I'm a curious engineer, a savage astrophotographer and a super enthusiast data scientist.

My scientific studies and my eternal passion for innovations and technologies led me to have a strong analytic approach that helps me to easily solve problems or technical difficulties.

After my Master Degree in Civil Engineering, and taking note of the real situation of this field in my country, I realized to never want to work as a Civil Engineer: a job too static for me and still full of inertia. So I took the path of Computer Science.

I think my variegated and indipendent studies, combined with my insatiable curiosity, allowed me to reach a high rate of learnability and flexibility.

From 2018 to 2019 I swam in the restless ocean of Internet of Things, Data Analysis and Big Data working on cutting edge projects for big companies in the ELIS Innovation Hub. Currently as incurable curious I work as a Data Scientist in a Space, Industry & IoT focused company called Sitael.

Here is my story...

   

The First "Civil" Age

#engineering, #modeling, #photography

During this period I studied the basics of civil engineering: calculus, physics, analytical mechanics, fluid dynamics and continuum mechanics. In 2014 I ended the three-year Bachelor Degree in Civil Engineering with the discussion of the thesis "Open-source models for Maritime Hydraulic Engineering Applications" (Fig.1, 2). In 2014 I approached astrophotography for first time, an extremely technical branch of photography (Fig.4).

Ocean Hydraulic Models
Fig. 1 - One of the global oceanic model analyzed during the thesis.
Fig. 2 - One of the mid size coastal model explored during the thesis.
Fig. 3 - Design of a water supply system. One of the dozens of projects realized during the Civil Engineering degree.
Fig.4 - A photo of a huge portion of the night sky obtained through advanced image processing methods and mosaicing.
 

 

The ImAge

#signalprocessing, #remotesensing, #astrophotography

After completing my Bachelor's Degree I enrolled in the Master course of Civil and Environmental Engineering and independently started my studies about digital imaging, astrophotography, sensoristics and image calibration techniques. The circle closed with the Satellitary Remote Sensing and GIS extra courses. These led me to explore environmental modeling (Fig.5), machine learning, computer vision and microcontrollers such as Arduino and RaspberryPi.

The academic course was concluded with the Master Thesis focused on the complete calibration and automation (with R, Python and Arduino) of a hyperspectral imaging systems for drones (UAV) purchased by the University of Palermo (Fig.7).

During this adventure I'm been having the immense pleasure of publishing my astropics in magazines like Coelum, Astronomy Now, Astronomy Magazine, BBC Sky at Night and many more. If you want to take a look please click here to go to the dark side .

Fig.5 - An image showing sea temperature obtained applying remote sensing models to multispectral images taken from MODIS satellite.
Fig.6 - A photo of the Pleiades Nebula after and before calibration process.
Fig.7 - The Hyperspectral Camera for drones inspected and calibrated during the Master Thesis: the Rikola HSI.
Fig.8 - The setup I use during the astrophotography sessions.
 

 

The Data Age

#machinelearning, #dataanalytics, #cloudcomputing

Computer Science, these are the keywords that are bouncing inside my life since november 2017. At the end of that year I decided to take the path just tasted during the Remote Sensing course: that of Data Science and Machine Learning. So I decided to move to Rome to enroll in a postgraduate Master course provided by ELIS Digital University named MIDAS "Managing IoT, Data Analytics & Security". During this 9 months practice-and-theory course I studied and experimented on topics in the field of Machine Learning, Cloud Computing, Internet of Things and Big Data.

After the Master course I have been offered me to start an industrial PhD, a program in which I do research on topics faced during work, with the Università Campus Bio-Medico in the Computer Systems & Bioinformatics research group. I accepted.

Fig.9 - A graph extracted from a Dimensionality Reduction model. This is an aggregation of similar time series.
Fig.10 - Plant Anomaly Score: aggregated physiology of systems obtained with machine learning models to detect abnormal behaviors.
Fig.11 - DBSCAN: a classical density based algorithm in this case applyied to detect abnormal network traffic activities (network data).
 

Now, after some months of running-in I have worked in the field of Big Data and Machine Learning for big companies (A2A, Generali Assicurazioni, ENEL Global Thermal Generation, ENEL Green Power). I have been working on projects that covered areas ranging from Web Scrapers to Web API development, from Natural Language Processing to Time Series Forecasting. My main activities are: Data Modeling, Project Management, Mentoring and Teaching.

Some of the latest projects:

  • Development and deploy of an anomaly detection model for the discovery of cyber attacks to power plants. The particularity of this model is that it analyzes in realtime both IT network traffic data and OT signals like plant temperatures and pressures. The output is a KPI indicating the “anomaly status” of the plant.
  • Development of a scalable tool for the inspection and forecast of chilean power plants panels' soiling using available field sensors with the final aim of optimizing cleaning sessions.
  • Development of a fault detection model to help recognize incipient failures and malfunctions of General Electric turbine combustors in power plants using probes data.

Other projects involved Web Scraping with Python for database building, prototyping of an IIoT energy harvesting solution for data collection and inspection of hard-to-reach areas in which resides pipes and open channels.

For these projects we used both traditional and machine/deep learning models such as: SARIMAX, SOMs, Extreme Gradient Boosting or LSTM Neural Networks.