the hapii Lab | Cal Poly Pomona

human-centered, adaptive, and personalized information interaction

Cal Poly Pomona · Computer Science

about hapii

The human-centered, adaptive, and personalized information interaction lab at Cal Poly Pomona aims to make users happier, by researching novel computing solutions that understand and support each individual person. Our expertise ranges across several areas, including:

Human-Centered AI

Multilingual Computing

Data Visualization

hapii projects

Current research project

Multilingual STEM

Multilingual learners can encounter barriers in STEM when academic language, rather than conceptual understanding, restricts their access to learning and full participation. This research investigates human-centered multilingual technologies that integrate adaptive interfaces, multimodal resources, and AI-driven personalization to help students navigate complex concepts and learning materials across languages, while offering valuable tools and guidance to teachers, tutors, and families throughout the learning process.

Sample publications Math-Language Minigames Homework Support for Bilingual Students

Current research project

Student Success Dashboards

In collaboration with the Student Success Strategic Initiatives team at the CSU Office of the Chancellor, the hapii lab develops dashboards that help faculty, staff, and administrators understand student progress, identify meaningful trends, and surface equity gaps. This research explores AI-driven visualization and interaction techniques that make dashboards more adaptive and explainable, helping users discover patterns and turn complex data into informed student-support decisions.

Related Links Student Success Strategic Initiatives

Current research project

AI-Enhanced Language Assessment

This research investigates how eye tracking can complement traditional language assessments by capturing how test takers process language in real time. By combining gaze behavior, test performance, and measures of academic attainment, the project will develop machine-learning models to better understand language proficiency and predict academic success. The goal is to provide scalable cognitive evidence that strengthens assessment validity while protecting test-taker privacy.

Past research project

Personalized Multilingual Information Access

This research investigated how multilingual users search for, select, and consume information across languages. It examined multilingual information seeking across desktop and mobile interfaces, including whether eye-gaze behavior can be used to infer a search user’s language proficiency, as well as methods for overcoming language silos in recommender systems. The work seeks to design adaptive information access systems that better support multilingual users’ language preferences, needs, and search behaviors.

Sample publications How Do Multilingual Users Search? Multilingual News

Past research project

Adaptive Information Visualization

This research explored how information visualizations can adapt to individual differences in users’ characteristics, abilities, and preferences. It investigated interactive visualization aids and used eye-tracking data with computational modeling to infer human factors that influence how users process visual information during complex visual analysis tasks. The findings support adaptive, personalized visualization systems that provide appropriate assistance based on each user’s characteristics and behavior.

Sample publications Towards Adaptive Information Visualization Computational Methods to Infer Human Factors

Past research project

Misleading Visualizations and Human-AI Synergy

This research investigated why people are susceptible to misleading data visualizations and how AI can reduce misinterpretation. It examined how visualization literacy, user characteristics, graph design, and device context affect comprehension, while comparing human and AI performance to determine when personalized assistance is most valuable. The work advances context-aware support that helps users recognize manipulation and interpret visual evidence accurately and confidently.

Sample publication Humans and AI against Misleading Information Visualizations (in press)

Full publication list

Book chapter

Steichen, B. Computational Methods to Infer Human Factors for Adaptation and Personalization Using Eye Tracking. A Human-Centered Perspective of Intelligent Personalized Environments and Systems, Human-Computer Interaction Series, Springer, 2024.

