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Research Interests

My research lies at the confluence of industrial statistics, machine learning, and energy analytics. I am particularly interested in active learning, causal inference, deep learning, design of experiments, statistical process control, and time series analysis. Recently, my work has focused on the intersection of these methodologies with electricity markets, leveraging causal inference to uncover the relationships between renewable energy integration and market dynamics. The overarching goal of my research is to enhance the accuracy, reliability, and adaptability of monitoring and predictive models for industrial and energy systems, addressing both theoretical and applied challenges.

Core competencies: data analytics, machine learning, active learning, anomaly detection, causal inference.

Application areas: energy, business analytics, manufacturing.

Education

• Technical University of Denmark (DTU), Ph.D. in Applied Mathematics and Computer Science (Double Degree), 2020 – 2024

• Norwegian University of Science and Technology (NTNU), Ph.D. in Mathematical Sciences (Double Degree), 2020 – 2024

• Sapienza University of Rome, M.Sc. in Management Engineering, 2016 – 2019

• Sapienza University of Rome, B.Sc. in Management Engineering, 2012 – 2016

Academic Appointments

• Imperial College London, Analytics & Markets Lab, Research Associate, 03/2024 – Present

• Technical University of Denmark (DTU), Section for Statistics and Data Analysis, Ph.D. Researcher, 11/2020 – 02/2024

• Georgia Tech, H. Milton Stewart School of Industrial & Systems Engineering, Visiting Researcher, 09/2023 – 12/2023

• Norwegian University of Science and Technology (NTNU), Section for Statistics, Visiting Researcher, 01/2022 – 06/2022

Industrial Experience

• National Energy System Operator (NESO), Research Consultant, 03/2024 – Present

• British Telecom (BT), Business Analyst (Graduate Programme), 09/2019 – 10/2020

• WWF, Data Analyst, 01/2018 – 03/2019

• Generali, Data Analyst Intern, 10/2017 – 01/2018

Other Positions

• European Network for Business and Industrial Statistics (ENBIS), Leader of the Special Interest Group "Young Statisticians", 2023 – Present

Service

• Professional affiliations: European Network for Business and Industrial Statistics (ENBIS), Institute for Operations Research and the Management Sciences (INFORMS), International Statistical Engineering Association (ISEA).

• Journal reviewing: 

  • International Journal of Forecasting (Elsevier)

  • Expert Systems with Applications (Elsevier)

  • Journal of Data and Information Quality (ACM)

  • Journal of Artificial Intelligence Research (JAIR)

  • Infrared Physics and Technology (Elsevier)

  • Journal of the Taiwan Institute of Chemical Engineers (Elsevier)

• Program committee: Interactive Adaptive Learning Workshop at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD).

• Conference volunteer: ENBIS Annual Meeting 2022, ENBIS Spring Meeting 2023.