I'm an incoming Digital Innovation Intern at Dow, where I'll be working on data-driven process optimization. As a PhD student, I leverage mathematical and computational tools to tackle complex real-world optimization and decision problems in Networked Dynamical Systems. My core skillsets include Algorithm Design, Operations Research & AI, Statistics & Data Science, with domain expertise in Network Science. Apart from research, I enjoy reading, writing, hiking music, and working out.
‍
I'd love to hear from you! Please feel free to reach out to me here
April 17 - May 1 2024: Participating in NSF's Mid-South I-CORPS program representing ASTERS Inc
February 23-25 2024: Recognized for Outstanding Participation at the DHSÂ Designing Actionable Solutions for a Secure Homeland (DASSH) 2024 Student Design Challenge
November 3 2023: Accepted an offer for the role of Digital Innovation Intern at Dow Chemical foe Summer 2024
October 25 2023: Our proposal won a grant from the Department of Homeland Security
May 20 2023: Earned an MS and Graduate Certificate in Data-Driven Decision-Making en route to PhD.
February 13 2023: "The effectiveness of naive optimization of the egress path for an active-shooter scenario" is published in Heliyon.
‍
December 12 2022: Defended my research proposal titled "Analysis and Optimization of Influence in Networked Systems" for the PhD Qualifying Exam.
May 20 2023: Earned an MS en route to PhD
‍
February 13 2023: "The effectiveness of naive optimization of the egress path for an active-shooter scenario" is published in Heliyon.
‍
December 12 2022: Defended my research proposal titled "Analysis and Optimization of Influence in Networked Systems" for the PhD Qualifying Exam.
November 7-8 2022: Presented our research and a live demo at the NSF Cyber-Physical Systems Principal Investigators Meeting 2022 in Washington DC.
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.
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.
A novel simulation-proven evacuation strategy in active-shooter scenarios that minimizes evacuees' causalities by 56% and injuries by 52%.
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.
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.
Designed a control algortihm for landing a 2-D rocket and evaluated its trajectory tracking.
Developed a pair of 2-D object detectors and cross-tested them across different domains to quantify domain-shift error.
AlexNet and VGG16 deep learning architectures were used for tomato crop disease classification with an accuracy of 96.16%