Nathan Giovanni has a general background in statistical and quantitative methods. He has over 6 years’ experience teaching and mentoring students at all levels in statistical methods. He has counseled students on statistical methods for thesis research in subjects like biostatistics, engineering, and finance. He has over 3 years of research project experience, implementing statistical methods for academic, corporate, and national laboratory clients. He has advised university department heads on statistics and data analytics curriculum.
His software experience involves Excel, LaTeX, Python, R, and SAS.
Nathan has conducted research in several technical areas. His masters’ research was in heavy-tailed distributions with applications to noisy time series data. The subject of his Ph.D. thesis was optimal transportation and its applications to statistical algorithms and analysis. He has implemented an unsupervised latent clustering algorithm for product defect identification for a corporate client. He has conducted research on fitting network meta-analysis models for evaluating data from several experiments with non-overlapping treatment groups
On a project for a major national laboratory, using optimal transportation-based techniques, he has devised highly flexible and scalable methods for combining experimental data into a consensus model. He has advised an engineering professor on fitting and evaluating ANOVA models for oil and gas exploration, and he has advised an engineering department head on incorporating data science and statistics into a modernized curriculum. Currently, he is starting a role at an analytics consulting company and working toward publishing journal articles from his Ph.D. research.
In greater detail, some of his areas of background include: descriptive statistics, hypothesis testing, confidence & credible intervals, power calculations, sampling theory, Bayesian statistics, linear & nonlinear regression models, ANOVA models, experimental design, network meta-analysis, time series, probability theory, decision theory, computational statistics, heuristic methods, decision tree methods, neural networks, clustering & classification, optimal transportation, and data visualization.
“Nathan helped me get the hang of Bayesian stats for my research. He helped me choose the right prior for my analysis and explained how to do Bayesian confidence intervals. He was also helpful guiding me through interpretation in a Bayesian setting.”
Nathan’s help was valuable on my dissertation research. He pointed out inconsistencies in the data, and how to deal with missing data. This step saved me a lot of time on my analysis. He also guided me on how to interpret the random and fixed effects in my model.
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