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SUMMARY:Centennial Workshop on Quantum Probability\, Causality\, and Infor
 mation
DTSTART:20260925T170000Z
DTEND:20260927T220000Z
DTSTAMP:20261027T003200Z
UID:indico-event-1711829@indico.cern.ch
CONTACT:nadolsky@msu.edu\;(517) 884-5613
DESCRIPTION:Speakers: Xiao-li Meng (Harvard University)\, Pavel Nadolsky (
 Michigan State University)\n\nWe are pleased to announce The Centennial Wo
 rkshop on Quantum Causality\, Probability\, and Information\, to be held a
 t Harvard University in Cambridge\, Massachusetts\, from September 25–27
 \, 2026.\nIn the year when we celebrate the 100th anniversary of the publi
 cation of Schrödinger equation and Born’s rule\, this interdisciplinary
  workshop will bring together leading researchers from physics\, statistic
 s\, philosophy\, and data science to examine foundational questions at the
  intersection of causality\, probability\, and information in modern scien
 ce. Hosted in collaboration with the Harvard Data Science Initiative and H
 arvard Data Science Review\, the meeting aims to foster deep dialogue acro
 ss disciplines at a time when advances in quantum theory\, large-scale dat
 a\, and artificial intelligence are reshaping scientific inference.\nScope
  and Vision\nCausality and probability lie at the core of scientific reaso
 ning\, yet their interpretation and application vary significantly across 
 fields. In quantum physics\, probability enters in non-classical settings\
 ; in statistics and AI\, it underpins inference from data and prediction\;
  in philosophy\, it raises enduring conceptual questions about explanation
  and knowledge.\nThis workshop seeks to clarify these perspectives and exp
 lore their connections\, with particular emphasis on the role of informati
 on as a unifying theme across disciplines.\nKey guiding questions include:
 \n\nWhat does causality mean in modern science\, particularly in quantum c
 ontexts?\nHow should probability be interpreted across physical theory\, s
 tatistics\, and machine learning?\nWhat is the relationship between causal
  models\, probabilistic inference\, and information-theoretic frameworks?\
 nCan quantum phenomena be understood within broader probabilistic or infor
 mational paradigms?\nHow do advances in AI reshape our understanding of un
 certainty\, inference\, and scientific explanation?\n\n \nTopics of Inter
 est\nThe program will span a range of interconnected themes\, including:\n
 \nFoundations of causation and causal modeling\nInterpretations of probabi
 lity (frequentist\, Bayesian\, quantum\, and beyond)\nQuantum probability 
 and its relation to classical probability theory\nInformation-theoretic ap
 proaches to physics and inference\nThe relationship between correlation\, 
 causation\, and signaling\nUncertainty quantification in modern physics an
 d data-driven science\nThe role of AI and machine learning in scientific r
 easoning\n\n \nPractical information\nParticipation in the workshop requi
 res accepted registration. The registration form and a webpage with inform
 ation about the venue\, lodging\, and travel can be accessed through the m
 enu on the left-hand side.\nZoom recordings\nZoom recordings of presentati
 ons are available for workshop participants in Workshop Materials at the b
 ottom of this webpage.\n \nOrganizers\n\nChristine Aidala (University of 
 Michigan)\nJacob Barandes (Harvard University)\nHanti Lin (University of C
 alifornia\, Davis)\nXiao-Li Meng (Harvard University)\nPavel Nadolsky (Mic
 higan State University)\n\n\nhttps://indico.cern.ch/event/1711829/
IMAGE;VALUE=URI:https://indico.cern.ch/event/1711829/logo-476034194.png
LOCATION:Lehman Hall (Harvard University)
URL:https://indico.cern.ch/event/1711829/
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