Dan H. TRAN

Academic Assistant Professor of Finance, Católica-Lisbon School of Business & Economics.
Builder ML & agentic-AI systems for finance, with the Alan Turing Institute, Mastercard, and EU investment firms.

"Only in darkness we can see the stars" — Thomas Carlyle

About

Short Bio

Favorite books


Research

Fields of interest

Working papers

Shock diffusion in large regular networks: the role of transitive cycles

with Noemí Navarro — working paper

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This paper studies how the presence of transitive cycles in the network affects the extent of financial contagion. In a regular networks where the same pattern of links repeats for each node, we allow an external shock to propagate losses through the system of linkages. The extent of contagion (contagiousness) of the network is measured by the limit of the losses when the initial shock is diffused into an infinitely large network. This measure indicates how a network structure may or may not facilitate shock diffusion, independently to other external factors. Our analysis highlights two main results. First, contagiousness decreases as the length of the minimal transitive cycle increases, keeping the degree of connectivity constant. Second, the extent of contagion is non-monotonic as degrees of connectivity increases. Our results provide new insights to better understand systemic risk and could be used to build complementary indicators for financial regulation.

Bank runs, fast and slow: from behaviors to dynamics

working paper

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This paper studies how bank runs occur, providing detailed dynamics on the emergence and trajectories of runs. While existing models mainly regard runs as symmetric equilibria in simultaneous games, the present paper considers continuous withdrawals that arise from a dynamic system. Depositors make decisions based on (i) their types, (ii) their private information on total withdrawal, and (iii) the observed actions of others within a network. Using both analytical and computational methods, this paper makes two main contributions. First, the model can capture both the speed and abruptness of runs, showing two distinct dynamic patterns: slow runs build up progressively vs. sudden runs occur abruptly without visible signs. Second, the model establishes empirically testable links between behavioral factors and the dynamics of runs. These results might be useful to predict large panic episodes.

Dynamics and tipping point of panics

with Emmanuelle Augeraud-Veron — working paper

Draft available on request

This paper models systemic events as dynamic cascades of actions, providing a complementary view to the coordination-game framework. The aim is to better understand how bank runs emerge and develop in continuous time, without imposing an exogenous sequence of actions. Agents employ a switching strategy that combines strategic actions and heuristics to make decisions. When a fraction of random agents withdraw, under the right conditions, some depositors withdraw preemptively in response, increasing the probability that other depositors will withdraw subsequently. As the main result, the paper provides explicit computations of the tipping point, i.e. when the panic bursts out, to determine the time windows for interventions.

Works in progress


Industry

Industry collaborations

Mastercard

Alan Turing Institute Data Study Group · 2024

Mastercard's global ML engine scores 10 million transactions a day and needed an independent audit. We built a framework to measure and mitigate intersectional bias, and to form AI/ML safety policies for sustainable business performance in the long run.

Team from

Alan Turing Institute Cambridge Católica-Lisbon Harvard Mastercard Northeastern London Queen Mary London Warwick
Final report

Innovate UK BridgeAI

Alan Turing Institute Data Study Group · 2026

A UK-government project to reshape education and labour-market policy, with the Bank of England as benefactor. Partnering with Lightcast, the UK's largest job-postings platform, we ingested large-scale hiring data and official statistics to measure AI-skill supply–demand gaps, align competing competency frameworks, and nowcast hiring demand.

Team from

Alan Turing Institute Cambridge Católica-Lisbon Cornell King's College London MIT Oxford UCL World Bank
Final report

Top-3 German retail bank

Confidential · 2023

ML credit scoring; automated data ingestion and reporting.

EU investment firms

Confidential, ongoing · 2025–

Automation of financial research, factor models, signal discovery.

AI competitions

Outstanding Achievement Award — Agentic Retrieval Grand Challenge

ACM ICAIF'25, NUS, Singapore

Agentic retrieval over SEC filings with open-source models: the highest score reached with an open-source model, and the most robust and reproducible solution.

Host & sponsors

NUS QRT Google Databricks Linq Alpha
Code

7th place — FinanceRAG Challenge

ACM ICAIF'24, NYU, New York

Retrieval-augmented generation over textual and tabular financial documents.

Host & organizer

Top-20 global — ECOM1

BitGN Arena · 2026

The largest agentic e-commerce challenge to date: 1,000+ engineers across 97 cities stress-testing autonomous commerce agents on production-grade tasks. Built a self-improving harness: the agent plays, evaluates, and patches itself.

Benchmark

Teaching

Current

  • Data science for Finance (graduate)
  • Python for Finance (graduate)
  • Machine Learning (graduate)
  • M.Sc. thesis supervision: Applied Machine Learning

Past

  • Computational Modeling
  • Microeconomics
  • Macroeconomics Intermediate
  • Programming


Education

M.Sc. in Financial Risk Engineering

University of Bordeaux
2013

B.A. in Economics

University Of California San Diego
University of Bordeaux
2011

Skills

AI stack
OpenAI SDK Vertex AI SDK Agentic RAG Hybrid retrieval fusion FastAPI React + Tailwind
Machine Learning
Python scikit-learn PyTorch
Computation
Matlab VBA NetLogo
Languages
Vietnamese - fluent French - fluent English - fluent Chinese - basic Portuguese - basic