Journal, conference, and workshop articles

Steichen, B., Isaacs, M., To, S., Tran, K., and Varela, D. Humans and AI against Misinformation-Understanding the Role of User Characteristics on Susceptibility to Misleading Visualizations & the Potential of AI Assistance. SYNERGY 2026, co-located with AVI 2026, in press, 2026.
Steichen, B., Mahadevappa, S., Gershman, M., Geronimo, M., Kermad, A., Stoehr, K., and Figueira, S. Math-Language Minigames-Helping Bilingual Children Learn Mathematics by Closing the Language Gap. International Workshop on Designing Advanced Interfaces for Learning and Teaching, co-located with AVI 2026, 2026.
Mahadevappa, S., Dasari, W. W., Nagalapatti, G., Steichen, B., and Figueira, S. MathTarea: Mathematics Homework Support for Bilingual Students. IDC ’26, pp. 1179-1185, 2026.
Steichen, B., Ling, C., and Figueira, S. Multilingual News Search-A Comparative User Study of Desktop and Mobile Interfaces. International Journal of Human-Computer Interaction, 40(19), pp. 5648-5663, 2024.
Steichen, B., Kosasih, W., and Becerra, C. Inferring Search User Language Proficiency from Eye Gaze Data. CHIIR 2022, pp. 211-220, 2022.
Fu, B., and Steichen, B. Impending Success or Failure? An Investigation of Gaze-Based User Predictions During Interaction with Ontology Visualizations. AVI 2022, Article 7, pp. 1-9, 2022.
Fu, B., and Steichen, B. Supporting User-Centered Ontology Visualization: Predictive Analytics Using Eye Gaze to Enhance Human-Ontology Interaction. International Journal of Intelligent Information and Database Systems, 15(1), 2022.
Steichen, B., and Lowe, R. How Do Multilingual Users Search? An Investigation of Query and Result List Language Choices. Journal of the Association for Information Science and Technology, 72(6), pp. 759-776, 2021.
Steichen, B., and Fu, B. Cognitive Style and Information Visualization-Modeling Users Through Eye Gaze Data. Frontiers in Computer Science, 2, 49, 2020.
Steichen, B., Castillo, C., and Scroggins, K. Personalized Multilingual Search-Predicting Search Result List Language Preferences. UMAP 2020, pp. 343-347, 2020.
Steichen, B., Fu, B., and Nguyen, T. Inferring Cognitive Style from Eye Gaze Behavior During Information Visualization Usage. UMAP 2020, pp. 348-352, 2020.
Fu, B., Steichen, B., and McBride, A. Tumbling to Succeed: A Predictive Analysis of User Success in Interactive Ontology Visualization. WIMS ’20, 2020.
Ling, C., Steichen, B., and Figueira, S. Multilingual News-An Investigation of Consumption, Querying, and Search Result Selection Behaviors. International Journal of Human-Computer Interaction, 36(6), 2019.
Steichen, B., and Fu, B. Towards Adaptive Information Visualization-A Study of Information Visualization Aids and the Role of User Cognitive Style. Frontiers in Artificial Intelligence, 2, 22, 2019.
Fu, B., Steichen, B., and Zhang, W. Towards Adaptive Ontology Visualization-Predicting User Success from Behavioral Data. International Journal of Semantic Computing, 13(4), 2019.
Fu, B., and Steichen, B. Using Behavior Data to Predict User Success in Ontology Class Mapping-An Application of Machine Learning in Interaction Analysis. ICSC 2019, pp. 216-223, Best Paper nominee, 2019.
Ling, C., Steichen, B., and Choulos, A. A Comparative User Study of Interactive Multilingual Search Interfaces. CHIIR ’18, pp. 211-220, 2018.

Conference posters

Gadon, A., Steichen, B., and Leon, J. Bridging the Online Language Gap with Polyvox. Southern California Conferences for Undergraduate Research, 2019.
Castillo, C., Scroggins, K., and Steichen, B. Supporting Multilingual Users Through Personalized Multilingual Search. Southern California Conferences for Undergraduate Research, 2019.
Daroy, P., and Steichen, B. Personalized Information Visualization Through the Use of Adaptive Overlays. Southern California Conferences for Undergraduate Research, 2019.
Castillo, C., Chen, K., and Steichen, B. Towards Multilingual Search-Gathering Multilingual Query Logs Using Crowdsourcing. Southern California Conferences for Undergraduate Research, 2018.
Pierre-Louis, D., Arima, J., and Steichen, B. Cookies for Breakfast, Lunch, and Dinner: Designing a GDPR-Compliant Accountability Tool for Web Services and Web Apps. Southern California Conferences for Undergraduate Research, 2018.

hapii people

Meet the faculty, students, and alumni who shape hapii through their research, collaboration, and contributions to the lab.

