Ph.D. in mathematics, Senior Optimization and Operation Research Analyst
President and Chairman at SATWII Solutions
HELLO,
Welcome to my web site
I obtained a Ph.D. in mathematics with a specialization in number theory at the Université des Sciences et technologies de Lille I. This experience ignited my passion for industrial research, particularly in the fields of optimization and operations research.
Throughout my career, I have gained significant experience in research and development (R&D), as well as in managing and fostering scientific development. I have worked in various settings, including universities, research centers, and industrial environments. My expertise lies in software development, identifying and analyzing management issues, devising solutions for operational challenges, and providing training and supervision to teams.
I am fluent in both French and English, and possess strong organizational and communication skills. Additionally, I am highly analytical, rigorous, autonomous, creative, and innovative.
ABOUT ME
Dr. Lo strengths are:
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Mathematical modeling,
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Creating and validation of mathematical algorithms.
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Knowledge of computer programming (C / C ++ Object-Oriented Programming (OOP)).
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Dr. Lo values are : credibility and reliability.
FAVORITE QUOTE
"All obstacles in life are mere opportunities. - (Written on a beach at Hobson park)".
The only real mistakes are the ones we make repeatedly. The others are learning opportunities.
Dalai Lama
RECENT PROJECTS
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Optimization of Multi-Satellite Operational Schedules Towards Enhanced Communication Capacity.
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Optimization of UAV Operational Schedules Towards Enhanced Communication Capacity. Communication constraints add an additional layer of complexity to our project. This could mean that the UAVs need to maintain a certain level of communication with a base station or each other during their flights. Communication constraints can impact the path planning and scheduling decisions to ensure that the UAVs remain within communication range.
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In this project, we focus on optimizing the allocation of search units for detecting a target. Our approach adopts a Bayesian framework, considering a prior distribution for the target's location that is known to the searcher. Additionally, we incorporate a function that links the conditional probability of detecting a target to the applied effort at a specific location.