I have studied and implemented recent advances in high dimensional machine learning, functional statistical modeling, high dimensional multivariate modeling, risk modeling, portfolio management, derivatives valuation. My broad and deep knowledge of the machine learning, econometrics and mathematical finance fields has afforded me the ability to utilize multiple methods to further generalize time series modeling; to rigorously criticize diagnostic results; and to fully model marginal processes, dependency structure and the characteristics of high dimensional portfolios. I have utilized these methods in conjunction with recent advances in "stylized facts robust" regularized regression/classification, time series motif/ discord discovery learning, wavelet/state space based hedging, cross-sectional factor construction, composite risk based position sizing and tail based hierarchical clustering to increase predictive power and extract non-spurious alpha whilst reducing path dependent draw-down risk and path independent tail risk.