BEGIN:VCALENDAR VERSION:2.0 PRODID:-//CERN//INDICO//EN BEGIN:VEVENT SUMMARY:Advanced Statistical Modeling for Sustainable Finance (ASMSF) DTSTART;VALUE=DATE-TIME:20260907T060000Z DTEND;VALUE=DATE-TIME:20260911T215900Z DTSTAMP;VALUE=DATE-TIME:20260907T091723Z UID:indico-event-116@cern.ch DESCRIPTION:Composition Partnership\n\n\n Università degli Studi di Napol i Federico II\, Italy (Coordinator and Host Institution)\n Athens Universi ty of Economics and Business\, Greece (Sending Institution)\n Technische U niversität Dortmund\, Germany (Sending Institution)\n University of Twent e\, The Netherlands (Sending Institution)\n University College Dublin\, Ir eland (Sending Institution)\n University of Economics in Bratislava\, Slov akia (Sending Institution)\n Babeș-Bolyai University\, Cluj-Napoca\, Roma nia (Sending Institution)\n\n\nTopic of the program\n\nThis program provi des advanced training in statistical and machine learning techniques for s ustainable finance. A key component is the Hybrid Approach for the Analysi s of Complex Data Structures\, where participants will learn to combine tr aditional statistical methods with modern computational tools to address s ustainability-related financial challenges.\n\nThe course focuses on the i ntegration of Environmental\, Social\, and Governance (ESG) factors into f inancial decision-making\, risk management\, and investment strategies. Pa rticipants will explore hybrid methods to analyze complex data from variou s sources\, including ESG metrics\, financial time series\, and corporate reports\, using tools like R and Python.\n\nLearning Outcomes\n\nBy the en d of the program\, participants will:\n\n\n \n Apply hybrid approaches to analyze complex data structures in sustainable finance.\n \n \n Build stat istical and machine learning models to assess ESG factors and their financ ial implications.\n \n \n Understand and integrate sustainability metrics into financial decision-making.\n \n \n Perform advanced data wrangling\, cleaning\, and analysis for financial datasets.\n \n \n Utilize dashboards and reporting frameworks to present actionable insights.\n \n\n\nSchedule description\n\n1- Description of the physical component\n\nDates: 07.09.2 026 to 11.09.2026\nLocation: Naples\, University of Naples Federico II\n\n  \n\nStructure:\n\n \n\nSeptember 7\, 2026\n\n09:30–10:00 – Welcome Addresses by Institutional Representatives and Introduction to the Trainin g School – Maria Iannario\n\n10:00–17:00\n\n      Sustainable Finan ce: Spatial Statistics and Machine Learning for Climate and ESG Risk [1]\n \nInstructors:\n\n      Adriano Morales\, University of Twente\, The Ne therlands\n\n      Abdulaziz Yusuf Ali\, University of Twente\, The Net herlands\n\nShort description\n\nThis course introduces participants to su stainable finance through the lens of spatial data\, spatial statistics\, and machine learning. It begins with an overview of climate finance and sp atial finance\, emphasizing how asset locations\, environmental hazards\, and geospatial information can be combined to assess physical and transiti on risks. Participants will then explore key spatial statistical methods\, including geostatistics\, Bayesian modelling\, and spatial point processe s\, with applications to housing prices and climate risk\, deforestation a nd asset exposure\, hazard-related losses\, and transport emissions.\n\nMa terials > Download    \n\n-------------------------------------------- ---------------------------------\n\nSeptember 8\, 2026\n\n10:00–17:00\n \n      Sustainable Finance: Spatial Statistics and Machine Learning fo r Climate and ESG Risk [2]\n\nInstructors:\n\nAdriano Morales\, Universit y of Twente\, The Netherlands\n\nAbdulaziz Yusuf Ali\, University of Twent e\, The Netherlands\n\nShort description\n\nThe second part of the course focuses on ESG and financial materiality\, showing how spatial data can su pport the identification\, measurement\, and interpretation of financially relevant sustainability risks. Machine learning methods for spatial class ification and clustering will also be introduced\, with examples related t o deforestation and mining mapping\, rooftop damage classification\, envir onmental risk mapping\, and broader asset-level exposure assessment. The c ourse combines conceptual lectures with hands-on laboratory sessions in wh ich participants apply spatial and machine learning tools to develop risk maps and classification outputs.\n\nEvening session\n\nLaboratory lectures – supervised tutorial\, individual and team work.\n\nMaterials > Downlo ad      \n\n-------------------------------------------------------- ---------------------\n\nSeptember 9\, 2026     \n\n09:00 – 10:30: L atent Variable Models for Sustainable Finance\n\nInstructor:\nMaria Iannar io\, University of Naples Federico II\, Italy.\nRosa Fabbricatore\, Univer sity of Naples Federico II\, Italy.