Hi, I’m Vaibhav
Mangroliya
Mathematics Student • Quantitative & AI/ML Focus
Mathematics Master’s student who loves solving tough problems, whether it’s pricing options, building ML models, or extracting insights from messy data. With ~2 years in fintech, I bridge the gap between mathematical theory and practical applications.
Get to know me
About Me
My Journey
I am currently pursuing an M.Sc. in Mathematics at the University of Luxembourg, building a strong foundation in the mathematical principles that underpin both quantitative finance and machine learning.
My journey into quantitative work began during my time as a Full Stack Developer at the National Stock Exchange of India Ltd. There, while working with Java, Spring Boot, and database systems, I found myself increasingly drawn to the quantitative side of the business—the models, the pricing logic, the risk frameworks. That curiosity eventually led me to pivot toward a more math-focused career path.
Along the way, I discovered my passion for AI/ML and computational methods—building NLP pipelines, training neural networks, and applying machine learning to real-world problems. Now I sit at the intersection of quantitative finance and AI, which I find incredibly exciting.
Quantitative Finance & Risk
Options pricing & the Greeks, Black-Scholes modeling, implied volatility dynamics, hedging strategies. VaR methodologies, Expected Shortfall (CVaR), GARCH forecasting, and portfolio risk assessment. Credit derivatives (CDS, CDO, CLO) and equity-linked instruments.
Regulatory Frameworks
UCITS regulations (5/10/40 rule, diversification requirements, KIID) and Luxembourg’s CSSF standards.
AI/ML & Computational Methods
Stochastic modeling, time series analysis, LSTM, XGBoost, Monte Carlo simulations, and statistical inference.
What drives me? Honestly, I just love solving hard problems. Give me something messy and complicated, like figuring out how to price a tricky option or stress-test a portfolio, and I'm happy. There's a real satisfaction in finding a clean answer to something that looked like chaos at first.
I'm also a firm believer that teaching sharpens understanding. Through my YouTube channel, I've helped 200,000+ viewers learn Assembly language programming, Engineering Physics, and other technical subjects—proof that if you can explain it simply, you truly understand it.
What I’m Looking For
What I work with
Technical Expertise
Quantitative Finance
Regulatory & Products
Programming & Tools
Machine Learning
Scientific Computing
Data & Visualization
Career path
Work Experience
Intern, ENVISION Unit (LEO Observatory)
Luxembourg Institute of Science and Technology (LIST)
- Python-based environmental data validation, QA/QC pipelines, automated reporting.
Student Assistant, Dept. of Mathematics(Part-time)
University of Luxembourg
- Preparation of technical documents and research materials using LaTeX.
Associate Systems Analyst
National Stock Exchange of India (NSE)
- Regulatory compliance systems (Java/Spring Boot) for 2,700+ listed companies.
- NAV calculation tool (Python, Oracle DB) automating Fair Value hierarchy classification.
- XBRL parsing system transforming unstructured financial data into 23-table SQL schema. 40% error reduction.
Academic background
Education
University of Luxembourg
M.Sc. in Mathematics
Mathematical Modelling & Computational Sciences
09/2024 – Present
Vidyalankar Institute of Technology, India
B.E. in Electronics & Telecommunication
Grade: 1.4/1
08/2018 – 05/2022
Credentials
Certifications
Things I’ve built
Key Projects
VKKM Aegis
- Open-source MCP tool with 22 commands for AI-powered security analysis, submitted to Anthropic's MCP directory.
- Provides automated vulnerability scanning, dependency auditing, and security report generation via Claude.
Fed Rate Hike Impact Analysis
- Quantified differential impact of 2022-2023 Fed hiking cycle on Growth vs Value stocks using DiD regression.
- Implemented EGARCH volatility modeling; found Growth stocks exhibited 3-4x larger abnormal returns around FOMC.
- Built LSTM and XGBoost ensemble for price prediction with VADER sentiment scores.
Finance-Informed Neural Networks for Option Pricing
- Developed a PINN that embeds Black-Scholes PDE constraints directly into the neural network loss function.
- Achieved 99.8% accuracy against analytical solutions with 40x faster inference than finite difference methods.
- Implemented custom automatic differentiation for Greeks computation (Delta, Gamma, Theta).
Agent-Based Market Simulation
- Simulated market dynamics with heterogeneous agents (fundamentalists, chartists, noise traders, institutional).
- Utilized Geometric Brownian Motion and Heston stochastic volatility models.
- Applied Monte Carlo methods to analyze emergent price behaviors and volatility clustering.
WWI Historical Text Causal Graph Builder
- Built NLP pipeline using Hugging Face models to extract cause-effect relationships from 1,500+ WWI documents.
- Constructed cross-document temporal causal chains with graph visualization.
- Applied transformer-based models for semantic understanding and entity extraction.
Quant Lab
Explore my quantitative finance experiments, interactive pricing tools, and research notebooks.
Visit Quant LabTestimonials
What People Say
“Vaibhav consistently stood out as a sharp and dependable professional. He showed a high level of ownership in his work, often handling critical modules with minimal guidance. Beyond his technical skills, Vaibhav is a collaborative team player with a professional attitude. I confidently recommend him for roles that require strong analytical thinking and problem-solving ability.”
Rahil Kamani
National Stock Exchange of India (7.1 yrs exp.)