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Research Fellows Directory

Ke Tang

Professor Ke Tang

Research Fellow


University of Birmingham

Research summary

My research falls into the general area of artificial intelligence. In particular, I mainly focus on metaheuristic search, e.g., evolutionary algorithms, which is a technology of choice for highly nonlinear and/or discontinuous real-world optimization problems. Recent development of computing and storage technologies (e.g., cloud computing) has offered powerful infrastructures/facilities to record and analyze the execution data of metaheuristic search methods. Under the support of a Newton Advanced Fellowship, I am closely collaborating with Professor Xin Yao at the University of Birmingham (the UK host of the Fellowship) on data-driven metaheuristic search. That is, leveraging on the abundant execution data generated during the search courses to design smarter meta-heuristic search methods, so as to tackle challenging optimization problems that cannot be solved by any existing approaches. Through both fundamental and applied research, we aim to advance the state-of-the-art of metaheuristic search, as well as to facilitate the solving of hard real-world optimization problems with ever-growing complexity.

Interests and expertise (Subject groups)

Grants awarded

Data-Driven Metaheuristic Search

Scheme: Newton Advanced Fellowship

Dates: Mar 2016 - Mar 2019

Value: £90,000

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