Profile picture

Marco Mussi

Ph.D. Student
Politecnico di Milano


ORCID   ORCID

GitHub   GitHub

LinkedIn   LinkedIn

Google Scholar   Google Scholar



About Me


Marco Mussi is a Ph.D. student in Information Technology at the Department of Electronics, Information and Bioengineering of Politecnico di Milano. He received his Master's degree in Computer Science and Engineering at Politecnico di Milano in 2019. After a period as a research fellow in the AIRLab research team, he started the Ph.D. in collaboration with ML cube. His main research topics revolve around Artificial Intelligence and Machine Learning, focusing on reinforcement learning applied to advertising. He contributed to several industrial research projects funded by both private and public Italian companies.

Publications


International Conferences

  • Marco Mussi, Gianmarco Genalti, Francesco Trovò, Alessandro Nuara, Nicola Gatti and Marcello Restelli. Pricing the Long Tail by Explainable Product Aggregation and Monotonic Bandits. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022. (A* Core Ranking - Oral Presentation - 54/753 - top 7%)
    [Link] [Paper] [Poster] [Slides]

Journal

  • Marco Mussi, Luigi Pellegrino, Marcello Restelli and Francesco Trovò. An Online State of Health Estimation Method for Lithium-Ion Batteries based on Time Partitioning and Data-Driven Model Identification. Journal of Energy Storage, 55, 2022. (Q1 Scimago)
    [Link] [Paper]
  • Marco Mussi, Luigi Pellegrino, Marcello Restelli and Francesco Trovò. A voltage dynamic-based state of charge estimation method for batteries storage systems. Journal of Energy Storage, 44, 2021. (Q1 Scimago)
    [Link] [Paper]

Workshops

  • Marco Mussi, Alberto Maria Metelli and Marcello Restelli. Dynamical Linear Bandits for Long-Lasting Vanishing Rewards. Complex Feedback in Online Learning Workshop at International Conference of Machine Learning (ICML). 2022.
    [Paper] [Poster]
  • Gianmarco Genalti, Marco Mussi, Alessandro Nuara and Nicola Gatti. Dynamic Pricing with Online Data Aggregation and Learning. European Workshop on Reinforcement Learning (EWRL). 2022. (Oral Presentation - 10/96)
    [Link] [Paper] [Poster] [Slides]

Under Review

  • Marco Mussi, Alberto Maria Metelli and Marcello Restelli. Dynamical Linear Bandits. Under review. Neural Information and Processing Systems (NeurIPS) 2022.
  • Marco Mussi, Davide Lombarda, Alberto Maria Metelli, Francesco Trovò and Marcello Restelli. ARLO: A Framework for Automated Reinforcement Learning. 2022. arXiv preprint arXiv:2205.10416.
    [Link] [Paper]
  • Marco Mussi, Gianmarco Genalti, Alessandro Nuara, Francesco Trovò, Nicola Gatti and Marcello Restelli. Dynamic Pricing with Volume Discounts in Online Settings. Under review. Innovative Applications of Artificial Intelligence (IAAI) 2023.
  • Marco Mussi, Luigi Pellegrino, Oscar Francesco Pindaro, Francesco Trovò, and Marcello Restelli. A Reinforcement Learning Framework Optimizing Costs and State of Health for Secondary Batteries in Smart Grids. Under review. Engineering Applications of Artificial Intelligence.

Education


Ph.D. in Information Technology - Politecnico di Milano (Nov 2020 - now)
Ph.D. in Machine Learning. Focus on Pricing and Advertising Reinforcement Learning solutions.
Supervisor: Prof. Marcello Restelli
Relevant coursework: Reinforcement Learning, Online Learning and Monitoring

M.Sc. in Computer Science and Engineering - Politecnico di Milano (Sep 2017 - Dec 2019)
Main focus: Artificial Intelligence and Machine Learning
Scholarship: Tuition waiver for high academic performance
Relevant coursework: Machine Learning, Artificial Intelligence, Game Theory, Autonomous Agents and Multi- agent Systems, Foundations of Operational Research, Software Engineering, Principles of Programming Languages, Data Bases II

B.Sc. in Computer Science and Engineering - Politecnico di Milano (Sep 2014 - Jul 2017)
Relevant coursework: Software Engineering, Theoretical Computer Science, Communication Networks and Internet, Information Systems, Data Bases I, Computer Architecture and Operating Systems, Automatic Control, Calculus I, Calculus II, Linear Algebra and Geometry, Logics and Algebra, Statistics and Probability, Physics, Applied Physics

High School Diploma in Computer Science - IIS Galileo Galilei Crema (Sep 2008 - Jul 2014)
Main Focus: C, Java, HTML, CSS, Javascript

Experience


AI Researcher - ML cube (Nov 2020 - now)
Goal: develop algorithms for dynamic pricing and advertising optimization

Research Assistant - Politecnico di Milano (Jan 2020 - Oct 2020)
Supervisor: Prof. Marcello Restelli

Projects


Reinforcement Learning in Smart-grids - Ricerca Sistema Energetico (Feb 2020 - Feb 2020)
Focus: Exploit Reinforcement Learning solutions to preserve the battery State of Health in smart-grids, optimizing economic variables

Last-mile delivery optimization - PaxMile (May 2020 - Oct 2020)
Focus: Delivery allocation using Reinforcement Learning and bikers load estimation using Supervised Learning techniques

AD cube product release - ML cube (Nov 2020 - now)
Focus: Release of AD cube, a product for advertising optimization in online campaigns

Dynamic pricing for e-commerce - Euroffice (Feb 2021 - now)
Focus: Implementation of a dynamic pricing model for an e-commerce with over 20000 products

Master's Students Supervision


  • Gianmarco Genalti, "A Multi-Armed Bandit Approach to Dynamic Pricing". Co-supervision. Supervisor: Prof. Nicola Gatti (M.Sc. in Mathematical Engineering, December 2021)
  • Amedeo Cavallo, "A Combinatorial Multi-Armed Bandit Approach to Online Advertising Budget Optimisation". Co-supervision. Supervisor: Prof. Marcello Restelli (M.Sc. in Computer Science and Engineering, December 2021)
  • Oscar Francesco Pindaro. "Controlling Lithium-Ion Batteries Through Reinforcement Learning". Co-supervision. Supervisor: Prof. Marcello Restelli (M.Sc. in Computer Science and Engineering, April 2022)
  • Davide Lombarda. "Towards Automated Reinforcement Learning". Co-supervision with Alberto Maria Metelli. Supervisor: Prof. Marcello Restelli (M.Sc. in Mathematical Engineering, April 2022)
  • Thomas Petrone. "Hidden Markov Model for Single User Response Prediction in Digital Advertising Campaigns". Co-supervision. Supervisor: Prof. Marcello Restelli (M.Sc. in Mathematical Engineering, July 2022)
  • Alessandro Montenegro. Co-supervision with Alberto Maria Metelli. Supervisor: Prof. Marcello Restelli (M.Sc. in Computer Science and Engineering, in progress)
  • Andrea d'Silva. Co-supervision with Alberto Maria Metelli. Supervisor: Prof. Marcello Restelli (M.Sc. in Computer Science and Engineering, in progress)