Summary

Cao (Danica) Xiao is the VP of AI at GE Healthcare.  At GE Healthcare, she is leading the AI science and AI engineering teams in developing medical imaging foundation models, large language models for patient care and operational efficiencies, as well as responsible AI in GE Healthcare’s AI transformation. Previously she has held leadership positions at multiple technology and healthcare companies, including VP of AI/ML at Relativity, Senior Director of Machine Learning of Amplitude, Global Head of Machine Learning of IQVIA, and research lead of MIT-IBM Watson AI Lab at IBM Research. She has successfully driven the creation of machine learning innovation for industry AI transformation, particularly in healthcare and medicine. Besides, she is a passionate machine learning researcher and thought leader with over 140+ highly cited papers (citation 8725, h-index 40, i10-index 82) published in leading AI/ML venues, with strong focus on the topic of AI for healthcare (EHR analysis, RWE, clinical trials, health monitoring, medical imaging, and drug discovery ). She also co-authored a textbook on deep learning for healthcare which is used in top CS graduate programs such as UIUC, GaTech, and PSU. Recently she was named by the largest Chinese internet company Baidu as “Top Chinese Young Scholars in Artificial Intelligence” in 2022 and “Top Chinese Female Scholars in Artificial Intelligence” in 2023. Danica got her Ph.D. degree in machine learning from University of Washington, Seattle in 2016.

News

1. [ICML 24] 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!

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

3. [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!

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

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

6. [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).

7. [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).

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

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

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

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

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

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

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

15. [COLING 2022] Sept 2022, our paper on clinical language simplification is accepted by COLING 2022.

16. Named by Baidu as Top Chinese Young Scholars in Artificial Intelligence, May, 2022

17. [ICLR 22] Jan 2022, our paper on Differentiable scaffolding tree for molecular optimization is accepted by ICLR 2022.

18. [WWW 22] Jan 2022, our paper on real-time population-level disease prediction is accepted by WWW 2022

19. [Cell Patterns] Jan 2022, our paper on clinical trial outcome prediction was accepted by Cell Patterns.

20. [AAAI 22] Dec 2021, 1 paper accepted by AAAI 2022, on control the overall prediction risk of classification with rejection options.

21. [Cell Patterns] Nov 2021, our paper on "Machine Learning Applications for Therapeutic Tasks with Genomics Data" was published on Cell Patterns.

22. [NeurIPS 21] Our paper on Therapeutics Data Commons was accepted by 2021 Conference on Neural Information Processing Systems (NeurIPS).

23. [KDD 21] Our tutorial titled "Advances in Mining Heterogeneous Healthcare Data" is accepted by KDD 2021.

24. [KDD 21] May 2021, 2 papers are accepted by KDD 2021. One on tensor decomposition for noisy data completion, the other on drug discovery.

25. [ACL 21] One paper is accepted by Findings of ACL 2021 on automated ICD coding.

26. [IJCAI 21] April 2021, 2 papers on recommendations are accepted by IJCAI 2021

27. [WWW 21] Jan 2021, 3 papers are accepted by WWW 2021.

 Awards

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

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

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

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

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

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

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

8. 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