Rocco De Rosa
curriculum vitae
EDUCATION
2011–2014 PhD - Mathematics and Statistics for Computational Science, Università degli
studi di Milano, Milan, supervised by Nicolò Cesa-Bianchi.
Thesis: Confidence-based and Nonparametric Classification: Applications to Credal
Classification, Online and Active Learning.
2009–2011 MSc (110/110 cum laude) - Informatics, Università degli studi di Milano, Milan.
Thesis: “Time series classification by precise and imprecise Markov models with applications
in action recognition”
2001–2003 BSc - Digital Communications, Università degli studi di Milano, Milan.
1996–2001 Upper School - Informatics, I.T.I.S Stanislao Cannizzaro, Milan.
RELEVANT WORK EXPERIENCE
2015–current Data Scientist - Business Intelligence, KeyOS - Sisal , Milan.
Machine Learning solution for: Maximizing the Player Lifetime Value.
Skill acquired:
{ Predictions with Partial feedback (multi-armed bandit algorithms);
{ Recommendation Systems;
{ Personalized Promotions;
2014–current Postdoctoral Researcher, Università degli studi di Milano, Milan, supervised by
Nicolò Cesa-Bianchi.
Online Learning - Time Series Mining - Real Time Bidding.
Skill acquired:
{ Predictions with Partial Feedback (bandit algorithms);
{ Recommendation Systems;
{ Scalable prediction algorithms (Stream Data Mining);
{ Real Time Bidding;.
2014–2014 Research Assistant, Oxford Brookes Vision Group, Oxford.
Computer Vision. Gesture-Action recognition.
Skill acquired:
{ Feature extraction from video frames;
{ Real Time Activity Recognition;
{ Multilinear classification (linear classifier where each sample has more than one label).
2012–2013 Data Scientist, Agenzia delle dogane, Rome.
Risk analysis of customs bills.
Skill acquired:
{ Analysis of complex system;
{ Data integration and transformation from heterogeneous source data;
{ Real-time automated detection of trade customs frauds based on the analysis of customs
declarations.
2011–2012 Research Assistant, Swiss RE Ltd, Zurich.
Text mining applications and complex data extraction.
Skill acquired:
{ Auto Categorization of documents.
{ Data Extraction from documents.
2010–2013 Research Assistant, IDSIA, Lugano.
Time series classification with imprecise models.
Skill acquired:
{ Robust classification algorithms;
{ General imprecise probabilistic methods (credal sets, convex optimizations, utility
measures).
OTHER WORK EXPERIENCE
2013–2013 Informatics consulting, Esalpi, Milan.
Optimized Web application for smartphone.
2009–2010 Informatics consulting, Links, Milan.
Java Desktop application.
2007–2008 Software designer, Linkas, Milan.
Java J2ME mobile application.
2004–2007 Analyst-Developer, Esalpi, Milan.
Java Web enterprise application.
2003–2004 Developer, Accenture, Milan.
Java Web and Desktop application.
2002–2003 Junior developer, Plug-in, Milan.
Visual basic Desktop application.
LANGUAGES
Language mother tongue
Italian
Language intermediate
English
KEY SKILLS
IT skills { Experienced in programming in Java, C++, MATLAB, and using database such
as Sql server , MySQL and Access;
{ Proficient in many Microsoft Office packages, UNIX and Windows operating
systems;
{ Experienced in Data Mining Frameworks: WEKA (Data Mining Software in Java),
MOA (MASSIVE ONLINE ANALYSIS), Vowpal Wabbit (Scalable implementation
of Online Machine Learning).
Data Mining { KEYwords (Field|Application): Data Mining (Data Analysis), Classification (Pheskills
nomena Predictions), Clustering (e.g. Customer Segmentation), Recommendation
System (e.g. Rating - Preferences User-Products), Filtering (e.g. Spam Filtering, Real-time automated detection of frauds),Real Time Bidding (e.g. place
Automatically Auction Reserve Price), Computer Vision (e.g. Human Activity
Recognition on Video Stream, Continuous Activity Recognition), Text Mining
(Auto Categorization of Documents), Big Data (Scalable Prediction Models),
Learning with Partial Feedback (e.g. Optimizing Online Display Ads, Ad Click
Prediction), Active Learning (Save expensive annotation process in prediction
models learning, rare class detection (fraud detection)).
{ Designer/analyst/programmer for Data Mining problems: data analysis, data
extraction and transformation, feature engineering, model selection, model tuning,
experiment setting, testing protocol, report and results analysis;
{ Know-how in statistical learning theory: major classification algorithms (Bayesian
methods, general linear models, decision trees, time series classification, credal
classification), classification paradigms (batch, online, incremental, active, selective sampling, filtering, predictions with Partial feedback), optimization tasks
(convex, quadratic, heuristics), major clustering techniques (partitioning methods,
hierarchical methods, density-based methods), probabilistic models (Bayesian
models, Imprecise probabilities models, Markov models, frequentist models).
People & { Supervised the laboratory project of an undergraduate, both day-to-day running
Project
of the project and its overall planning;
Management { Mentor to other postgraduate students.
Problem { Working across different scientific areas of Machine Learning, demonstrated
solving
independent though in analysing problems, adopting suitable strategies and
developing new techniques. The results are being reviewed for publication (see
my website).
INTEREST AND ACTIVITIES
Sports runner, skier, walk in nature
Chess amateur player
Travel an avid backpacker traveller