I am a Principal Consultant in the Quant Services division of LSEG, where I partner with tier-one investment banks, hedge funds, and risk technology teams to design, validate, and implement quantitative models for counterparty credit risk and derivative pricing. My work spans CVA, DVA, FVA, and PFE model validation, regulatory compliance with frameworks such as SS1/23 and FRTB, and the deployment of open-source risk infrastructure using the Open Source Risk Engine (ORE).
I hold a PhD in Applied Mathematics from the University of Limerick, where my research focused on describing the dynamics of complex systems (such as online social networks and popularity dynamics) using mathematical modelling and large-scale data analysis. This background in applied statistics and network science now informs my approach to building robust, mathematically rigorous financial risk analytics.
My technical toolkit includes Python (NumPy, SciPy, pandas, QuantLib Python), C++ for performance-critical pricing components, and R for statistical analysis and diagnostics. I also regularly contribute to LSEG’s Ahead of the Curve podcast series, discussing ORE developments, model risk regulation, and the future of quantitative risk management.
Interests
- Counterparty Credit Risk (CCR)
- XVA (CVA / DVA / FVA)
- Derivative Pricing & Valuation
- Model Risk Management
- Quantitative Finance
Education
PhD in Applied Mathematics, 2017-2021
University of Limerick
BSc. in Financial Mathematics, 2013-2017
University of Limerick
Expertise
Quantitative Risk & Model Validation
I specialise in the validation and implementation of quantitative models across the trade lifecycle, with particular depth in:
- XVA Modelling: CVA, DVA, FVA, MVA, and KVA frameworks; sensitivities and Greeks; regulatory capital
- Counterparty Credit Risk: PFE simulation, ISDA SIMM, netting and collateral agreements
- Derivative Pricing: Interest rate products (swaps, caps, floors, swaptions), credit derivatives, FX derivatives
- Model Validation: Independent model review, backtesting, stress testing, regulatory documentation
- Regulatory Compliance: SS1/23 (PRA Model Risk Management), FRTB, Basel IV
- Open Source Risk Engine (ORE): Configuration, customisation, and validation against bespoke systems
Publications
Academic & Applied Mathematical Research
This case study focuses on backtesting methodologies for Future Risk Factors within derivative exposure models, validating Simulated Exposure profiles against actual historical market realizations to meet regulatory expectations like …
Persistence of chimera states and the challenge for synchronization in real-world networks
Eur. Phys. J. BCalibrating COVID-19 susceptible-exposed-infected-removed models with time-varying effective contact rates
Phil. Trans. R. Soc. AA Generalized Framework for Simultaneous Long-Short Feedback Trading
IEEE Transactions on Automatic ControlA generalization of the classical Kelly betting formula to the case of temporal correlation
IEEE Control Systems LettersRecent Posts
Articles on Mathematics, Data, & Technology
Historical simulation VaR in ORE
Media & Speaking
Podcasts, Keynotes, Industry Labs & Academic Seminars
Agentic AI in Quantitative Risk Management
Discussing the real-world operational transformation and governance challenges of deploying autonomous AI agents and Open Source Risk Engine (ORE) architectures on institutional trading and risk desks.
- Autonomous agents for model validation & stress testing
- ORE architecture & multi-asset valuation workflows
- Bank of England PRA SS1/23 supervisory alignment
Recent Summits, Keynotes & Appearances
LSEG Ahead of the Curve Podcast: Agentic AI in Quantitative Risk Management
Joined the latest episode of LSEG’s Ahead of the Curve podcast alongside Xabier Anduaga and Stuart Smith to discuss the real-world impact …
LSEG’s Internal Adoption of ORE
Presented at the LSEG Quant Summit London 2026, discussing LSEG’s internal adoption, integration, and scaling of the Open-Source Risk Engine …
ORE in the Era of Agentic AI
Presented at the LSEG Quant Summit London 2026, exploring the capabilities and limitations of Large Language Models (LLMs) in writing …
ORE & Risk Analytics Lab Tokyo
Attended and supported our APAC team’s ORE & Risk Analytics Lab event in Tokyo, which gathered Japanese market participants and risk …
LSEG Ahead of the Curve Podcast: UK Model Risk Regulatory Landscape & SS1/23
Featured on LSEG’s Ahead of the Curve podcast alongside Scott Sobolewski and Xabier Anduaga to discuss the UK model risk regulatory …
LSEG Ahead of the Curve Podcast: 2024 Year in Review
Joined the Ahead of the Curve: 2024 Year in Review podcast episode alongside Scott Sobolewski and Roland Stamm to reflect on key ORE …
News & Updates
- July 2026 — Podcast: Featured on LSEG’s Ahead of the Curve podcast discussing the real-world impact and future of Agentic AI in quantitative risk management. Listen & Watch →
- November 2025 — Conference & Lab: Attended and supported our APAC team’s ORE & Risk Analytics Lab in Tokyo, presenting to risk professionals from leading financial institutions on CVA frameworks, custom risk models, and capital efficiency.
- March 2021 — New Scientist & PLOS ONE: Our mathematical research on Fantasy Premier League published in PLOS ONE and featured in New Scientist: Maths reveals the top strategies to win at fantasy football. Read Paper (PLOS ONE) | arXiv