Blind Lifter Saad Inspires UP Trainers with Immense Strength Gains
posted 14 Dec 2016Everyone who comes to Ultimate Performance has their own struggles to overcome as they work to revolutionise their...
My career spans from theoretical AI research to practical high-performance software development. During my PhD in Constraint Programming at the University of St Andrews, I developed novel approaches to complex optimisation problems across vehicle routing, shift scheduling, resource allocation, and supply chain management. My research culminated in ATHANOR, a constraint local search solver that outperforms existing solutions like Google’s OR-tools on large-scale problems through innovative dynamic scaling constraint techniques.
In financial markets where microseconds translate directly to competitive advantage, a comprehensive understanding of the entire tech stack is crucial. I have experience designing and maintaining both low-level C++/Rust applications and high-level multi-service, distributed systems.
Through my ventures Fielder and SmartRev, I’ve developed sophisticated AI agents for lead nurturing, appointment setting, sales automation, and staff management. This work has expanded into consulting, where I help businesses implement AI solutions to streamline their operations—from automated procurement to intelligent customer service systems.
Beyond technology, I find joy in playing piano, maintaining an active lifestyle at the gym, and exploring the culinary arts. I’m always open to discussing challenging technical problems or potential collaborations. Feel free to reach out.
This paper presents ATHANOR, a novel local search solver that operates on abstract constraint specifications of combinatorial problems in the ESSENCE language.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19)We propose a local search solver that operates directly on the high level structures found in the essence abstract constraint specification language.
Modeling and Reformulation 2018Structured Neighbourhood Search (SNS) is a framework for constraint-based local search for problems expressed in the ESSENCE abstract constraint specification language.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)educational HISTORY
Automatically exploiting high-level problem structure in local-search
Degree classification: 1:1 .
Work HISTORY