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Steve Paul

Ph.D. Candidate

Steve Paul is a highly skilled professional in Optimization, Surrogate Modelling, Robotics, Machine Learning, and Artificial Intelligence.

With a strong background in Navigation, Motion Planning, Controls, and Collision Avoidance for autonomous robots, Steve excels in Algorithm Development and applying Heuristic Optimization with Machine Learning to create optimal decision-making processes for autonomous agents.

Steve specializes in Optimization Modelling with constraints handling for complex problems, as well as developing Bio-inspired Algorithms for autonomous agents and Decentralized Decision-Making algorithms.

His expertise spans across various algorithms including Genetic Algorithms, Particle Swarm Algorithm, Ant Colony Optimization, Simulated Annealing, Predator-Prey Algorithms, and more. He is well-versed in Gradient-based optimization, Neural Networks, and Control Systems.

In his Master's thesis titled 'A Bio-inspired Neural System for Energy Optimal Collision Avoidance by Unmanned Aerial Vehicles,' Steve demonstrated his proficiency in developing optimal collision avoidance strategies using Neural Network classifiers and regression.

His knowledge extends to numerous areas such as Traveling Salesman Problems, Design of Experiments (DOE), Surrogate Modeling, Machine Learning techniques like Logistic Regression, Support Vector Machines (SVM), and Pattern Recognition.

Steve Paul also has hands-on experience with advanced concepts like Reinforcement Learning, Neuroevolution, Control Systems including PID, PD, State Estimation, Sensor Fusion, and various filters like Kalman Filters, Extended Kalman Filters (EKF), and Particle Filters (Monte-Carlo Localisation).

Steve Paul
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Location

Buffalo, New York