Hossam Faris
Chief AI And Analytics Officer at Altibbi
Amman, Amman, Jordan
Summary
👤
Senior🎓
Top SchoolHossam Faris is a Chief AI and Analytics Officer and academic with over a decade of experience translating AI research into production across telemedicine, e-commerce, automotive tech, education, and national platforms. At Altibbi he has built and scaled AI/ML teams, led LLM fine-tuning and deployment, and operationalized recommendation and analytics systems that serve millions in MENA. He combines strategic advisory to C-level leaders with hands-on engineering—contributing optimization algorithms and benchmark suites to open-source projects like EvoloPy. A co-founder of the Evo-ML research group and a Clarivate Top Cited Researcher (top 1% in Computer Science), he bridges rigorous scholarship with pragmatic product delivery. Based in Amman and a professor at the University of Jordan, he is known for turning high-level AI roadmaps into measurable business impact.
10 years of coding experience
4 years of employment as a software developer
Master of Computer Science, Computer Science, Excellent, Master of Computer Science, Computer Science, Excellent at Jami'at Al-Balqa Al-TatbiqiyyaBachelor of Science - BS, Computer Science, 85.8 (Excellent), Bachelor of Science - BS, Computer Science, 85.8 (Excellent) at Yarmouk UniversityDoctor of Philosophy - PhD, e-Business, Doctor of Philosophy - PhD, e-Business at Università del Salento
Github Skills (9)
algorithm10
application-framework10
app-framework10
web-framework10
algorithms10
metaheuristics10
python10
implement10
optimization10
Programming languages (2)
Github contributions (5)
7ossam81/EvoloPy
May 2016 - Jul 2022
EvoloPy toolbox provides classical and recent nature-inspired metaheuristic for the global optimization.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:81 commits, 21 PRs, 77 pushes in 6 years 2 months
Contributions summary:Hossam primarily contributed to the development of benchmark functions and optimizers for the EvoloPy toolbox. Their commits added and updated benchmark functions in `benchmarks.py`, which are essential for evaluating the performance of optimization algorithms. The user also implemented a Particle Swarm Optimization (PSO) algorithm in `PSO.py`, and integrated it with the benchmark functions via the `optimizer.py` file. Further commits added MVO, GWO, CS, BAT, WOA and FFA optimizers and included updating results export functionality.
optimization-methodsglobal-optimizationevolutionary-algorithmstoolboxoptimization
7ossam81/MLP-optimizer
Mar 2017 - Jun 2018
Contributions:5 commits, 4 pushes, 1 branch in 1 year 3 months
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