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Stanford Continuing Studies

Biography

Stanford Continuing Studies

Oleg Melnikov has taught quantitative financial risk management courses at Rice University in Python and in R. He received an MS in computer science from Georgia Institute of Technology, an MS in mathematics from UC Irvine, an MBA from UCLA, and a PhD in statistics from Rice University.

Experienced researcher, educator, and professional with a PhD in Statistics from Rice University. My buzzwords: statistics, R, Python, machine learning, data science, data munging and engineering, SQL, SQL Server, clustering, JMP, dimensionality reduction, regularization, boosting, bagging, random forests, classification, regression trees, ensemble, bootstrapping, multivariate analysis, collaborative filtering, non-negative matrix factorization (NMF), principal component analysis, logistic regression, convolutional neural network, vector autoregressive models, time series, numpy, pandas, scikit-learn, GloVe, word2vec, fastText, BERT, LASER, NLTK, SpaCy, RegEx, OpenCV, OpenAI, fast.ai, natural language processing (NLP/NLU), regression, classification, clustering, predictive analytics,NMF, PCA, SVD, kNN, k-means, bias-variance, Kaggle, data manipulation, visualization, missing data, dimension reduction, feature selection and extraction, time series analysis: regime switching, ARIMA and GARCH families, nonlinear and multivariate, density estimation, kernel smoothing, auto/cross-spectrum and covariance estimation, VAR models, forecasting, bootstrapping, irregular spacing, Numerical solutions: MCMC, optimization, lattice trees, finite differences methods, probability theory, common distributions, parameter estimation, hypothesis testing, ANOVA, Modeling of and forecasting a pollution profile in Houston area via dynamic PCA, regression, non-negative matrix factorization (NMF), GO-GARCH and DCC-GARCH volatility models. Financial modeling: Valuation and of exotic derivatives, bonds, stocks, portfolios, indices via exact computation (like Black-Scholes model), lattice trees, MCMC, finite differencing. Estimation of model-implied volatilities. Modeling risk measures (Greeks, duration, convexity).

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