Multi-Period Portfolio Optimization using Sample Average Approximation

Used Stochastic Optimization- based Sample Average Approximation algorithms for Multi-period Portfolio Optimization with the goal of saving $200K within 10 years using the S&P 500 dataset. Sensitivity towards parameters was studied.

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Decentralized Optimal Control of Connected and Automated Vehicles

Used nonlinear programming and pseudo-spectral algorithms to optimally control CAVS for 3 cost functions - time, fuel, & mixed. Analyzed the cost decentralization by comparing the loss in loss in efefficiency between individual & cumulative optima.

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Testing the effectiveness of naive optimization of the egress path for an active-shooter scenario

A novel simulation-proven evacuation strategy in active-shooter scenarios that minimizes evacuees' causalities by 56% and injuries by 52%.

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Modeling and Simulation of Optimal Egress algorithm for an active-shooter scenario

As part of bachelor's thesis, I designed a expected utility-maximization based algorithm that optimizes risk and reward thereby increasing evacuation safety in active-shooter scenarios.

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Markov Chain Monte Carlo: Implementation and Analysis

Implemented and Studied the Sampling performance of the Algorithm. Metropolis-Hastings. Performed a Sensitivity study to measure the effect of parameters such as step size, total steps on the run time, convergence and acceptance ratio.

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Control Algorithm for landing a 2-D rocket

Designed a control algortihm for landing a 2-D rocket and evaluated its trajectory tracking.

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Synthetic-to-Real Domain Shift Error: A case study in 2-D object detection

Developed a pair of 2-D object detectors and cross-tested them across different domains to quantify domain-shift error.

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Tomato crop disease classification using pre-trained deep learning algorithm

AlexNet and VGG16 deep learning architectures were used for tomato crop disease classification with an accuracy of 96.16%

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