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BEGIN:VEVENT
DTSTAMP:20260526T151254Z
DTSTART;VALUE=DATE-TIME:20260608T130000
DTEND;VALUE=DATE-TIME:20260608T140000
SUMMARY:WCPM\, Kevin Huang\, Warwick
TZID:Europe/London
UID:20260608-8ac672c59d8bfea7019daa1cd0024e12@warwick.ac.uk
CREATED:20260521T094626Z
DESCRIPTION:Networking Lunch: Outside L5\, from 12:30pm - 1pm. Title: Dia
 gonal Symmetrization of Neural Network Solvers for the Many-Electron Sch
 rödinger Equation Abstract: Incorporating group symmetries into neural n
 etworks has been a cornerstone of success in many AI-for-science applica
 tions. Diagonal groups of isometries\, which describe the invariance und
 er a simultaneous movement of multiple objects\, arise naturally in many
 -body quantum problems. Despite their importance\, diagonal groups have 
 received relatively little attention\, as they lack a natural choice of 
 invariant maps except in special cases. We study different ways of incor
 porating diagonal invariance in neural network ansätze trained via varia
 tional Monte Carlo methods\, and consider specifically data augmentation
 \, group averaging and canonicalization. We show that\, contrary to stan
 dard ML setups\, in-training symmetrization destabilizes training and ca
 n lead to worse performance. Our theoretical and numerical results indic
 ate that this unexpected behavior may arise from a unique computational-
 statistical tradeoff not found in standard ML analyses of symmetrization
 . Meanwhile\, we demonstrate that post hoc averaging is less sensitive t
 o such tradeoffs and emerges as a simple\, flexible and effective method
  for improving neural network solvers. Bio: Kevin is a postdoctoral rese
 arch fellow funded by the Engineering and Physical Sciences Research Cou
 ncil (EPSRC) through the ProbAI Hub. They are currently based at the Uni
 versity of Warwick\, working with Gareth Roberts\, and collaborate with 
 Boris Hanin at Princeton University. They completed a PhD in machine lea
 rning at the Gatsby Computational Neuroscience Unit\, University College
  London\, under the supervision of Peter Orbanz and Morgane Austern. Dur
 ing this time\, they were also a visiting researcher with the LIPS group
  at Princeton Computer Science\, hosted by Ryan P. Adams. Prior to this\
 , they completed both their undergraduate and master’s degrees in mathem
 atics at the University of Cambridge. Their research lies at the interse
 ction of machine learning theory\, probability\, and statistics. They st
 udy the emergence of universal structures in large-scale stochastic syst
 ems\, drawing on tools from random matrix theory\, high-dimensional stat
 istics\, symmetry-based inference\, and stochastic optimisation. Alongsi
 de this theoretical work\, they increasingly engage with applied challen
 ges\, particularly around scaling laws in neural networks\, AI for scien
 tific discovery\, and the robustness and safety of machine learning mode
 ls. For the 2025–2026 academic year\, he is co-organising the ProbAI onl
 ine seminar series and will lead the ProbAI Theory of Scaling Laws Works
 hop at Warwick in summer 2026.
LOCATION:
CATEGORIES:WCPM
LAST-MODIFIED:20260521T094626Z
ORGANIZER;CN=Jin Kang:
END:VEVENT
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