BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Florence data science - ECPv6.15.20//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Florence data science X-ORIGINAL-URL:https://datascience.unifi.it X-WR-CALDESC:Events for Florence data science REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/Rome BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20180325T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20181028T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20190331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20191027T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20200329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20201025T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20210328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20211031T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20220327T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20221030T010000 END:STANDARD END:VTIMEZONE BEGIN:VTIMEZONE TZID:UTC BEGIN:STANDARD TZOFFSETFROM:+0000 TZOFFSETTO:+0000 TZNAME:UTC DTSTART:20180101T000000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20210629T160000 DTEND;TZID=Europe/Rome:20210629T170000 DTSTAMP:20260509T221647 CREATED:20210628T132200Z LAST-MODIFIED:20210628T132617Z UID:3479-1624982400-1624986000@datascience.unifi.it SUMMARY:DISEI Seminar: Assessing and designing research\, projects and policies in energy\, climate and AI DESCRIPTION:DETAILS \nTitle: Assessing and designing research\, projects and policies in energy\, climate and AI \nSpeaker: Francesco Fuso-Nerini (KTH) \nhttps://www.disei.unifi.it/vp-360-seminari.html \nMeeting Link: https://unifirenze.webex.com/unifirenze/j.php?MTID=m3908103502626beac5efee7bf699f135\nMeeting Number: 121 695 9503\nPassword: NkFUXXWP566 URL:https://datascience.unifi.it/index.php/event/disei-seminar-assessing-and-designing-research-projects-and-policies-in-energy-climate-and-ai/ CATEGORIES:Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20210629T140000 DTEND;TZID=Europe/Rome:20210629T150000 DTSTAMP:20260509T221647 CREATED:20210618T134216Z LAST-MODIFIED:20210629T082930Z UID:3447-1624975200-1624978800@datascience.unifi.it SUMMARY:DISEI Seminar: A soft introduction to applied Machine Learning for Social Sciences DESCRIPTION:THE SEMINAR HAS BEEN CANCELLED \n  \nDETAILS \nTitle: A soft introduction to applied Machine Learning for Social Sciences \nSpeaker: Rafael Boix\, Universidad de Valencia \nhttps://www.disei.unifi.it/vp-360-seminari.html \nMeeting Link: https://unifirenze.webex.com/unifirenze/j.php?MTID=m3908103502626beac5efee7bf699f135\nMeeting Number: 121 695 9503\nPassword: NkFUXXWP566 URL:https://datascience.unifi.it/index.php/event/disei-seminar-a-soft-introduction-to-applied-machine-learning-for-social-sciences/ CATEGORIES:Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20210618T140000 DTEND;TZID=Europe/Rome:20210618T153000 DTSTAMP:20260509T221647 CREATED:20210527T141606Z LAST-MODIFIED:20211008T082232Z UID:3287-1624024800-1624030200@datascience.unifi.it SUMMARY:3rd Seminar of the “D2 Seminar Series” – Florence Center for Data Science DESCRIPTION:The Florence Center for Data Science is happy to present the third Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 18th of June 2021\, from 2-3.30 pm.\n\nThe Seminar will be held by two new Fellows of the Center: the research unit COPERNICUS Earth Observation and Spatial Analysis and some researchers of the Magnetic Resonance Center (CERM). The speakers are Gherardo Chirici from the Department of Agriculture\, Food\, Environment and Forestry (University of Florence) and Enrico Ravera from the Magnetic Resonance Center (CERM) and the Department of Chemistry (University of Florence).\n\nRegister in advance for the seminar (free of charge): https://us02web.zoom.us/webinar/register/WN_I9bCjz_cQ_K_8nOP94usTw\n\nAfter registering\, you will receive a confirmation email containing information about joining the webinar.\n\n—————-\n\nSpeaker: Enrico Ravera – Magnetic Resonance Center (CERM) and Department of Chemistry\, University of Florence\n\nTitle: From algebra to biology: what does the math of ensemble averaging methods can tell us\n\nAbstract: Our work aims at a quantitative comparison of different methods for reconstructing conformational ensembles of biological macromolecules integrating molecular simulations and experimental data. This field has evolved over the years reflecting the evolution of computational power and sampling schemes\, and a plethora of different methods have been proposed. These methods can vary extensively in terms of how the prior information from the simulation is used to reproduce the experimental data\, but can be coarsely attributed to two categories: Maximum Entropy or Maximum Parsimony. In any case\, the problem is severely underdetermined and therefore additional information needs to be provided on the basis of the chemical knowledge about the system under investigation. Maximum entropy looks for the minimal perturbation of the prior distribution\, whereas Maximum Parsimony looks for the smallest possible ensemble that can explain in full the experimental data. On these grounds\, one can expect radically different solutions in the reconstruction\, but surprises are still possible – and can be justified by a rigorous geometrical description of the different methods.