Edward Valachovic, Ph.D., M.A., M.S., is an Assistant Professor at the Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, State University of New York. Prior to joining the UAlbany SPH, Dr. Valachovic was the Program Manager for Research and Data of the Washington State Sentencing Guidelines Commission and afterward a Senior Research Associate at the Washington State Center for Court Research within the Administrative Office of the Courts investigating courtroom operations and criminal justice statistics. He is a triple alumnus of the University of Albany, earning a Master of Arts in Mathematics from the College of Arts and Sciences, followed by a Master of Science and then Doctorate in Biometry and Statistics at the School of Public Health. He earned his undergraduate degree from Union College in Schenectady, New York, with a double major in Mathematics and Economics.
The overarching goal of Dr. Valachovic's research program is the simultaneous advancement of statistical theory and analysis methods, particularly in the field of spatiotemporal time series analysis, and the application of these and general statistical methods to epidemiology, public health, and other external fields of research. Some of his research interests include the investigation, development, extension, and demonstration of spatiotemporal analysis frequency separation, time series decomposition, missing data multiple imputation, forecasting, and resampling of time series data, as well as applications to cancer epidemiology, pollution, and environmental determinants of health.
- Mathematical statistics
- Time series and spatio-temporal analysis
- Spectral analysis
- Computational statistics
- Higher dimensional data analysis
- Applications to cancer epidemiology, pollution and environmental determinants of health
Learn more about Dr. Valachovic's work:
- Introduction to Theory of Statistics
- Analysis of Categorial Data
- Methods of Data Analysis
- Introduction to Bayesian Inference
- Principals of Statistical Inference