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UID:20260512T163343EDT-8057oN7Zl1@132.216.98.100
DTSTAMP:20260512T203343Z
DESCRIPTION:Virtual Informal Systems Seminar (VISS) Centre for Intelligent 
 Machines (CIM) and Groupe d'Etudes et de Recherche en Analyse des Decision
 s (GERAD)\n	\n	Zoom Link\n	Meeting ID: 910 7928 6959        \n	Passcode: VISS
 \n	\n	Speaker: Derek Onken\, PhD candidate\, Computer Science and Informatic
 s Program\,  Emory University\n	\n	Abstract: Optimal control (OC) problems a
 im to find an optimal policy that control given dynamics over a period of 
 time. For systems with high-dimensional state (for example\, systems with 
 many centrally controlled agents)\, OC problems can be difficult to solve 
 globally. We propose a neural network approach for solving such problems. 
 When trained offline in a semi-global manner\, the model is robust to shoc
 ks or disturbances that may occur in real-time deployment (e.g.\, wind int
 erference). Our unsupervised approach is grid-free and scales efficiently 
 to dimensions where grids become impractical or infeasible. We demonstrate
  the effectiveness of our approach on several multi-agent collision-avoida
 nce problems in up to 150 dimensions.\n	\n	Bio: Derek Onken is a current PhD
  candidate in the Computer Science and Informatics Program at Emory Univer
 sity and is advised by Lars Ruthotto. Prior to joining Emory\, he received
  his BS in Mathematics and Computer Science from the University of Georgia
 . His research examines design and applications of neural ordinary differe
 ntial equations that arise from the blending of neural networks and differ
 ential equations. In addition to his dissertation research\, Derek enjoys 
 applying machine learning to applications arising in the health industry. 
 He recently published work using cell phone billing records for privacy-pr
 eserving epidemic surveillance. In internships with the US Air Force Resea
 rch Labs and UnitedHealth Group R&D\, he applied machine learning methods 
 to toxicology and lung cancer detection. He recently accepted a position a
 t Eli Lilly and Co.\n
DTSTART:20210226T190000Z
DTEND:20210226T200000Z
LOCATION:CA\, ZOOM
SUMMARY:A Neural Network Approach for High-Dimensional Optimal Control
URL:https://www.mcgill.ca/cim/channels/event/neural-network-approach-high-d
 imensional-optimal-control-328640
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