\n\nSpeaker: Gherardo Chirici – COPERNICUS Earth Observation and Spatial Analysis and Department of Agriculture\, Food\, Environment and Forestry\, University of Florence\n\nTitle: Big data from space. Recent Advances in Remote Sensing Technologies\n\nAbstract: Since the 1970s\, remote sensing technologies for terrestrial observation have generated a constant flow of data from different platforms\, in different formats and with different purposes. From these\, through successive steps\, spatial information is generated to support the Earth resources monitoring and planning. Indispensable in various sectors: from urban planning to geology\, from agriculture to forest monitoring and\, more generally\, any type of information to support environmental monitoring. For this reason\, remotely sensed information is recognized as a typical example of big data ante litteram. Today the new cloud computing technologies (such as Google Earth Engine) allow facing the complex problem of data management and processing of big data from remote sensing with new strategies that have revolutionized these data sources are used. From experiments on small areas\, today we have moved to the possibility of operationally processing vast multidimensional and multitemporal datasets on a global scale. The increased availability of information from space is exemplified by the numerous services offered by the European Copernicus program. The presentation\, starting from a brief introduction to remote sensing techniques\, illustrates some examples of applications developed within the geoLAB – Geomatics Laboratory of the Department of Agriculture\, Food\, Environment and Forestry (DAGRI) and the UNIFI COPERNICUS Research Unit.\n\n  URL:https://datascience.unifi.it/index.php/event/3rd-seminar-of-the-d2-seminar-series-florence-center-for-data-science/ LOCATION:Online CATEGORIES:Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20210604T140000 DTEND;TZID=Europe/Rome:20210604T153000 DTSTAMP:20260509T221647 CREATED:20210525T105728Z LAST-MODIFIED:20210527T103647Z UID:3340-1622815200-1622820600@datascience.unifi.it SUMMARY:2nd Seminar of the “D2 Seminar Series” – Florence Center for Data Science DESCRIPTION:The Florence Center for Data Science is happy to present the second Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 4th of June 2021\, from 2-3.30 pm. \nThe seminar will be held by Assistant Professor Cesare Bracco\, PhD from the Department of Mathematics and Computer Science “Ulisse Dini” and Professor Leonardo Boncinelli from the Department of Economics and Management of the University of Florence. \nRegister her for the event (free of charge): Registration 2nd Seminar \n  \nSpeaker: PhD Cesare Bracco \nDepartment of Mathematics and Computer Science “Ulisse Dini”\, University of Florence\nTitle: Scattered data: surface reconstruction and fault detection (joint work with O. Davydov\, C. Giannelli\, D. Großmann\, S. Imperatore\, D. Mokriš\, A. Sestini) \nAbstract: We will consider two aspects concerning scattered data approximation. The first is reconstructing a (parametrized) surface from a set of scattered points: the lack of structure in the data requires approximation methods that automatically adapt to the distribution and shape of the data themselves. We will discuss an effective approach to this issue based on hierarchical spline spaces\, which can be locally refined\, and therefore naturally lead to adaptive algorithms. The reconstruction problem contains another interesting problem: detecting the discontinuities the surface may have in order to reproduce them. Finding the discontinuity curves\, usually called faults (or gradient faults when gradient discontinuities are considered)\, is actually an important issue in itself\, with several applications\, for example in image processing and geophysics. I will present a method to determine which points in the scattered data set lie close to a (gradient) fault\, based on indicators obtained by using numerical differentiation formulas. \nSpeaker: Professor Leonardo Boncinelli \nDepartment of Economics and Management\, University of Florence\nTitle: Game-based education promotes sustainable water use \nAbstract: In this study\, we estimate the impact of a game-based educational program aimed at promoting sustainable water usage among 2nd-4th grade students and their families living in the municipality of Lucca\, Italy. To this purpose\, we exploited unique data from a quasi-experiment involving about two thousand students\, one thousand participating (the treatment group)\, and one thousand not participating (the control group) in the program. Data were collected by means of a survey that we specifically designed and implemented for collecting students’ self-reported behaviours. Our estimates indicate that the program has been successful: the students in the program reported an increase in efficient water usage and an increase in the frequency of discussions with their parents about water usage; moreover\, positive effects were still observed after six months. Our findings suggest that game-based educational programs can be an effective instrument to promote sustainable water consumption behaviors in children and their parents. URL:https://datascience.unifi.it/index.php/event/2nd-seminar-of-the-d2-seminar-series-florence-center-for-data-science/ LOCATION:Online CATEGORIES:Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20210521T140000 DTEND;TZID=Europe/Rome:20210521T153000 DTSTAMP:20260509T221647 CREATED:20210512T093422Z LAST-MODIFIED:20210514T135957Z