Overview
My work is on Systems Design and Optimization under Uncertainty, focusing on the development and evaluation of new computational techniques, digital processes, and algorithms to support the design of complex engineered systems, with applications in energy, transportation, and space systems. It covers methodological topics such as concept generation and selection, decision-making under uncertainty, machine learning, stochastic optimization, uncertainty modeling, and virtual/augmented reality.
An important theme in my research is the concept of Flexibility in Engineering Design. This paradigm emerged from the theory of Real Options and seeks to design complex systems that adapt, reconfigure and change in the face of uncertainty and risks, with the goal of improving economic performance, sustainability, and resilience. Research on this emerging paradigm is highly needed, especially given ongoing threats from climate change, healthcare emergencies, geo-political tensions, and cyber-physical terrorism. Latest efforts focus on the development of a theory for Data-Driven Systems Design for Uncertainty, which explores the roles of AI and machine learning as part of this emerging paradigm.
Below are some examples of recent work and contributions.
Design Decision Support System
This software platform was developed in collaboration with the National Environment Agency of Singapore. It aims to support decision-making in system design and deployment of waste-to-energy (WTE) facilities in the city state. It integrates seamlessly data analytics, economic evaluation, optimization, and multi-dimensional analysis of system design configurations. The software is licensed under TechID 2017-098.
Decision Rule Based Real Options
This work aims to adapt the economic theory of real options into an application-driven paradigm for systems design.
Real options provide the "right, but not the obligation, to change a system in the face of uncertainty." The flexibility provided by real options have been shown in many studies to provide significant improvements over standard methods not only for design, but also for economic evaluation of real investment projects. However, subtle mathematical and theoretical intricacies have delayed their widespread use.
Decision rule based real options integrates the decision-making process along with the systems design considerations into a paradigm that leads to elegant and intuitive design solutions that fully exploit the concept of flexibility in design.
The paper can be accessed here
Impact and Outreach
Features on Decision-Making Under Uncertainty
This feature article written by Enterprise presents research done by several colleagues on Decision-Making Under Uncertainty. The follow-on panel event discusses cutting edge developments in the field, from probalistic modeling to portfolio optimisation and real options analysis, mail delivery to space systems design, with insights from policy-making and business collaborations. This news article captures the key insights!
Associate Editor for ASME Journal of Mechanical Design
In September 2020, I joined the Editorial Board of the ASME Journal of Mechanical Design as an Associate Editor. This flagship journal is a leading reference in Engineering Design and Systems Design research. Read the story on Imperial news!
IISE Transactions Award
The paper "An Approach Based on Robust Optimization and Decision Rules for Analyzing Real Options in Engineering Systems Design" received the 2019 Best Application Paper Award in Design & Manufacturing at the Honors & Awards Ceremony, IISE Conference and Expo, Orlando, USA.
ISE Magazine
The work on resilient deployment of power grid systems published in the IISE Transactions award-winning paper was featured in the ISE Magazine. As the pre-eminent voice of the Industrial and Systems Engineering profession, ISE strives to enhance professional capabilities, and improve the performance of organizations and the profession as a whole.
Business Times
This article provides a view on the need to account for uncertainty and flexibility in infrastructure finance and megaprojects design. The full article is available here
United Nations Development Program
In September 2017 I joined the Research Council for UNDP's SDG Impact Finance initiative. The goal is to investigate, research and develop new tools to support the design of sustainable infrastructure systems worldwide.
Data-Driven Systems Design
This work focuses on the development of a new theory to design complex engineered systems relying on data science and machine learning principles. The idea is to enable a system to learn the best adaptation strategies to deal with uncertainty, risks, and disruptions.
We have so far demonstrated the concept on the analysis of Waste-to-Energy systems in Singapore, and also working on the development of new models to support design and operations of nano-grid systems for nomadic tribes in Mongolia. This work complements the approach based on decision rules also developed in my group.
The paper can be accessed here