import pandas as pd
pd.set_option('max_colwidth', 1000)
from IPython.core.display import display, HTML
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
display(HTML("""
<style>
.output {
display: flex;
align-items: center;
text-align: center;
}
</style>
"""))def load_map_frame():
with open('map_frame.html') as f:
return f.read()
import sqlite3
conn = sqlite3.connect('cv.db')
lf = pd.read_sql_query('SELECT * FROM users WHERE id="luca fontanili"', conn)
lf = lf.set_index('id')
display(HTML('<center><table><tr><td><img src="profile.jpg" width=200/>{}</td><td>{}</td></table></center>'
.format(lf.T.to_html(escape=False), load_map_frame())))
| <iframe src="https://www.google.com/maps/embed?pb=!1m18!1m12!1m3!1d2831.1517727885257!2d10.327056116089658!3d44.798095479098734!2m3!1f0!2f0!3f0!3m2!1i1024!2i768!4f13.1!3m3!1m2!1s0x47806ae0cec03315%3A0x63674c9ddee412c0!2sBorgo+Riccio+da+Parma%2C+34%2C+43121+Parma+PR!5e0!3m2!1sen!2sit!4v1544393049528" width="600" height="450" frameborder="0" style="border:0" allowfullscreen></iframe> |
def plot_working_experience(employee=None, size=700):
if employee is None:
raise Exception('Do you work on your own?')
from bokeh.plotting import figure, output_notebook, show
from bokeh.models import ColumnDataSource, LabelSet
output_notebook()
work_experiences = pd.read_sql_query('SELECT * FROM work_experiences where employee="{}"'.format(employee), conn)
keywords = work_experiences.keyword
randoms = np.random.random_sample((len(keywords),))
source = ColumnDataSource(data=dict(
x=np.random.random_sample((len(keywords),))*2,
y=np.random.random_sample((len(keywords),)),
keywords=keywords,
descriptions=work_experiences.description
))
TOOLTIPS = """
<div style="width:300px">
<span style="font-size: 17px;"><b>@keywords</b></span>
<span style="font-size: 15px;">@descriptions</span>
</div>
"""
p = figure(plot_width=size, plot_height=size, tooltips=TOOLTIPS, toolbar_location=None)
p.y_range.start = -0.1
p.y_range.end = 1.1
p.x_range.start = -0.2
p.x_range.end = 2.2
p.circle('x', 'y', size=15, source=source, color="#480968")
labels = LabelSet(x="x", y="y", text="keywords", y_offset=-30, text_font_size="10pt", text_color="#555555",
source=source, text_align='center')
p.add_layout(labels)
show(p)
plot_working_experience('Ubiq')<div class="bk-root">
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Business or sector Computer Vision
plot_working_experience('Datalogic', 500)<div class="bk-root">
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- Computer vision, data mining, use of Hadoop framework, information security, artificial intelligence, computer networks, mobile systems, OpenCV, Java, C/C++, VHDL, IT, Real-Time systems.
Thesis Development of parallel processing approach to compute Zero-mean Normalized Cross- Correlation. CUDA framework has been used to develop fast approach to detect characters in images, using Template Matching approach, based on Zero-mean Normalized Cross-Correlation measure.
Final Grade 110/110 with Honours
Thesis A Machine Learning project based on arm gesture recognition and humanoid imitation. Arm gestures are acquired by inertial motion sensors and modelled in an OpenGL 3D virtual space. A new method based on Functional Principal Component Analysis is used in MATLAB for both a supervised clustering of training data and gesture recognition. Recognized gestures are imitated by a small humanoid robot
Final grade 107/110
| Language | Listening | Reading | Spoken Interacion | Spoken Production | Writing |
|---|---|---|---|---|---|
| Italian | MT | MT | MT | MT | MT |
| English | C2 | C2 | C2 | C2 | C1 |
| German | A1 | A1 | A1 | A1 | A1 |
Levels: A1/2: Basic user - B1/2: Independent user - C1/2 Proficient user - MT Mother Tongue
I gained good communication skills speaking and interacting with customers and clents. I'm also part of the Data Science and Artificial Intelligence Group in Parma, and I hold a speeck about Machine Learning topics once every few months, usually in front of dozens of peoples.
