Dr. Nikolay Bliznyuk
Associate Professor, Statistics and Machine Learning
Areas of Specialization:
- Statistical Methodology: Bayesian statistics, computational statistics, computer experiments, high-dimensional data, inverse problems, Monte Carlo methods, spatial and spatio-temporal modeling, statistical machine learning
- Applications: digital health technologies, agricultural, biological/biomedical and environmental applications
Bliznyuk Lab for Applied Statistics in IFAS (UF/IFAS BLAST) focuses on statistical methodology and modern applications motivated by large complex data from environmental and life sciences. Methodological work has three tightly intertwined methodological statistics thrusts: (i) statistical machine learning (ML) for predictive modeling, (ii) Bayesian modeling strategies for integrative informatics, predictive modeling and uncertainty quantification (UQ) and (iii) modeling for dependent data (e.g., spatial, temporal, spatiotemporal).
Major areas of applications include digital health technologies in humans and animals (particularly for high-throughput data produced by modern sensors and their networks), predictive modeling for natural resources optimization (water, nutrients) and smart agriculture, and spatiotemporal modeling for infectious diseases. My research acts as a catalyst for interdisciplinary collaborative research in ABE and IFAS/UF and has a wide-reaching practical impact. It is tightly integrated with graduate teaching and mentorship.
I typically teach 2-3 graduate courses per academic year (all with 100% responsibility) on modern statistical and machine learning methodology, specifically
- STA6703 Statistical Machine Learning,
- STA6348 Bayesian Analysis for ML and UQ,
- STA6709 Spatial Statistics & Hierarchical Modeling for Dependent Data.
My teaching is instrumental to new UF/IFAS Artificial Intelligence (AI) initiatives as it contributes to building sound foundations for AI/ML education and training new generations of interdisciplinary data scientists equipped with critical thinking and integrative informatics skills imperative for lifelong learning in the rapidly evolving modern technological landscape.
In both my research and teaching endeavors, I employ modern analytical and computational technologies to develop cutting-edge statistics and machine learning tools to empower decision-makers, as well as to train a new generation of interdisciplinary data scientists under the ABE umbrella to address pressing challenges that the State of Florida, U.S., and the humankind face in the new millennium.
More information about my research and teaching can be obtained from my personal webpage.
Contact Information
352-294-6734
Office:
120 Frazier Rogers Hall
Mailing Address:
P.O. Box 110570
Gainesville, FL 32611-0570
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Teaching
- STA6709: Spatial Statistics
- STA6703: Statistical Machine Learning
- ABE6933: Applied Statistical Machine Learning
- STA6348: Bayesian Analysis for ML and UQ
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Education
- Ph.D. Operations Research and Information Engineering, Cornell University, 2008
- Publications