Machine Learning for Big Data - Machine learning is ideal for exploiting the opportunities hidden in big data. It delivers on the promise of extracting value from big and disparate data sources with far less reliance on human direction. It is data driven and runs at machine scale. It is well suited to the complexity of dealing with disparate data sources and the huge variety of variables and amounts of data involved - and unlike traditional analysis, machine learning thrives on growing datasets, meaning the more data fed into a machine learning system, the more it can learn and apply the results to higher quality insights. Freed from the limitations of human scale thinking and analysis, machine learning is able to discover and display the patterns buried in the data.

Missing Data Imputation - Dealing with missing data is an important step in dataset pre-processing since most statistical analysis techniques, data reduction tools, machine learning methods and recommender systems require complete datasets. There are many techniques that can be used to deal with the missingness, but the common approach is to make the most of the available data through minimizing the loss of statistical power and the bias inevitably brought by inferring values for the missing data. A more interesting problem arise when the size of the data grows in a way that the state-of-the-art techniques are no longer applicable due to the high computational time and memory required - here new distributed and online techniques should be studied and developed in order to face this new challenge.

Software Development Effort Estimation - Development effort is considered the dominant cost of software projects, thus effort estimation is a critical activity for planning and monitoring software project development and for delivering the product on time and within budget. Significant over or under-estimates can be very expensive for a company and the competitiveness of a software company heavily depends on the ability of its project managers to accurately predict in advance the effort required to develop software systems. In the literature several methods have been proposed in order to estimate software development effort. Among them, widely employed estimation methods try to explain the effort to develop a software system in terms of some relevant factors (named cost drivers), e.g., Linear and Stepwise Regression, Regression Tree, and Case Based-Reasoning. These methods exploit data from past projects, consisting of both factor values that are related to effort and the actual effort to develop the projects, in order to estimate the effort for a new project under development. The main research topics related to the software development effort estimation regard the definition and empirical evaluation of search-based approach for building novel estimation models and the definition and the empirical evaluation of functional metrics for sizing software products.

PhD in Computational Intelligence: Supervised Machine Learning Methods for Complex Data • University of Portsmouth - 2019

Bachelor Degree in Computer Science: 1st with Honours • University of Salerno - 2014

Improving Applicability of Nature-Inspired Optimisation • Paris - Oct 18-24 2017

International School on Mathematics “Guido Stampacchia” Workshop “Graph Theory, Algorithms and Application (3rd Edition) • Erice - Sep 8-16 2014

International Summer School on Software Engineering (11th edition) • University of Salerno - July 2014

Data Mining of satellite data for the study of natural hazards • University of Salerno - September 2013

Senior Manager of Data & Machine Learning Science • Expedia Group - Mar 2021 - Present

Data Science Team Lead, Machine Learning • Expedia Group - Sep 2020 - Mar 2021

Senior Data Scientist, Machine Learning • Expedia Group - Nov 2019 - Sep 2020

Data Scientist, Machine Learning • Expedia Group - Sep 2017 - Nov 2019

Data Scientist Intern, Machine Learning • Expedia Group - Jun 2017 - Sep 2017

Data Scientist Intern, Machine Learning • Expedia Group - Jun 2016 - Oct 2016

Honorary Senior Research Fellow • University College London (UCL) - Nov 2019 - Present

Visiting Researcher at the University of Oxford (Big Data Institute) under the supervision of Dr Antoniya Georgieva. • University of Oxford - Jan 2018 - Dec 2018

Erasmus+ Traineeship at the University of Salerno (DISA-MIS) under the supervision of Dr Roberto Tagliaferri. • University of Salerno - Mar 2017 - Apr 2017

Erasmus+ Traineeship at the University of Portsmouth (School of Computing) under the supervision of Dr Ivan Jordanov. • University of Portsmouth - Mar 2015 - Jul 2015

Research Assistant at the Penn State University (Department of GeoInformatics) under the supervision of Dr Guido Cervone. • Penn State University - Mar 2014 - May 2014

1. A. Petrozziello and C. Sommeregger, "Search Ranking At Hotels.com". (Under Review)

2. F. Sarro, A. Petrozziello, D.-Q. He, S. Yoo "A New Approach to Distribute MOEA Pareto Front Computation", in Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO’20 Companion).

3. A. Petrozziello and I. Jordanov, "Automated Deep Learning for Threat Detection in Luggage from X-ray Images". Special Event on Analysis of Experimental Algorithms (SEA 2019).

4. A. Petrozziello and I. Jordanov, "Feature Based Multivariate Data Imputation". The 4th Annual Conference on machine Learning, Optimization and Data science (LOD 2018).

5. A. Petrozziello, I. Jordanov, A.T. Papageorghiou, C.W.G. Redman, and A. Georgieva, "Deep Learning for Continuous Electronic Fetal Monitoring in Labor", IEEE 40th International Engineering in Medicine and Biology Conference (EMBC 2018).