UID:3276-1621605600-1621611000@datascience.unifi.it SUMMARY:1st Seminar of the "D2 Seminar Series" - Florence Center for Data Science DESCRIPTION:The Florence Center for Data Science is happy to present the first Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 21st of May 2021\, from 2-3.30 pm. \nHere the link for the Registration:  https://forms.gle/1AvggjTGfKs1Xxy3A \nSpeaker: Professor Fabrizia Mealli\nDepartment of Statistics\, Computer Science\, Applications “G. Parenti”\, University of Florence\nTitle: Assessing causality under interference \nAbstract: Causal inference from non-experimental data is challenging; it is even more challenging when units are connected through a network. Interference issues may arise\, in that potential outcomes of a unit depend on its treatment as well as on the treatments of other units\, such as their neighbours in the network. In addition\, the typical unconfoundedness assumption must be extended—say\, to include the treatment of neighbours\, and individual and neighbourhood covariates—to guarantee identification and valid inference. These issues will be discussed\, new estimands introduced to define treatment and interference effects and the bias of a naive estimator that wrongly assumes away interference will be shown. A covariate-adjustment method leading to valid estimates of treatment and interference effects in observational studies on networks will be introduced and applied to a problem of assessing the effect of air quality regulations (installation of scrubbers on power plants) on health in the USA. \nSpeaker: Professor Andrew Bagdanov\nDepartment of Information Engineering\, University of Florence\nTitle: Lifelong Learning at the end of the (new) Early Years \nAbstract: Lifelong learning\, also often referred to as continual or incremental learning\, refers to the training of artificially intelligent systems able to continuously learn to address new tasks from new data while preserving knowledge learned from previously learned ones. Lifelong learning is currently enjoying a sort of renaissance due to renewed interest from the Deep Learning community. In this seminar\, I will introduce the overall framework of continual learning\, discuss the fundamental role played by the stability-plasticity dilemma in understanding catastrophic forgetting in lifelong learning systems\, and present a broad panorama of recent results in class-incremental learning. I will conclude the discussion with a look at current trends\, open problems\, and low-hanging opportunities in this area. URL:https://datascience.unifi.it/index.php/event/1st-seminar-of-the-d2-seminar-series-florence-center-for-data-science/ LOCATION:Online CATEGORIES:Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20210215T170000 DTEND;TZID=Europe/Rome:20210215T183000 DTSTAMP:20260509T221647 CREATED:20210212T110842Z LAST-MODIFIED:20210212T110842Z UID:3234-1613408400-1613413800@datascience.unifi.it SUMMARY:Kick-off Meeting - Master II Livello DATA SCIENCE AND STATISTICAL LEARNING - MD2SL DESCRIPTION:We are pleased to announce the kick-off meeting for the MD2SL Master\, which will be held on February 15th at 17:00.\nFurther details are available at this link. URL:https://datascience.unifi.it/index.php/event/kick-off-meeting-master-ii-livello-data-science-and-statistical-learning-md2sl/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20200928T150000 DTEND;TZID=Europe/Rome:20200928T170000 DTSTAMP:20260509T221647 CREATED:20200903T110017Z LAST-MODIFIED:20200903T125103Z UID:2852-1601305200-1601312400@datascience.unifi.it SUMMARY:CEN-IBS/GMDS Invited Session on "Causal Inference and Machine Learning" DESCRIPTION:“Causal Inference and Machine Learning” – a virtual\nsatellite invited session of the 2020 joint conference of the GMDS &\nCEN-IBS – 28 September\, 3-5pm (CEST\, Berlin time). \nMore information on the website https://www.eventbrite.de/e/cen-ibsgmds-invited-session-on-causal-inference-and-machine-learning-tickets-116222778459 \nNote: the event is free\, but you need to register via this eventbrite site https://www.eventbrite.de/e/cen-ibsgmds-invited-session-on-causal-inference-and-machine-learning-tickets-116222778459 URL:https://datascience.unifi.it/index.php/event/cen-ibs-gmds-invited-session-on-causal-inference-and-machine-learning/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20200608T080000 DTEND;TZID=Europe/Rome:20200612T170000 DTSTAMP:20260509T221647 CREATED:20191206T150618Z LAST-MODIFIED:20191229T170154Z UID:2430-1591603200-1591981200@datascience.unifi.it SUMMARY:2020 Applied Bayesian Statistics Summer School: BAYESIAN CAUSAL INFERENCE DESCRIPTION:The Applied Bayesian Statistics summer school has been running since 2004. Since 2012 it is organised by: \n\nIMATI CNR Istituto di Matematica Applicata e Tecnologie Informatiche\, Consiglio Nazionale delle Ricerche\, Milano\nDipartimento di Scienze Statistiche Università Cattolica\, Milano\n\nThe 2020 Applied Bayesian Statistics Summer School (17th edition) will be held in Florence\, Italy. The topic chosen for the 2020 school is\nBayesian Causal Inference. The lecturer is Prof. Fan Li (Department of Statistical Science\, Duke University\, Durham\, NC\, USA).