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DESCRIPTION:\n\nMoving towards Interpretable Models of Functioning Brains F
 or Adaptive Real-World Behavior and Sustainable Health\n\nRaghu Machiraju
 \n\nProfessor of Biomedical Informatics\, Computer Science and Engineering
  (CSE)\, and Pathology at the Ohio State University (OSU)\n\nWith High-Lev
 el Panel of Leaders in Science\, Technology\, On-the-Ground Action\, and P
 olicy\n\nRegister & watch the webinar\n\nView poster\n\nAs exemplified by 
 the Virtual Brain Platform that is part of the EU-lead e-Brain initiative\
 , the brain can be best modeled as a complex\, dynamic and adaptive system
  that is adept at engaging in non-trivial decision making or learning task
 s. In previous work we have shown that appropriate state- space models can
  depict how the various regions of a functioning brain are recruited in a 
 cascacadic manner to complete mental arithmetic. These models were used to
  explain the different functioning of normal\, dyscalculic and dyslexic br
 ains. I will describe this work in sufficient detail and especially highli
 ght the crucial role of state-space models. In more recent interactions\, 
 I explored how these state-space models can be used for more general decis
 ion making tasks. The goal was to create “models similar to a functioning 
 brain” rather than replicate the brain as is often done in the annals of n
 euromorphic computing. When successful\, it will be then possible to simul
 ate the behavior of humans when they interact with both natural and manage
 d-natural systems. Thus\, the models of experts can be shared to teach nov
 ices particular tasks. Further\, I will also propose that is possible to m
 odel living systems of human brain and body as integrated into a larger en
 vironment as they adapt to change. Thus\, cancers like glioblastomas\, liv
 ing plant(s)\, and other organisms can be modeled as adaptive precision ca
 re. Interestingly\, one could create specialized virtual E-brains and real
 ize them either as von-Neuman systems at the least\, or as material system
 s (e\,g.\,photonic systems emulating photo-synthesis) that sense\, compute
  and store information and interact with each other. I therefore offer a b
 lueprint for an e- brain-in-a-box which\, in turn\, will rest on many tool
 s from difference projects assembled in high-performing computing integrat
 ive architecture like C-Brain.\n\nAbout the speaker\n\nRaghu Machiraju is 
 a Professor of Biomedical Informatics\, Computer Science and Engineering (
 CSE)\, and Pathology at the Ohio State University (OSU). He founded the $1
 70M\, 55-faculty strong\, Translational Data Analytics Institute dedicated
  to the adoption of data science and analytics on the campus of Ohio State
 . Currently\, he is the Associate Chair for Growth in the Department of Co
 mputer Science and Engineering and an essential member of a leadership tea
 m overseeing tremendous growth in size and reputation. Over the last two y
 ears\, CSE@OSU has risen 11 spots and is now a top-25 department and is se
 eking to transform itself into a school of computing. Raghu’s own research
  interests span areas where computing intersects with various domains. As 
 a Co-PI of a $20M NSF-funded AI Institute\, he helps with the adoption of 
 AI by various domain specialists while contributing to AI foundations. As 
 an independent researcher\, he has contributed to developing machine learn
 ing methods to characterize unsteady flow\, model state transitions of a f
 unctioning brain\, integrate multiple omics data to predict patient outcom
 es with both semin- supervised and unsupervised tools\, create weakly supe
 rvised models that rely on weak labels and enable robust grading of large 
 whole slide histopathology images\, and develop tools of GenAI to convert 
 text describing branching processes to flow graphs.\n\n\nAbout the series
 \n\nThe Precision Convergence series is launched to catalyze unique synerg
 y between\, on the one hand\, novel partnerships across sciences\, sectors
  and jurisdictions around targeted domains of real-world solutions\, and o
 n the other hand\, a next generation convergence of AI with advanced resea
 rch computing and other data and digital architectures such as PSC’s Bridg
 es-2\, and supporting data sharing frameworks such as HuBMAP\, informing i
 n a real time as possible the design\, deployment and monitoring of soluti
 ons for adaptive real-world behaviour and context.\n\nThe Precision Conver
 gence Webinar Series is co-hosted by The McGill Centre for the Convergence
  of Health and Economics (MCCHE) at McGill University and The Pittsburgh S
 upercomputing Center\, a joint computational research centre between Carne
 gie Mellon University and the University of Pittsburgh.\n\n \n
DTSTART:20230927T150000Z
DTEND:20230927T170000Z
SUMMARY:MCCHE Precision Convergence Webinar Series with Raghu Machiraju
URL:https://www.mcgill.ca/desautels/channels/event/mcche-precision-converge
 nce-webinar-series-raghu-machiraju-350435
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