working experience

data science




mobility, numeric
e-commerce, advertising

Dr Giovanni Marelli

Numerics - simulations

Research assistant 06-12

Signal theory

C++, qt graphic libraries

Studies and programming

LaTeX, fortran, c++

solid state

Monte Carlo simulations, openGL, c++

viral fusion

fusion pathway and activation probability for viral fusion

Molecular dynamics

Force decomposition, parallel high performance software, openMP, MPI

inclusion objects

event collection
Monte Carlo, Configurational Bias, Widom insertion. C++, openGL, CGAL

numeric calculation

event collection
Finite differences, 4th order 2d differential equation with boundary conditions


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Statistical mechanics, biophysics exercises

Enterprise planning

Data consultant 13-14

Offgrid electrification

php-mysql forms, R scripts

Databases - forms

php-mysql, spss, gtk

ERP CRM inventory

openErp, odoo, postres

webapp - portals

openLayers, facebook connector


Android studio, cordova

electric violins

arduino, teensy, openFrameworks

creative coding - arduino

openCV, openGL, openFrameworks, processing, arduino

d3 data visualization

data visualization and dashboards with d3.js

conversion uplift

A/B tests, conversion uplift


Data scientist 14-15


maxymiser, tealium

a/b testing

monitoring a/b tests

text mining

NLP, stemming, classification

customer feedback

Sentiment, promoter score, personas


webtrends, tealeaf, tealium, maxymiser

targeted advertising

look alike, conversion uplift


Sr Data Scientist 16-17

audience affinity

R, clustering, labelling


weblogs, geographical classification

time series

arma, exp decay, periodic decomposition


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Forecast on signal components

socio demo

verticality, crm data

data pricing

costs and performances

data visualization

data exploration with d3.js


content classification

time series dashboard

highcharts, html, css

audience offer

presenting a large number of targets


dmp, dsp, ssp, ad server, census

page labelling

content analysis, loop-ai, admantix

Mobility - transportation

movement patterns, commercial capture rate


ML Expert 17-19

event collection

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We collect an handover between cells per device (D1-Netz)

Events to movements

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We groups events into activities and trips

Activity probability

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triangulation, signal cleaning

Daily patterns

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train on census data

Tourist patterns

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BI tools, tibco, tableau

age, gender, device types

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movements across the country

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origin destination matrices

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important for local transportation companies


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we work on an efficient routing and infrastructure

local network

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Special modules consider city centers

subway module

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Distinguish the mean of transportation labelling cells (training data)

subway passangers

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Collecting information about commuters

Intensity of activities

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Activity intensity: breaks of motorway drivers

collect important features

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we select the most relevant features

training set

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geographical information to train system


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We train our models to predict important features

compare external sources

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Public information from different sources

capture rate

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Calculate the coverage of a commercial activity

whitespot analysis

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Customer potential on regions not covered within a isochrone

time dependent data

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Weather/time dependent data

geo static information

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Predict absolute values

learning process

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Scoring, cross validation, classification

evaluate data and time series

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Series clustering, distribution, class stability

Shared mobility

demand prediction, fleet optimization


AI lead 19-20

demand - offer areas

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Activation, revenues, app opening, errors

location intelligence

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Spot the relevant geo data for predictions

motion patterns

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Start, end, route, deviations, graphs

fleet dispatch

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optimization engine for fleet dispatch

graph routing

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from street network

ai - reinforcement learning

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reinforcement learning to tune the simulation

infrastructure design

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microservice based, parallel, asynchronous


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interaction frontend, ui interaction with the solution

documentation libraries

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documentation libraries