CV

Curriculum Vitae

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Professional Profile

AI Researcher and agent engineer specializing in robust machine reasoning, retrieval-augmented generation (RAG), LLM evaluation, and alignment. Builds grounded agentic workflows, vector-retrieval systems, and dataset generators for context-management evaluation in LLM agents. Former Military Intelligence Operator who held a Top Secret clearance, bringing a threat-modeling mindset to AI security and model capability evaluation.

Technical Skills

• Languages: Python, C++, Java, SQL, LaTeX
• AI & Agents: PyTorch, Reinforcement Learning (RL), RLVR, LangGraph, LangChain, RAG, Hugging Face, vLLM, Transformers, Gemini/OpenAI/Anthropic APIs
• RAG & Evals: Vector storage, embeddings, BM25, hybrid retrieval, cross-encoder reranking, MRR/nDCG/recall, groundedness, validation
• Systems: Linux, SQLite, GitHub Actions CI, AWS, Apache Flink, Distributed GPU Clusters

AI Research & Agent Engineering

• ArbiGraph (2026 – Present) [arXiv] | [GitHub] | [Hugging Face]
  • Built an open-source benchmark and dataset generator for evaluating context management in tool-assisted LLM agents across arbitrarily scalable, long-horizon task graphs.
  • Generated math, word-problem, and Python-tracing workflows with executable ground truth, enabling exact evaluation of state retention, updates, propagation, and stale-state reuse without manual labels.
  • Built reinforcement learning with verifiable rewards (RLVR) environments supporting dataset-backed and on-demand episodes, hidden verifier state, exact binary rewards, and per-node diagnostics.
• arXiv Research Agent (2026) [GitHub] | [Evals]
  • Built an evaluation-first RAG literature-review agent with LangGraph, LangChain, Gemini, and Chroma vector storage; orchestrates arXiv search, PDF parsing, hybrid retrieval, reranking, and citation-grounded synthesis.
  • Benchmarked dense, BM25, hybrid, and cross-encoder retrieval on 50 hand-labeled questions over 570 chunks using MRR, nDCG, recall, and paired-bootstrap confidence intervals; evaluated groundedness and claim support.
  • Engineered deterministic map-reduce fan-out, retry-aware partial results, and resumable SQLite checkpointing; shipped 163 offline tests in CI and measured a median end-to-end cost of $0.054 per run.
• Robust Reasoning Benchmark (RRB) (2026) [arXiv] | [DOI] | [GitHub]
  • Designed a highly creative adversarial evaluation framework leveraging 13 deterministic textual perturbations to decouple an LLM’s mechanical deciphering from its underlying mathematical logic.
  • Demonstrated that the attention drift occurs within a single query’s Chain-of-Thought, empirically showing that intermediate reasoning steps pollute the dense attention mechanism.
  • Engineered custom mechanistic interpretability pipelines in PyTorch for layerwise attention-allocation analysis across token index boundaries, testing models ranging from 7B to 30B parameters.
• Fusing Adds and Shifts for Efficient Dot Products (2026) [IEEE CAL] | [GitHub] Hardware ML Research — Toronto, ON
  • Proposed and validated a novel algorithmic optimization for dot-product computations, demonstrating a strong foundational understanding of hardware-level ML primitives and efficiency.

Engineering & Professional Experience

• Graduate Researcher (PhD, on leave) — ML Systems & Agent Evaluation (2022 – Present) University of Toronto, EcoSystem Research Group
  • Conducted research spanning ML systems and efficient computation, with later work focused on robust machine reasoning, agent evaluation, context management, and AI alignment, in the EcoSystem Research Group and as a member of the Vector Institute.
  • Developed open-source evaluation frameworks, agentic RAG systems, and mechanistic-interpretability analyses, resulting in first-author research on context management and reasoning robustness.
• Graduate Researcher (MSc) — Distributed Systems (2020 – 2022) University of Toronto, EcoSystem Research Group
  • Engineered a flexible IoT distributed data-streaming framework from scratch, designed to automatically partition computational streaming queries between edge devices and cloud instances.
  • Built the full software stack: programmed Arduino/C++ sensors for real-time biological data collection (EMG/ECG), developed custom socket networking protocols, and deployed cloud infrastructure using AWS and Apache Flink.
• Intelligence Operator (2013 – 2018) Canadian Armed Forces
  • Formerly held a Top Secret security clearance while conducting rigorous analysis of classified information streams to produce actionable intelligence reports for command elements.
  • Developed a strong adversarial threat-modeling mindset, emphasizing operational security, rigorous data validation, and the identification of logical vulnerabilities in complex, multi-agent scenarios.
• Mathematics Teacher (2012 – 2013 & 2018 – 2019) Blyth Academy
  • Taught foundational mathematics to students in Grades 10, 11, and 12, developing the ability to distill and communicate complex quantitative concepts.

Education

• PhD in Computer Science (on leave as of September 2026), University of Toronto, 2022 – Present
• Master of Science (MSc) in Computer Science, University of Toronto, 2020 – 2022
• Bachelor of Science (BSc) in Mathematics and Philosophy (Formal Logic), University of Toronto, Graduated 2011