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Nijash Sooriyakumaran

Hello, I'm

Nijash
Sooriyakumaran

ML Researcher · Quantitative Finance · Biomedical Engineering

Graduate researcher at the University of Toronto with dual master's training in Engineering and Financial Mathematics. I build uncertainty-aware ML systems, quantitative models, and optimization algorithms for hard, high-dimensional problems.

Background & Expertise

I am a graduate researcher at the University of Toronto completing my M.A.Sc. in Biomedical Engineering (CGPA 4.0/4.0), with a focus on uncertainty-aware optimization in high-dimensional systems. I also hold an MFM in Financial Mathematics from McMaster University (CGPA 3.9/4.0).

My research spans machine learning, Bayesian optimization, and stochastic modelling — with applications ranging from autonomous scientific experimentation to quantitative finance. I love solving hard problems at the intersection of rigorous mathematics and modern ML.

Previously, I interned at the Royal Bank of Canada where I built deep generative models for AML risk analysis, and at Ciena Corporation as a software developer. I also TA at the Rotman School of Commerce for Risk Analytics.

Technical Skills

Languages
PythonC++MATLABRSQLBash
Frameworks
PyTorchScikit-LearnXGBoostBoTorchOptunaRay TuneAx
Infrastructure
LinuxGitTableauBloomberg TerminalEigen
4.0/4.0
M.A.Sc. CGPA
University of Toronto
17×
Speedup vs naive baseline
Research thesis result
30%
Improved AML detection
RBC Internship
81%
Sharpe ratio improvement
vs XGBoost baseline

Education

Sep 2024 – Expected Sep 2026

Master of Applied Science, Biomedical Engineering

University of Toronto · Toronto, ON
CGPA: 4.0 / 4.0 · IBBME Fellowship
  • Thesis: Uncertainty-aware optimization in high-dimensional systems
  • Built a risk-aware autonomous experimentation algorithm — 17× speedup vs naive, 6× vs XGBoost/MLP baselines
  • TA, Rotman School of Commerce: Risk Analytics (MMA program)
Sep 2023 – Aug 2024

Master of Financial Mathematics

McMaster University · Hamilton, ON
CGPA: 3.9 / 4.0 · Mathematics Graduate Scholarship
  • Relevant courses: Portfolio Theory & Optimization, Risk & Financial Markets, Stochastic Calculus, Deep Learning
Sep 2018 – Apr 2023

Bachelor of Electrical Engineering

McMaster University · Hamilton, ON
  • Thesis: Computational Efficiency of the Weiss-Weinstein Lower Bound for Non-linear Filtering
  • Relevant courses: Partial Differential Equations, Digital Signal Processing, Probability

Professional Experience

RBC

AML Risk Intern

Royal Bank of Canada · Toronto, ON · May – Sep 2024
  • Built Variational Autoencoder sensitivity analysis to assess AML risk across 200+ holdings
  • Created NLP model to identify and flag high-risk / illegal transactions
  • Increased high-risk transaction detection by 30%
CNA

Software Developer Intern

Ciena Corporation · Ottawa, ON · Sep 2021 – Apr 2022
  • Developed Python bindings for ~100 complex C++ libraries for cross-language interoperability
  • Developed and debugged Python unit test suites validating PCEP and Segment Routing protocols

Projects

Quant Finance · 2026

Dynamic Portfolio Allocation with Deep Gaussian Processes

DGP for t+1 log-return prediction on 3-month high-frequency crypto data. Achieved +81% Sharpe vs XGBoost (65.5 vs 36.1) and 50% lower max drawdown in 3-fold walk-forward backtest.

Deep GPsPyTorchPython
Quant Finance · 2026

Heath-Jarrow-Morton Interest Rate Simulator

Multi-factor HJM model in C++ for forward-rate curve simulation using PCA factors. Achieved ~30× speedup vs Python in Monte-Carlo simulations via Eigen. Computed key rate durations and curve sensitivities.

C++EigenMonte CarloPCA
ML · 2023

Variational Autoencoder for IV Surfaces

VAE in Python to learn latent implied volatility surfaces and moneyness. Modelled volatility surface structure across strikes and maturities, capturing nonlinearity in the latent space.

PyTorchVAEOptions Pricing

Conference Submissions

1

"Developing Algorithms for the Discovery of Serum-Free Cell Culture Media"

Sooriyakumaran, Audet & Feng

Omics4CMeat 2025

Download CV

Resume

1-page resume highlighting research, experience, and key achievements.

Download PDF

Contact

I'm always open to discussing research collaborations, quant opportunities, or just connecting. Feel free to reach out!