Candidate for Bachelor's of Mathematics student @ UWaterloo specializing in Applied Mathematics - Scientific Machine Learning and Statistics, with a minor in Computing. Building ML-powered financial analysis tools using Python, Classical ML, and Deep Learning techniques, and real market data. Focused on making sophisticated data science accessible through practical applications.
Over the next year, I’m building and shipping products that reflect how people actually experience finance, learning, and connection—not just how they look in theory.
Part 1 — Navigating Finance with ML
My journey starts with understanding money at the most personal level.
PlainCents
focuses on day-to-day financial behavior—spending, habits,
and patterns. From there,
RiskFecta
steps into uncertainty, risk,
and decision-making under volatility using modern portfolio theory and forecasting.
The path ends with an AI-powered financial education platform , designed
to help Canadian newcomers build confidence navigating an unfamiliar financial system.
Together, these projects move from personal insight → institutional tools → accessible education.
Part 2 — Wanderers (Coming Soon)
Wanderers is an offline-first social discovery platform built around a simple belief:
meaningful connections don’t come from infinite scrolling.
The goal is to create low-pressure pathways for students to meet in the real world—through
shared curiosity, exploration, and spontaneity. This project reflects my interest in
building technology that nudges people off their screens, not deeper into them.
And because I love challenges and strategic thinking, I am also building a neural network-driven chess engine—purely to push myself and learn how machines reason in complex decision spaces. Check out VectorMate.
Curious to collaborate or learn more? Get in touch or Connect on LinkedIn.
I'm an Applied Mathematics student at UWaterloo specializing in Scientific Machine Learning, with minors in Statistics and Computing.
With access to Bloomberg Terminal, I'm bridging the gap between institutional-grade financial data and accessible ML applications. My goal: democratize sophisticated financial analysis by building tools that anyone can use, not just those who can afford $24K/year professional platforms.
I'm not just learning ML theory—I'm shipping real products that solve real problems.
English, Hindi, Tamil, Marathi, French (elementary proficiency)
Toronto, Canada
Innovation, Entrepreneurship, Finance, Soccer, Chess, Martial Arts, Content Creation
Relevant Coursework: Calculus I, II, Linear Algebra, Data Structures, Probability, Algorithm Design, Financial Statements
Specialization Focus: Machine Learning theory, Statistical modeling, Scientific computing, Numerical optimization
Applied Skills: Building ML models from scratch, Financial data analysis with Bloomberg Terminal, Mathematical modeling for real-world problems
Research-focused ML project modeling volatility contagion across U.S. equity markets using temporal graph neural networks to capture dynamic inter-asset dependencies.
Built a production-grade AI tutoring system for Canadian financial certifications (CSC, IFIC), transforming early prototypes into scalable RAG pipelines powering contextual exam question generation and tutor responses.
Coordinating corporate partnership outreach for Canada's largest student-run data science hackathon (CxC 2026) for 300+ participants, building strategic relationships with industry leaders.
Led educational initiatives combining STEAM for young learners and fostered project-based learning and mentoring.
Established and led a student-driven coding club focused on preparing for Waterloo CCC coding competition, collaborative learning and creative projects.
Kapil made a strong contribution to our AI education project, demonstrating both technical depth and professional maturity. He independently owned full-stack features across the ML and application layers, strengthened system reliability, and maintained a clear, well-documented development process. His ability to combine AI modeling, prompt engineering, and backend workflows meaningfully advanced the project from prototype to production-ready.
What if managing your finances was as easy as uploading a CSV?
How do institutional investors optimize portfolios? Now retail investors can too.
Can a neural network learn to play chess like an expert? Watch it think.
k22iyer@uwaterloo.ca
kapiliyer29@gmail.com
+1 (647) 248-2756
Waterloo, Canada