\nMaterials > Download      \n\n  \n\n11:00 – 13:00  Sustainability Analytics and Carbon Footprint Est imation in Sports \n\nInstructor:\n\nProfessor Ioannis Ntzoufras\, Depart ment of Statistics\, Athens University of Economics and Business\, Greece\ n\nShort description\n\nThis lecture introduces the fundamental principles and methods for analysing survey data in the context of sports sustainabi lity. Using data collected from spectators attending a match between the G reek National Football Team and the National Team of Ireland\, participant s will explore how survey analytics can be used to understand mobility pat terns\, transportation choices\, and environmental attitudes among footbal l fans.\n\nThe course demonstrates how survey data can provide valuable in sights into spectator behaviour and its environmental impact. Particular e mphasis will be placed on estimating the carbon footprint associated with match attendance\, with a focus on travel-related emissions and the role o f sustainable mobility practices.\n\nAll analyses will be conducted using R. Starting from descriptive and exploratory analytics\, participants will learn how to summarise and visualise fans’ attitudes towards sustainabi lity\, identify key mobility trends\, and quantify the environmental impac t of spectator travel. The course will then progress to more advanced anal ytical approaches for estimating the overall carbon footprint of the event and assessing factors associated with environmentally responsible behavio urs.\n\nBy the end of the lecture\, participants will have gained practica l experience in managing and analysing survey data\, interpreting sustaina bility-related indicators\, and producing evidence-based assessments of th e environmental impact of major sporting events.\n\nEvening session\n\nLab oratory lectures – supervised tutorial\, individual and team work.\n\nMa terials > Download      \n\n---------------------------------------- -------------------------------------\n\nSeptember 10\, 2026\n\n10:00–17 :00\n\nMethods for Dimension Reduction and Clustering for ESG Analysis\n\n Instructor:\n\nDimitris Karlis\, Athens University of Economics and Busine ss\, Greece\n\nShort description\n\nThis course introduces the basic ideas and principles of dimension reduction. It focuses on Principal Components Analysis\, including the underlying methodology\, the choice of the numbe r of components\, variants of the method\, and applications with real data .\n\nEvening session\n\nLaboratory lectures – supervised tutorial\, indi vidual and team work.\n\nMaterials > Download    \n\n \n\n----------- ------------------------------------------------------------------\n\nSept ember 11\, 2026\n\n10:00–17:00\n\n       Machine Learning and AI in Sustainable Finance\n\nInstructor:\n\nAssociate Professor Liana Stanca\, B abeș-Bolyai University / FinTech LivingLab\n\nShort description\n\nThis l ecture introduces the fundamental principles and practical methods of appl ying Machine Learning and Artificial Intelligence in the context of sustai nable finance and ESG-driven decision-making. Using real-world financial a nd sustainability datasets\, participants will explore how data-driven app roaches can be used to understand ESG performance\, climate risk exposure\ , and sustainability-related financial indicators across firms and markets .\n\nThe course demonstrates how machine learning models and visual analyt ics can support evidence-based sustainability assessment in finance. Parti cular emphasis is placed on the integration of predictive modelling and in teractive data visualization for ESG analysis\, including the development of dynamic dashboards for exploring sustainability metrics\, carbon exposu re\, and financial performance relationships.\n\nAll analyses will be cond ucted in R/Shiny or Python\, depending on the implementation\, combining e xploratory data analysis\, machine learning techniques\, and interactive v isualization tools such as Shiny\, Plotly\, or Dash. Starting from descrip tive analytics and data visualization\, participants will learn how to sum marize and interpret ESG indicators\, identify sustainability patterns acr oss firms\, and visualize key financial and environmental relationships th rough interactive dashboards.\n\nThe course then progresses toward supervi sed and unsupervised learning methods for ESG risk classification\, sustai nability profiling of companies\, and clustering of firms based on environ mental and governance characteristics. A dedicated component is included o n explainable AI techniques to ensure transparency and interpretability of machine learning outputs in financial decision contexts.\n\nBy the end of the lecture\, participants will have gained hands-on experience in buildi ng and interpreting machine learning models for sustainable finance\, desi gning interactive dashboards for ESG data exploration\, and producing data -driven insights that support sustainable investment and risk assessment d ecisions.