\nFurther details are provided on the conference webpage. \nLocal contributing organization:\nFlorence Center for Data Science (FDS)\nDepartment of Statistics\, Computer Science\, Applications (DISIA)\, University of Florence URL:https://datascience.unifi.it/index.php/event/2020-applied-bayesian-statistics-summer-school-bayesian-causal-inference/ LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20200219T120000 DTEND;TZID=Europe/Rome:20200219T133000 DTSTAMP:20260509T221647 CREATED:20200207T143635Z LAST-MODIFIED:20200207T143635Z UID:2544-1582113600-1582119000@datascience.unifi.it SUMMARY:DISIA Seminar: From logit to linear regression and back DESCRIPTION:Title: From logit to linear regression and back \nSpeaker: Giovanni Maria Marchetti (University of Florence) \nLocation: Aula 32 – DISIA – Viale Morgagni 59 \nAbstract URL:https://datascience.unifi.it/index.php/event/disia-seminar-from-logit-to-linear-regression-and-back/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20200218T170000 DTEND;TZID=Europe/Rome:20200218T193000 DTSTAMP:20260509T221647 CREATED:20200129T094327Z LAST-MODIFIED:20200129T094327Z UID:2537-1582045200-1582054200@datascience.unifi.it SUMMARY:DSG Seminar: The Future of Artificial Intelligence and Fundamental Rights DESCRIPTION:Title: The Future of Artificial Intelligence and Fundamental Rights \nSpeaker: Marc Rotenberg (Georgetown Law School – EPIC) \nLocation: Building D4\, Room 102 \nSeminar poster URL:https://datascience.unifi.it/index.php/event/dsg-seminar-the-future-of-artificial-intelligence-and-fundamental-rights/ LOCATION:D4 Via delle Pandette 9\, Via delle Pandette 9\, Firenze\, Italy END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20200218T120000 DTEND;TZID=Europe/Rome:20200218T133000 DTSTAMP:20260509T221647 CREATED:20200207T143056Z LAST-MODIFIED:20200207T143056Z UID:2542-1582027200-1582032600@datascience.unifi.it SUMMARY:DISIA Seminar: On Spatial Lag Models estimated using crowdsourcing\, web-scraping or other unconventionally collected Big Data DESCRIPTION:Title: On Spatial Lag Models estimated using crowdsourcing\, web-scraping or other unconventionally collected Big Data \nSpeaker: Giuseppe Arbia (Catholic University of the Sacred Heart Milan) \nLocation: Aula 32 – DISIA – Viale Morgagni 59 \nAbstract URL:https://datascience.unifi.it/index.php/event/disia-seminar-on-spatial-lag-models-estimated-using-crowdsourcing-web-scraping-or-other-unconventionally-collected-big-data/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20200129T113000 DTEND;TZID=Europe/Rome:20200129T123000 DTSTAMP:20260509T221647 CREATED:20200114T134059Z LAST-MODIFIED:20200114T134143Z UID:2528-1580297400-1580301000@datascience.unifi.it SUMMARY:Adversarial Machine Learning DESCRIPTION:Speaker: Prof. Fabio Roli\, Università di Cagliari \nMachine learning algorithms are widely used for cybersecurity\napplications\, including spam\, malware detection\, biometric recognition.\nIn these applications\, the learning algorithm has to face intelligent\nand adaptive attackers who can carefully manipulate data to purposely\nsubvert the learning process. As machine learning algorithms have not\nbeen originally designed under such premises\, they have been shown to be\nvulnerable to well-crafted attacks\, including test-time evasion and\ntraining-time poisoning attacks (also known as adversarial examples).\nThis talk aims to introduce the fundamentals of adversarial machine\nlearning and some techniques to assess the vulnerability of machine\nlearning algorithms to adversarial attacks. We report application\nexamples including object recognition in images\, biometric identity\nrecognition\, spam and malware detection. \n\n\n\n\n\nFabio Roli is a Full Professor of Computer Engineering at the University\nof Cagliari\, Italy\, and Director of the Pattern Recognition and\nApplications laboratory (http://pralab.diee.unica.it/). He is partner\nand R&D manager of the company Pluribus One that he co-founded\n(https://www.pluribus-one.it ). He has been doing research on the design\nof pattern recognition and machine learning systems for thirty years.\nHis current h-index is 66 according to Google Scholar (December 2019).\nHe has been appointed Fellow of the IEEE and Fellow of the International\nAssociation for Pattern Recognition. He was a member of NATO advisory\npanel for Information and Communications Security\, NATO Science for\nPeace and Security (2008 – 2011). URL:https://datascience.unifi.it/index.php/event/adversarial-machine-learning/ LOCATION:Aula Caminetto\, Via di Santa Marta 3\, Firenze\, Italy END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20200120 DTEND;VALUE=DATE:20200123 DTSTAMP:20260509T221647 CREATED:20191230T120049Z LAST-MODIFIED:20191230T120049Z UID:2514-1579478400-1579737599@datascience.unifi.it SUMMARY:DISIA hosts a 3-day Stats Camp Seminar DESCRIPTION:The Department of Statistics is hosting a Todd D. Little (Texas Tech University) short course: \nTitle: The Craft of Model Construction Using Structural Equation Modeling\nComprehensive 3-day Stats Camp Seminar by Todd Little (Texas Tech University)\n\nSyllabus of the short course \nOnline registration \n(For DiSIA members\, please contact Carla Rampichini) \n  \nBIO \nTodd D. Little\, Ph.D. is a Professor of Educational Psychology at Texas Tech University (TTU) where\, in 2013\, he became the founding director of the Institute for Measurement\, Methodology\, Analysis and Policy (IMMAP). The IMMAP at TTU is a University-designated research and support center that provides expert consulting and assistance on all manner of data collection\, data management\, and advanced statistical analyses. Little is internationally recognized for his quantitative work on various aspects of applied SEM (e.g.