Excellent social/organisational skills acquired during my experience as Team Leader. During my time in Ubiq I had to completely manage different projects with many clients, organizing work packages and distributing them across the team members.
def show_skills_plot(x_label, skills=None):
if skills is None:
raise Exception("you should have at least one skill, shouldn't you?")
from bokeh.io import show, output_notebook
from bokeh.plotting import figure
from bokeh.palettes import RdPu9
from bokeh.transform import factor_cmap
from bokeh.models import HoverTool, ColumnDataSource
output_notebook()
source = ColumnDataSource(skills)
p = figure(plot_width=800, plot_height=400, title="Experise level",
x_range=list(skills.description), toolbar_location=None)
index_cmap = factor_cmap('type', palette=RdPu9, factors=sorted(skills.type.unique()), end=1)
p.vbar(x='description', top='level', width=1, source=source,
line_color="white", fill_color=index_cmap)
p.y_range.start = 0
p.y_range.end = 10
p.x_range.range_padding = 0.1
p.xgrid.grid_line_color = None
p.xaxis.axis_label = x_label
p.xaxis.major_label_orientation = 1.2
p.outline_line_color = None
hover = HoverTool()
hover.tooltips = [
('Name', '@description'),
('Type', '@type'),
("Details", "@tooltip")
]
p.tools.append(hover)
show(p)
job_skills = pd.read_csv('job_skills.csv', sep=';')Programming Languages
show_skills_plot('Programming Languages', job_skills[job_skills.type == 'Programming Language'])<div class="bk-root">
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Other job-related skills
show_skills_plot('Skills grouped by type', job_skills[job_skills.type != 'Programming Language'])<div class="bk-root">
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</div>
Sport:
- American Football, 4 years’ experience in the Parma Panthers, interaction with American staff and players. Participation in European Championship with the Italian National American Football team
- Development of projects of personal interest using latest mobile and web programming languages (Android, HTML, CSS, SQL, JavaScript, Python) and tools (Nutch crawler, SonarQube, Tesseract).
Driving License: B (own car)
Publications
Arm Gesture Recognition and Humanoid Imitation Using Functional Principal Component Analysis,
J. Aleotti, A. Cionini, L. Fontanili, S. Caselli,
IEEE/RSJ International Conference on Intelligent Robotics and Systems (IROS),
Tokyo, November 2013
Honours and awards
Bronze medal of sporting merit given by CONI
Certifications
- Machine Learning, Stanford University, Coursera, #FGYRSK7XD8XG
- Using Python for Research, Harvard University, edX, #f079493d7801420388179e219e2a9d63
Projects
- Ti Frutta – The very first "cash back" app in Italy that allows the customer to earn by shopping
- F Abbigliamento – development of the Fontanili Abbigliamento website using Wordpress CRM.
- CV – Eye Detector: development of an eye detector in C++ based on Normalized Cross Correlation, robust to light variation
- DSS – Hadoop: implementation in Apache Hadoop environment of the Distributed Solving Set algorithm for outlier detection in large distributed data sets, using the MapReduce model
- Parma Panthers Official: development of the Android official application of the Parma Panthers, the American Football team 4 times champion of Italy in the main championship
- PicoPic: VHDL project of a Peripheral Interface Controller for a stereo vision system on FPGA, with a 8 bit RISC microprocessor PicoBlaze
- Wikiquote Search Engine: implementation of a web crawler for Wikiquote based on Apache Nutch and development of a Java search engine based on Apache Solr
- pysqoop: a Python package that lets you sqoop into HDFS data from RDBMS using Apache Sqoop (installable via pip)
- Member & Speaker of the Data Science & AI Group in Parma
pd.read_sql_query('SELECT * FROM books order by status desc, author asc', conn)
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
</style>
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| title | author | status | |
|---|---|---|---|
| 0 | Mastering Java Machine Learining | Kamath, Uday | reading |
| 1 | The Art of Computer Programming, Fundamental Algorithms | Knuth, Donald | reading |
| 2 | Test-Driven Development | Beck, Kent | read |
| 3 | Effective Java | Bloch, Josua | read |
| 4 | Design Patterns | Gamma, Helm, Johnson, Vlissides | read |
| 5 | 97 Things Every Programmer Should Know | Henney, Kevlin | read |
| 6 | Clean Code | Martin, Robert C. | read |
| 7 | The Clean Coder | Martin, Robert C. | read |
from math import pi
import pandas as pd
from bokeh.io import output_notebook, show
from bokeh.palettes import Category20
from bokeh.plotting import figure
from bokeh.transform import cumsum
output_notebook()
x = {
'Sleep': 6.5/24,
'Work': 9/24,
'Gym': 1.5/24,
'Study': 2/24,
'Other': 5/24
}
data = pd.Series(x).reset_index(name='value').rename(columns={'index':'country'})
data['angle'] = data['value']/data['value'].sum() * 2*pi
data['color'] = Category20[len(x)]
p = figure(plot_height=350, title="Pie Chart", toolbar_location=None,
tools="hover", tooltips="@country: @value%", x_range=(-0.5, 1.0))
p.wedge(x=0, y=1, radius=0.4,
start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
line_color="white", fill_color='color', legend='country', source=data)
p.axis.axis_label=None
p.axis.visible=False
p.grid.grid_line_color = None
show(p)<div class="bk-root">
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