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Basics of Statistical Analysis by José Ocariz
Dr. José Ocariz is a French-Venezuelan experimental particle physicist.
He is a member of the ATLAS experiment at the Large Hadron Collider (LHC) at CERN.
He was an active contributor to the discovery of the Higgs Boson in 2012, and is now interested in understanding the properties of the Higgs field, and in searches for New Physics beyond the Standard Model, with a recent motivation for possible signatures of Dark Matter in colliders. He has been involved in several programs of international scientific cooperation, in particular between France and Latin America.
- Basics of Machine Learning by Raquel Pezoa
Dr. Raquel Pezoa is an assistant professor at the Computer Science Department at Universidad Técnica Federico Santa María (UTFSM), and an associate researcher of CCTVal-UTFSM in Valparaíso, Chile. Her research focuses on developing machine learning and explainable artificial intelligence algorithms for analyzing data from the high-energy physics, astrophysics, and biomedical fields.
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Application of Machine Learning in Nuclear Physics Experiments by David Lawrence
Dr. David Lawrence holds a Ph.D. in experimental nuclear physics from Arizona State University and has over 25 years of experience in nuclear physics experiments at particle accelerators. He has extensive expertise in software development and the application of artificial intelligence to experiments. Dr. Lawrence has worked on the calibration and maintenance of the CLAS detector, contributed to the PrimEx experiment, and dedicated 15 years to the GlueX detector. In 2020, he founded and now leads the Experimental Software and Computing Infrastructure (EPSCI) group at Jefferson Lab, focusing on data acquisition, efficient data reconstruction in high-throughput computing systems, and AI applications in experimental programs.
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Application of Machine Learning in Astrophysics by Pia Amigo
PhD in Astrophysics from the Pontifical Catholic University of Chile. Since 2020, she has been working on machine learning projects, focusing on text analysis using natural language processing (NLP) techniques. She has participated in large-scale initiatives at the national level, such as the Mineduc's Dialogues for Rural Education (2023), Citizen Dialogues on the Constitutional Process (2022-2023), and analysis of Citizen Consultations for Tenemos que Hablar de Chile (2021-2023). Since 2023, she has been working as a part-time professor at the Federico Santa María Technical University (UTFSM), teaching courses for Engineering and Astronomy, and this semester she is in charge of the Introduction to Machine Learning for Astronomy course at the Casa Central and Santiago campuses.
- Machine Learning in Action: The algorithms that transformed the industry by Felipe Rojas
Felipe Rojas earned a PhD in particle physics from Universidad Técnica Federico Santa María. He then spent five years researching the theory and phenomenology of elementary particle physics and cosmology beyond the Standard Model. His work explored the cosmological connection to dark matter, collaborating with colleagues in Chile, Brazil, and England. In 2020, Felipe transitioned to a role as a data scientist at Walmart Chile, where he applies data-driven techniques to enhance business decision-making. His contributions span multiple projects, including the development of recommender systems for e-commerce products, text classification, sentiment analysis to improve chatbot performance, and sales forecasting.
- University-Industry Linkage through R&D: The Case of Wildsense and Aquaculture by Marcos Zúñiga.
Dr. Marcos Zúñiga is an associate researcher of CCTVal in Valparaíso, CEO of its spin-off Wildsence, and professor at the Telematics Civil Engineering in the Department of Electronics at the Universidad Técnica Federico Santa María.