\, indicator selection\, parceling\, modeling developmental processes) as well as his substantive developmental research (e.g.\, action-control processes and motivation\, coping\, and self-regulation). Prior to joining TTU\, Little has guided quantitative training and provided consultation to students\, staff\, and faculty at the Max Planck Institute for Human Development’s Center for Lifespan Studies (1991-1998)\, Yale University’s Department of Psychology (1998-2002)\, and researchers at KU (2002-2013\, including as director of the RDA unit at the Lifespan Institute and as director of the Center for Research Methods and Data Analysis). In 2001\, Little was elected to membership in the Society for Multivariate Experimental Psychology\, a restricted-membership society of quantitative specialists in the behavioral and social sciences. \nIn 2009\, he was elected President of APA’s Division 5 (Evaluation\, Measurement\, and Statistics). He founded\, organizes\, and teaches in the internationally renowned ‘Stats Camps’ each June (see statscamp.org for details of the summer training programs) and has given over 150 workshops and talks on methodology topics around the world. As an interdisciplinary-oriented collaborator\, Little has published with over 280 persons from around the world in over 65 different peer-reviewed journals. His work has garnered over 11\,000 citations. He published Longitudinal Structural Equation Modeling in 2013 and he has edited five books related to methodology\, including the Oxford Handbook of Quantitative Methods and the Guildford Handbook of Developmental Research Methods (with Brett Laursen and Noel Card). Little has served on numerous grant review panels for federal agencies such as NSF\, NIH\, and IES\, and private foundations such as the Jacobs Foundation. He has been the principal investigator or co-principal investigator on over 15 grants and contracts and he has served as a statistical consultant on over 70 grants and contracts. In the conduct of his collaborative research\, he has participated in the development of over 12 different measurement tools\, including the CAMI\, the Multi-CAM\, the BALES\, the BISC\, the I FEEL\, and the form/function decomposition of aggression. URL:https://datascience.unifi.it/index.php/event/disia-hosts-a-3-day-stats-camp-seminar/ LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy CATEGORIES:Short course ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2019/12/little.jpg.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20200116T150000 DTEND;TZID=Europe/Rome:20200116T160000 DTSTAMP:20260509T221647 CREATED:20200112T213154Z LAST-MODIFIED:20200112T213154Z UID:2523-1579186800-1579190400@datascience.unifi.it SUMMARY:Self-Organizing Migrating Algorithm - Progress\, Innovations and Applications DESCRIPTION:Speaker: Prof. Ivan Zelinka\nLocation: Sala riunioni DINFO – Via di S. Marta 3\nThis seminar discusses recent advances\, progress and application on SOMA algorithm (http://somaalgorithm.com)\, that has been developed in the year 2000 and is based on swarm intelligence principles. During its existence it has been published twice in a book form\, numerous journals and was a subject of a few keynotes and tutorials. Recent improvements of this algorithms\, published on Congress on Evolutionary Algorithms\, GECCO and SEMCCO have set up its performance on the same level as the differential evolution is (actually on the 3rd place amongst the 34 state-of-art algorithms of global optimization like PSO\, DE\, ABC\, FireFly\,…). In the seminar will be audience introduced to its latest improvements and modification\, testing on representative test functions as well as applications of the SOMA. Selected applications will be from domain of chaos control\, real-time control of the plasma reactor\, symbolic regression – program synthesis\, swarm robot control\, design of electronic circuits\, neural network synthesis\, computer games and astrophysical data processing amongst the others. The seminar is designed as an introduction into SOMA\, its structure\, results\, and content are based on our previous research\, journal publications\, keynotes\, and tutorials and reflect the latest progress in that field. URL:https://datascience.unifi.it/index.php/event/self-organizing-migrating-algorithm-progress-innovations-and-applications/ LOCATION:Sala riunioni DINFO\, Via di Santa Marta 3\, Firenze\, Italy CATEGORIES:Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20200114T150000 DTEND;TZID=Europe/Rome:20200114T160000 DTSTAMP:20260509T221647 CREATED:20200112T212553Z LAST-MODIFIED:20200112T213238Z UID:2519-1579014000-1579017600@datascience.unifi.it SUMMARY:Recent Advances and Progress in Evolutionary Algorithms and its Dynamics DESCRIPTION:Title: Recent Advances and Progress in Evolutionary Algorithms and its Dynamics \nSpeaker: Prof. Ivan Zelinka \nLocation: Sala riunioni DINFO \n\n\n\n\n\n\n\n\n\n\n\n\n\nThis seminar is focused on recent progress in the mutual intersection of a few exciting fields of research whose core topic is evolutionary algorithms in general and will be explained relations between evolutionary dynamics\, its visualization as a complex network and its control. It discusses research in evolutionary algorithms that can be considered a discrete dynamical complex system with inherent nonlinear dynamics. These dynamics is visualized and analyzed as a complex network and CML (coupled