- Worked at Estimation, Tracking and Fusion Research Laboratory1- Modelling and Generation of Soft Data in Kinematic Scenarios ° Demonstrated extensive computer programming by developing a software in C++ for generating soft data ° Designed the software based on object-oriented programming ° Used Standard Template Library(STL) in different part of the project ° Modelled and generated soft measurements from soft sensors ° Showed ability to analyze data, solve problems and implement solutions- Data Science and Machine Learning Projects: Customer Churn analysis for a Telecommunication Company ° Cleaned and preprocessed data ° Wrote a helper function in Python to apply multiple supervised learning algorithm ° Applied upsampling method to make the unbalanced data balance ° Applied ensemble/stacking method to increase AUC score Unsupervised learning algorithm for customer segmentation ° Applie natural logarithm technique for feature scaling ° Applied PCA transformations and clustering algorithms to segment the transformed customer data ° Applied Gaussian Mixture Model clustering algorithm to identify the various customer segments hidden in the data

° Teaching assistance for Signal and Systems and Introduction to Control Systems° Supervised labs which were designed to help students learn the practical aspects of the course° Held office hours to answer the students’ questions° Graded the assignments, quizzes, lab reports, midterm and final exams