Hello there! Thanks for stopping by.
I'm Hongjin (鸿瑾), a Ph.D. candidate in Computer Science at Harvard University. I am very fortunate to have Krzysztof Gajos as my primary advisor and Catherine D'Ignazio and Kentaro Toyama on my committee. I admire their work and who they are as people.
My research lies at the intersection of AI and social justice, through both qualitative critical evaluation and technology development. I am currently analyzing funding for AI for Good initiatives, drawing on feminist epistemology. I am an affiliate of the Data + Feminism Lab at MIT, part of the Harvard Climate Leaders Program cohort 2024 - 2025, and the World Economic Forum Global Shapers community since 2024.
I was born and raised in Guangzhou, a lush subtropical city in China. I moved to the US for my undergraduate degree in Mathematics and Computer Science at Occidental College. I received my master’s degree in data science from the London School of Economics and Political Science. Before Harvard, I worked as a research fellow at Stanford Law School with Dan E. Ho, where I developed and evaluated Machine Learning systems to help reduce water pollution, in partnership with the EPA.
I am deeply passionate about social impact. Outside of research, I have completed projects with nonprofits in China, the US, the UK, and Malawi and worked as a data for development intern at UNDP. When I am away from my computer, I love to spend time in nature, with communities, dance, run, do yoga, and play music. I recently completed a 200-hour yoga training program in Indonesia and love to teach yoga informally!
Always happy to chat about research, grad school, passion projects, life journeys, and anything else! Feel free to drop me an email.
Sunrise at Mount Ijen, 2022
Research Values
Like many in academia, I grapple with imposter syndrome and could feel disheartened by traditional metrics like the h-index. Inspired by brilliant thinkers like Radhika Nagpal, I want to define success on my own terms, guided by a set of core values rather than simplified numbers. I would love to learn about yours!
Relationship-Driven
Research is a team sport. I see every project and collaboration as not just an intellectual pursuit but an opportunity to forge lasting friendships and connections with fellow thinkers.
Joy-Led
Research is a marathon and keeping the fire alive is hard. I prioritize projects that spark joy in the daily grind, making the journey as rewarding as the destination.
Wellbeing-First
As a yoga practitioner with a love for nature and community, I make physical and mental wellness a priority, knowing it's what fuels my best work.
Climate-Aware
We’re living in a climate crisis. I aim to channel my energy and resources into projects that actively contribute to a sustainable future for the planet.
Work in progress
[Under review] Hongjin Lin, Anna Kawakami, Catherine D’Ignazio, Kenneth Holstein, Krzysztof Z. Gajos. Funding AI for Good: A Call for Meaningful Engagement.
[Under review] Vishal Sharma, Hongjin Lin, Asra Sakeen Wani, Jared Katzman, Anupriya Tuli, Naveena Karusala, Shaowen Bardzell, Christoph Becker, Martin Tomitsch, Neha Kumar. Advancing Post-growth HCI.
[Under review] Amelia Lee Doğan, Hongjin Lin, Lindah Kotut. "Down to Earth": Design Considerations for Sustainable AI from the Environmental and Climate Movement.
Publications
Please see my Google Scholar page for the most up-to-date list: https://scholar.google.com/citations?user=46y1bUEAAAAJ&hl=en
[CSCW 2024] Hongjin Lin, Naveena Karusala, Chinasa T. Okolo, Catherine D’Ignazio, and Krzysztof Z. Gajos. 2024. “Come to us first”: Centering Community Organizations in Artificial Intelligence for Social Good Partnerships. Proc. ACM Hum.-Comput. Interact. 8, CSCW2, Article 470 (November 2024), 28 pages. Preprint; Paper
[EAAMO 2024] Chinasa T. Okolo and Hongjin Lin. 2024. Explainable AI in Practice: Practitioner Perspectives on AI for Social Good and User Engagement in the Global South. ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), San Luis Potosí, Mexico. Preprint; Paper
[ASSETS 2024] Hongjin Lin, Tessa Han, Krzysztof Z. Gajos, and Anoopum S. Gupta. 2024. Hevelius Report: Visualizing Web-Based Mobility Test Data For Clinical Decision and Learning Support. In The 26th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS ’24), October 27–30, 2024, St. John’s, NL, Canada. ACM, New York, NY, USA, 12 pages. Preprint; Paper
[CHI 2024] Chinasa T. Okolo and Hongjin Lin. 2024. "You can’t build what you don’t understand": Practitioner Perspectives on Explainable AI in the Global South. In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems (CHI EA '24). Association for Computing Machinery, New York, NY, USA, Article 11, 1–10. Paper
[ICML 2023] Dan Ley, Leonard Tang, Matthew Nazari, Hongjin Lin, Suraj Srinivas, Himabindu Lakkaraju. 2023. Consistent Explanations in the Face of Model Indeterminacy via Ensembling. Workshop on Interpretable Machine Learning in Healthcare at ICML 2023, Honolulu, HI, United States. Preprint; Paper
[AAAI 2023] Hongjin Lin*, Matthew Nazari*, and Derek Zheng*. PCTreeS—3D Point Cloud Tree Species Classification Using Airborne LiDAR Images. Workshop on Artificial Intelligence for Socal Good At AAAI 2023, Washington DC, United States. Feb 14th, 2023. Paper
[FAccT 2021] Elinor Benami, Reid Whitaker, Vincent La, Hongjin Lin, Brandon R. Anderson, and Dan E. Ho. The Distributive Effects of Risk Prediction in Environmental Compliance: Algorithmic Design, Environmental Justice, and Public Policy. In Toronto 2021: ACM FAccT. Paper
[Master Thesis 2019] Hongjin Lin*, Qiang Ha*, and Pablo Barbera. Fake and True News on Twitter During the 2016 U.S. Presidential Election. MS in Data Science thesis at the London School of Economics and Political Science. Sept 2019. (Manuscript available upon request)
Workshops and Events
[FAccT 2024] CRAFT session: Centering communities’ visions of success: a data feminist approach to impact evaluation of cross-sector partnerships. Organizers: Hongjin Lin, Dasha Pruss, Pablo Nunes, Thallita Lima, Chinasa T. Okolo, Helena Suárez Val, Alessandra Jungs de Almeida, Isadora Araujo Cruxen, Lauren Klein, Catherine D’Ignazio
Abstract: There has been little proven tangible benefit to the communities involved in cross-sector partnerships aimed at developing AI technologies for social applications. In this bridge-building workshop, we bring together community organizations, activists, technologists, and academics to discuss and co-design impact evaluation guidelines that prioritize the objectives of local communities and align with their visions of success. Grounding our discussions in the intersectional data feminism framework, we highlight principles of challenging power, elevating emotion and embodiment, rethinking binaries and hierarchies, embracing pluralism, considering context, and making labor visible. Beginning with a brief presentation to establish a common understanding, participants will then delve into small group discussions and engage in hands-on brainstorming activities. Post-workshop, organizers will consolidate participants’ proposals and outstanding open questions and share them with the broader FAccT community.
[AAAI 2023] Co-organized the Women's Mentoring Lunch with Paula Rodríguez-Díaz and Elizabeth Bondi-Kelly
Teaching and Mentorship
[Pre-Concentration Advising] Katherine Alderete ('28), Mark Li ('28)
[Teaching Fellowship] CS 276 Design, Technology, and Social Impact at Harvard University (2024 Spring) by Professor Krzysztof Gajos.