Sérgio Novaes

  • Principal InvestigatorUNESP


Full Professor of Physics at the São Paulo State University (Unesp), he obtained the B.Sc. and Ph.D. degrees in Physics from the University of São Paulo (USP) and was a Postdoctoral Research Fellow at the Lawrence Berkeley National Laboratory. He was visiting researcher at the University of Wisconsin, University of Valencia, and at the Fermi National Accelerator Laboratory. He started his scientific career as a theoretical physicist working on particle phenomenology and field theory. By the end of 90’s, he became an experimental high energy physicist at the DZero Collaboration from Fermilab, and currently he is part of the Compact Muon Solenoid (CMS) Collaboration from the European Organization for Nuclear Research (Cern). He is the leader of the CMS group in São Paulo and the Principal Investigator of the São Paulo Research and Analysis Center (SPRACE) since 2003. He and his team deployed the GridUnesp, the first Campus Grid in Latin America. He is now the Scientific Director of the Center for Scientific Computing from Unesp and has been the PI of several R&D projects associated to the private sector (Padtec, Intel, Huawei, etc.) which include an Intel Parallel Computing Center and a Center of Excellence in Machine Learning. He was member of the International Committee for Future Accelerators, ICFA (2005-2007), the Brazilian representative to the Particles and Fields Commission from the IUPAP (2012-2020) and member of the Technical and Scientific Committee of the National Network for High Energy Physics, Renafae (2008-). He led several outreach programs such as “Elementary Structure of Matter: A Chart in Every School”, which distributed a chart to 25,000 Middle Schools in Brazil and the SPRACE Game translated to English and German. He coordinated several international agreements with USA (Madison, MIT, and Texas Tech), United Kingdom (Edinburgh, Imperial College, and Southampton), and Portugal (IST, Lisbon).

Summary of Activities/Interests

  • High Energy Physics
  • High Performance Computing
  • Artificial Intelligence
  • Machine Learning