6. A. Petrozziello, C. Sommeregger and I. Jordanov, "Distributed Neural Networks for Missing Big Data Imputation", IEEE International Joint Conference on Neural Networks (IJCNN 2018).

7. A. Petrozziello and I. Jordanov, "Column-wise Guided Data Imputation", 17th International Conference on Computational Science (ICCS 2017).

8. A. Petrozziello and I. Jordanov "Data Analytics for Online Traveling Recommendation System: A Case Study", IASTED's 36th International Conference on Modelling, Identification and Control (MIC 2017).

9. I. Jordanov, N. Petrov and A. Petrozziello, "Supervised Radar Signal Classification", IEEE International Joint Conference on Neural Networks (IJCNN 2016).

10. F. Sarro, A. Petrozziello and M. Harman, "Multi-Objective Effort Estimation", ACM 38th International Conference on Software Engineering (ICSE 2016). Supplementary materials

1. A. Petrozziello, A. Serra, L. Troiano, M. La Rocca, G. Storti, I. Jordanov and R. Tagliaferri, "Deep Learning for Volatility Forecasting in Asset Management", Soft Computing (2022).

2. A. Petrozziello, X. Liu and C. Sommeregger "A Scale Invariant Ranking Function for Learning-to-Rank: A Real-World Use Case", in arXiv preprint (2021)

3. V. Tawosi, F. Sarro, A. Petrozziello and M. Harman, "Multi-Objective Software Effort Estimation: A Replication Study", IEEE Transactions on Software Engineering (2021).

4. F. Sarro, R. Moussa, A. Petrozziello and M. Harman, "Learning From Mistakes: Machine Learning Enhanced Human Expert Effort Estimates", IEEE Transactions on Software Engineering (2020).

5. A. Petrozziello, I. Jordanov, A.T. Papageorghiou, C.W.G. Redman, and A. Georgieva, "Multimodal Convolutional Neural Networks to detect fetal compromise during labor and delivery", IEEE Access (2019).

6. A. Petrozziello and F. Sarro, "Linear Programming as a Baseline for Software Effort Estimation", ACM Transactions on Software Engineering and Methodology (2018).

7. I. Jordanov, N. Petrov and A. Petrozziello, "Classifiers accuracy improvement based on missing data imputation", Journal of Artificial Intelligence and Soft Computing Research (2018).

8. A. Petrozziello, G. Cervone, P. Franzese, S.E. Haupt, R. Cerulli, "Source Reconstruction of Atmospheric Releases with Limited Meteorological Observations Using Genetic Algorithms", Applied Artificial Intelligence Journal (2017).

1. A. Petrozziello, "Supervised Machine Learning Methods for Complex Data", PhD Thesis, University of Portsmouth (UK), June 2019.

1. Erasmus+ Traineeship Scholarship (960€) • University of Portsmouth - April 2017

2. 3 years full time PhD Bursary (£42000) • University of Portsmouth - September 2015

3. Erasmus+ Traineeship Scholarship (2400€) • University of Salerno - February 2015

4. Scholarship "Messaggeri della Conoscenza) (12000€)• MIUR (Italian Ministry of Education University and Research) - October 2013

Grants

Currently (co)advising PhD Students

1. Two PhD candidates co-supervised at University College London (UK) with Dr Federica Sarro.

Formerly (co)advised Bachelor and Master Students

1. One Master thesis co-supervised at University College London (UK) with Dr Federica Sarro.

2. Two Erasmus students (master thesis) co-supervised at University College London (UK), with Dr Federica Sarro (University College London, UK) and Filomena Ferrucci (University of Salerno, Italy).

Academic Year 2017/2018

1. First term: Artificial Neural Networks and Genetic Algorithms (NENGA), BSc year 3, Dr Ivan Jordanov, School of Computing, University of Portsmouth, Portsmouth (UK).

2. First term: Advanced Programming Concepts (ADPROC), BSc year 2, Dr Ivan Jordanov, School of Computing, University of Portsmouth, Portsmouth (UK).

Academic Year 2016/2017

1. Second term: Artificial Neural Networks and Genetic Algorithms (NENGA), BSc year 3, Dr Ivan Jordanov, School of Computing, University of Portsmouth, Portsmouth (UK).

2. First term: Advanced Programming Concepts (ADPROC), BSc year 2, Dr Ivan Jordanov, School of Computing, University of Portsmouth, Portsmouth (UK).

Academic Year 2015/2016

1. Second term: Artificial Neural Networks and Genetic Algorithms (NENGA), BSc year 3, Dr Ivan Jordanov, School of Computing, University of Portsmouth, Portsmouth (UK).

2. First term: Advanced Programming Concepts (ADPROC), BSc year 2, Dr Ivan Jordanov, School of Computing, University of Portsmouth, Portsmouth (UK).