I have been fortunate to work for a lot of amazing researchers, who have helped me grow as a researcher.
I have been fortunate to work for a lot of amazing researchers, who have helped me grow as a researcher.
This was the first project where I had to do actual software development, and I learned a lot from it. However, the main takeaways from this project came from the weekly meeting with all co-authors, where I got to understand how top-quality public policy research works. This project helped solidify my passion for using mathematical modeling for public policy.
For this project, I:
Simplified the SEIR model used for finding the optimal lockdown targeting for pandemic response by reducing the number of SEIR groups and age groups, working primarily with Python.
Collaborated to identify a scaling factor that maintained consistent model dynamics across different age group partitions.
This was the first time (outside of courses) I encountered mathematical proofs, complexity theory, and machine learning theory. This project helped me understand that I am interested in recommender systems and would like to contribute more to this field.
For this project, I:
Conducted an in-depth literature review on Matrix Completion, diversity in recommender systems, and limitations of recommender systems, summarizing key research findings for weekly discussions.
Prepared brief summaries of selected papers, providing the professor with clear overviews to streamline discussions.
Explained complex mathematical proofs to the professor during weekly meetings, facilitating a deeper understanding of theoretical aspects.
Independently identified and explored relevant papers, ensuring alignment with the research goals through regular feedback.
I was a research assistant for Prof. T. T. Niranjan from June 2022 - May 2023. I mostly worked on behavioral operations projects. I got to learn a lot of new things as this was my first time learning about behavioral research and how it is conducted. I loved my experience and kept on working with Prof. T. T. Niranjan on two projects as a co-author after my tenure as a research assistant.
During my time as a research assistant, I:
analyzed eye-tracking data from 130+ participants using Python for data extraction, cleaning, and modeling, contributing to insights into decision-making processes.
assisted in designing and conducting eye-tracking experiments, gathering both qualitative and quantitative data from 75+ participants.
was responsible for creating all data visualizations and plots used in the paper.
supported hypothesis testing through the application of Generalized Linear Models in R, contributing to statistical analysis for publication.
reviewed drafts to improve readability, provided detailed feedback, and assisted with refining the final manuscript for the paper published in Production and Operations Management.
maintained and developed new features for the beer game website, which is used as a pedagogical tool for operations management/analysis courses at IIT Bombay and Auburn University, among other universities.
I went to Montreal in the summer of 2023 for three months for a research internship as part of the Mitacs Globalink Research Internship. It was my first non-IITB research experience, and it helped me understand how research works in North America. I also got to travel around the beautiful Canada.
I developed and optimized a risk-averse optimization model for a modular off-site construction supply chain using Pyomo and Gurobi. The model aimed to minimize a combination of the inventory cost, transportation cost, and lateness penalty while assembling a modular building in a limited time. The model also considered disruptions relating to the weather, wind, transportation, and worker efficiency.