Career
Experience
Software Engineer, GenAI Team · Royal Bank of Canada
Jan 2026 – Present
Toronto, Canada
- Working on LLM optimization with RBC's GenAI team, improving inference performance for production language models
Software Engineer · Royal Bank of Canada
Jul 2025 – Aug 2025
Toronto, Canada
- Automated ATM service restarts across 40+ servers in RBC Caribbean with Python, replacing manual processes
- Reduced downtime by 20% via self-service ticket-based tooling and saved 27 developer-hours per month
Research Intern · Dartmouth College
Jun 2025 – Present
Remote
- Designed a Bayesian neural network in Python for spatial transcriptomic uncertainty quantification
- Ran large-scale experiments on Dartmouth's HPC cluster; will present this work at AACR 2026 in San Diego
Research Assistant · University of Toronto
Sep 2024 – Present
Toronto, Canada
- Modeling radiative transfer in protoplanetary disks to study planet-forming environments
- Contributing to Astraeus, a research project on black hole imaging and dynamics
Machine Learning Engineer · Triage
Apr 2024 – Dec 2024
Toronto, Canada
- Built a production RAG pipeline using LangChain and ChromaDB, ingesting 10k+ medical documents to power a clinical LLM
- Scraped and cleaned large-scale medical data, managing datasets with Databricks and SQL
- Iterated quickly with the CEO and CTO, driving measurable improvements in diagnostic response quality
Research Intern · University of Waterloo
Mar 2023 – Present
Waterloo, Canada
- Ran 500+ experiments via the Anthropic API studying SWE-Bench-Verified benchmark leakage on Claude Sonnet models
- First-authored and published a research paper based on the experimental findings
Research
Research & Publications
- Publication· December 22, 2025
Does SWE-Bench-Verified Test Agent Ability or Model Memory?
University of Waterloo · First Author, with Noble Saji Mathews and Meiyappan Nagappan
500+ experiments via the Anthropic API asking whether SWE-Bench-Verified scores reflect real coding-agent ability or memorized training data. Finds substantial overlap between benchmark tasks and model pretraining corpora, which complicates using the benchmark to compare agent capability.
Read the paper → - Abstract· April 3, 2026
Quantifying Uncertainty in Virtual Spatial Transcriptomics Using Bayesian Neural Networks
Dartmouth College · AACR Annual Meeting 2026, San Diego (Abstract 4189)
A Bayesian neural network that predicts gene expression from H&E tissue images and separates epistemic from aleatoric uncertainty across 991 genes and ~290k tissue spots. Metabolic and cell-cycle pathways turn out to be the most reliably inferred from morphology alone.
Read the abstract →