I'm a Digital Innovation Intern at Dow, with Robotics team in Core R&D. As a PhD student, I design and develop optimization and control algorithms for real-world problems in Networked Dynamical Systems. My core skillsets include Algorithm Design, Operations Research & AI, Statistics & Data Science, with domain expertise in Network Science.
I'm a co-founder of ASTERS Inc - a seed-stage startup spinoff from our research, which is building an AI-powered end-to-end public safety system. Additionally, I'm an active member of the Student Leadership Council of SENTRY - a Dept. of Homeland Security Center of Excellence.
With my diverse background, proven track record, and core competencies, I'm confident I'm uniquely positioned to spearhead innovative solutions to some of the industry's most pressing challenges. Outside of work, I do pro-bono mentorship and give career guidance to young adults. I also love to hike, read, and play the violin.
‍
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%