Miguel's research is concerned with using mathematical optimization to provide guaranteed optimal or near-optimal solutions for important classes of large-scale optimization problems arising in engineering applications. Convex optimization is a privileged means to obtain high-quality solutions to hard optimization problems, and much of his research is concerned with improving models and algorithms for the application of convex optimization in electricity systems, facility layout, and pricing.

Burak's research interests mainly deals with problems involving parameter uncertainty in the broad areas of queueing theory and revenue management. He uses and develops stochastic programming techniques to solve real world problems. The application areas for his current research are pricing of tickets in airline networks and designing flexible systems under uncertainty.

Chris works broadly across energy systems analysis, with particular interests in electricity security of supply risk analysis, and in the use of computer models for decision support (the latter including both statistical uncertainty quantification, and also people/institutional aspects of the use of modelling). He also takes a general interest in optimisation methods for electricity generation scheduling, including for optimal power flow and unit commitment.

Sergio's research addresses the development of methods for integer programming and combinatorial optimization problems. He has been working on facility location problems, p-median problems and the split delivery vehicle routing problems.

Jacek is interested in the theory and implementation of optimization methods for linear, quadratic and nonlinear programming. He is also interested in the use of linear algebra techniques and sparse matrix factorisation methods applied in optimization. His interests include the use of parallel and distributed computing for solving real-life very large optimization problems arising in telecommunications, energy sector and finance.

Andreas is interested in decomposition methods for large scale nonlinear nonconvex constrained optimization; bundle methods; warmstarts for interior point methods; pooling problems.

Akshay's primary research interests are in theory, algorithms, and applications of nonconvex optimization. On the theoretical side, he works mainly on convexification techniques for mixed-integer and nonlinear (polynomial optimization) problems. He also has secondary research interests in computational discrete mathematics, particularly the structural study of convex polytopes and structural graph theory, and the use of tools from computational algebraic geometry in polynomial optimization. The theoretical results of his research have direct impact on accelerating convergence of global optimization algorithms for nonconvex problems. Motivating applications come from chemical engineering, resilient network design, electric power systems, and financial portfolio management, facilitated by interdisciplinary collaborations with groups in civil engineering and in research labs in the petrochemical industry in the US.

Julian has a long-term interest in the development of algorithmic and computational techniques for solving large scale linear programming (LP) problems using the revised simplex method on both serial and parallel computers. A consequential research interest is the application of these techniques in other areas of computational optimization and linear algebra.

Joerg is interested in developing and implementing exact and heuristic algorithms to solve large-scale real-life problems from diverse areas of application, like facility location, districting, or service scheduling, taking time dynamic, uncertainty and reliability issues into account. He is also keen on combining different disciplines from mathematics and computer science, like combinatorial optimization, complexity theory, computational geometry, and calculus, to obtain structural results and exact solution approaches, e.g., for facility location problems with continuous demand.

Ken works on global optimization, parallel linear programming and industrial applications of optimization in the chemical, oil and electricity industries.

John is interested in the numerical solution of large-scale optimization problems with PDE constraints, in particular problems arising from applications in fluid dynamics, chemical and biological processes, multiscale particle dynamics, and data science. One of his main areas of research is in the design of fast and robust numerical linear algebra techniques and preconditioners to solve the matrix systems resulting from these applications. In addition, John is interested in interior point methods for quadratic and nonlinear programming problems, as well as associated linear algebra challenges.

Lars is interested in mixed-integer optimization, specifically using techniques from discrete optimization to solve mixed-integer nonlinear problems. He has worked on a variety of applications, but in the last years he has mainly worked on gas network optimization and mathematical models of energy markets.

Alper's research interests encompass several facets of convex and nonconvex optimization, including theory, algorithms, and applications. He is particularly interested in developing optimization models with desirable structural properties for various real-life applications, exploiting problem-specific properties to develop effective solution methods, understanding the worst-case behaviour of such algorithms, and implementation of algorithms. On the theoretical side, his recent research interests focus on exact and approximate representations of discrete and nonconvex optimization problems via convex conic optimization. His work on applications includes problems arising from electricity markets, wireless networks, logistics, graph theory, and machine learning.

Nick is interested in optimization, particularly in numerical methods for solving nonlinear, non-convex optimization problems involving a large number of unknowns and/or constraints. He is also interested in numerical linear algebra, particularly in aspects that arise from optimization applications. He has written a number of software packages for solving a variety of optimization and simultaneous equation problems. His particular favourite areas are (non-convex) quadratic programming, nonlinearly constrained optimization, trust-region methods, and methods for solving linear systems that arise from saddle-point problems.

Nicholas Radcliffe's research interests focus on evolutionary search algorithms. The particular focus of much of his work is the development of a formalism (forma analysis) to allow beliefs about the structure of a domain of search problems to be captured in such a way as to allow generic, problem-independent search algorithms to be applied to them mechanically. He also has research interests in machine learning, feature creation and stochastic programming.

Philippe's research addresses smooth nonlinear optimization problems, with an emphasis on the algorithmic viewpoint, ranging from convergence theory to numerical considerations and software development (LANCELOT, CUTEr, GALAHAD), as well as practical and multidisciplinary applications of optimization techniques. He is also interested in the analysis of transportation systems, including dynamic trafic modelling and demand estimation, as well as advanced behavioural models with applications in regional, national and european strategic transportation planning. Read Philippe's brief biography.