Discrete-Event Systems (Automata, Markov Chains, Petri Nets, ...) and Reinforcement Learning are the areas of my thesis and horizontal modeling approaches that can be used in several fields.
On the other hand, I'm passionate about problems of all fields since it's the connections between apparently different areas that I find more enticing.
Techniques Gonçalo Neto Uses:
Identification of abstraction layers; Solution transfer from other problems and fields; Modeling and computer simulation; Breakdown of the problem into smaller manageable ones; Creative and lateral thinking; Brainstorming if in a group; Hypothesis testing and Trial-and-Error.
Gonçalo Neto's Problem Solving Skills:
– Familiarity with several operative systems (Mac OS X, Linux and Windows).
– Working knowledge of several discrete event mathematical models.
– Experience with setting up small networks.
– Working knowledge of statistical learning/regression methods.
– Working knowledge of robotics related algorithms (vision, navigation, path planning, decision)
– Programming in several languages and taste for learning new ones.
– Experience in writing research papers using LaTeX.
Gonçalo Neto's Problem Solving Experience:
– I have worked in a team based robotics project where I developed path planning, navigation and vision algorithms.
– I have developed a novel systems modeling methodology that combines the fields of Discrete Event Systems (Finite State Automata, Petri Nets, ...) and Reinforcement Learning (Controllers that adapt to external input).