Where Data Science
Meets Domain Expertise

Commodity Price Forecasting

Using deep learning architectures including LSTM networks and Transformer models to forecast commodity market prices across multiple time horizons. The models incorporate multi-variate time-series data and custom attention mechanisms for improved prediction accuracy.

Key Techniques

Approach

• • Multi-horizon forecasting with custom neural architectures
• • Feature engineering from macroeconomic and market indicators
• • Ensemble methods combining statistical and DL models
• • Backtesting framework for strategy evaluation

Retirement Decumulation Modeling

Ruin probability comparison across 10 withdrawal strategies at varying initial wealth levels

Developing stochastic models for optimal retirement portfolio withdrawal strategies. This work applies Monte Carlo simulation and risk-adjusted frameworks to determine sustainable decumulation rates under various market conditions and longevity scenarios.

Key Techniques

Research Focus

• • Optimal withdrawal rate modeling under market volatility
• • Longevity risk integration into decumulation strategies
• • Dynamic asset allocation during retirement phase
• • Core researcher at Canada’s Financial Wellness Lab

Hydrogen Integrated Greenhouse Horticultural (HIGH) Energy Project

Funded by Ontario’s Independent Electricity System Operator (IESO) through the Hydrogen Innovation Fund, the HIGH Energy project explores using wind-powered green hydrogen to decarbonize the greenhouse sector in Southwestern Ontario. Led by the University of Windsor in collaboration with Kruger Energy and the Ontario Greenhouse Vegetable Growers, this research assesses the technical and economic feasibility of blending hydrogen with natural gas to provide clean heat and power for large-scale horticultural facilities.

The Part 1 report identifies a lowest levelized cost of hydrogen (LCOH) of CAD $4.6/kg using a 10% H₂/90% natural gas blend at $33.07/MWh electricity rate. Economic viability is optimized at 5 days of storage and 2,190 annual operating hours, with analysis covering 9 scenarios across various blending ratios and delivery methods.

Key Techniques

Research Scope

• • 9 scenarios across H₂ blending ratios (10%, 20%, 100%) and delivery methods
• • Wind-powered PEM electrolysis coupled with 200MW wind farm
• • Levelized cost, payback period, IRR, and discounted payback analysis
• • Market analysis and regulatory review for Ontario grid integration

Project Links

The Alternative Deicer Paradox

Demonstrates that "green" alternative road deicers (calcium chloride, magnesium chloride, potassium chloride) mobilize trace metals (Cu, Pb, Zn) at higher ecotoxicity than conventional sodium chloride, contradicting the prevailing assumption that alternatives are environmentally safer. Submitted to Environmental Pollution. Joint work with A. Provost-Savard (ETS Montréal), E. Verkuil, C. Power, and A. Levasseur.

Key Techniques

Key Findings

• • Alternative deicers mobilize Cu, Pb, Zn at higher ecotoxicity than NaCl
• • Challenges the "green alternative" narrative in municipal de-icing policy
• • Data-driven evidence for policy recommendations on deicer selection
• • Submitted to Environmental Pollution (Elsevier), 2026

Microplastics Detection via ML

DETR object detection output: microplastics classified as Glitter (1.00) and Fragment (0.99) from Great Lakes samples

Using DETR (Detection Transformer) and CNN-based deep learning to automate the detection and classification of microplastics gathered from the Great Lakes. Applying state-of-the-art object detection models for rapid, accurate identification of microplastic particles in environmental samples.

Key Techniques

Methodology

• • DETR (Detection Transformer) for microplastic detection
• • CNN-based classification of microplastic types
• • Great Lakes environmental sample analysis
• • Object detection pipeline for automated identification

Medical Imaging AI

Brain image generation using Denoising Diffusion Probabilistic Models (DDPM), created by Dr. Petrov

14+ years of medical imaging research at the Robarts Research Institute. Expertise spans deep learning for MRI motion correction, metal artifact reduction in CT, cardiac micro-CT imaging, and diffusion-based generative models for brain image synthesis.

Key Techniques

Selected Work

• • Deep learning motion correction for multichannel MRI (NMR in Biomedicine, 2024)
• • Brain image generation with DDPM diffusion models
• • Metal artifact reduction in CT scans
• • Cardiac micro-CT iterative reconstruction
• • 3D cardiac imaging with portable cone-beam scanner

Credibility Depletion in Dynamic Multi-Issue Bargaining

Belief evolution trajectories: DP-optimal vs bluff-and-retreat bargaining strategies

A decision-analytic benchmark for multi-issue bargaining where a single focal actor chooses bundles (tariffs, side payments, security concessions) against a reduced-form opponent with a two-type acceptance rule. The key mechanism is credibility depletion: visible retreat from hardline positions reduces future bargaining leverage, whereas consistent conduct rebuilds it. Joint work with Matt Davison.

