Francesco Battistone
Data AnalystProject & Product Engineer at Tattile
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Project & Product Engineer at Tattile
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Francesco Battistone is System Engineer at Tattile Brescia, Italy. He has got MSc in computer science at University Parthenope of Naples with Honor and academic mention. During his studies he has been working as research fellow and he has publisced some articles on tracking, action recognition and segmentation of human action. In the last five years, he has been working as System Engineer at Tattile Brescia, Italy..
University of Naples ‘Parthenope’
Master's degree, Computer Science
January 1, 2014 – January 1, 2017
University of Naples ‘Parthenope’
Bachelor's degree, Computer Science
January 1, 2010 – January 1, 2013
Tattile srl
Ingegnere di prodotto
June 1, 2022 – Present
Tattile srl
System Engineer
June 1, 2022 – Present
MERMEC
Algorithmic Engineer
December 1, 2017 – June 1, 2022
Treviso, Italia
Tattile srl
Algorithmic Engineer
June 1, 2017 – December 1, 2017
Brescia, Italia
ALTEN Italia
Algorithmic Engineer
June 1, 2017 – December 1, 2017
Milano, Lombardia, Italia
Prometeo - Fondazione Mario Diana Onlus
Vincitore dell'hackathon "Alberthon"
October 1, 2016 – October 1, 2016
Caserta, Campania, Italy
University of Naples, Parthenope
Fellow Researcher in Machine Learning, Computer Vision and Data Analysis
October 1, 2013 – May 1, 2017
Napoli, Italia
CVPRLab, Department of Science and Technology, University of Naples Parthenope
Tirocinio
March 1, 2013 – October 1, 2013
Naples, Campania, Italy
Best Structured Tracker (BST)
June 1, 2016 – Present
BST is based on the idea of Flock of Trackers: a set of local trackers tracks a little patch of the original target and then the tracker combines their information in order to estimate the resulting bounding box. Each local tracker separately analyzes the features extracted from a set of samples and then classifies them using a structured Support Vector Machine as Struck. Once having predicted local target candidates, an outlier detection process is computed by analyzing the displacements of local trackers. Trackers that have been labeled as outliers are reinitialized. At the end of this process, the new bounding box is calculated using the Convex Hull technique.
A parallel implementation of image denoising using Cuda and cuFFT.
January 1, 2015 – Present
A parallel implementation for image denoising on a Nvidia GPU using Cuda and the cuFFT Library The sofware: Automatically selects the most powerful GPU (in case of a multi-GPU system) Executes denoising Saves output as text file and image
Python 3.10
Udemy
June 24, 2026 – Present
Summer School Rough, Fuzzy and Beyond
University of Naples, Parthenope
June 24, 2026 – Present
Summer School Embedded Vision Systems
University of Naples, Parthenope
June 24, 2026 – Present
Machine Learning
Stanford University School of Engineering
June 24, 2026 – Present
CCNA 1 – Networking Basics
UNITeS CISCO Networking Academy
June 24, 2026 – Present
Deep Learning e Reti Neurali con Python
Udemy
June 24, 2026 – Present
Deep Learning Standard & Advanced Class
Cognex Corporation
June 24, 2026 – Present
Halcon training course
MVTec Software
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a diverse background spanning academic research, hackathon participation, and industry roles in companies like Tattile srl and MERMEC. The projects, particularly 'Best Structured Tracker' and 'A parallel implementation of image denoising', show a strong inclination towards complex analytical and computational tasks. While the experience is heavily skewed towards computer vision and algorithmic engineering, the 'Data Analyst' target role aligns with the data analysis aspects of their past work. The breadth of skills and certifications suggests a continuous learning mindset. However, the lack of explicit 'Data Analyst' roles or projects focused purely on business intelligence, reporting, or specific data visualization tools might indicate a slight misalignment with typical Data Analyst expectations, leaning more towards a Data Scientist or ML Engineer profile.
Soft Skills & Operational Fit
The candidate's experience in various engineering roles (Algorithmic Engineer, System Engineer, Product Engineer) suggests problem-solving capabilities and adaptability. Participation in an hackathon indicates teamwork and innovation potential. However, specific details on communication, stress handling, and team collaboration are not explicitly provided in the resume or assessment data.