Summary

I am an AI executive and healthcare technology leader focused on building AI-native platforms and intelligent systems that turn complex, multimodal data into real-world decisions.

Currently, I serve as Vice President of AI at GE HealthCare, where I lead a global organization of scientists, engineers, and technical leaders developing enterprise AI platforms, multimodal foundation models, LLM-powered systems, and agentic AI applications across clinical workflows, medical imaging operations, service, commercial, and enterprise productivity use cases. My work focuses on translating frontier AI into scalable, trustworthy, production-grade systems that can operate in regulated, high-impact environments and create measurable business and customer value.

My career spans executive AI leadership, research-to-product translation, global team building, enterprise AI strategy, and AI transformation across healthcare, life sciences, legal technology, and enterprise software. I have led AI organizations at GE HealthCare, Relativity, Amplitude, IQVIA, and IBM Research / MIT-IBM Watson AI Lab, building platforms and products that apply machine learning, LLMs, multimodal AI, and decision intelligence to real-world workflows.

I am particularly interested in the intersection of AI platforms, agentic systems, multimodal intelligence, healthcare transformation, and responsible deployment. I believe the most important AI opportunities ahead will come from connecting data, workflow, governance, and human expertise into systems that are not only powerful, but also trustworthy, auditable, and usable at scale. In parallel with my industry leadership, I remain active in the AI research community, with 180+ peer-reviewed publications, 15,600+ citations, and a Springer textbook, Introduction to Deep Learning for Healthcare. My work has appeared in leading AI and healthcare venues including NeurIPS, ICML, ICLR, KDD, ACL, AAAI, Nature Communications, Nature Scientific Data, and JAMIA. 

I am passionate about shaping the next generation of AI-native organizations — building the systems, teams, and platforms that turn complex data into trusted decisions, meaningful human impact, and durable value across healthcare, life sciences, and other industries where AI can transform how work gets done.

Recent Publications (2026 & 2025) 

2026

• [KDD 2026] Sicheng Zhou ~Sicheng_Zhou1 , Zaifu Zhan, Lei Wu, Cao Xiao, Parminder Bhatia, Taha Kass-Hout, Rui Zhang. Salient-Q: A Saliency-Guided Vision-Language Framework for Medical Image De-Identification. KDD, 2026.

• [ICML 2026] Aofei Chang, Le Huang, Alex James Boyd, Parminder Bhatia, Taha Kass-Hout, Fenglong Ma, Cao Xiao. MedSIGHT: Towards Grounded Visual Comprehension in Medical Large Vision-Language Models. ICML, 2026.

• [Nature Health 2026] Junyi Gao, Cao Xiao, Lucas Glass, EM Harrison, Jimeng Sun. Matching clinicians with clinical trials using AI. Nature Health, 2026.

• [ICASSP 2026] Sicheng Zhou, Lei Wu, Cao Xiao, Parminder Bhatia, Taha Kass-Hout, Mammodino: Anatomically aware self-supervision for mammographic images, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2026

2025

• [NeurIPS 2025] Yuxin Chang, Alex Boyd, Cao Xiao, Taha Kass-Hout, Parry Bhatia, P Smyth, Deep continuous-time state-space models for marked event sequences, NeurIPS, 2025. (spotlight)

• [NeurIPS 2025] Haoyan Yang, Runxue Bao, Cao Danica Xiao, Jun Ma, Parminder Bhatia, Shangqian Gao, Taha Kass-Hout. Any large language model can be a reliable judge: Debiasing with a reasoning-based bias detector. NeurIPS, 2025.

• [Nature Scientific Data] Tianfan Fu, Jintai Chen, Yaojun Hu, Yingzhou Lu, Yue Wang, Xu Cao, Miao Lin, Hongxia Xu, Jian Wu, Xiao Cao, Jimeng Sun, Lucas Glass, Kexin Huang, and Marinka Zitnik, TrialBench: Multi-Modal AI-Ready Datasets for Clinical Trial Prediction, Nature Scientific Data 2025.

• [NPJ Digital Medicine 2025] Zifeng Wang, Hanyin Wang, Benjamin Danek, Ying Li, Christina Mack, Luk Arbuckle, Devyani Biswal, Hoifung Poon, Yajuan Wang, Pranav Rajpurkar, Cao Xiao, Jimeng Sun,  A perspective for adapting generalist ai to specialized medical ai applications and their challenges, NPJ Digital Medicine 8 (1), 429, 2025

• [Cell Patterns 2025] Brandon Theodorou, Cao Xiao, Lucas Glass, Jimeng Sun, MediSim: Multi-granular simulation for enriching longitudinal, multi-modal electronic health records, Cell Patterns 2025

• [ACL 25] Aofei Chang, Le Huang, Alex James Boyd, Parminder Bhatia, Taha Kass-Hout, Cao Xiao, Fenglong Ma, Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning, ACL 2025.

• [KDD 25] Longchao Da, Rui Wang, Xiaojian Xu, Parminder Bhatia, Taha Kass-Hout, Hua Wei, Cao Xiao, FlanS - A Foundation Model for Free-Form Language-based Segmentation in Medical Images, KDD 2025

• [CVPR 25] Aishik Konwer, Zhijian Yang, Erhan Bas, Cao Xiao, Prateek Prasanna, Parminder Bhatia, Taha Kass-Hout, Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation, CVPR 2025

• [ICLR 25] Pengcheng Jiang, Cao Xiao, Minhao Jiang, Parminder Bhatia, Taha Kass-Hout, Jimeng Sun, Jiawei Han. Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval, ICLR 2025

• [NAACL 25] Shuyang Yu, Runxue Bao, Parminder Bhatia, Taha Kass-Hout, Jiayu Zhou, Cao Xiao. Dynamic Uncertainty Ranking: Enhancing In-Context Learning for Long-Tail Knowledge in LLMs, NAACL 2025

• [NAACL 25] Xiyao Wang, Jiuhai Chen, Zhaoyang Wang, Yuhang Zhou, Yiyang Zhou, Huaxiu Yao, Tianyi Zhou, Tom Goldstein, Parminder Bhatia, Taha Kass-Hout, Furong Huang, Cao Xiao, Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement, NAACL 2025

• [AAAI 25] Pengcheng Jiang, Cao Xiao, Tianfan Fu, Parminder Bhatia, Taha Kass-Hout, Jimeng Sun, Jiawei Han. Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations, AAAI 2025

 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