Courses & Seminars


Students can select courses, under the guidance of supervisors, from those organized by the PhD program in Smart Computing as listed below as well as from those proposed by partner Universities:
In addition, courses can be also taken from the other PhD programs of the School of Engineering of the University of Florence: For all the above courses the authorization of the PhD commitee is not needed. It is instead required for any other courses. Rules for accounting credits (CFU) for courses and seminars can be found under the Procedures page.

Courses
  • Instructor: Prof. Luigi Chisci (UniFI)
    Period: 2-3-5-9-10 February 2026 (h. 9:00-13:00)
    Location: online via Google Meet (mandatory registration using the following link)
    Duration: 20h
    Abstract: The course will provide an overview of advanced research in estimation, specifically concerning the two topics of multi-agent and multi-object estimation. Multi-agent estimation deals with a network of agents with sensing, processing and communication capabilities that aim to cooperatively monitor a given system of interest. Multi-object estimation aims to detect an unknown number of objects present in a given area and estimate their states. Special attention will be devoted to the Kullback-Leibler paradigm for fusion of possibly correlated information from multiple agents and on the random-finite-set paradigm for the statistical representation of multiple objects. Applications to distributed cooperative surveillance, monitoring and navigation tasks will be discussed.
    Additional information: Links to online lectures
    link lecture 1, 2 February 2026, h. 9:00-13:00
    link lecture 2, 3 February 2026, h. 9:00-13:00
    link lecture 3, 5 February 2026, h. 9:00-13:00
    link lecture 4, 9 February 2026, h. 9:00-13:00
    link lecture 5, 10 February 2026, h. 9:00-13:00
  • Instructor: Dr. Alessandro Betti (IMT Scuola Alti Studi, Lucca)
    Period: 16-20 March 2026 (h. TBD)
    Location: TBD
    Duration: TBD
    Abstract: This course explores the structural parallels between physical laws and learning dynamics through the unifying lens of variational principles. Starting from the calculus of variations and the principle of least action in classical mechanics, the course develops a rigorous framework in which learning algorithms are interpreted as dynamical systems evolving in an energy landscape. The course introduces novel perspectives on optimization, regularization, and stability in machine learning. A central goal is to formulate online and recurrent learning as an optimal control problem, uncovering connections with Pontryagin’s principle and Hamiltonian dynamics. Students will gain both theoretical insight and practical tools to design learning algorithms grounded in physical and variational reasoning.
    Additional information: link
  • Instructor: Prof. Pericle Perazzo (UniPI)
    Period: 5-6-7-8 May 2026 (h. 14:30-17:30)
    Location: Dept. of Information Engineering (DII), Largo Lazzarino 1, Pisa
    Duration: 12 hours
    Abstract: This PhD course provides an in-depth exploration of Bitcoin and Blockchain technologies, offering participants a comprehensive understanding of their foundational principles and advanced concepts. The course will examine the cryptographic primitives and the decentralized paradigm that underpins Bitcoin, the structure of transactions, their validation and propagation within the network, the Proof-of-Work Consensus, the concept of Lightweight Clients. Building on this, we will explore Bitcoin’s scripting capabilities and the evolution toward programmable money. Through theoretical insights and practical examples, participants will gain a solid foundation for research and innovation in blockchain systems.
    Additional information: link
  • Instructor: Dr. Cosimo Rulli (ISTI-CNR, Pisa), Dr. Franco Maria Nardini (ISTI-CNR, Pisa), Prof. Rossano Venturini (UniPI), Dr. Salvatore Trani (ISTI-CNR, Pisa)
    Period: 24-25-26-27 June 2026 (h. 9:00-13:00)
    Location: room “seminari est”, ISTI-CNR, via G. Moruzzi 1, Pisa
    Duration: 16 hours
    Abstract: This PhD course focuses on Information Retrieval and discusses the state-of-the-art and the challenges in the two main areas of Web search. i) indexing and ii) query processing. The course introduces each area by discussing the state of the art in the field and by presenting the open research questions. The course emphasizes query processing, a research line where machine learning is important to advance the state of the art. After introducing the different query processing techniques, the course introduces supervised techniques explicitly focused on targeting the ranking problem and discusses several time and space efficiency/effectiveness trade-offs in query processing. The course will also provide an in-depth analysis of query processing techniques employing transformer-based large language models. Four hands-on sessions will cover indexing and query processing of public Web collections.
    Additional information: link
  • Instructor: Prof. Sajal Das, IEEE Fellow, Missouri University of Science and Technology, USA
    Period: 17-18-19 November, 2025 (h. 10:30-12:30 and 14:00-16:30), 20 November, 2025 (h. 10:30-12:30)
    Location: Aula Riunioni, Sixth Floor, Dept. of Information Engineering (DII), Largo Lazzarino 1, Pisa
    Duration: 16 hours
    Abstract:
    Additional information: link
Seminars
  • Instructor: Prof. Sajal Das, IEEE Fellow, Missouri University of Science and Technology, USA
    Period: 21 November 2025, h.15:00
    Location: room 211 CDM, viale Morgagni 44
    Duration: 1h
    Abstract: Global crop losses due to pests and diseases currently exceed 40%, resulting in economic losses of over $220 billion annually, which are projected to worsen under climate change. In this talk, we will present a cyber-agriculture framework that enables real-time, data-driven integrated pest management (IPM) by integrating AI/ML, IoT sensors, drones, and edge-cloud computing with a goal to design scalable AgTech solutions for resilient and sustainable crop protection. After outlining agronomic challenges and our vision of smart connected farms, we will present our novel machine learning pipeline for IPM. (i) CNN classifiers for pest detection achieving up to 95% mean average prediction, (ii) RL-motivated dynamic split-learning algorithm that adapts computation across devices and cloud server under resource constraints; (iii) a multimodal scheme that fuses weather, spectral indices, and soil data through tailored network branches and late-fusion regression, achieving robust pest-pressure forecasts; and (iv) personalized agronomic guidance via LLMs. Prototype testbed implementation using Raspberry Pi, camera sensors, and Long Range (LoRa) communication with real datasets will validate predictions and inference for pest detection
    Additional information: link
  • Instructor: Prof. Mohamed Daoudi, University of Lille, France
    Period: 13 November 2025, h.11:00,
    Location: room 204 CDM, viale Morgagni 44
    Duration: 1h
    Abstract: In this talk, I will present some of our recent results on human body shape analysis, learning human interactions, and how computer vision can serve as a tool for understanding mental health disorders.
    Additional information: link
  • Instructor: Dr. Thomas Besnier, University of Lille, France
    Period: 6 November 2025, h.11:15
    Location: room 006 CDM, viale Morgagni 44
    Duration: 1h
    Abstract: We introduce a novel framework to learn mesh deformations and interpolations at the point level, allowing localized control for Partial Non-Rigid Deformations and interpolations of Surfaces (PaNDaS). PaNDaS learns a per-face feature field on the source mesh and fuses it with a global encoding of the target, a deformation generator predicts a Jacobian field and recovers a smooth displacement, enabling precise regional control, pose mixing, and transferable local edits. Unlike previous approaches, our method can restrict the deformations to specific parts of the shape in a versatile way. Across various human body part datasets, PaNDaS achieves state-of-the-art interpolation accuracy and stronger locality than methods based on global shape codes or handles, while remaining robust to remeshing. We also demonstrate several localized shape manipulation tasks and show that our method can generate new shapes by combining different input deformations.
    Additional information: link

Universities of Florence, Pisa and Siena • 2015-2026