About me

Hi there! I’m Emanuele, a final-year BSc student in Physics and volunteer research assistant in Artificial Intelligence and Cyber-Physical Systems with the Bortolussi Group (a.k.a. [email protected]) at the University of Trieste (Italy).

During my studies I focused mainly on statistical/computational methods for data analysis in experimental physics and signal processing. More recently, I moved toward more general artificial intelligence, (statistical) machine learning and analysis of massive datasets, mainly centered around decision problems and planning/control, both theoretical and applied.

My main research interests include:

  • Core methods in classical statistics and machine learning (i.e. decision trees, boosting, Petri nets, genetic programming);
  • Core methods for optimization in highly-dimensional manifolds with minimal assumptions (i.e. SGD, MonteCarlo-based, tempering);
  • Artificial neural computation and deep learning (i.e. CNNs, RNNs, autoencoders/VAEs, GANs, novel neural architectures, neural ODEs);
  • Statistical network models for inference (i.e. energy-based NN, Boltzmann machines, Belief Networks) or simulation (i.e. diffusive processes on networks, computational epidemiology);
  • Bayesian statistics and Bayesian ML methods - shallow and deep - including approximate variational inference;
  • Prediction and control - through reinforcement (including Deep RL), logic and hybrid approaches - under uncertainty and/or in complex environments;
  • Scientific high-performance computing, and, more generally, ways to harness computational power and efficiency from dedicated hardware architectures (i.e. GPGPU computing, neuro-/physio-morphic hardware).

I am deeply fascinated, too, by the bidirectional interplay between artificial and biological intelligence (i.e. bio-inspired AI methods, unconventional computing; AI applied to systems biology and neuroscience), and between artificial intelligence and physics (i.e. quantum machine learning; machine learning in high-energy physics).

When not about any of the above, I usually rant about optimality in policy-making and politics, energy policy, and open science/software.

Also, I usually don’t bite.

My (computational) toolbox

In trying to solve whichever problem I have at hand, I will happily use any combination of the following, provided they run on Linux.

  • Python & its ecosystem (NumPy, SciPy, Pandas, PyTorch, Keras, modern TensorFlow, );
  • Modern C++ (>=2011) with libraries (ROOT, Armadillo, Boost, );
  • Integrated computational languages/environments, i.e. MATLAB and Wolfram Mathematica;
  • Bash/Zsh as a scripting language for basic orchestration and system automation;
  • Fortran, F# or R if no other option exist or I feel brave enough.

I’m also a diehard user of mechanical pencils and liquid-ink-based pens, for the purposes of problem solving - computational or otherwise.