Rate My Professor Matteo Cremonesi

Prior to CMU, he was LHC Physics Center Artificial Intelligence Fellow and Research Associate at the University of Notre Dame from 2021 to 2022 and Research Associate at Fermi National Accelerator Laboratory from 2015 to 2021. His research specializes in experimental high energy physics, focusing on dark matter searches at the Compact Muon Solenoid (CMS) experiment at CERN's Large Hadron Collider. Cremonesi co-led the observation of s-channel single top quark production with the CDF experiment at the Tevatron and has driven dark matter analyses including mono-top and mono-Higgs channels. He pioneered big data technologies in high energy physics, managing the LHC Big Data project with Apache Spark in partnership with Intel and leading the Coffea project for columnar analysis, which reduced analysis turnaround times by two orders of magnitude. As CMS MET Group Convener from 2020 to 2022, he developed graph neural networks for missing transverse momentum reconstruction and their deployment on field-programmable gate arrays for real-time triggers. Cremonesi founded AccelerateAI to commercialize AI on FPGAs and serves as CMS delegate to the LHC Dark Matter Working Group. His accolades include the LPC AI Fellowship (2021), CMS Award (2020), FNAL Reward and Recognition Award (2020), and Junior CMS LPC Distinguished Researcher Fellowship (2017, 2018). Key publications encompass 'Observation of s-Channel Production of Single Top Quarks at the Tevatron' (Phys. Rev. Lett., 2014), 'Search for dark matter in events with energetic, hadronically decaying top quarks and missing transverse momentum at √s = 13 TeV' (JHEP, 2018), 'Search for dark matter produced in association with a Higgs boson decaying to a pair of bottom quarks' (Eur. Phys. J. C, 2019), and 'Coffea – Columnar Object Framework For Effective Analysis' (EPJ Web Conf., 2020).