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Dr Duy-Minh Dang

Senior Lecturer

School of Mathematics and Physics
Faculty of Science
duyminh.dang@uq.edu.au
+61 7 336 52686

Overview

I joined UQ in September 2014 as Senior Lecturer in Mathematics and Director of the Master of Financial Mathematics (MFinMath) Program. Through strategic and effective leadership, I've overseen the Program's transformation into one of Australia's largest. My commitment to enhancing teaching methodologies, fostering a vibrant student and alumni community, and emphasising industry relevance and collaboration, has significantly contributed to this growth. Additionally, I've had the privilege of supervising well over 100 MFinMath graduates and several PhD candidates, many of whom are making significant contributions in corporations worldwide. My commitment to academic rigour, industry relevance and collaboration ensures our graduates are well-prepared for their careers.

My research focuses on the development of reliable numerical methods for stochastic control problems in finance. In particular, I have worked on complex mathematical challenges such as Defined Contribution superannuation and valuation adjustments, which stem from governance issues and broader societal needs. My robust collaboration with key sectors including FinTech, Superannuation, Energy, Investment, Banking & Finance, Information Technology, and Commercial, reinforces the practical relevance of my academic endeavors and strengthens the bridge between academia and industry.

My ongoing commitment is focused on fostering an enriching educational environment, promoting impactful research, and strengthening industry-academia collaborations at UQ.

Beyond my professional commitments, I find balance through a range of personal interests. I am a blackbelt in Judo and an enthusiastic CrossFit practitioner.

Furthermore, I have a deep appreciation for music, particularly piano compositions. My daughter, now an advanced pianist, has been a source of both inspiration and amusement for me. Despite enduring her initial stages of piano practice, filled with the typical off-key notes and stumbles that come with learning an instrument, I've been rewarded with the joy of her progress. Her dedication to mastering the piano serves as a continual source of motivation and a reminder of the beauty found in commitment and growth.

I hold a PhD in Computer Science from the University of Toronto, Canada.

Research Interests

  • Computational finance
  • Numerical analysis
  • Scientific computing

Qualifications

  • Doctor of Philosophy, University of Toronto

Publications

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Grants

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Supervision

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Available Projects

  • Machine Learning for Defined Contribution Superannuation

    In a landscape of economic uncertainty and rising inflation, managing retirement savings and wealth has become a pressing challenge in finance. This complexity is amplified by a significant global shift towards Defined Contribution (DC) superannuation plans, particularly prominent in Australia. Under DC plans, individuals shoulder the entire investment risk through both the accumulation (pre-retirement) and decumulation (post-retirement) phases, which together constitute a full-life cycle DC plan extending over potentially 50 years or more.

    With Australia being the world's fourth-largest holder of pension fund assets and with over 87% of its 2.77 trillion USD superannuation assets in DC plans, a vast majority of Australian employees and retirees face considerable risk in retirement. Alarmingly, the fear of outliving retirement savings often surpasses the fear of death among many pre-retirees.

    Given this background, we offer a range of projects designed to harness the power of machine learning in modelling and managing Defined Contribution superannuation through a stochastic control approach. These projects aim to:

    • Identify and quantify the diverse risk factors in DC plans, providing insights for suitable risk measures for effective wealth management.
    • Develop robust, efficient, and reliable investment strategies for both the pre- and post-retirement phases through a stochastic control framework
    • Deliver personalised, effective wealth management solutions that cater to individual needs, thus alleviating the fear of outlivig retirement savings.
    • Promote a quantitative understanding of retirement savings among Australian employees and retirees, particularly emphasisng the challenges faced during the decumulation phase.

    These projects, suitable for Honours, Master and PhD level students, present students with the opportunity to work at the forefront of financial mathematics, leveraging machine learning methods to enhance the competitiveness of Australian super funds. These endeavors aim to drive significant economic and societal benefits, particularly relevant to Australia, while offering students the chance to make a real-world impact in addressing one of the most challenging issues in today's society.

