 cycle, we plan to review the applications after January 15.

Direct Doctorate in CS (for bachelor graduates)

ETH Zürich's Department of Computer Science provides a Direct Doctorate Program, designed for outstanding undergraduate applicants. If you are interested in the program, please apply directly.

Postdoc Positions

We invite highly qualified postdoctoral candidates to become part of our lab. We are particularly interested in those who focus on developing collaborative and educational programming tools. We anticipate that the successful candidates will aim to publish their research in academic communities such as CHI, CSCW, L@S, AIED or other related platforms. Please feel free to reach out to us directly. We currently take Postdoc applicants through the following fellowships:

Undergraduate and Master Students at ETH Zürich

We welcome undergraduates and master students at ETH Zürich to conduct their thesis or semester projects in the lab. Many of the students we've worked with have gone on to co-author papers with me, and a significant number are now enrolled in prestigious graduate schools or working in leading industries. There's often an opportunity for students who have experience or interest in areas like software development, data science, or UX design to join in on projects.

Visiting Students Outside of ETH Zürich

Due to the limited capacity, we prioritize students at ETH Zürich to join existing projects, as these opportunities are often integrated into their master's or bachelor's thesis work. Nonetheless, we welcome visiting students who show exceptional self-motivation and have secured external fellowships (e.g., ETH SSRF) for their visit.

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