map lattices) systems. As already reported in many research papers and books\, these dynamics can generate different kinds of behavior including chaotic one. Selected evolutionary algorithms discussed in this seminar will be differential evolution\, genetic algorithm\, particle swarm\, artificial bee algorithm\, and others. Methodology\, converting evolutionary algorithms to the complex network will be introduced including demonstrations. The seminar then continues by explaining how evolutionary dynamics can be converted into so-called CML systems\, which are used to model spatiotemporal behavior (including chaotic one) and also will be explained its relation to complex networks. At the end will be demonstrated how we can control EAs dynamics using a feedback loop control scheme. The seminar is designed as a topic overview\, its structure\, results\, and content are based on our previous research\, journal publications\, keynotes\, and tutorials and reflect the latest progress in that field. URL:https://datascience.unifi.it/index.php/event/recent-advances-and-progress-in-evolutionary-algorithms-and-its-dynamics/ LOCATION:Sala riunioni DINFO\, Via di Santa Marta 3\, Firenze\, Italy CATEGORIES:Seminar ATTACH;FMTTYPE=image/jpeg:https://datascience.unifi.it/wp-content/uploads/2019/11/Linfa_Universita_Firenze_DINFO_Logo.jpg END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20191205T120000 DTEND;TZID=Europe/Rome:20191205T133000 DTSTAMP:20260509T221647 CREATED:20191202T092509Z LAST-MODIFIED:20191202T093203Z UID:2418-1575547200-1575552600@datascience.unifi.it SUMMARY:Lezione aperta DISEI Nobel 2019 DESCRIPTION:Title: Lezione aperta DISEI Nobel 2019\nSpeakers: Prof. Fabrizia Mealli (Director of the Florence Center for Data Science\, University of Florence) and Prof. Mario Biggeri (University of Florence)\nLocation: Building D6 Room 0.18 – Via delle pandette\, 9\n  \nDISEI is pleased to present the 2019 DISEI class on the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel.\nProf. Fabrizia Mealli and Prof. Mario Biggeri will present an overview of the main contributions of Abhijit Banerjee\, Esther Duflo and Michael Kremer who have received the 2019 “Nobel” prize in economics for their experimental approach to alleviating global poverty. \n  URL:https://datascience.unifi.it/index.php/event/2418/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20191126T120000 DTEND;TZID=Europe/Rome:20191126T133000 DTSTAMP:20260509T221647 CREATED:20191122T123023Z LAST-MODIFIED:20191122T123023Z UID:2386-1574769600-1574775000@datascience.unifi.it SUMMARY:Seminar @ DISEI : Il diritto On line DESCRIPTION:Title: Blockchain\, smart contract e cryptovalute per le imprese \nSpeaker: Giovanni Brancalion Spadon (Foro di Venezia) \nLocation: Aula D5 001 – Polo delle Scienze Sociali – Viale delle Pandette 9 URL:https://datascience.unifi.it/index.php/event/seminar-disei-il-diritto-on-line/ LOCATION:D5 Via delle Pandette\, Via delle Pandette 9\, Firenze\, Italy END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20191104T101500 DTEND;TZID=Europe/Rome:20191104T120000 DTSTAMP:20260509T221647 CREATED:20191023T162604Z LAST-MODIFIED:20191023T162641Z UID:2177-1572862500-1572868800@datascience.unifi.it SUMMARY:DISIA-DSG-DISEI seminar: Nello Cristianini DESCRIPTION:Title: Living with Intelligent Machines\nSpeaker: Nello Cristianini (University of Bristol)\nLocation: Building D6 Room 1.18 – Via delle pandette\, 9\nABSTRACT \nThe way we build intelligent machines today involves two components: a (fairly) general learning algorithm\, and a (very) large set of training examples. These two elements are combined to create machine translation\, computer vision\, online recommendations\, spelling correction\, and so on. In many cases we know that the intended behaviour can be closely emulated\, but we have done away with modeling the mechanism that generates it\, replacing that with statistical correlations. As the training examples are often obtained “from the wild”\, they might contain information that we are not aware about\, including various cultural biases. If these biases enter in the statistical models\, they can be difficult to detect\, and become part of shaping the behaviour of the intelligent agent. As we train machines by exposing them to media content and other samples of human behaviour\, and these machines have the capability to emulate those behaviours\, it is important that we understand the biases present in the data: this is one case in which computational social sciences become an important element of AI design. Other cases include the automation of psychometrics\, and the risk of addiction. The social\, ethical and legal consequences of using modern-type of AI can be better understood and managed when we think about AI also from a social science perspective.  \nBIO \nNello Cristianini is Professor of Artificial Intelligence (AI) at the University of Bristol. His current research covers the large-scale analysis of media content (news and social media)\, using various AI methods\, the design of new AI methods\, their application to digital humanities and computational social science\, and the social impact of Big Data and AI technologies. Cristianini is the co-author of two widely known books in machine learning\, An Introduction to Support Vector Machines and Kernel Methods for Pattern Analysis\, as well as a book in bioinformatics\, Introduction to Computational Genomics. He is also a recipient of the Royal Society Wolfson Research Merit Award and a current holder of a European Research Council Advanced Grant. In 2014\, Thomson-Reuters included him in a list of the most influential computer scientists of the decade. Before joining the University of Bristol\, he has been a professor of statistics at the University of California\, Davis. Currently he is working on social and ethical implications of AI. URL:https://datascience.unifi.it/index.php/event/disia-dsg-disei-seminar-nello-cristianini/ LOCATION:D6 Via delle Pandette 9\, Via delle Pandette 9\, Firenze\, Italy CATEGORIES:Seminar ATTACH;FMTTYPE=image/jpeg:https://datascience.unifi.it/wp-content/uploads/2019/10/medium-142795.jpg END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20191028T110000 DTEND;TZID=Europe/Rome:20191028T123000 DTSTAMP:20260509T221647 CREATED:20191003T090716Z LAST-MODIFIED:20191003T090716Z UID:2034-1572260400-1572265800@datascience.unifi.it SUMMARY:Seminar @DISIA : Current Challenges in the Analysis of Brain Signals DESCRIPTION:Title: Current Challenges in the Analysis of Brain Signals \nSpeaker: Hernando Ombao (KAUST) \nLocation: Aula 32 – DISIA – Viale Morgagni 59 \nAbstract URL:https://datascience.unifi.it/index.php/event/seminar-disia-current-challenges-in-the-analysis-of-brain-signals/ LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20191004T113000 DTEND;TZID=Europe/Rome:20191004T130000 DTSTAMP:20260509T221647 CREATED:20190927T101356Z LAST-MODIFIED:20190927T102225Z UID:1996-1570188600-1570194000@datascience.unifi.it SUMMARY:Seminar: Complexity of nonconvex optimization DESCRIPTION:Seminar @Dipartimento di Ingegneria Industriale\nRoom 108 – Plesso didattico Morgagni \nPhilippe Toint (University of Namur) \nComplexity of nonconvex optimization \nAbstract.\nWe present a review of results on the worst-case complexity of minimization algorithms for nonconvex problems using potentially high-degree models.\nGlobal complexity bound are presented that are valid for any model’s degree and any order of optimality\, thereby generalizing known results for first- and second-order methods. An adaptive regularization algorithm using derivatives up to degree p will produce an epsilon-approximate q-th order minimizer in at most O(epsilon^( -(p+1)/(p−q+1) ) evaluations. We will also extend these results to the case of inexact objective function and derivatives with an application to subsampling algorithms for machine learning. URL:https://datascience.unifi.it/index.php/event/seminar-complexity-of-nonconvex-optimization/ LOCATION:Plesso didattico Morgagni\, Viale Morgani 40\, Firenze\, 50134\, Italy CATEGORIES:Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20191004T113000 DTEND;TZID=Europe/Rome:20191004T123000 DTSTAMP:20260509T221647 CREATED:20191003T085708Z LAST-MODIFIED:20191003T085933Z UID:2028-1570188600-1570192200@datascience.unifi.it SUMMARY:Seminar: A Next Step for Bioinformatics DESCRIPTION:Title: A Next Step for Bioinformatics: Integrated Machine Learning and Stochastic Modeling for Understanding Disease-related Cell Signaling and Regulation \nSpeaker: Juan Cui (Univ. of Nebraska) \nLocation: Aula Caminetto – Via di S. Marta 3 URL:https://datascience.unifi.it/index.php/event/seminar-a-next-step-for-bioinformatics/ LOCATION:via di Santa Marta 3\, FIrenze CATEGORIES:Seminar ORGANIZER;CN="Lorenzo Mucchi":MAILTO:lorenzo.mucchi@unifi.it END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Rome:20190722T123000 DTEND;TZID=Europe/Rome:20190722T133000 DTSTAMP:20260509T221647 CREATED:20190718T152341Z LAST-MODIFIED:20190718T152341Z UID:1888-1563798600-1563802200@datascience.unifi.it SUMMARY:MICC seminar: Xavier Alameda-Pineda DESCRIPTION:Title: Probabilistic and deep methods for human behavior understanding\nSpeaker: Dr. Xavier Alameda-Pineda\nLocation: Aula Anfiteatro Ex-Farmacologia – Viale Morgagni 65\nABSTRACT \nIn this talk we will discuss several works on automatic understanding of human behavior. First\, I will present recent variational expectation-maximisation techniques for multi-speaker tracking\, where a combinatorial problem over time yields to a high-complexity exact solution that is intractable in practice. Variational approximation of the posterior leads to an efficient\, yet performant\, way to tackle the problem. Secondly\, I will present robust deep regression techniques based on probabilistic models and discuss the joint training of the graphical model and the network parameters. Finally\, I will propose one way to exploit behavioral diversity for data generation. \nBIO \nXavier Alameda-Pineda received M.Sc. in Mathematics (2008)\, in Telecommunications (2009) and in Computer Science (2010). He obtained his Ph.D. in Mathematics and Computer Science from Universite Joseph Fourier in 2013. Since 2016\, he is a Research Scientist at Inria Grenoble\, with the Perception Team. He served\nas Area Chair at ICCV 2017\, at ACM MM 2019 and at ICIAP 2019. He is the recipient of several paper awards\, and of the ACM SIGMM Rising Star 2018. He is a Senior Member of the IEEE. His scientific interests lie in computer vision\, machine learning and signal processing for human behavior understanding and robotics. URL:https://datascience.unifi.it/index.php/event/micc-seminar-xavier-alameda-pineda/ LOCATION:Aula Anfiteatro Ex-Farmacologia\, Viale Morgagni 65\, Firenze\, Italy ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2019/07/Screenshot-2019-07-18-at-17.19.09.png