BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Santiago BEGIN:DAYLIGHT DTSTART:20250907T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0300 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=9 TZNAME:-03 END:DAYLIGHT BEGIN:STANDARD DTSTART:20260404T230000 TZOFFSETFROM:-0300 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=1SA;BYMONTH=4 TZNAME:-04 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260224T205441Z UID:07497CBD-8D94-4479-8FE4-D8D98193712D DTSTART;TZID=America/Santiago:20260120T110000 DTEND;TZID=America/Santiago:20260120T130000 DESCRIPTION:[]\n\nEl Departamento de Ingeniería Civil Eléctrica (DIE) de la Universidad de Chile\, en colaboración con\nla Iniciativa de Datos e I nteligencia Artificial (IDIA)\; el iHealth del Millennium Institute for\nI ntelligent Healthcare Engineering y el IEEE Computational Intelligence Soc iety\, invita a la\ncomunidad académica y pública a la charla magistral del distinguido profesor Jose C. Principe\,\ndistinguished professor of El ectrical and Computer Engineering\, University of Florida\, e IEEE\nFellow .\n\nBajo el título "A Self-Learning Cognitive Architecture for Scene Und erstanding Using Causality"\nel expositor presentará avances pioneros en arquitecturas cognitivas autoaprendientes que imitan\nel sistema visual an imal. Esta propuesta permite reconocer objetos en videos sin necesidad de\ netiquetas supervisadas\, reduciendo drásticamente el ancho de banda comp utacional requerido.\nJose C. Principe es Distinguished Professor de Ingen iería Eléctrica\, Computacional y Biomédica\nen la Universidad de Flori da\, fundador y director del Computational NeuroEngineering\nLaboratory (C NEL). Autor de más de 1.000 publicaciones\, con un H-index de 104\, ha di rigido 110\ntesis doctorales y recibido el prestigioso IEEE Neural Network Pioneer Award en 2012. Es autor de\nlibros clave en el campo\, como Infor mation Theoretic Learning y Kernel Adaptive Filtering.\nLa actividad -en i nglés- se realizará el martes 20 de enero de 2026\, entre las 11:00 y 13 :00 horas\,\nen el Auditorio Enrique D'Etigny de la Facultad de Ciencias F ísicas y Matemáticas (FCFM) de la\nUniversidad de Chile (Beauchef 851\, Santiago).\nEntrada liberada con inscripción previa a través del código QR disponible en la convocatoria o\npueden ingresar [AQUÍ](https://docs. google.com/forms/d/e/1FAIpQLSeUPoMF1GnByDgWBlH1YGhw4VrQcR351SPBd-irbG5dpPj GuA/viewform?usp=header) y completar el formulario de inscripción -son cu pos limitados-.\nUna oportunidad imperdible para explorar las fronteras de la inteligencia artificial biológicamente\ninspirada.\n¡Inscríbete y n o te la pierdas!\n\nAbstract. This talk describes our efforts to abstract from the animal visual system the computational\nprinciples to recognize o bjects in video without using labels and decreasing the computational\nban dwidth required. We develop a hierarchical\, distributed architecture of d ynamical systems that\nself-organizes and mimics the foveal vision in huma ns using an empirical Bayes criterion. The\nsystem learns from reinforceme nt with the world\, and uses causality to identify objects of interest\nin the environment. When trained in video games its learning speed is much f aster when compared\nwith the traditional Deep Reinforcement Learning algo rithms.\n\nJose C. Principe (M’83-SM’90-F’00) is a Distinguished Pro fessor of Electrical and Computer\nEngineering and Biomedical Engineering at the University of Florida\, where he teaches advanced\nsignal processin g\, machine learning\, and artificial neural networks (ANNs) modeling. He is the Eckis\nEndowed Professor and the Founder and Director of the Univer sity of Florida Computational\nNeuroEngineering Laboratory (CNEL) www.cnel .ufl.edu. His primary area of interest is processing\nof time-varying sign als with adaptive neural models. The CNEL Lab has been studying signal and \npattern recognition principles based on information-theoretic criteria ( entropy and mutual\ninformation).\nDr. Principe is an IEEE Fellow and rece ived the prestigious IEEE Neural Network Pioneer Award in\n2012. He was th e past Chair of the Technical Committee on Neural Networks of the IEEE Sig nal\nProcessing Society\, Past-President of the International Neural Netwo rk Society\, and Past-Editor in\nChief of the IEEE Transactions on Biomedi cal Engineering. Dr. Principe has more than 1000\npublications\, and an H- index of 104 (Google Scholar). He directed 110 Ph.D. dissertations and 66\ nMaster theses. He wrote in 2000 an interactive electronic book entitled “Neural and Adaptive\nSystems” published by John Wiley and Sons and mo re recently co-authored several books on\n“Brain Machine Interface Engin eering”\, Morgan and Claypool\, “Information Theoretic Learning”\,\n Springer\, and “Kernel Adaptive Filtering”\, Wiley.\nActividad con ins cripción previa\, a través del código QR disponible en la convocatoria o pueden\ningresar [AQUÍ](https://docs.google.com/forms/d/e/1FAIpQLSeUPoM F1GnByDgWBlH1YGhw4VrQcR351SPBd-irbG5dpPjGuA/viewform?usp=header).\n\nRoom: Auditorio Enrique D'Etigny\, Beauchef 851\, Santiago\, Santiago\, Region Metropolitana\, Chile LOCATION:Room: Auditorio Enrique D'Etigny\, Beauchef 851\, Santiago\, Santi ago\, Region Metropolitana\, Chile ORGANIZER:christhian.escudero@uchile.cl SEQUENCE:33 SUMMARY:A Self-Learning Cognitive Architecture for Scene Understanding Usin g Causality URL;VALUE=URI:https://events.vtools.ieee.org/m/533259 X-ALT-DESC:Description: <br /><p><img src="https://events.vtools.ieee.org/v tools_ui/media/display/9bb6c02a-2453-41fb-a5c3-4a4fe76436b3" alt="" width= "600" height="750"></p>\n<p>El Departamento de Ingenier&iacute\;a Civil El &eacute\;ctrica (DIE) de la Universidad de Chile\, en colaboraci&oacute\;n con<br>la Iniciativa de Datos e Inteligencia Artificial (IDIA)\; el iHeal th del Millennium Institute for<br>Intelligent Healthcare Engineering y el IEEE Computational Intelligence Society\, invita a la<br>comunidad acad&e acute\;mica y p&uacute\;blica a la charla magistral del distinguido profes or Jose C. Principe\,<br>distinguished professor of Electrical and Compute r Engineering\, University of Florida\, e IEEE<br>Fellow.</p>\n<p>Bajo el t&iacute\;tulo "A Self-Learning Cognitive Architecture for Scene Understan ding Using Causality"<br>el expositor presentar&aacute\; avances pioneros en arquitecturas cognitivas autoaprendientes que imitan<br>el sistema visu al animal. Esta propuesta permite reconocer objetos en videos sin necesida d de<br>etiquetas supervisadas\, reduciendo dr&aacute\;sticamente el ancho de banda computacional requerido.<br>Jose C. Principe es Distinguished Pr ofessor de Ingenier&iacute\;a El&eacute\;ctrica\, Computacional y Biom&eac ute\;dica<br>en la Universidad de Florida\, fundador y director del Comput ational NeuroEngineering<br>Laboratory (CNEL). Autor de m&aacute\;s de 1.0 00 publicaciones\, con un H-index de 104\, ha dirigido 110<br>tesis doctor ales y recibido el prestigioso IEEE Neural Network Pioneer Award en 2012. Es autor de<br>libros clave en el campo\, como Information Theoretic Learn ing y Kernel Adaptive Filtering.