\n\nEvening session\n\nLaboratory lectures – supervised tutoria l\, individual and team work.\n\nMaterials > Download      \n\n \n\ nb. Description of the virtual component \n\nThe programme includes onlin e sessions delivered via Microsoft Teams. These virtual meetings will be u sed for lectures\, discussions\, supervision\, and interaction with partic ipants.\n\nLink to the virtual sessions: Microsoft Teams classroom \n\n  \n\nStructure:\n\nAugust 26\, 2026\n\n\n \n 10:00 CEST– Welcome and in troductions by the academic partners to greet the students\n \n 10:20– 12:00 CEST– Lecture: Hybrid Data Analysis for Sustainability\n\n\nThis l ecture introduces the concept of hybrid data analysis in the context of su stainability-related decision-making. It explores how quantitative and qua litative data\, structured and unstructured information\, and different an alytical approaches can be combined to address complex environmental\, soc ial\, and economic challenges. Participants will gain insight into key sus tainability data sources\, common methodological approaches\, and the role of expert knowledge in hybrid analytical frameworks.    \n\nMaterials > Download\n\nMarcos R. Machado\, University of Twente\, The Netherlands\n\ n \n\nSeptember 2\, 2026\n\n\n 10:00–12:00 CEST– Lecture: Data Prepro cessing Treatment & Applications in Sustainability\n\n\nThis course provid es an introduction to the theoretical foundations and practical applicatio ns of data preprocessing in data mining and analytics. Participants will e xamine the main stages of preprocessing\, including data cleaning\, transf ormation\, reduction\, and other preparation techniques that enhance the q uality and usability of data for subsequent analysis. The course also expl ores different categories of preprocessing methods and demonstrates their implementation through Python and Jupyter Notebooks. A sustainability-rela ted application case is included to illustrate how preprocessing technique s can be applied in practice to support meaningful and robust data-driven insights. \n\nMaterials > Download\n\nMarcos R. Machado\, University of Tw ente\, The Netherlands\n\nWouter van Heeswijk\, University of Twente\, The Netherlands\n\n \n\nSeptember 25\, 2026\n\n\n 10:00–12:00 CEST– Lect ure: Quarto for Reproducible Documents and Dashboards with R\n\n\nMateria ls > Download\n\nAlfonso Iodice D’Enza University of Naples Federico II\ , Italy\n\n\nKey Features\n\n\n Level: Master’s and PhD students.\n ECTS : 3\n Language: English\n Online Support: Weekly mentorship from sustainab ility and finance experts.\n\n\nHybrid Approach for Complex Data Structure s\n\nThis course focuses on blending traditional statistical approaches (e .g.\, regression\, dimension reduction) with machine learning and AI metho ds (e.g.\, clustering\, predictive analytics) to address the multifaceted challenges in sustainable finance. Emphasis is placed on handling large-sc ale\, heterogeneous datasets\, developing scalable models\, and deriving a ctionable insights for ESG evaluation and decision-making.\n\n \n\nPracti cal information\n\n- Level of students: Master and PhD students\n\n- Numbe r of ECTS: 3 \n\n- Main language of instruction/training: English\n\n- Ve nue of Activities (City\, Institution): Naples\, Department of Political S ciences\, University of Naples Federico II (Statistics Laboratory and G4 r oom)\n\n\n\nIn addition\, a tutor will be available for each participant d uring the training period and a dedicated programme manager provided by BI P Faculty will be available virtually. These figures will act as support i n the learning and skills development phase.\n\n \n\nSocial Event Session : Guided Tour of Hidden Naples – Organized by Insolitaguida\n\nDates:   Thursday at 16:00 (4:00 PM)\n\nAs part of the physical component in Napl es\, participants will have the opportunity to take part in a guided walki ng tour organized by Insolitaguida\, dedicated to the discovery of some of the most fascinating legends and hidden treasures of the city.\n\nThe tou r will include a visit to the Fontana di Spinacorona\, one of Naples’ mo st distinctive fountains\, and to the Church of San Giovanni Maggiore\, wh ich houses the memorial stone associated with the legendary siren Partheno pe\, the mythical founder of Naples.\n\nThe itinerary will conclude at the Church of Santa Maria la Nova\, where\, according to a fascinating local tradition\, the tomb of Dracula\, Vlad III of Wallachia\, may be located.\ n\nThe cost of the guided tour is €20 per person\, including entrance fe es and audio headsets.\n\nTo participate\, students should email info@inso litaguida.it with the subject line: ASMSF2026 – Guided Tour\n\n \n\nFo r additional information about the association organizing the event\, plea se visit the following link: Social Event\n\nhttps://indico.unina.it/even t/116/ LOCATION:Naples University of Naples Federico II URL:https://indico.unina.it/event/116/ END:VEVENT END:VCALENDAR