Name:	Emily Wang
Position:	Statistics PhD Student
Institution:	Rice University
My Work:	Research Interests: Bayesian modeling and inference, variable selection, state-space models, time series, predictive modeling and analytics, machine learning, and deep learning. With applications in: Biomedical informatics, electronic health records, clinical texts, seizure risk assessment and prevention. Publications: 1. Wang, E.T., Chiang, S., Haneef, Z., Rao, V.R., Moss, R., Vannucci, M. A Bayesian switching linear dynamical system for estimating seizure chronotypes. Proceedings of the National Academy of Sciences. 2. Wang, E.T., Chiang, S., Cleboski, S., Rao, V.R., Vannucci, M., Haneef, Z. Seizure count forecasting to aid diagnostic testing in epilepsy. Epilepsia. 3. Wang, E.T., Chiang, S., Haneef, Z., Rao, V.R., Moss, R., Vannucci, M. Bayesian non-homogeneous hidden Markov model with variable selection for investigating drivers of seizure risk cycling. Annals of Applied Statistics. 4. 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.
Publications:	(1) A Bayesian switching linear dynamical system for estimating seizure chronotypes PMID 36343269 (2) Seizure count forecasting to aid diagnostic testing in epilepsy. PMID 36149301