- Ph.D. in Statistics, Purdue University University, West Lafayette, IN
- M.S. in Statistics, Renmin University of China, Beijing, P.R. China
- B.S. in Statistics, Renmin University of China, Beijing, P.R. China
Associate Director -- Real-World Evidence and Digital Health, Biogen Aug, 2021 - present
Research Staff Member, IBM Research Jul 2015 - Aug 2021
- - Technical Lead for IBM-CHDI collaboration on Huntington's Disease Progression Modeling
- - Lead for Disease Progression Modeling Research Challenge
- - Co-Chair, IBM Health Informatics Professional Interest Community
Postdoctoral Researcher, IBM Research Aug 2014 - Jul 2015
- Amrita Mohan, Zhaonan Sun, Soumya Ghosh, Ying Li, Swati Sathe, Jianying Hu, Cristina Sampaio. (2022) A Machine‐Learning Derived Huntington's Disease Progression Model: Insights for Clinical Trial Design. Movement Disorder. 37(3): 553-562.
- Bum Chul Kwon, Vibha Anand, Kristen A Severson, Soumya Ghosh, Zhaonan Sun, Brigitte I Frohnert, Markus Lundgren, Kenney Ng. (2020) DPVis: Visual analytics with hidden markov models for disease progression pathways. IEEE transactions on visualization and computer graphics. doi: https://doi.org/10.1109/TVCG.2020.2985689
- Bin Liu, Ying Li, Soumya Ghosh, Zhaonan Sun, Kenney Ng, Jianying Hu. (2019) Complication risk profiling in diabetes care: A bayesian multi-task and feature relationship learning approach. IEEE Transactions on Knowledge and Data Engineering. 32 (7), 1276-1289.
- Zhaonan Sun, Soumya Ghosh, Ying Li, Yu Cheng, Amrita Mohan, Cristina Sampaio, Jianying Hu. (2019) A probabilistic disease progression modeling approach and its application to integrated Huntington’s disease observational data. Journal of American Medical Informatics Association Open. 1(1), 123-130.
- Zach Shahn, Ying Li, Zhaonan Sun, Amrita Mohan, Cristina Sampaio, Jianying Hu. (2019) G-Computation and Hierarchical Models for Estimating Multiple Causal Effects From Observational Disease Registries With Irregular Visits. AMIA Summits on Translational Science Proceedings. 789.
- Sandra Liu, Jie Chen, Zhaonan Sun, Yu Zhu. (2018) From good to great: nonlinear improvement of healthcare service. International Journal of Pharmaceutical and Healthcare Marketing. 12(4), 391-408.
- Jaehee Shim, Zhaonan Sun, Amos Cahan. (2018) Patient specific Vancomycin Dose Recommendation with Baseline Information at the Time of the First Dose. Journal of Pharmacokinetics and Pharmacodynamics. 45, S48-S49.
- Bin Liu, Ying Li, Zhaonan Sun, Soumya Ghosh, and Kenney Ng. (2018): Early Prediction of Diabetes Complications from Electronic Health Records: A Multi-task Survival Analysis Approach. AAAI Conference on Artificial Intelligence(AAAI). 32 (1).
- Zhengping Che, Yu Cheng, Shuangfei Zhai, Zhaonan Sun, and Yan Liu. (2017): Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records. The IEEE International Conference on Data Mining(ICDM). 787-792.
- Xiang Li, Zhaonan Sun, Xin Du, Haifeng Liu, Gang Hu, and Guotong Xie. (2017): Bootstrap-based Feature Selection to Balance Model Discrimination and Predictor Significance: A Study of Stroke Prediction in Atrial Fibrillation. American Medical Informatics Association Annual Symposium(AMIA). 1130-1139.
- Zhaonan Sun, Ying Li, Soumya Ghosh, Yu Cheng, Amrita Mohan, Cristina Sampaio, and Jianying Hu. (2017): A Data-Driven Method for Generating Robust Symptom Onset Indicators in Huntington's Disease Registry Data. American Medical Informatics Association Annual Symposium(AMIA). 1635-1644.
