University of Toronto

I have gone to University of Toronto for my graduate degree from 2017 to 2018.

I earned my degree in Master of Engineering (M.ENG) in Mechanical and Industrial Engineering with Emphasis in Analytics.

I have received 3.8 CGPA. My courses and projects are shown below.

Courses Taken

Mechanical Engineering

Computational Fluid Mechanics (MIE 1210)
Applied CFD (MIE 1214)
Multiphase Flows (MIE 1222)
Wind Power (MIE 1240)
Energy Management (MIE 1241)

Industrial Engineering

Financial Engineering (APS 502)
Computational Finance and Risk Management (MIE 1622)
Introduction to Healthcare (MIE 1623)
Introduction to Data Science (MIE 1624)
Financial Engineering 2
Quality Control for Engineering Management (APS 1040)

include: Data Science and Analytics Projects  Python 3 with iPython Notebook used with Numpy, Scipy, Pandas, NLTK, and Scikit-learn  Obtained data, web-scraping, data cleaning, exploratory analysis using APIs  Modelled using regression and different machine learning algorithms (logistic, KNN, SVM, Random Forests, N-B) with hyper parameter tuning and cross validation  Projects: Sentimental Analysis of Tweets during presidential election, Prediction of Income Brackets Computational Finance and Risk Management Projects  MATLAB was used with CPLEX and IPOPT  Used modern portfolio theory (Markowitz model) to construct and analyze efficient frontier (with or without short selling and/or risk-free asset) and numerous portfolios such as min variance, max return, max sharpe ratio, equal risk contribution, and robust portfolios using CPLEX from IBM for linear problems and IPOPT for non-linear problems  Modelled and analyzed a credit-risky portfolio of corporate bonds with 100 counterparties by calculating creditworthiness, losses, and VaR/CVaR using Monte Carlo simulations and Cholesky decomposition  Comparison of European options pricing, with knock in barrier, using Black-Scholes formula and Monte Carlo simulations using Geometric Brownian Motion Computational Fluid Dynamics and Heat Transfer Projects  Python 3 was used with Numpy and Scipy  Coded to solve numerous complex fluid mechanics and heat transfer problems, such as lid driven cavity, back step, Couette, fully developed channel flows using Navier-Stokes Equation  Discretized using Finite Volume Method – method used in ANYSYS Fluent CFD  Coded for three different scenarios: diffusion only, advection-diffusion, and 2D incompressible N-S using SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithm