With over 10 years of experience in financial analyses, data analytics and consulting, and with master’s degrees in Statistics and Economics, and a Ph.D. my research focuses on Mathematical Statistics. Thus, my education and experiences are geared towards a career in artificial intelligence, machine learning, financial analyses, economic analyses, statistical analyses, predictive modeling, big data and data analytics in general. These communication and technical skills along with relevant work and educational experiences give me the versatility and skills necessary to work with highly qualified academics, managers and leaders in all sectors of industry, to streamline deliverables, target and focus efforts to increase efficiency, and add value to quantitative teams in a multitude of ways.
My research interest are varied from Bayesian Methodologies of Generalized Linear Models; Model Evaluation; Discrete Choice; Artificial Intelligence and Machine Learning; Outlier Detection; Efficient Frequentist and Bayesian Optimization Algorithms for Large-scale Data; Nonparametric Bayes; Non-convex Optimization; Dynamical Systems in Markets; Complex Networks; Mathematical Modeling; Utility Theory and Stochastic Choice Under Uncertainty; Algorithmic Game Theory; Variational Methods; Image Processing; and Graph Theory. Broadly these interests span Statistics, Probability Theory, Econometrics, Computing in Mathematics, Natural Science, Engineering and Medicine, Computing in Social science, Arts and Humanities. The overarching theme amongst these disciplines and interest is a research methodology based on robust, replicable scientific inference.
The website is organized into pages. Please navigate by clicking on them accordingly. Also, I am constantly updating and upgrading the research, teaching, blog and other aspects of the website. So please reach out should you have any questions regarding the content. I am eager to engage with you in a lively discussion to enhance and improve all aspects of research, to improve human knowledge.
Duke's Research Website 