Rural 21 The International Journal for Rural Development published an article co-authored by Concern Worldwide’s Cecilia Benda and Feinstein’s Anastasia Marshak. The article discusses how to build resilience among rural…
Alexander Liss’ research interests include multivariate statistical data analysis and machine learning for time series and longitudinal data (including GLM regression, deep learning neural networks and non-parametric boosted decision tree), and their application to disease surveillance, exposure assessment, forecasting, and decision support frameworks. His expertise comprises Satellite Remote Sensing and GIS, econometric and financial forensics, business valuation and cost-benefit analysis, digital signal processing and network modeling, and other quantitative topics.
In addition to his academic interests, Alexander has extensive industry experience. For example, he has developed models for readmission risk prediction for healthcare organizations, crop yield forecasting, statistical anomaly (fraud) detection, and biometric identification algorithms and software for secure documents.
Alexander has a PhD from Tufts University, an MBA, Summa Cum Laude, from Babson College, and a M.Sc. from St. Petersburg University.