Dario Landa-Silva

Dario Landa-Silva is a Professor of Computational Optimization at the University of Nottingham and a key member of the Computational Optimization and Learning (COL) Lab. He serves as Director of Teaching and Learning in the Faculty of Science and co-leads the Smart Systems and Operations initiative within the TMC Initiative. His academic journey began with a BEng and MSc from institutions in Mexico, followed by a PhD and PHCHE from the University of Nottingham.

• Education:
  • BEng (Mexico)
  • MSc (Mexico)
  • PhD (University of Nottingham)
  • PHCHE (University of Nottingham)

Landa-Silva's research focuses on the intersection of computer science, operations research, and artificial intelligence, applying metaheuristics, hyper-heuristics, and machine learning to solve constrained combinatorial problems like vehicle routing, personnel scheduling, and space allocation. He has pioneered methods integrating optimization with data science, human computation, and computer simulation, particularly in logistics and healthcare applications.

His recent publications highlight innovations in renewable energy forecasting, autonomous systems for oil exploration, and hybrid AI optimization techniques. Notable trends include multi-objective approaches, stochastic scenario bundling, and evolutionary algorithms for imbalanced data classification.

Scientific Awards:

• Fellow of Advance Higher Education
• FHEA (Fellow of the Higher Education Academy)

Landa-Silva has supervised numerous PhD students in topics such as workforce scheduling, nurse rostering, and logistics optimization. He has led over £1.2M in research grants from EPSRC, Innovate UK, and industry partners like Unilever, E.ON Energy, and Netacea Ltd.

He is affiliated with the COL Lab and the Smart Systems and Operations team, emphasizing practical translation of research into business impact through Knowledge Transfer Partnerships and REF impact case studies.