I am thrilled to apply for the Hong Kong Laureate Forum to engage with pioneering scientists and emerging researchers. As a mathematics researcher at Shenzhen Polytechnic University specializing in stochastic control, I am eager to learn from laureates at the forefront of theoretical and applied sciences. Their perspectives on breakthrough methodologies will directly inform my work on probabilistic modeling, while their guidance will shape my development as a rigorous researcher.

The forum’s interdisciplinary nature presents an unparalleled platform to exchange ideas with international peers. I am particularly excited to share my work on financial mathematics applications and absorb innovative approaches from fellow young scientists. These interactions will catalyze future collaborations and enhance my analytical toolkit for complex systems.

I am keen to contribute to discussions at the intersection of mathematics and emerging fields, especially AI-driven financial modeling. Hong Kong’s position as a global research hub makes it the perfect venue for such cross-pollination of ideas.

Participation would profoundly impact my trajectory by exposing me to transformative scientific thinking. I am committed to actively engaging in all sessions and bringing my expertise in stochastic systems to enrich discussions. This opportunity aligns perfectly with my ambition to advance quantitative research with real-world

My research focuses on optimal investment strategies for defined-contribution (DC) pension plans under regulatory and contractual constraints. Specifically, I study dynamic asset allocation across risk-free bonds, inflation-linked bonds, and equities, while incorporating joint Value-at-Risk (VaR) constraints (both initial and intermediate-horizon) and terminal wealth guarantees. The objective is to maximize expected utility of surplus wealth above a minimum guaranteed annuity, balancing growth and risk control.

Methodologically, I employ martingale duality techniques to transform the stochastic control problem into a tractable static optimization framework, deriving closed-form optimal strategies. A key emphasis is on analyzing how intermediate risk constraints shape the tail behavior of terminal wealth, ensuring solvency and downside protection in adverse markets. Numerical simulations demonstrate that these constraints effectively reduce extreme loss exposure without overly compromising long-term returns.

This work contributes to retirement finance and risk-aware portfolio management by:
- Developing tractable models for DC pensions under real-world regulatory requirements.
- Quantifying the trade-offs between growth, risk limits, and guarantees.
- Providing insights for policymakers and fund managers on constraint design.

Keywords: Pension optimization, dynamic asset allocation, risk constraints, martingale methods, guaranteed annuities, tail risk management.