END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20190630 DTEND;VALUE=DATE:20190707 DTSTAMP:20260509T221647 CREATED:20190611T195718Z LAST-MODIFIED:20190611T195832Z UID:1829-1561852800-1562457599@datascience.unifi.it SUMMARY:Summer school: Optimization\, Big Data and Applications DESCRIPTION:DINFO/UNIFI organizes the Second Edition of the Summer School on Optimization\, Big Data and Applications (OBA) which will take place from June 30th to July 06th\, 2019 in Veroli (FR)\, Italy \nThe speakers will be: \nNicolò Cesa-Bianchi\, University of Milano\nJulien Mairal\, INRIA\, Grenoble\nMiguel Á. Carreira-Perpiñán\, Univ of California at Merced\nAndrew Bagdanov\, Unviersità di Firenze \nMore information on the website https://webgol.dinfo.unifi.it/oba/ URL:https://datascience.unifi.it/index.php/event/summer-school-optimization-big-data-and-applications/ END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20190618 DTEND;VALUE=DATE:20190622 DTSTAMP:20260509T221647 CREATED:20190611T175401Z LAST-MODIFIED:20190611T190010Z UID:1812-1560816000-1561161599@datascience.unifi.it SUMMARY:Short course of Tamas Terlaky at DINFO DESCRIPTION:Prof. Tamas Terlaky will give a PhD course on “Modern convex optimization: Duality\, Algorithms\, Solutions and Interpretations ” \nLectures will be offered on Tuesday June 18\, Wed June 19\, Thu June 20\, Fri June 21 from 10:30 to 13:00 in room 107\, via di Santa Marta 3\, Firenze. \nAbstract \nOptimization methodology is the engine of prescriptive analytics. This short course gives a gentle\, rigorous introduction to modern convex optimization models and algorithms.\nDuality provides optimality conditions and serve as the platform of algorithm design.\nFirst we focus on duality in linear optimization (LO) and convex conic linear optimization (CLO) problems.\nCLO includes the LO\, second order conic and semidefinite optimization problems\, which are solvable by Interior Point Methods (IPMs) in polynomial time\, and also the NP-hard classes of copositive and completely positive CLO problems. Robust LO models will motivate the introduction of second order conic optimization problems.\nThen algorithmic concepts\, such as pivot algorithms and interior point methods (IPMs) are discussed.\nInitialization of the algorithms\, the computational cost and efficient computation of an iterative step\, and characteristics of the produced optimal solutions are discussed.\nAs time allows we shortly available software packages\, sensitivity analysis\, and some applications will be discussed. \nNote:\nPreliminary knowledge of optimization\, operations research models and methods is a plus\, but everyone with good linear algebra and multi-dimensional calculus skills should be able to follow the course. \nPS: if you wish to be informed on the intiatives of the PhD program in Information Engineering\, please refer to the official website\, where a calendar is also available URL:https://datascience.unifi.it/index.php/event/1812/ LOCATION:DINFO\, via di Santa Marta 3\, Firenze END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20190612 DTEND;VALUE=DATE:20190614 DTSTAMP:20260509T221647 CREATED:20190611T185552Z LAST-MODIFIED:20190611T185855Z UID:1818-1560297600-1560470399@datascience.unifi.it SUMMARY:Short course Prof. Giovanni Parmigiani at DiSIA DESCRIPTION:Prof. Giovanni Parmigiani will give a PhD course on “Multi-Study Biomarker Analysis” \nCheck here for more details URL:https://datascience.unifi.it/index.php/event/short-course-prof-giovanni-parmigiani/ LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20190610 DTEND;VALUE=DATE:20190611 DTSTAMP:20260509T221647 CREATED:20190424T175442Z LAST-MODIFIED:20190603T091734Z UID:1590-1560124800-1560211199@datascience.unifi.it SUMMARY:First FDS Meeting DESCRIPTION:The first meeting of the Florence Center for Data Science will be held on June 10\, 2019\, in Viale Morgagni 40\, Auditorium B. \nThe meeting will feature the participation of outstanding researchers in Data Science. Speakers include Francesca Dominici\, Giovanni Parmigiani and Donato Malerba. \nAn overview of research projects and activities conducted at UNIFI around Data Science will also be provided. \nDetailed program. URL:https://datascience.unifi.it/index.php/event/kickoff-meeting/ LOCATION:Auditorium B\, Viale Morgagni 40\, Firenze\, Italy END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20190605 DTEND;VALUE=DATE:20190608 DTSTAMP:20260509T221647 CREATED:20190514T210529Z LAST-MODIFIED:20190514T210529Z UID:1707-1559692800-1559951999@datascience.unifi.it SUMMARY:ITACOSM 2019 DESCRIPTION:The 6th ITAlian COnference on Survey Methodology – ITACOSM 2019 – will be held in Florence from 5 to 7 June 2019 and will take place at the Department of Statistics\, Computer Science\, Applications “G. Parenti” of the University of Florence.\nCheck here for the final program. URL:https://datascience.unifi.it/index.php/event/itacosm-2019/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20190529T093000 DTEND;TZID=UTC:20190529T093000 DTSTAMP:20260509T221647 CREATED:20190507T145945Z LAST-MODIFIED:20190507T145945Z UID:1664-1559122200-1559122200@datascience.unifi.it SUMMARY:Artificial Intelligence in health and well-being DESCRIPTION:One-day Workshop organised by IFAC-CNR and DISIA-UNIFI URL:https://datascience.unifi.it/index.php/event/artificial-intelligence-in-health-and-well-being/ LOCATION:Area di Ricerca CNR\, Via Madonna del Piano\, 10\, Sesto Fiorentino\, FI END:VEVENT END:VCALENDAR