cv

PhD

Politecnico di Milano

Reinforcement Learning for Financial Trading

• Algorithimic Trading
• Reinforcement Learning
• Machine Learning

MSc in Computer Science and Engineering

Politecnico di Milano

Reinforcement Learning for Financial Trading

• Machine Learning
• Reinforcement Learning
• Applied AI in Biomedicine
• Artificial Neural Networks and Deep Learning
• Foundation of Operations Research

Performance Benchmarking of Deep Learning Applications

Research Project. Supervisors: Prof. Danilo Ardagna, Federica Filippini

• This framework is part of the AI-SPRINT Project funded by the European Union Horizon 2020 research and innovation programme
• Adaptation of the tf-slim by Google on Tensorflow 2
• a-GPUBench allows training a model on a target machine (e.g.a Server or AWS service) remotely
• The Architecture, the hyperparameters and the dataset are specified using an XML file

Premature Ventricular Complexes (PVCs) and Premature Atrial Complexes (PACs) detector using an ECG-based Deep Learning approach

Course Teamwork

• 1-D Convolutional Neural Network (1D-CNN) that classifies a beat as Normal Sinusoid Beat (N),Premature Ventricular Complexes (S) and Premature Atrial Complexes (V) relying on the ECG signal
• The starting dataset was composed by about 92% of N, 4.5% of V and 3.5% of S samples
• Our model reached 95% of accuracy on N, 83% on V and 76% on S on a separated test set with unseed patients
• We applied XAI techniques as LIME and GradCam to cross-check that the model was learning meaningful features as R-R peak distance.