Key Techniques

Key Results

• • Forward-looking credibility premium reaches +0.107 utility in adversarial settings
• • Credibility premium is zero in benign environments, confirming mechanism specificity
• • Boundary alternating-offer check shows responder-strategic case adds minimal separation
• • Manuscript in preparation for submission

Belief-State DP for Submarine-Launched UUV Reserve Management

Formulating the reserve-management problem for submarine-launched unmanned underwater vehicles (UUVs) as a partially observable stochastic control problem. The mothership carries a typed inventory of ISR, mine countermeasures (MCM), and anti-submarine warfare (ASW) vehicles, with a shared handling bottleneck preventing simultaneous launches. The hidden threat follows a three-state continuous-time Markov chain (peacetime, tension, conflict). Joint work with Matt Davison.

Key Techniques

Key Results

• • Optimal policy yields mean reward of 12.54 vs 8.36 (myopic) over 4,000 Monte Carlo missions
• • Hold region occupies ~77% of belief simplex at mission start
• • ISR dominates in tension; MCM/ASW emerge only under high conflict posterior
• • Identifies a cooldown parity effect: terminal alternation in the optimal policy boundary driven by the handling cycle, not regime dynamics
• • Manuscript in review (v17)

Optimal Stockpiling of Defence-Critical Minerals

Regime-switching price dynamics for nickel, copper, and cobalt (1990–2024)

Canada produces ten of twelve NATO defence-critical minerals yet lacks a quantitative stockpiling framework. This work develops a regime-switching model where the stockpile manager minimizes procurement, holding, and shortage costs via backward DP on joint price–inventory–belief state. Calibrated to World Bank Ni/Cu and Investing.com Co data. Joint work with Matt Davison, targeting Resources Policy.

Key Techniques

Key Results

• • Regime-aware policy reduces shortage months by 98–100% vs fixed-rate benchmarks
• • Cost savings of 19–59% versus fixed-rate procurement
• • Cobalt shows persistent volatility consistent with HHI = 5399 supply concentration
• • Robust across 16-fold range of penalty assumptions

Dual-Belief Framework for UUV Payload Management

Concept note extending a belief-state payload-allocation model with a second uncertainty dimension: the internal health state of stored vehicles. Combines (i) belief-state control for external threat, (ii) a reduced-order digital-twin library for latent structural health states, and (iii) an interpretable classifier mapping noisy diagnostics to health posteriors. The health posterior modifies expected deployment value through effectiveness reduction and health-related penalties.

Key Techniques

Architecture

• • External: three-state threat regime (peacetime / tension / conflict)
• • Internal: latent health states estimated via physics-informed digital twin library
• • Interpretable classifier for high-stakes operational decisions
• • Extends Petrov & Davison (2024) belief-state payload management model

Autocorrelation Hole Signature of Refractoriness

Proves that every refractory event train produces a characteristic "hole" in its autocorrelation function at zero lag. This universal spectral signature enables a three-way classification of event train generators (hard refractory, soft refractory, Poisson) from a single FFT computation, without parameter estimation. The theorem applies to neuroscience spike trains, queueing systems, and any temporal point process with a dead-time mechanism. Joint work with Matt Davison.

Key Techniques

Key Results

• • Theorem on hole structure classifies event trains into hard / soft / Poisson
• • Recovery of refractory floor from a single FFT pass
• • All 16/16 numerical verification tests passing
• • Manuscript (v19) ready for submission to Physical Review X

Boundary Functionals for Refractoriness Classification

Introduces a curvature pair (κ, Λ) extracted from the empirical forward-recurrence CDF that provides a three-way classification of refractoriness type (hard, soft, Poisson). The pair has bounded influence under contamination and is locally unique within a rational function class. A companion methods paper to the PRX autocorrelation hole work. Joint work with Matt Davison.

Key Techniques

Key Results

• • Bounded-influence curvature pair resists contamination in observed data
• • Local uniqueness within 2-jet-factoring rational function class
• • Manuscript (v18) complete; targeting Annals of Applied Statistics
• • Real-data validation on single-molecule FRET datasets in progress

Terminal Parity: Discrete Impulse Control with Refraction

Identifies and characterizes a structural signature in finite-horizon optimal control problems: when an agent faces a discrete handling cooldown (refraction constraint), the optimal policy boundary oscillates near the terminal horizon. This "terminal parity" effect is driven by the interaction between the countdown clock and the handling cycle, not by the underlying regime dynamics. A theoretical refinement of the cooldown-parity phenomenon first identified in the TKMS UUV payload management paper. Joint work with Matt Davison.

Key Techniques

Key Results

• • First documented treatment of terminal-horizon oscillation in impulse-control literature
• • Oscillation traced to handling cycle, independent of regime dynamics
• • Abstract structural result applicable beyond naval operations
• • Manuscript in preparation, targeting SIAM Journal on Control and Optimization