Organizations frequently need to take decisions based on uncertain or incomplete information (e.g., about future customer demands, raw material prices or exchange rates). As a group, we strive to explore how quantitative methods can assist prudent decision-making under these challenging conditions. Even under the idealized assumptions that are typically made in the mathematical models of real-life decision problems, optimal decisions are typically difficult to obtain if uncertainty is involved. Our work therefore concentrates on the design of tractable approximation schemes that can be justified through rigorous error bounds.
Our research is interdisciplinary and combines methodological work with applications. Many of our projects are motivated by real-life problems faced in operations management, energy systems and financial engineering, which are frequently inspired by and carried out in collaboration with industrial partners. In this regard, we gratefully acknowledge funding from the Engineering and Physical Sciences Research Council, the UK Cabinet Office, Imperial Business Analytics, the Imperial College Research Fellowship scheme and various industrial collaborators.
Our research regularly features in peer-reviewed management (e.g., Management Science and Operations Research) and mathematical (e.g., Mathematical Programming, Mathematics of Operations Research and SIAM Journal on Optimization) journals. Moreover, Wolfram currently serves as an area editor for Operations Research Letters as well as an associate editor for Computational Management Science, Computational Optimization and Applications, Manufacturing & Service Operations Management, Operations Research and SIAM Journal on Optimization. Wolfram also serves on the board of the Stochastic Programming Society.
Our recent research on elective care prioritization in the wake of the COVID-19 pandemic has been featured on the Daily Mail, the Financial Times and France24.