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Industrial Engineering, Multi-criteria Decision Making, Optimization Techniques, Global/International Facility Location, Agent-based Modeling
Metaheuristics In Social and Industrial Problems.
Bio-inspired Optimization.
Agent-Based Modeling.
Chaos Theory.
Natural Language Processing.
Alma Mater: FT Ranked No. 10 Business Economics school.
Ranked No 1 in an engineering mathematics national level test.
Ranked No 1 in an analytics program at IIT Bombay.
B.E. Mechanical Engineering.
MTech 1st year Modelling and Simulation.
PhD 1st year Strategy Simulation at The University of Texas at Dallas.
Tuition scholarships at the Santa Fe Institute.
GMAT 730
5 years of operations research work experience.
Published and presented a poster at the The Operational Research Society, UK Annual Conference 2021 integrating strategy and applied math. Took on and resolved a longstanding problem.
Solo authored leadership article in the Analytics magazine Nov/Dec 2021 issue from INFORMS.
Solo authored theoretical optimization abstract at the ICORES 2022 Conference.
Authoring the black-tie, board room manual - The Change Management Series Volume 1 Kindle edition on Amazon March, 2022.
I am a participant at the Financial Modeling World Cup 2022.
Build spiders for scraping web data.
Agent-based computer simulation in strategy, the resource-based view in strategy, agency theory and top & middle management incentives, organizational economics, algorithmic game theory, financial friction, financial econometrics.
Dr. Cheick Amed Diloma Gabriel Traore is a researcher specializing in modeling multi-agent systems. He earned his PhD from Cheikh Anta Diop University (UCAD) in Senegal. His doctoral research focused on the formalization and simulation of Sahelian transhumance as a complex adaptive system. Utilizing mathematical and computational techniques, he developed agent-based models to analyze the spatiotemporal dynamics of transhumant herds, taking into account factors such as herd behavior, environmental conditions, and socio-economic pressures.
To design the models for his dissertation, Dr. Traore conducted extensive fieldwork in Senegal. He collaborated with interdisciplinary teams to collect data on transhumant practices within the Sahelian ecosystem. With this data, he created a multi-objective optimization framework to model the movement decisions of transhumants and their herds. Additionally, he developed a real-time monitoring system for transhumant herds based on discrete mathematics. His doctoral research was funded by the CaSSECS project (Carbon Sequestration and Sustainable Ecosystem Services in the Sahel).
Before pursuing his PhD, Dr. Traore obtained both a master’s and a bachelor’s degree in mathematics from Nazi Boni University in Burkina Faso. During his studies, he developed a rectangular grid for image processing and applied the Hough transform to detect discrete lines. His master’s and bachelor’s degrees were funded by the Burkinabe government.
Currently, Dr. Traore is an Assistant Professor at the Institute of Computer Engineering and Telecommunications at the Polytechnic School of Ouagadougou. In addition to his role in student training, he is working on integrating viability theory with agent-based modeling to address sustainable development challenges in rapidly changing and complex socio-economic systems. His research has been published in several renowned conferences and scientific journals, and he continues to actively contribute to the fields of complex systems modeling and image processing.
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My research interests consist of
* Artificial Intelligence
* Machine Learning
* Data Mining
* Lead Scoring
* Search Engine Optimization
* Digital Marketing
* Healthy Living
* Health & Wellness
Operations Management Production Planning Optimization Agribusiness Management Agent Based Modeling Complex Systems Biology Agent Based Intelligent Systems Complex Systems Complex Adaptive Systems Complex System Optimization, Optimization-simulation models.
Simulation and Optimization
Supply Chain Management
Data Analytics
Agent-Based Modeling
Dr. Mariam Kiran is a Research Scientist at LBNL, with roles at ESnet and Computational Research Division. Her current research focuses on deep reinforcement learning techniques and multi-agent applications to optimize control of system architectures such as HPC grids, high-speed networks and Cloud infrastructures.. Her work involves optimization of QoS, performance using parallelization algorithms and software engineering principles to solve complex data intensive problems such as large-scale complex decision-making. Over the years, she has been working with biologists, economists, social scientists, building tools and performing optimization of architectures for multiple problems in their domain.