Develop a Bot using Deep Q-Learning that automatically learns how to play the game Snake
Implement a Deep learning technique to transfer the style of a painting to a chosen image
Modeling and predicting the delay of 7 million US domestic flights using Big Data Tools.
Develop a Java application using Flink, implementing functionalities of Speed Radar, Average Speed Control and Accident Reporter on data streams.
Develop a Java program using HBase to create, load tables, and implementing four different queries to simulate the activity of any company that must manage, sell, and distribute products or services.
Implementation of different standard Monte Carlo approaches in path tracing, and comparison with Next event estimation
Master thesis researching Q-Learning, Deep Q-Learning and Sparse Neural Networks for Importance Sampling in physically based rendering.
Supervisor: Prof. Elmar Eisemann - TU Delft
Supervisor: Prof. Decebal Mocanu - TU Eindhoven
A two-year Master double degree programme, coordinated by the European Institute of Innovation and Technology (EIT). Major in Data Science, and Minor in Innovation and Entrepreneurship.
- Machine Learning algorithms and Deep Learning (NLP, Computer vision)
- Big Data Ecosystem: creation of Big Data application with Spark (Scala), Flink, HBase
- Data analysis with R
- Innovation and Enterpreneurship, focused on creation of Start-ups.
The Program is focused on Computer Science and Business IT. I followed joint courses with Delft University and Standford University. Courses:
- Software Systems Module (Programming, Network and Client-Server Interaction, Software Design)
- Finance for Engineering
- Communication technology for global work (Standford University)
- Cyber Crime Science (Delft University)
- Data Science - Human Computer Interaction
Thesis Project: "Mechanical design and virtual prototyping for thermal turbomachinery"
- Cost accounting and decision making, economic criteria for the dimensioning of industrial plants and facilities, layout planning
- C++ and Matlab programming
- Operations Research and linear planning for optimization
- Design methods and CAD Softwares (Autodesk Inventor, SolidWorks, SolidEdge, PTC Creo)
Focus on researching and applying machine learning techniques - such as Deep Reinforcement Learning and Inverse Reinforcement Learning - for autonomous driving.
Achieved 87% accuracy using custom Deep Learning algorithms for Image detection in aerospace applications (Python, Tensorflow, Keras).
- Product management: market analysis for military and civil helicopters, market research and analysis for unmanned aerial veichle
- Mechanical design and optimization for aerospace systems (drones, Airbus components)
- Fluid dynamics analysis and turbomachinery optimization by computational fluid dynamics method (CFD)