About me

I am an Assistant Research Professor at Cornell’s Center for Data Science for Enterprise and Society, where I am mentored by Jon Kleinberg, Maryam Fazel, and Sarah Dean. I am affiliated with the Department of Computer Science.

I seek to improve the ability of machine learning models to capture the preferences and identities of minority populations. I work on applications to recommender systems, rank-choice voting, and algorithmic fairness.

I completed my Ph.D. in computer science at Northeastern University, affiliated with the Network Science Institute. I obtained a Bachelor of Science in Engineering from Princeton University.

I have worked as a research-scientist intern at Meta (Central Applied Science and FAIR AI), sociotechnical researcher at Taraaz, and software engineer at Bloomberg LP.

News

[Jun ‘26] Released ranked-choice forecasts of the 2026 Maine primaries.
[May ‘26] Work on data mixing for collaborative filtering accepted to Pluralistic Alignment @ ICML 2026.
[Jan ‘26] Discussed my work on recommender systems on the Data Skeptic podcast.
[Jan ‘26] Paper on power-niche users accepted to WWW ‘26

Publications

	Identifying and Upweighting Power-Niche Users to Mitigate Popularity Bias in Recommendations David Liu, Erik Weis, Moritz Laber, Tina Eliassi-Rad, and Brennan Klein WWW 2026 paper · code
	Bypassing Skip-Gram Negative Sampling: Dimension Regularization as a More Efficient Alternative for Graph Embeddings David Liu, Arjun Seshadri, Tina Eliassi-Rad, and Johan Ugander KDD 2025 paper · code · video
	When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations David Liu, Jackie Baek, and Tina Eliassi-Rad FAccT 2025 paper · code · video
	Group fairness without demographics using social networks David Liu, Virginie Do, Nicolas Usunier, and Maximilian Nickel FAccT 2023 paper · video
	STABLE: Identifying and Mitigating Instability in Embeddings of the Degenerate Core David Liu and Tina Eliassi-Rad SDM 2023 paper · code
	BiaScope: Visual Unfairness Diagnosis for Graph Embeddings Agapi Rissaki, Bruno Scarone, David Liu, Aditeya Pandey, Brennan Klein, Tina Eliassi-Rad, and Michelle A. Borkin VDS 2022 at IEEE VIS paper
	Examining Responsibility and Deliberation in AI Impact Statements and Ethics Reviews David Liu, Priyanka Nanayakkara, Sarah Ariyan Sakha, Grace Abuhamad, Su Lin Blodgett, Nicholas Diakopoulos, Jessica R. Hullman, and Tina Eliassi-Rad AIES 2022 paper · video
	RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity David Liu, Zohair Shafi, William Fleisher, Tina Eliassi-Rad, and Scott Alfeld AIES 2021 paper · code · video
	Successes and struggles with computational reproducibility: Lessons from the Fragile Families Challenge David Liu and Matthew J. Salganik Socius 2019 paper

Public Scholarship

	A Guiding Framework for Vetting Technology Vendors Operating in the Public Sector Cynthia Conti-Cook, Roya Pakzad, Sarah Ariyan Sakha, and David Liu · A research report conducted with Taraaz and the Ford Foundation to assist the public sector in vetting technology vendors.
	A New AI Lexicon: Power David Liu and Sarah Sakha · An essay contribution to AI Now's 2021 A New AI Lexicon project.

Archives of my sports reporting for The Daily Princetonian are available here.

Teaching

DS 4400: Machine Learning and Data Mining I

Undergraduate course at Northeastern University, Spring 2024

David M. Liu

News

Publications

Public Scholarship

Teaching

DS 4400: Machine Learning and Data Mining I