Summary

Danica Xiao is a seasoned AI leader with over a decade of experience driving AI transformation and translating cutting-edge research into real-world impact across healthcare, legal, product intelligence, and technology sectors. She has led AI organizations at both global enterprises and high-growth startups, consistently focusing on responsible, scalable innovation that delivers meaningful outcomes for patients, customers and end users.

In her current role as Vice President of AI at GE Healthcare, Danica leads a global team of over 90 scientists and engineers. Under her leadership, the team has launched a suite of generative AI (GenAI), large language model (LLM), and agentic AI solutions, contributing to measurable improvements in revenue, operational efficiency, and team productivity across GEHC’s business units.

Beyond industry, Danica is an active contributor to the AI research community. She has authored more than 160 peer-reviewed papers (12000+ citation), co-written a widely adopted textbook on deep learning for healthcare, and was recognized by Baidu as a Top Chinese Female Scholar in AI. She is deeply committed to cultivating collaborative, high-performing teams and ensuring AI strategy aligns with real-world impact for patients, customers, and business. Danica holds a Ph.D. in machine learning from the University of Washington, Seattle.

News

1. [Nature Scientific Data] Our paper "TrialBench: Multi-Modal AI-Ready Datasets for Clinical Trial Prediction" has been accepted for publication by Nature Scientific Data!

2. [ICLR 2025] Our paper "Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval" has been accepted by ICLR 2025!

3. [NAACL 2025] We've got one paper accepted by NAACL 2025 Main conference, and two papers accepted by NAACL Findings!

4. [AAAI 2025] Our paper "Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations" has been accepted by AAAI 2025!

5. [NeurIPS 2024] Our paper "Knowledge Graph Fine-Tuning Upon Open-World Knowledge from Large Language Models" has been accepted by NeurIPS 2024!

6. [EMNLP 2024] We've got one paper accepted by EMNLP Main conference, and two papers accepted by EMNLP Findings!

7. [KDD 2024] Our paper on "Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models" has been accpted by KDD 2024!

8. [ACL 2024] Our paper on "Unity in Diversity: Collaborative Pre-training Across Multimodal Medical Sources" has been accepted by ACL 2024!

9. [ICML 2024] Our paper on federated conformal prediction that address both data privacy and data quality issues for predictive models especially for healthcare tasks has been accepted by ICML 2024!

10. [IJCAI 2024] Our survey paper on AI for healthcare has been accepted by IJCAI 2024!

11. [IJCAI 2024] Our foundation framework on scaling medical tabular data predictors via data consolidation, enrichment, and refinement using LLM has been accepted by IJCAI 2024!

12. [NAACL 2024] Two papers accepted by NAACL 2024!

13. [ICLR 2024] Our paper "GraphCare: enhancing healthcare predictions with personalized knowledge graphs" has been accepted by ICLR 2024!

14. [AAAI 2024] Our paper "ConSequence: Synthesizing Sequences for Electronic Health Record Generation" has been accepted at the 38th AAAI Conference on Artificial Intelligence (AAAI-24).

15. [EMNLP 2023] Our paper "AutoTrial: Prompting Language Models for Clinical Trial Design" got accepted by The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023).

16. [Nature Communications 2023] Our paper "Synthesize high-dimensional longitudinal electronic health records via hierarchical autoregressive language model" got published in Nature Communications!

17. [ACM BCB' 2023] Three papers accepted by The 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB) 2023.

18. [KDD 2023] Our paper "MedLink: De-Identified Patient Health Record Linkage" is accepted by KDD 2023.

19. [ICML 2023] Our paper "Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control" is accepted by ICML 2023.

20. Named by Baidu as Top Chinese Female Scholars in Artificial Intelligence, March, 2023

21. [Nature Chemical Biology 2022] Nov 2022, our paper on Artificial Intelligence Foundation for Therapeutic Science was published on Nature Chemical Biology.

22. [NeurIPS 2022] Sept 2022, our paper on augmented tensor decomposition to adapt data construction toward downstream tasks is accepted by NeurIPS 2022.

 Awards

1. The Top 50 Women Leaders of Washington for 2024, Women We Admire, 2024

2. Top Chinese Female Scholars in Artificial Intelligence, Baidu, 2023

3. Top Chinese Young Scholars in Artificial Intelligence”, Baidu, 2022 

4. Best paper published in 2018 in “AI in Health”. IMIA Yearbook on Medical Informatics, 2019.

5. First runner-up for IEEE-TASE best paper of 2019, 2019

6. Manager's Choice Award, IBM Research, 2018

7. Winner of the 2016 Parkinson's Disease PPMI Data Challenge, Michael. J. Fox Foundation, 2016

8. Third Place of National IIE-CIS mHealth App Competition, IISE, 2016

9. Outstanding Female Award, Society of Women Engineers (SWE), 2015-2016

10. GSFEI Top Scholar Award, University of Washington, Seattle, 2012-2014

11. Spring Research Scholarship, American Statistical Association/Society for Industrial and Applied Mathematics, Chicago IL, 2016

Research Interest

1. ML/DL for user behavioral data modeling

2. ML/DL for marketing cohort targeting and product recommendation

3. ML/DL for online experimentation based on user data

4. knowledge graph and graph inference for SaaS solutions

5. Auto-ML for scalable SaaS model serving 

6. ML for scalable and automatic customer success monitoring and business growth