Welcome to my personal GitHub page!
I am a researcher in Reinforcement Learning and Control methodology. I have a Ph.D. in Electrical Engineering from KTH Royal Insitute of Technology (Stockholm), where I was supervised by Prof. Proutiere Alexandre at the Division of Decision and Control Systems.
My research involves the study of Reinforcement Learning and data-driven control, including
- Exploration in Reinforcement Learning
- Reinforcement learning methodology
- Conformal prediction
- Learning in control
- Poisoning attacks on Sequential Decision Makers (Reinforcement Learning, Adaptive Control, Data-Driven Control)
- Attack detectability
Check out also my website
- Reading list: here I keep track of interesting papers
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- Model Free Active Exploration in Reinforcement learning
- Conformal Prediction for Off-Policy Evaluation in MDPs
- Data poisoning of linear systems
- OSRL-SC, Optimal Representation Learning in Multi-Task Bandits
- TZDDPC, Tube-based Zonotopic Data-Driven Predictive COntrol
- STTMPC, Self-Tuning Tube-based MPC
- Privacy Stochastic Systems
- DPE, Optimal algorithms for multi-agent bandit models
- Poisoning of Data Driven Controllers (TBA)
- Optimal Attacks on RL Policies (TBA)
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- PyZPC: an implementation of ZPC in Python (Zonotopic Data Driven Predictive Control)
- PyZonotope: a library that enables the usage of Zonotopes in CVXPY
- PythonVRFT: a python implementation of the VRFT algorithm
- PyDataDrivenReachabilityAnalysis: a python implementation of data-driven rechability analysis based on zonotopes
- PyQuadraticFormNormal: Python library that implements the computation of the distribution of a linear combination of non-centered χ2 random variables
- PyDeePC: A python implementation of the data-driven predictive control algorithm DeePC
- py-lower-bound-bai: A python library that solves the best-arm identification problem in classical multi-armed bandit models.
- nnGA: Neural Network Genetic Algorithm library used for deep learning problems
- PytorchRBFLayer: Pytorch RBF Layer implements a radial basis function layer in Pytorch. Radial Basis networks can be used to approximate functions.
- AdaptiveControlLibrary: Adaptive Control Library in Matlab
- TBC
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- Algorand Royalty Fees: a tutorial that shows how to implement royalty fees on the Algorand blockchain
- TBC
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