Reshmi Ghosh


Ph.D. Applied Scientist, Microsoft (MSAI)

   ABOUT   ACADEMICS   PROJECTSReviewing Activities


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:

  1. Introduction to Deep Learning (Ph.D. level)
  2. Decision Analytics for Business and Policy (Operations Research Math)
  3. Practical Data Science
  4. Applied Machine Learning
  5. Applied Data Analysis
  6. Data Analytics for Engineered Systems
  7. Ph.D. Microeconomics
  8. Investment Planning and Pricing
  9. Energy Policy and Economics
  10. Risk Analytics
  11. Business Intelligence and Data Mining
  12. Python for Developers
  13. Theory and Applications of Policy Analysis
  14. Graduate Probability and Statistics
  15. Multi-Criteria Decision Making
  16. Inroduction to Sustainable Engineering
  17. Data Warehousing

Teaching Experience

  1. [April 2021] Guest lecturer for practical uses of MATLAB, and linear alegbra for a graduate level class, College of Engineering, CMU.
  2. [October 2020] Held recitation class on the theory behind Convolutional Neural Networks, forward pass and backpropogation, and its application to real world image classification problems, School of Computer Science, CMU.
  3. [October 2019] Conducted a graduate level class on introductory microeconomics, College of Engineering, CMU.
  4. [January 2019] Conducted a undergraduate level class on Applied Statistics methods, and Statistical Inference, College of Engineering, CMU.
  5. [October 2018] Conducted a graduate level class on “Best practices for Data Analytics and Visualizations, College of Engineering, CMU.
  6. [October 2018] Conducted a graduate level class on Monte Carlo simulation, and use of probabilistic techniques to model risk, College of Engineering, CMU.