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Algorithms and complexity
Design and analysis of provably efficient algorithms, with attention to the structural boundary between tractable and intractable problems.
forming · Abu Dhabi · Distributed
Algorithms with guarantees for decisions at scale.
Theory Lab studies the mathematical foundations of efficient decision-making: algorithms, optimization, complexity, and strategic interaction under uncertainty.
Research
Proofs before promises.
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Optimization under uncertainty
Continuous, discrete, and robust optimization for problems whose data, constraints, or objectives cannot be treated as perfectly known.
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Games, incentives, and multi-agent decisions
Algorithmic study of strategic systems: mechanisms, pricing, stochastic games, and decisions shaped by the behaviour of other agents.
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Theory for high-impact systems
Mathematical methods for power, logistics, machine learning, and autonomous systems where scale and uncertainty make naive optimization unreliable.
Selected foundations
Guarantees readers can inspect.
Representative publications by the lab lead and coauthors establish the programme's foundations without being recast as lab outputs.
Dual bounded generation: Polynomial, second-order cone and positive semidefinite matrix inequalities
Khaled Elbassioni
Discrete Applied Mathematics
Heuristic (S, T) Solutions via an FPTAS for a One-Warehouse Multiretailer Problem
Waleed Najy, Ali Diabat, Khaled Elbassioni
Operations Research
Anti Tai mapping for unordered labeled trees
Mislav Blažević, Stefan Canzar, Khaled Elbassioni, Domagoj Matijević
Information Processing Letters
Method
Rigour with a useful target
Theory Lab shares REQS standards for evidence and credit while keeping proof, complexity, and optimization at its methodological core.
Make assumptions visible
Every theorem, model, and experiment states the conditions under which its conclusion can be trusted.
Prove the useful part
Correctness, running time, solution quality, and robustness are made explicit rather than implied.
Let applications sharpen theory
Power, logistics, learning, and autonomous systems supply constraints that reveal where new mathematics is needed.
Publish limits with results
Hardness, counterexamples, failed assumptions, and negative results are part of the scientific record.
Research lead
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Khaled Elbassioni
Lab lead · Professor of Computer Science · Department of Computer Science, Khalifa University, Abu Dhabi, UAE
Research leadership remains connected to public scholarly records, explicit assumptions, and independently stated affiliations.
People and affiliations