  • Numerical Methods for Hamilton-Jacobi-Bellman Equations in Finance

    Many popular problems in financial mathematics can be posed in terms of a stochastic optimal control formulation, leading to the formulation of nonlinear Hamilton-Jacobi-Bellman (HJB) equations. The inherent challenges in solving these HJB equations include the lack of analytical solutions under realistic scenarios where controls are constrained, and the non-uniqueness and lack of smooth classical solutions due to their nonlinear nature. Consequently, our pursuit is directed towards finding the financially relevant solution for these HJB equations – the viscosity solution in this context.

    A number of my projects are centered around the development of efficient numerical methods that ensure convergence to the viscosity solution for HJB equations arising in finance. Potential applications include portfolio optimisation (superannuation), variable annuities with riders (pension products), and valuation adjustments (regulations).

    These projects, suitable for Honours, Master and PhD level students, emphasize the practical and real-world relevance of research in mathematical finance, offering opportunities for intellectual growth and for making meaningful contributions to understanding and controlling complex financial systems.

View all Available Projects

Publications

Journal Article

Conference Publication

PhD and MPhil Supervision

Current Supervision

  • Numerical methods for stochastic control problems in finance

    Doctor Philosophy — Principal Advisor

    Other advisors:

Completed Supervision

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Machine Learning for Defined Contribution Superannuation

    In a landscape of economic uncertainty and rising inflation, managing retirement savings and wealth has become a pressing challenge in finance. This complexity is amplified by a significant global shift towards Defined Contribution (DC) superannuation plans, particularly prominent in Australia. Under DC plans, individuals shoulder the entire investment risk through both the accumulation (pre-retirement) and decumulation (post-retirement) phases, which together constitute a full-life cycle DC plan extending over potentially 50 years or more.

    With Australia being the world's fourth-largest holder of pension fund assets and with over 87% of its 2.77 trillion USD superannuation assets in DC plans, a vast majority of Australian employees and retirees face considerable risk in retirement. Alarmingly, the fear of outliving retirement savings often surpasses the fear of death among many pre-retirees.

    Given this background, we offer a range of projects designed to harness the power of machine learning in modelling and managing Defined Contribution superannuation through a stochastic control approach. These projects aim to:

    • Identify and quantify the diverse risk factors in DC plans, providing insights for suitable risk measures for effective wealth management.
    • Develop robust, efficient, and reliable investment strategies for both the pre- and post-retirement phases through a stochastic control framework
    • Deliver personalised, effective wealth management solutions that cater to individual needs, thus alleviating the fear of outlivig retirement savings.
    • Promote a quantitative understanding of retirement savings among Australian employees and retirees, particularly emphasisng the challenges faced during the decumulation phase.

    These projects, suitable for Honours, Master and PhD level students, present students with the opportunity to work at the forefront of financial mathematics, leveraging machine learning methods to enhance the competitiveness of Australian super funds. These endeavors aim to drive significant economic and societal benefits, particularly relevant to Australia, while offering students the chance to make a real-world impact in addressing one of the most challenging issues in today's society.

  • Numerical Methods for Hamilton-Jacobi-Bellman Equations in Finance

    Many popular problems in financial mathematics can be posed in terms of a stochastic optimal control formulation, leading to the formulation of nonlinear Hamilton-Jacobi-Bellman (HJB) equations. The inherent challenges in solving these HJB equations include the lack of analytical solutions under realistic scenarios where controls are constrained, and the non-uniqueness and lack of smooth classical solutions due to their nonlinear nature. Consequently, our pursuit is directed towards finding the financially relevant solution for these HJB equations – the viscosity solution in this context.

    A number of my projects are centered around the development of efficient numerical methods that ensure convergence to the viscosity solution for HJB equations arising in finance. Potential applications include portfolio optimisation (superannuation), variable annuities with riders (pension products), and valuation adjustments (regulations).

    These projects, suitable for Honours, Master and PhD level students, emphasize the practical and real-world relevance of research in mathematical finance, offering opportunities for intellectual growth and for making meaningful contributions to understanding and controlling complex financial systems.

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