Aakil Caunhye Headshot

Lecturer in Business Analytics

Background

Biography

Broadly speaking, my research belongs to the area of optimisation, through mathematical programming, under uncertainty. My main interests span the areas of robust optimisation and stochastic programming, both of which are mathematical modelling techniques that can be applied to a wide range of cross disciplinary decision-making problems. I have used mathematical methods to derive novel reformulations and algorithmic solution approaches for classes of multi-stage robust optimisation and stochastic programming models such as network optimisation, combinatorial optimisation, and mixed integer optimisation. I have applied my results to integrated power grid expansion planning, disaster response planning, real options analysis in engineering systems design, and resilience improvement for critical infrastructure systems. More recently, I have been working on the concept of ambiguity aversion through distributionally robust optimisation as a tool to merge, solidify, and extend the links between the fields of big data analytics and robust optimisation

Qualifications

Experience

  • Senior Research Fellow, National University of Singapore and ETH Zurich, Singapore-ETH Center, Jan '18–Jul '18
  • Research Fellow, National University of Singapore and ETH Zurich, Singapore-ETH Center, Aug '14–Dec '17
  • Research Associate, Nanyang Technological University, Aug'13–Aug'14

Academic Qualifications

  • PhD Systems and Engineering Management, Nanyang Technological University, 2014
  • MSc Industrial and Systems Engineering (specialization: Logistics and Operations Research), National University of Singapore, 2009 - Standard Chartered Bank Book Prize for being the best graduate in the graduating cohort in MSc (Industrial and Systems Engineering), with GPA 4.95 / 5.00
  • BEng Industrial and Systems Engineering (specialization: Logistics and System Optimization), National University of Singapore, 2008

Research Taxonomy

Research Interests

Methodological

  • Robust optimisation, especially data-driven ambiguity-averse robust optimisation
  • Stochastic programming
  • Cutting plane algorithms (Benders decomposition, row generation, approximations)
  • Decision rules for stochastic programming and robust optimisation(Conditional-go, binary, finite adaptability, linear) 

Application areas so far:

  • Humanitarian logistics
  • Flexibility and real options in engineering systems design
  • Resilience of critical infrastructure systems
  • Power grid expansion planning
  • Route restoration

Research Area

Publication Type Actions