Faculty

Dr. Ben Steichen

Dr. Ben Steichen is an Associate Professor in the Computer Science Department at California State Polytechnic University, Pomona (CPP). He received his PhD from Trinity College Dublin (TCD), Ireland, an M.Sc. degree in Computer Science from TCD, and a First Class Honours B.Sc. degree in Computing Sciences from the University of East Anglia (UEA), UK.

Current students

Kenneth Ang

Undergraduate Student

Khine Zar Hein

Undergraduate Student

Lucia Iniguez

Undergraduate Student

Anthony Penaloza

Undergraduate Student

Ayla Santos

Undergraduate Student

Jack Schoebel

Undergraduate Student

Khoi Tran

Graduate Student

Trang Tran

Graduate Student

Yong Thu La Wong

Undergraduate Student

Alumni

Kyle Ah-Tye

Graduate Student

Ethan Bautista

Undergraduate Student

Christian Becerra

Graduate Student

Carla Castillo

Undergraduate Student

Kailie Chang

Graduate Student

Siva Charan Mallena

Graduate Student

Wei Hsuan Chen

Undergraduate Student

Caleb Chung

Undergraduate Student

Nate Colbert

Undergraduate Student

Yennhi Dang

Graduate Student

Patriz Daroy

Undergraduate Student

Isabel DeLaPaz

Undergraduate Student

Huy Doan

Graduate Student

Angel Gadon

Undergraduate Student

Alex Garcia

Graduate Student

Diego Garcia

Undergraduate Student

Miguel Geronimo

Undergraduate Student

Maribel Gershman

Graduate Student

Tony Gonzalez

Undergraduate Student

Rachel Goodrich

Undergraduate Student

Daeyoung Hwang

Undergraduate Student

Maddie Isaacs

Undergraduate Student

Brad Kim

Undergraduate Student

Wilsen Kosasih

Graduate Student

Chi Le

Undergraduate Student

Jinjing Lee

Undergraduate Student

Jesus Leon

Undergraduate Student

Rachel Lewis

Undergraduate Student

Chenjun Ling

Graduate Student

Antonio Loyola

Undergraduate Student

Raymond Luc

Graduate Student

Michelle Paino

Undergraduate Student

Manson Pham

Undergraduate Student

Danielle Makiling

Undergraduate Student

Colin Mcgough

Undergraduate Student

Nana Mimura

Undergraduate Student

Rucha Nandgirikar

Graduate Student

Peter Nguyen

Undergraduate Student

Thoa Nguyen

Undergraduate Student

Todd Nguyen

Graduate Student

Bryan Orellana de la Cruz

Undergraduate Student

Deep Parekh

Undergraduate Student

Nicholas Piazza

Undergraduate Student

Dimitri Pierre-Louis

Undergraduate Student

Jay Savla

Undergraduate Student

Kevin Scroggins

Undergraduate Student

William Sosropartono

Graduate Student

Keerthi Sreeram

Undergraduate Student

Camryn Sumabat

Undergraduate Student

Kayla Tang

Undergraduate Student

Sarah To

Undergraduate Student

Eli Tolentino

Undergraduate Student

Shannon Trinh

Undergraduate Student

Kelly Tsai

Graduate Student

Damian A. Varela

Undergraduate Student

Moina Vernon

Graduate Student

Evelyn Vu

Undergraduate Student

John West

Undergraduate Student

Alysia Wong

Undergraduate Student

Milush Yanev

Graduate Student

Eli Zupke

Undergraduate Student

hapii lab

The lab is equipped with state-of-the-art tools for user experience research and data analysis, including eye-tracking systems and high-performance computing resources. These capabilities allow us to conduct in-depth studies of natural user behavior and experience.

contact hapii

If you want to learn more about the lab, or if you are interested in working with us, please reach out!

Department of Computer Science, Cal Poly Pomona
3801 West Temple Avenue, Pomona, CA 91768

Building 8, Room 21

bsteichen@cpp.edu