My research explores how quantitative methods can help us understand complex systems, from the mathematical structures underlying music to the economic and social factors shaping financial inclusion. It is most meaningful to me when it connects abstract models with human experiences and real-world problems.
Quantifying Tonal Stability and Harmonic Motion
Computational musicology · Published in NHSJS
A Tonnetz maps harmonic relationships as a lattice: notes a fifth apart sit side by side, and thirds run along the diagonals. The C major and C minor triads are highlighted.
Music has been part of my life for over a decade, but I have always been curious about more than how it sounds. I wanted to understand the mathematical structures that make music feel stable, surprising or emotionally complex.
Using Python and symbolic MIDI data, I analysed 50 musical excerpts across five categories: Classical, Pop, Rock, Jazz and Stravinsky’s compositions. I combined the Krumhansl–Schmuckler key-finding algorithm with Tonnetz-based representations of harmonic relationships to build a quantitative framework for examining tonal ambiguity and movement.
The project showed me that mathematical modelling need not remove the human element from music. It can reveal new ways to study the structures through which we experience it.
Published in the National High School Journal of Science
CREST Gold Award, British Science Association
Lightning talk accepted at MIT’s URTC 2026
Presented at the Sigma Xi Student Research Showcase
One of 10 speakers at the YYGS Speaker Series, to around 300 participants
Submitted or presented to further audiences, including the Princetonian Conference
Beyond the Interface
Internet Penetration, Human Agency, and Structural Determinants of the Gender Gap in Financial Inclusion Across Indian States
Development economics
Mentored by Dr. Nand Kumar, Professor of Economics, Delhi Technological University
I examined state-level differences in women’s financial inclusion across 22 Indian states and Union Territories, using data from the Reserve Bank of India, the Telecom Regulatory Authority of India and the Ministry of Finance, and interviewed 15 women from digital-literacy programmes. My central argument is that digital infrastructure can create opportunities for inclusion, but individual agency and accessible education influence whether they become financial empowerment.
Regression analysis
Hypothesis testing
Model diagnostics
Field interviews
Published in the International Journal of Scientific Research and Review
Runner-up, Best Presenter, Global Confluence Review International Conference
Presented at the international seminar Women and Child Rights: Legal Perspectives and Challenges, organised by Law Mantra Trust with Maharashtra National Law University, Nagpur
Accepted at the Princetonian Conference, IFERP’s WGL 2026 and the IFoRE conference
Understanding and Simulating Standing Waves
Computational physics
Research internship with Dr. Nishchal Dwivedi, IIT Bombay
I studied how standing waves emerge from the superposition of waves travelling in opposite directions, and how boundary conditions determine nodes, antinodes and allowed frequencies. I then built Python simulations of wave displacement over time, adding Gaussian noise and damping to represent real-world conditions, and compared the results with theoretical predictions.
Python
NumPy
Matplotlib
Numerical methods
Research paper: Understanding and Simulating Standing Waves
Determinants of Foreign Direct Investment Inflows into India
An Empirical Analysis, 1993–2022
Applied economics
Research internship with Dr. Nand Kumar, Delhi Technological University
Using annual data from the World Bank’s World Development Indicators, I examined how GDP, domestic lending interest rates and exchange rates relate to FDI inflows in the short and long run, with careful attention to stationarity, residual behaviour and model stability.
OLS regression
ADF unit-root tests
ARDL bounds testing
Granger causality
GDP identified as the most consistent long-run determinant of FDI
Examined evidence of a mutually reinforcing relationship between exports and FDI
Extended Project Qualification
To what extent can machine learning algorithms improve the accuracy and efficiency of real-time fraud detection in credit card and bank account transactions?
Machine learning and finance
Jumeirah College
My EPQ examines the technical, economic and ethical dimensions of machine-learning-based fraud detection, evaluating the trade-offs between accuracy, speed, cost and fairness in real-world financial systems.
Machine learning
Technical analysis
Economic analysis
Ethical analysis
EPQ awarded A*
What Connects My Research
Across music, physics, economics and digital inclusion, each project has followed the same process: identifying a question, translating it into a framework that can be investigated, testing ideas against evidence, and considering what the findings reveal.
Mathematics and expression
Computational music research showed me how mathematics can illuminate creative expression.
Access and agency
My work on DigiSakhi showed how empirical analysis can reveal the gap between access and agency.
Modelling complex systems
Physics and economics taught me to approach complex systems through modelling, diagnostics and careful interpretation.