# 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.