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UID:30efb2143709fd39f44b1cfaa3b1a4bb
CATEGORIES:Mathematical Physics Seminar
CREATED:20260818T144251
SUMMARY:Webinar: Arup K Chakraborty - How the Immune System Learns
LOCATION:Zoom
DESCRIPTION:Arup K Chakraborty – MIT\nWednesday, September 9, 2026\nZoom opens: 10:30AM
  EDT\nSeminar begins: 10:45AM EDT\nHow the Immune System Learns\nThe humora
 l immune system, comprised of B cells and their antibody and memory B cell 
 products, plays an important role in protecting us from infection. This sys
 tem is also a learning algorithm. Antibodies and memory B cells are produce
 d by a Darwinian evolutionary process. This is a non-equilibrium stochastic
  dynamic process that allows the immune system to learn about a new antigen
  (pathogen or vaccine component). I will first describe results obtained fr
 om statistical physics-based models of these processes and complementary da
 ta from animals and humans. These studies show that the human immune system
  has a remarkable ability to learn to develop responses that can respond to
  previously unseen variant antigens upon “training” with only a few exposur
 es to the same antigen. The mechanism underlying this ability to generalize
  will be discussed. I will then discuss how exploring and exploiting analog
 ies between how the immune system learns and how machines learn is now enab
 ling us to address basic scientific questions that can potentially guide be
 tter strategies to cure and prevent disease. Finally, I will comment on sim
 ilarities and differences between learning in the immune system and deep le
 arning networks.\n
X-ALT-DESC;FMTTYPE=text/html:<p style="text-align: center;"><strong>Arup K Chakraborty – </strong><stron
 g>MIT</strong></p><p style="text-align: center;"><strong>Wednesday, Septemb
 er 9,&nbsp;2026</strong></p><p style="text-align: center;"><strong>Zoom ope
 ns: 10:30AM EDT</strong></p><p style="text-align: center;"><strong>Seminar 
 begins: 10:45AM EDT</strong></p><p style="text-align: center;"><strong>How 
 the Immune System Learns</strong></p><p style="text-align: justify;">The hu
 moral immune system, comprised of B cells and their antibody and memory B c
 ell products, plays an important role in protecting us from infection. This
  system is also a learning algorithm. Antibodies and memory B cells are pro
 duced by a Darwinian evolutionary process. This is a non-equilibrium stocha
 stic dynamic process that allows the immune system to learn about a new ant
 igen (pathogen or vaccine component). I will first describe results obtaine
 d from statistical physics-based models of these processes and complementar
 y data from animals and humans. These studies show that the human immune sy
 stem has a remarkable ability to learn to develop responses that can respon
 d to previously unseen variant antigens upon “training” with only a few exp
 osures to the same antigen. The mechanism underlying this ability to genera
 lize will be discussed. I will then discuss how exploring and exploiting an
 alogies between how the immune system learns and how machines learn is now 
 enabling us to address basic scientific questions that can potentially guid
 e better strategies to cure and prevent disease. Finally, I will comment on
  similarities and differences between learning in the immune system and dee
 p learning networks.</p>
DTSTAMP:20260928T035045
DTSTART;TZID=America/New_York:20260909T104500
DTEND;TZID=America/New_York:20260909T120000
SEQUENCE:0
TRANSP:OPAQUE
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