Ph.D. Applied Scientist, Microsoft (Office of Applied Research)
Along with my ongoing Ph.D. program, I have two other masters’ degrees from Carnegie Mellon University: M.S. in Engineering and Public Policy (Focus: Data Science techniques for Policy Analysis) and M.S. in Civil and Environmental Engineering (Focus: Data Analytics for Engineered Systems)
Inspired by the bright minds of CMU, working on interdisciplinary research, I challenged myself to pursue both my masters’ degree with a focus in data-driven analysis, a unqiue pathway to learn and use machine learning to inform decisions for businesses and policy making.
Currently in my Ph.D. program I am leveraging machine and deep learning techniques to stochastically analyze resource adequacy metrics required to measure the effect of large-scale integration of renewable energy sources in the existing grid.
I am proficient in Python, Pytorch, and SQL, I and familiar with the working of Matlab, and C. I also have experience working with python based Machine Learning and Deep Learning libraries like NumPy, Pandas, Scikit-Learn, SciPy, statsmodels, Seaborn, Plotly, Matplotlib, TensorFlow, etc.
Applying my skills to solve societal problems by leveraging my background in applied statistics, data science, economics, and consulting, this endeavor was only made possible by the courses I undertook at CMU, some of which are: