Suqi Liu

I am an Assistant Professor in the Department of Statistics at the University of California, Riverside. Previously, I was a Postdoctoral Research Fellow in Biomedical Informatics at Harvard University. I received my Ph.D. from Princeton University. My research interests span the theoretical foundations and practical applications of probability, statistics, machine learning, and data science. Specifically, I focus on complex structured health data, including biomedical networks, genomic sequences, and clinical notes.

Education

• Ph.D. in Operations Research and Financial Engineering, Princeton University, 2016–2022.

• M.S. in Computer Science, University of California, San Diego, 2013–2016.

• B.S. in Mathematics and Physics, Tsinghua University, 2009–2013.

Publications

Preprints

• Representation Learning to Advance Multi-institutional Studies with Electronic Health Record Data
  Doudou Zhou, Han Tong, Linshanshan Wang, Suqi Liu, Xin Xiong, Ziming Gan, Romain Griffier, Boris Hejblum, Yun-Chung Liu, Chuan Hong, Clara-Lea Bonzel, Tianrun Cai, Kevin Pan, Yuk-Lam Ho, Lauren Costa, Vidul A. Panickan, J. Michael Gaziano, Kenneth Mandl, Vianney Jouhet, Rodolphe Thiebaut, Zongqi Xia, Kelly Cho, Katherine Liao, and Tianxi Cai
  arXiv:2502.08547

• Representation-Enhanced Neural Knowledge Integration with Application to Large-Scale Medical Ontology Learning

Suqi Liu, Tianxi Cai, and Xiaoou Li

arXiv:2410.07454

Journal papers

• A probabilistic view of latent space graphs and phase transitions
  Suqi Liu and Miklos Z. Racz
  Bernoulli 29 (3), 2417-2441 (2023).
  arXiv:2110.15886

• Phase transition in noisy high-dimensional random geometric graphs
  Suqi Liu and Miklos Z. Racz
  Electronic Journal of Statistics 17 (2), 3512-3574 (2023).
  arXiv:2103.15249

Conference and workshop papers

• UniBioEmbed: Heterogeneous Multi-Modal Knowledge Graph for Unified Biomedical Concept Embeddings

Qizhao Chen, Suqi Liu, Jun Wen, Doris Yang, Hongyi Yuan, Alexandre Pereira, and Tianxi Cai

Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB) 2025

• Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning

Hongyi Yuan, Suqi Liu, Zongxin Yang, Kelly Cho, Katherine P. Liao, Alexandre Pereira, and Tianxi Cai

ICML 2025 Workshop on Multi-modal Foundation Models and Large Language Models for Life Sciences (FM4LS) 2025

arXiv:2410.10144

• Perfect Recovery for Random Geometric Graph Matching with Shallow Graph Neural Networks
  Suqi Liu and Morgane Austern
  Artificial Intelligence and Statistics (AISTATS) 2025
  arXiv:2402.07340

• Modeling Advertiser Bidding Behaviors in Google Sponsored Search with a Mirror Attention Mechanism
  Suqi Liu, Liang Liu, Sugato Basu, and Jean-Francois Crespo
  AdKDD 2019

• Who is .com? Learning to Parse WHOIS Records
  Suqi Liu, Ian Foster, Stefan Savage, Geoffrey M. Voelker, and Lawrence K. Saul
  Internet Measurement Conference (IMC) 2015

Theses

• Geometry of Random Graphs
  Ph.D. thesis, Princeton University, May 2022.
  Advisor: Miklos Z. Racz
  Committee: Miklos Z. Racz (Chair), Mykhaylo Shkolnikov, and Ramon van Handel

• Max-Margin Sum-Product Networks
  Diploma thesis, Tsinghua University, June 2013.
  Advisor: Jun Zhu

Talks

• Neural Knowledge Integration from Large-Scale Multimodal Health Data, Digital Health Faculty Candidate Colloquium, Rice, February 19, 2025.

• Neural Knowledge Integration from Large-Scale Multimodal Health Data, Statistics Department Seminar Series, Michigan, January 14, 2025.

• Neural Knowledge Integration from Large-Scale Multimodal Health Data, Applied & Computational Mathematics Seminar, Dartmouth, January 7, 2025.

• Random Geometric Graph Matching with Graph Neural Networks. INFORMS Annual Meeting, October 23, 2024.

• Representation-Enhanced Neural Knowledge Integration. DBMI Science Day, Harvard, September 12, 2024.

• Random Geometric Graph Matching with Graph Neural Networks. Joint Statistical Meetings, August 7, 2024.

• Random Geometric Graph Matching with Graph Neural Networks. IDEAL Workshop on Learning in Networks: Discovering Hidden Structure, April 10, 2024.

• Multimodal Representation Learning of Clinical Concepts and Genetic Variants. MVP Science Meeting, U.S. Department of Veterans Affairs, October 30, 2023.

• Phase transitions in soft random geometric graphs. Graduate Student Seminar, PACM, Princeton, March 15, 2022.

• Phase transitions in soft random geometric graphs. Stochastics Seminar, School of Mathematics, Georgia Tech, January 13, 2022.

• A probabilistic view of latent space graphs and phase transitions. Northeast Probability Seminar, November 18, 2021. [video] [slides]

• Phase transition in noisy high-dimensional random geometric graphs. Columbia–Princeton Probability Day, May 7, 2021. [video] [slides]

• Learning to parse WHOIS records. Internet Measurement Conference, Oct 30, 2015. [slides]

Teaching

• Instructor, STAT 255S (Topics in Applied Statistics: Machine Learning for Health), UCR, Fall 2025.

• Assistant in Instruction, ORF 309 (Probability and Stochastic Systems), Princeton, Spring 2022.

• Assistant in Instruction, ORF 526 (Probability Theory), Princeton, Fall 2018, Fall 2019, Fall 2020.

• Assistant in Instruction, ORF 387 (Networks), Princeton, Spring 2020, Spring 2021.

• Assistant in Instruction, ORF 350 (Analysis of Big Data), Princeton, Fall 2017, Spring 2019.

• Assistant in Instruction, ORF 307 (Optimization), Princeton, Spring 2018.

• Teaching Assistant, CSE 250A (Principles of Artificial Intelligence), UCSD, Spring 2016.

• Teaching Assistant, CSE 150A (Introduction to Artificial Intelligence), UCSD, Winter 2016.