- Zhaonan Sun, Ying Li, Soumya Ghosh, Yu Cheng, Amrita Mohan, Cristina Sampaio, and Jianying Hu. (2017): Exploring Factors that Contribute to Missing Values in Observational Huntington's Disease Study Data. American Medical Informatics Association Summit on Clinical Research Informatics(AMIA). Abstract.
- Soumya Ghosh, Zhaonan Sun Ying Li, Yu Cheng, Amrita Mohan, Cristina Sampaio, and Jianying Hu. (2017): An Exploration of Latent Structure in Observational Huntington's Disease Studies. American Medical Informatics Association Summit on Clinical Research Informatics(AMIA).
- Zhengping Che, Yu Cheng, Zhaonan Sun and Yan Liu. (2016): Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature Embedding. Neural Information Processing Systems Foundation(NIPS) ML4HC Workshop.
- Soumya Ghosh, Yu Cheng and Zhaonan Sun. (2016): Deep State Space Models for Computational Phenotyping. IEEE International Conference on Health Informatics(ICHI). 399-402.
- Ying Li, Ping zhang, Zhaonan Sun and Jianying Hu. (2016): Data-Driven Prediction of Beneficial Drug Combinations in Spontaneous Reporting Systems. American Medical Informatics Association Summit on Clinical Research Informatics(AMIA).
- Zhaonan Sun, Xu Liu, Ping Zhang, Jianying Hu, Juan Wisnivesky, and Fei Wang. (2016): Joint Modeling of Survival Events through Multi-task Learning Framework. American Medical Informatics Association Summit on Clinical Research Informatics(AMIA), Abstract.
- Zhaonan Sun, Yu Cheng, Amos Cahan, Fei Wang and Jianying Hu. (2016): Modelling the Progression of CKD with EMR Data: a Partially Hidden Markov Approach. American Medical Informatics Association Summit on Clinical Research Informatics(AMIA), Abstract.
- Ben Li, Zhaonan Sun, Qing He, Yu Zhu and Zhaohui Qin. (2015): Bayesian inference with historical data-based informative priors improves detection of differentially expressed genes. Bioinformatics. 32(5): 682-689.
- Zhaonan Sun, Fei Wang and Jianying Hu. (2015): LINKAGE: An Approach for Comprehensive Risk Prediction for Care Management. 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). Pages 1145-1154.
- Ping Zhang, Zhaonan Sun , Fei Wang and Jianying Hu (2015):Towards Computational Drug Repositioning: A Comparative Study of Single-task and Multi-task LearningAmerican Medical Informatics Association Annual Symposium (AMIA), Abstract.
- Yan Jiang, Jane Frankenberger, Laura Bowling and Zhaonan Sun. (2014): Quantification of Uncertainty in Estimated Nitrate-N Loads in Agricultural Watersheds. Journal of Hydrology. 59:A. Pages 106-116.
- Zhaonan Sun, Thomas Kuczek and Yu Zhu. (2014): Statistical calibration for qRT-PCR, microarray and RNA-Seq expression data with measurement error models. The Annals of Applied Statistics. 8:2. Pages 1022-1044.
- Zhaonan Sun, Han Wu, Zhaohui Qin and Yu Zhu. (2013): Model-Based Methods for Transcript Expression Level Quantification in RNA-Seq in Advances in Statistical Bioinformatics: Models and Integrative Inference for High-Throughput Data. edited by Do, K-A., Qin, S. and Vannucci, M. Cambridge University Press.
- Zhaonan Sun and Yu Zhu. (2012): Systematic Comparison of RNA-Seq Normalization Methods Using Measurement Error Models. Bioinformatics. 28:20. Pages 2584-2591.
- S. V. N. Vishwanathan, Zhaonan Sun, Nawanol Theera-Ampornpunt and Manik Varma. (2010): Multiple Kernel Learning and the SMO Algorithm. Neural Information Processing Systems Foundation (NIPS).
Pages 3311-3325
- Xi Wang, Xing Wang and Zhaonan Sun (2009): Comparison on confidence bands of decision boundary between SVM and Logistic Regression. Proceedings of fifth international joint conference on INC, IMS and IDC.
