Rate My Professor Matteo Cremonesi

Matteo Cremonesi is an Assistant Professor of Physics at Carnegie Mellon University since November 2022. His research centers on experimental particle physics, with a focus on dark matter searches using the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC). He develops machine learning tools, including graph neural networks for missing transverse momentum (MET) reconstruction, and explores their deployment on field-programmable gate arrays (FPGAs) for real-time data analysis. Cremonesi has advanced big data frameworks like Coffea for high-energy physics analysis and pioneered Apache Spark applications in the field. He earned a PhD in Physics from the University of Oxford in 2015, with a thesis on "Observation of s-Channel Single Top Quark Production at the Tevatron" as part of the CDF experiment. He holds an MSc (2012) and BSc (2009) in Physics from the University of Rome II, both summa cum laude. Previously, he was LHC Physics Center AI Fellow and Research Associate at the University of Notre Dame (2021-2022) and Research Associate at Fermi National Accelerator Laboratory (2015-2021).

During his PhD, Cremonesi co-led the validation of the Standard Model through the discovery of a top quark production mechanism. Key publications include "Coffea – Columnar Object Framework For Effective Analysis" (EPJ Web of Conferences 245, 06012, 2020), "Search for dark matter produced in association with a Higgs boson decaying to a pair of bottom quarks" (Eur. Phys. J. C 79, 280, 2019), "Search for dark matter in events with energetic, hadronically decaying top quarks" (JHEP 06, 027, 2018), and "Observation of s-Channel Production of Single Top Quarks at the Tevatron" (Phys. Rev. Lett. 112, 231803, 2014). Cremonesi has earned awards such as the 2021 LPC AI Fellowship, 2020 CMS Award, FNAL Reward 2020, Junior CMS LPC Fellowships (2017-2018), and Angelo Della Riccia Foundation Prize (2013). He led the CMS MET Group (2020-2022), served as delegate to the LHC Dark Matter Working Group (2021-present), managed the COFFEA project, and founded AccelerateAI. His contributions have influenced data analysis practices in particle physics, enhancing efficiency and discovery capabilities.