<br>La actividad -en ingl&eacute\;s- se re alizar&aacute\; el martes 20 de enero de 2026\, entre las 11:00 y 13:00 ho ras\,<br>en el Auditorio Enrique D'Etigny de la Facultad de Ciencias F&iac ute\;sicas y Matem&aacute\;ticas (FCFM) de la<br>Universidad de Chile (Bea uchef 851\, Santiago).<br>Entrada liberada con inscripci&oacute\;n previa a trav&eacute\;s del c&oacute\;digo QR disponible en la convocatoria o<br> pueden ingresar <a href="https://docs.google.com/forms/d/e/1FAIpQLSeUPoMF1 GnByDgWBlH1YGhw4VrQcR351SPBd-irbG5dpPjGuA/viewform?usp=header">AQU&Iacute\ ;</a> y completar el formulario de inscripci&oacute\;n -son cupos limitado s-.<br>Una oportunidad imperdible para explorar las fronteras de la inteli gencia artificial biol&oacute\;gicamente<br>inspirada.<br>&iexcl\;Inscr&ia cute\;bete y no te la pierdas!</p>\n<p><strong>Abstract. </strong>This tal k describes our efforts to abstract from the animal visual system the comp utational<br>principles to recognize objects in video without using labels and decreasing the computational<br>bandwidth required. We develop a hier archical\, distributed architecture of dynamical systems that<br>self-orga nizes and mimics the foveal vision in humans using an empirical Bayes crit erion. The<br>system learns from reinforcement with the world\, and uses c ausality to identify objects of interest<br>in the environment. When train ed in video games its learning speed is much faster when compared<br>with the traditional Deep Reinforcement Learning algorithms.&nbsp\;</p>\n<p><st rong>Jose C. Principe </strong>(M&rsquo\;83-SM&rsquo\;90-F&rsquo\;00) is a Distinguished Professor of Electrical and Computer<br>Engineering and Bio medical Engineering at the University of Florida\, where he teaches advanc ed<br>signal processing\, machine learning\, and artificial neural network s (ANNs) modeling. He is the Eckis<br>Endowed Professor and the Founder an d Director of the University of Florida Computational<br>NeuroEngineering Laboratory (CNEL) www.cnel.ufl.edu. His primary area of interest is proces sing<br>of time-varying signals with adaptive neural models. The CNEL Lab has been studying signal and<br>pattern recognition principles based on in formation-theoretic criteria (entropy and mutual<br>information).<br>Dr. P rincipe is an IEEE Fellow and received the prestigious IEEE Neural Network Pioneer Award in<br>2012. He was the past Chair of the Technical Committe e on Neural Networks of the IEEE Signal<br>Processing Society\, Past-Presi dent of the International Neural Network Society\, and Past-Editor in<br>C hief of the IEEE Transactions on Biomedical Engineering. Dr. Principe has more than 1000<br>publications\, and an H-index of 104 (Google Scholar). H e directed 110 Ph.D. dissertations and 66<br>Master theses. He wrote in 20 00 an interactive electronic book entitled &ldquo\;Neural and Adaptive<br> Systems&rdquo\; published by John Wiley and Sons and more recently co-auth ored several books on<br>&ldquo\;Brain Machine Interface Engineering&rdquo \;\, Morgan and Claypool\, &ldquo\;Information Theoretic Learning&rdquo\;\ ,<br>Springer\, and &ldquo\;Kernel Adaptive Filtering&rdquo\;\, Wiley.<br> Actividad con inscripci&oacute\;n previa\, a trav&eacute\;s del c&oacute\; digo QR disponible en la convocatoria o pueden<br>ingresar <a href="https: //docs.google.com/forms/d/e/1FAIpQLSeUPoMF1GnByDgWBlH1YGhw4VrQcR351SPBd-ir bG5dpPjGuA/viewform?usp=header">AQU&Iacute\;</a>.</p> END:VEVENT END:VCALENDAR