- Ying Li, Ping Zhang, Zhaonan Sun and Jianying Hu. Data-Driven Prediction of Drug Combinations That Mitigate Adverse Drug Reaction. United States Patent Application US 15/257535
- Xiang Li,Zhaonan Sun, Haifeng Liu, Jingjing Tao, Gang Hu and Guotong Xie. A Method to Identify Risk Factors with Both Predictive Power and Statistical Significance. ChinesePatent Application CN920160040.
- Zhaonan Sun, Fei Wang and Jianying Hu. A Method For Proactive Comprehensive Geriatric Risk Screening. United States Patent Application US 15/048413
- Zhaonan Sun, Ping Zhang, Fei Wang and Jianying Hu. Evidence Boosting In Rational Drug Design And Indication Expansion By Leveraging Disease Association. United States Patent Application 14/929995
- 2021, 16th Annual HD Therapeutics Conference, Palm Spring, CA. DSI - A Disease Status Index for Huntington's Disease.
- 2021, IBM Research Got Science Seminar, Yorktown Heights, NY. Disease Progression Modeling.
- 2019, 14th Annual HD Therapeutics Conference, Palm Spring, CA. Multi-modal HD progression model with clinical and morphometric data.
- 2019, Colloquium Seminars at Columbia Biostatistics, New York, NY. Disease Progression Modeling with Large-Scale Observational Data in Huntington's Disease.
- 2018, International Chinese Statistical Association Applied Statistics Symposium, New Brunswick, NJ. Disease Progression Modeling with Large-Scale Observational Data.
- 2018, 1st Enroll-HD Congress , Quebec City, Quebec. Understanding Huntington's Disease Progression: A Probabilistic Modeling Approach.
- 2017, American Medical Informatics Association Annual Symposium , Washington, DC. A Method for Generating Robust Symptom Onset Indicators in Huntington's Disease Registry Data.
- 2017, New England Statistics Symposium , Storrs, CT. Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature Embedding.
- 2017, Joint Summits of American Medical Informatics Association , San Francisco, CA. Exploring Factors that Contribute to Missing Values in Observational Huntington's Disease Study Data.
- 2016, The Second Statistical Forum on Huntington's Disease , Princeton, NJ. Machine learning for disease progression models.
- 2015, American Medical Informatics Association Annual Symposium, San Francisco, CA. A graph based methodology for temporal signature identification from EHR.
- 2015, Joint Statistical Meetings, Seattle, WA. Comprehensive risk prediction using interactive graph-guided fussed Lasso penalty.
- 2015, IBM Research Health Informatics PICs , Yorktown Heights, NY. Multi-task learning approach for comprehensive risk prediction.
- 2014, Eastern North American Region Meetings, Baltimore, MD. Statistical calibration of qRT-PCR, microarray and RNA-Seq gene expression data with measurement error models.
- 2014, Purdue Bioinformatics Seminar, West Lafayette, IN. Statistical calibration of high-throughput gene expression data using measurement error models.
- 2012, Joint Statistical Meetings, San Diego, CA. Differential gene expression pattern analysis using exon-level RNA-Seq data
- 2011, Joint Statistical Meetings, Miami, FL. An integrative approach to comparing and normalizing gene expression data generated from RNA-Seq, Microarray and RT-PCR technologies.
- IBM Research Outstanding Research Achivement Award, 2021
- IBM Research Invention Award, 2020
- IBM Research Outstanding Research Achivement Award, 2019
- IBM Research Outstanding Research Achivement Award, 2018
- IBM Research Invention Award, 2018
- IBM Research Manager's Choice Award, 2016
- IBM Research Manager's Choice Award, 2015
- International Joint Conference on Artificial Intelligence(IJCAI) 2015
- IEEE International Conference on Healthcare Informatics(ICHI) 2015
- KDD 2015 Workshop on "BigCHat: Connected Health at Big Data"
- 1st Workshop on Matrix Computations for Biomedical Informatics 2015
- International Joint Conference on Artificial Intelligence(IJCAI) 2016
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2016
- American Medical Informatics Association Annual Symposium (AMIA) 2016
- IEEE International Conference on Healthcare Informatics(ICHI) 2016
- NIPS Machine Learning for Health (ML4H) workshop 2017
- CM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2018
- CM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2021
- International Conference on Learning Representations (ICLR) 2021
- International Conference on Machine Learning (ICML) 2021