Helena Brandao Malbouisson | Rio de Janeiro University, Brazil
Helena is an associate professor at the Rio de Janeiro State University - UERJ - in Brazil.
She did her PhD at the D0 experiment at the Tevatron, studying diffractive Physics and structure functions. After her PhD she joined the CMS Collaboration and is a member since 2008, when she first started as a postdoctoral researcher with the University of Nebraska working on top quark, Higgs physics and search for physics beyond the standard model. Helena is also heavily involved in the Data performance of CMS, having served as co-coordinator of the Physics Performance and Datasets group of CMS.
Andrzej Nowicki | CERN, Geneva, Switzerland
Andrzej is a member of the CERN Database Team, specialising in Oracle, MySQL and PostgreSQL databases. With a passion for solving technical challenges and a commitment to sharing his knowledge of database systems, he makes valuable contributions to theteam's objectives. Andrzej began his journey with CERN in 2020 and recently joined the CSC team in 2023. Prior to joining CERN, he gained experience working with various companies, where he actively contributed to numerous IT projects as a database administrator. He holds a MEng from Poznan University of Technology. Outside of his professional pursuits, Andrzej finds joy in exploring the world around him. He nurtures his adventurous spirit by travelling, hiking and skiing.
Alberto Pace | CERN, Geneva, Switzerland
Alberto Pace is a member of the IT department at CERN where he holds the IT education responsibility.
In past years, Alberto has led the Compute and Devices group providing the scientific computing services and infrastructure needed by the laboratory and its experiments to accomplish their mission, and the Storage group ensuring a coherent development process for Physics Data management activities, strongly driven by operational and user needs. He also represents CERN in the Advisory Board of the Internet Society (isoc.org) and the World Wide Web Consortium (w3c.org).
He has more than 30 years experiences in computing services, infrastructure, software engineering, accelerator control and accelerator operation. He graduated in Electronic Engineering from Politecnico di Milano (Italy) in 1987, where he received the first prize among Electronic Engineering students of the North Italy section.
Jaime Romero Barrientos | CCHEN and SAPHIR Millenium Institute, Chile
Jaime Romero is a researcher at the Chilean Nuclear Energy Commission (CCHEN) and at SAPHIR Millenium Institute, specializing in Monte Carlo methods for neutron transport and reactor physics.
His work focuses on the implementation of time-dependent transport algorithms and variance reduction techniques to improve the statistical efficiency of simulations in complex nuclear systems.
He works regularly with a range of Monte Carlo codes, including OpenMC (and its time-dependent extensions), MCNP, Geant4, FLUKA, and Serpent, applying them to problems such as criticality
analysis, shielding studies, and detector modeling. He has participated in experiments at ISOLDE (CERN) and RIKEN (Japan), and is a member of the SND@LHC and SHiP collaborations,
contributing to neutron-related studies in high-energy beam environments.
Jaime enjoys teaching and mentoring, and is actively involved in supervising undergraduate and graduate students. He combines his interests in physics and computing to support
the development of simulation capabilities and scientific training in nuclear science.
Arturo Sánchez | Universidad de Los Andes, Mérida, Venezuela
Arturo Sánchez is a Venezuelan former Assistant Professor at Universidad de Los Andes, Mérida, a Physicist and Senior DevOps Engineer and Infrastructure Manager at inait AI, Switzerland, and a former System Administrator and Researcher with over 12 years of experience at CERN. Arturo holds a PhD in Particle Physics from the University of Naples "Federico II", Italy, on one of the multiple searches for the Higgs Boson at 7 TeV proton-proton collisions at the LHC. He has professional experience in education, high-performance computing, data analysis, and DevOps practices. Arturo has worked with several institutions, including CERN, as a System Administrator for the ATLAS Experiment, co-founder and co-coordinator of the ATLAS Open Data Project, and other outreach programs such as the Erasmus+ LA-CoNGA program. His experience spans postdoctoral positions and collaborations with the ICTP and the INFN in Italy, as well as the CNRS, including the ESCAPE project at LAPP in Annecy, France. He is passionate about open education and has developed numerous training programs and workshops in computing and physics for universities in Europe and Latin America. Actively involved in outreach through Creative Commons Venezuela and Creative Commons Switzerland, he continues to mentor students and professionals in the region.
Toni Šćulac | University of Split, Croatia
Toni was born and lives in Split, Croatia where he works as an associate professor at the Faculty of Science, University of Split. He started working for the CMS experiment at CERN in 2015 as a part of his PhD studies. In 2018, he successfully defended his PhD thesis at Ecole Polytechnique (Paris, France) and University of Zagreb (Croatia). He is currently the leader of the Split CMS group that focuses on Higgs boson physics and HGCAL upgrade of the CMS detector.
During his studies, Toni attended CSC in Madrid in 2017 and tCSC in Split in 2018 as a student. In addition to his scientific work he also loves to teach and mentor Bachelor’s, Master’s, and PhD students. In 2022 he became a lecturer at the CSC and he is extremely motivated and happy to be a part of the CSC team.
Leonid Serkin | University of Mexico (UNAM)
Leonid is a Mexican scientist and Associate Professor in the Faculty of Sciences at the National Autonomous University of Mexico (UNAM), where he is building a group integrating ML into research and teaching, and developing a graduate data-science specialization.
He earned his B.Sc. and M.Sc. in physics at UNAM and received his doctoral degree from the University of Göttingen in Germany, working on the ATLAS experiment. He was later a postdoc at ICTP in Italy and a special INFN cooperation associate at CERN, contributing to precision measurements of the top quark, Higgs boson studies, new physics searches, data-quality monitoring, MC simulations, and ATLAS open data.
Now a scientific member of the ALICE Collaboration, he brings over 17 years of LHC data-analysis experience and is always enthusiastic about fostering synergies between diverse physics disciplines and computer science. Somehow, he also manages to understand six languages, though none of them seem to help with compiler or ROOT errors.
Andre Sznajder | Rio de Janeiro University, Brazil
Andre Sznajder is an Associate Professor of Physics at the State University of Rio de Janeiro (UERJ) in Brazil. His research focuses on the fundamental constituents of matter—elementary particles—and their interactions. He earned his Ph.D. in experimental particle physics in 1998, working on b-quark production at the D0 Experiment at FERMILAB.
He is currently a member of the CMS experiment at CERN, where he works on Higgs physics and the investigation of the Electroweak Symmetry Breaking mechanism through Vector Boson Scattering. Since the 2012 discovery at CERN of a new particle consistent with the long-sought Standard Model Higgs boson, this area has become especially active. A central objective now is the measurement of the Higgs potential through its self-coupling—via Di-Higgs production—to test its consistency with the predictions of the Electroweak theory.
He also has a strong interest in the Monte Carlo simulation of physics processes, event generators, and advanced multivariate data analysis techniques, including the Matrix Element Method and Deep Neural Networks. In particular, he has been developing deep learning models implemented on FPGAs for deployment in the Level-1 trigger system of the CMS experiment during the upcoming High-Luminosity LHC (HL-LHC) phase. These trigger applications require ultra-fast inference—on the order of nanoseconds—which can only be achieved through FPGA-based implementations. To this end, he uses the HLS4ML framework to translate trained neural networks into optimized FPGA firmware suitable for real-time processing.
