
Student learning a bit of everything...
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Basic_Hydrogen_Model
April 7, 2026 – Present
Python script for visualizing hydrogen atomic orbitals from the quantum numbers n, l, and m. It generates a 2D slice in the xz-plane at y=0, a probability density plot, and a 3D scatter visualization of the orbital.
View ProjectOption-Pricing-on-Quantum-Computer
March 13, 2026 – Present
A from-scratch implementation of Stamatopoulos et al. (2020). "Option Pricing using Quantum Computers"
View Projecthybrid-qae-monte-carlo-finance
February 15, 2026 – Present
hybrid-qae-monte-carlo-finance — GitHub repository
View ProjectA-Guide-to-Quantum-Computing
December 27, 2025 – Present
Quantum Computing is a complicated topic. I hope my notes finds you well.
View Project2D_Interactive_Spacetime_Simulator
December 7, 2025 – December 7, 2025
This is a repository that runs an interactive 2D simulation of how mass 'curves' spacetime.
View ProjectGravity-Informed-Neural-Network
October 26, 2025 – November 13, 2025
This repository demonstrates how a simple neural network can learn fundamental physics laws using simulated data.
View ProjectRust-BlockChain
November 9, 2024 – December 28, 2024
A Blockchain featuring secure hashing and validation. Will be updated.
View ProjectDNS-Server-in-RUST
November 3, 2024 – November 3, 2024
A simple and efficient DNS Server built in Rust
View ProjectCultural Fit Analysis
The candidate's projects show a strong inclination towards theoretical physics, quantum computing, and novel computational approaches. While this demonstrates intellectual curiosity and a drive for complex problem-solving, the direct alignment with typical enterprise Data Scientist roles (e.g., business analytics, machine learning for product, A/B testing) is not explicitly evident. The project diversity is high in terms of scientific domains but less so in standard industry applications. The lack of professional experience or education details makes it difficult to fully assess cultural fit within a corporate data science team.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.