BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20251216T011850Z UID:7B73C08D-59EF-4A9A-A2F1-D5C58424D2B0 DTSTART;TZID=America/New_York:20251209T171500 DTEND;TZID=America/New_York:20251209T183000 DESCRIPTION:Talk Abstract: Collective animal behavior provides numerous exa mples of robust coordination and decision-making in complex environments. Fish schools\, in particular\, perform navigation and aggregation tasks by integrating noisy visual and hydrodynamic cues and propagating this infor mation across the group. This makes them a natural model system for studyi ng decentralized estimation and control problems for multi-agent systems. This talk will present recent theoretical advances in modeling and analyzi ng fish behavior through the lens of dynamical systems and control theory. I will introduce a data-driven framework that combines potential flow app roximations with stochastic differential equations to characterize hydrody namic coupling\, sensory fusion\, and their feedback into locomotor contro l. I will show how this framework leads to novel zero-order optimization s trategies for autonomous robotic navigation in unknown environments and pr ovides a quantitative description of the feedback control process underlyi ng predator-prey interactions. I will conclude by outlining emerging direc tions in multi-agent inference and collective decision-making informed by biological principles.\n\nSpeaker(s): Daniel Burbano\, \n\nAgenda: \n5:15 pm snacks and meet the speaker\n\n5:30 pm Talk\n\nRoom: Conference Room\, EE 240\, Bldg: Rutgers Busch Campus - Electrical Engineering Building\, 94 Brett Road\, Piscataway\, New Jersey\, United States\, 08854 LOCATION:Room: Conference Room\, EE 240\, Bldg: Rutgers Busch Campus - Elec trical Engineering Building\, 94 Brett Road\, Piscataway\, New Jersey\, Un ited States\, 08854 ORGANIZER:zgajic@soe.rutgers.edu SEQUENCE:32 SUMMARY:Towards Bio-inspired Estimation and Control for Autonomous Systems: Lessons from Collective Animal Behavior URL;VALUE=URI:https://events.vtools.ieee.org/m/515434 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="text-align: justi fy\;"><strong><span style="font-family: 'Times New Roman'\,serif\;">Talk A bstract: </span></strong><span style="font-family: 'Times New Roman'\,seri f\;">Collective animal behavior provides numerous examples of robust coord ination and decision-making in complex environments. Fish schools\, in par ticular\, perform navigation and aggregation tasks by integrating noisy vi sual and hydrodynamic cues and propagating this information across the gro up. This makes them a natural model system for studying decentralized esti mation and control problems for multi-agent systems. This talk will presen t recent theoretical advances in modeling and analyzing fish behavior thro ugh the lens of dynamical systems and control theory. I will introduce a d ata-driven framework that combines potential flow approximations with stoc hastic differential equations to characterize hydrodynamic coupling\, sens ory fusion\, and their feedback into locomotor control. I will show how th is framework leads to novel zero-order optimization strategies for autonom ous robotic navigation in unknown environments and provides a quantitative description of the feedback control process underlying predator-prey inte ractions. I will conclude by outlining emerging directions in multi-agent inference and collective decision-making informed by biological principles .</span></p><br /><br />Agenda: <br /><p>5:15 pm snacks and meet the speak er</p>\n<p>5:30 pm Talk</p>\n<p>&nbsp\;</p> END:VEVENT END:VCALENDAR