Lina al-Kanj
Sr Applied Scientist @Amazon
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WORK HISTORY
Sr Applied Scientist @Amazon
New York, NY, US
I am a Senior Applied Scientist at Amazon SCOT working in the Specialized Selection Team. I work at the intersection of the Search, Forecasting and Optimization teams.
EDUCATION
American University of Beirut
Doctor of Philosophy (PhD), Electrical and Computer Engineering
The University of Texas at Austin
Visiting PhD Student, Electrical Engineering
American University of Beirut
Master of Engineering (MEng), Computer and Communications Engineering
Technical University of Munich
Visiting Master Student, Information Technology
Lebanese University
Bachelor of Engineering (BE), Electrical, Electronics and Communications Engineering
SKILLS
ABOUT LINA AL-KANJ
My main research interest is sequential stochastic decision making with emphasis on reinforcement learning, machine learning, deep learning and lookahead models. I work on the integration of Machine Learning for predictive modeling and Stochastic Optimization for decision making. One of my main points of strengths is combining these two areas together in one framework. The applications that I have addressed have rich problem domains, due to inherent stochasticity and the curses of dimensionality (e.g, large state, action, and outcome spaces) which requires approximations to get attainable solutions. The techniques that I have used to address such complex problems is proper uncertainty modeling, state space reduction and lookahead/value function approximations. I did also convergence proofs when attainable and worked on real-time information collection for data-driven decision making.My main expertise and research interests are:1- Sequential Stochastic Decision Making (Stochastic Modeling and Optimization).2- Markov Decision Processes, Dynamic Programming and Reinforcement Learning.3- Machine Learning and Statistical Learning (Predictive Modeling). 4- Neural Networks, Deep Learning and Deep Reinforcement Learning.5- Artificial Intelligence: Machine Learning, Reinforcement Learning, Deep Learning, Deterministic and Stochastic Optimization, Mixed Integer Programming, Graph Theory and Networks.6- Supply Chain, Recommendation Systems, Resource Allocation and Scheduling.7- Experimentation: A/B testing, Hypothesis testing and Inference.9- Programming: Java, C++, Python, SQL, CPLEX, GUROBI.
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