BIMSA > Shailesh Lal
Assistant Professor Shailesh Lal

Group: Quantum Fields and Strings

Office: A3-3a-305

Email: shaileshlal@bimsa.cn

Research Field: Machine Learning and Neural Networks, Theory & applications to Physics

CV

Biography

Dr Shailesh Lal received his PhD from the Harish-Chandra Research Institute. His research interests are applications of machine learning to string theory and mathematical physics, black holes in string theory and higher-spin holography.

Research Interest

  • Machine Learning and Neural Networks
  • Theory & applications to Physics
  • AdS/CFT duality
  • Conformal Bootstrap
  • String Theory
  • Black Holes
  • Heat Kernel Methods

Education Experience

  • 2006 - 2013      Harish-Chandra Research Institute & Homi Bhabha National Institute      Physics      Doctor      (Supervisor: Rajesh Gopakumar)
  • 2004 - 2006      University of Delhi      Physics      Master
  • 2001 - 2004      University of Delhi      Physics      Bachelor

Work Experience

  • 2022 -      Beijing Institute of Mathematical Sciences and Applications      Assistant Research Fellow
  • 2022 - 2022      International Centre for Theoretical Sciences, Tata Institute of Fundamental Research      Visiting Post-doctoral Fellow
  • 2021 - 2021      Kenkou GmbH      Data Scientist (Research)
  • 2017 - 2020      Faculdade de Ciencias, Universidade do Porto      Post-Doctoral Fellow
  • 2015 - 2017      LPTHE, CNRS & Universite Sorbonne      Postdoctorant
  • 2013 - 2015      Seoul National University      Post-Doctoral Fellow
  • 2012 - 2013      International Centre for Theoretical Sciences, Tata Institute of Fundamental Research      Post-Doctoral Fellow

Publication

  • [1] Shailesh Lal, Suvajit Majumder, and Evgeny Sobko, The R-mAtrIx Net, Machine Learning: Science and Technology, 5(2024), 3, 035003
  • [2] Yang-Hui He, Shailesh Lal, M. Zaid Zaz, The world in a grain of sand: Condensing the string vacuum degeneracy, Physical Letters B, 849(2024), 138461
  • [3] Heng-Yu Chen, Yang-Hui He, Shailesh Lal, M. Zaid Zaz, Machine Learning Etudes in Conformal Field Theories, International Journal of Data Science in the Mathematical Sciences, 1(2023), 01, 71-104
  • [4] Shailesh Lal, Machine Learning Symmetry, Nankai Symposium on Mathematical Dialogues(2022)
  • [5] Heng-Yu Chen, Yang-Hui He, Shailesh Lal, and Suvajit Majumder, Machine learning Lie structures & applications to physics, Physics Letters B, 817(2021), 136297
  • [6] Nick Halmagyi, and Shailesh Lal, Mixed Moments for the Product of Ginibre Matrices, arXiv:2007.10181(2020)

 

Update Time: 2024-09-30 15:45:15