Ziqi Xu

I am a fourth-year Ph.D. student in Computer Science at Washington University in St. Louis, advised by Prof. Chenyang Lu in the AI for Health Institute. My research focuses on multimodal and trustworthy AI for healthcare, with the goal of supporting clinical decision-making. Prior to my Ph.D., I earned my Bachelor’s degree in Computer Science and Mathematics from WashU, graduating summa cum laude. During my undergraduate studies, I conducted research under the guidance of Prof. Lu on machine learning for healthcare.

Research Interests

My research focuses on developing reliable and deployable AI systems for healthcare. I am particularly interested in the following directions:

  • Multimodal AI for Healthcare: I develop predictive models that integrate wearable sensing data, ecological momentary assessments, structured electronic health records and unstructured clinical notes by leveraging complementary information across modalities and modeling their interactions.
  • Trustworthy and Uncertainty-Aware Machine Learning: I design methods to quantify uncertainty in both traditional models and large language models. My research introduces modality-specific uncertainty estimation and reliability-aware fusion strategies for safer deployment in clinical decision-making.
  • Clinical AI and Real-World Impact: I collaborate with interdisciplinary teams across Anesthesiology, Neurosurgery, and Public Health. My work spans applications including surgical predictions, treatment effect, disease phenotyping, and global health.

News

May 2026

I was invited to present our work “Using AI to Predict Post-surgical Headache Improvement for Chiari I and Syringomyelia” at the 2026 THINK TANK Meeting in San Antonio, TX.

May 2026

I will join Apple as a summer intern in San Diego, working on applied machine learning for display products. I’ll be focusing on leveraging large language models and transformer-based systems to support real-world products.

Apr 2026

My work with Sizhe Wang (first author), CURA: Clinical Uncertainty Risk Alignment for Language Model–Based Risk Prediction, has been accepted to the Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL) 2026 main conference.

Nov 2025

I successfully defended my Ph.D. dissertation proposal on Trustworthy Multimodal AI for Personalized Healthcare!

Mar 2025

I successfully passed my Ph.D. Qualifying Exam!

Aug 2022

I am excited to start my Ph.D. at Washington University in St. Louis, advised by Dr. Chenyang Lu.

Selected projects

IMWUT 2025

Incorporating Uncertainty in Predictive Models Using Mobile Sensing and Clinical Data: A Case Study on Persistent Post-surgical Pain

Ziqi Xu, Jingwen Zhang, Simon Haroutounian, Hanyang Liu, Zihan Cao, Gabrielle Rose Messner, Harutyun B. Alaverdyan, Saivee Ahuja, Rahul Koshy, Joel Hanns, Madelyn Frumkin, Thomas L. Rodebaugh, Chenyang Lu

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Vol. 9, No. 2, Article 58, June 2025 (33 pages)

Presented at ACM UbiComp / ISWC 2025 (Helsinki, Finland).

IMWUT 2024

Predicting Multi-dimensional Surgical Outcomes with Multi-modal Mobile Sensing: A Case Study with Patients Undergoing Lumbar Spine Surgery

Ziqi Xu, Jingwen Zhang, Jacob Greenberg, Madelyn Frumkin, Saad Javeed, Justin K. Zhang, Braeden Benedict, Kathleen Botterbush, Thomas L. Rodebaugh, Wilson Z. Ray, Chenyang Lu

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Vol. 8, No. 2, Article 81, June 2024 (30 pages)

Presented at ACM UbiComp / ISWC 2024 (Melbourne, Australia).

Neurosurgery

Using Artificial Intelligence to Identify Three Presenting Phenotypes of Chiari Type-1 Malformation and Syringomyelia

Vivek Prakash Gupta*, Ziqi Xu* (equal contribution), Jacob K. Greenberg, Jennifer Mae Strahle, Gabriel Haller, Thanda Meehan, Ashley Roberts, David D. Limbrick Jr., Chenyang Lu

Neurosurgery 96(6):1341–1352, June 2025.

Neurosurgery

Preoperative Mobile Health Data Improve Predictions of Recovery From Lumbar Spine Surgery

Jacob K. Greenberg*, Madelyn Frumkin*, Ziqi Xu* (equal contribution), Jingwen Zhang, Saad Javeed, Justin K. Zhang, Braeden Benedict, Kathleen Botterbush, Salim Yakdan, Camilo A. Molina, Brenton H. Pennicooke, Daniel Hafez, John I. Ogunlade, Nicholas Pallotta, Munish C. Gupta, Jacob M. Buchowski, Brian Neuman, Michael Steinmetz, Zoher Ghogawala, Michael P. Kelly, Burel R. Goodin, Jay F. Piccirillo, Thomas L. Rodebaugh, Chenyang Lu, Wilson Z. Ray

Neurosurgery 95(3):617–626, September 2024.

Nature Communications

Patterns of maternal and child health services utilization and associated socioeconomic disparities in sub-Saharan Africa

Claire Najjuuko, Ziqi Xu, Samuel Kizito, Chenyang Lu, Fred M. Ssewamala

Nature Communications vol. 16, Article 7840 (2025). Published 22 August 2025.

ACL 2025

Utilizing Semantic Textual Similarity for Clinical Survey Data Feature Selection

Benjamin C. Warner, Ziqi Xu, Simon Haroutounian, Thomas Kannampallil, Chenyang Lu

Findings of the Association for Computational Linguistics: ACL 2025, pages 502–520, Vienna, Austria, July 2025.

Presented at ACL 2025 (Vienna, Austria).