About Me
Bio
I am a data scientist at CommonSpirit Health, working on designing statistical and machine learning models for various healthcare applications, including medical device classification, demand forecasting, and inventory management and optimization.
I completed my PhD in statistics in 2022 under the direction of Dr. Marina Vannucci at Rice University, and my undergraduate degree at California Institute of Technology.
My dissertation work, “Bayesian State-Space Models with Variable Selection for Neural Count Data”, contributes to research in epilepsy by introducing novel Bayesian methods for count data and applying them to real seizure data to address various issues in epilepsy.
Research Interests
My broad research interests are in:
- Bayesian inference
- Biomedical and healthcare informatics
- Forecasting and time series analysis
- Variable selection
- NLP
Publications
- Wang, E.T., Vannucci, M., Haneef, Z., Moss, R., Rao, V.R. and Chiang, S. (2022). A Bayesian switching linear dynamical system for estimating seizure chronotypes. Proceedings of the National Academy of Sciences, 119(46), e2200822119.6.
- Wang, E.T., Chiang, S., Cleboski, S., Rao, V.R., Vannucci, M., Haneef, Z. (2022). Seizure count forecasting to aid diagnostic testing in epilepsy. Epilepsia, 63(12), 3156-3167.
- Wang, E.T., Chiang, S., Haneef, Z., Rao, V.R., Moss, R., Vannucci, M. (2023). Bayesian non-homogeneous hidden Markov model with variable selection for investigating drivers of seizure risk cycling. Annals of Applied Statistics, 17(1), 333-356.
- Chiang, S., Khambhati, A.N., Wang, E.T., Vannucci, M., Chang, E.F., Rao, V.R. (2021). Evidence of state-dependence in the effectiveness of responsive neurostimulation for seizure modulation. Brain Stimulation, 14(2), 366-375.
Contact Info
Work Address:
CommonSpirit Health, Denver Office
198 Inverness Drive W
Englewood, CO 80112, USA
Work E-mail: emily.wang@commonspirit.org
Personal E-mail: emily.ting.wang@gmail.com
Phone: (469)-396-3543