# Sean Gasiorowski > particle physicist **Wikidata**: [Q112844739](https://www.wikidata.org/wiki/Q112844739) **Source**: https://4ort.xyz/entity/sean-gasiorowski ## Summary Sean Gasiorowski is an American particle physicist specializing in high energy physics and machine learning. He currently serves as a research associate at the SLAC National Accelerator Laboratory, a position he has held since September 2021. Gasiorowski earned his Doctor of Philosophy in physics from the University of Washington, where he studied under the supervision of Anna Goussiou. ## Biography * **Nationality:** United States * **Education:** * Doctor of Philosophy (Physics), University of Washington (2016–2021) * Master of Science (Physics), University of Washington (2016–2018) * Bachelor of Arts (Honours) (Physics), University of Chicago (2012–2016) * Bachelor of Science (Honours) (Mathematics), University of Chicago (2012–2016) * **Known for:** Research at the intersection of particle physics and machine learning. * **Employer(s):** * SLAC National Accelerator Laboratory (Research Associate, Sept 2021–Present) * University of Washington Department of Physics (Doctoral Student, ended 2021) * **Field(s):** Particle physics, high energy physics, machine learning ## Contributions Sean Gasiorowski has contributed to the academic and research landscape primarily through his work in high energy physics, specifically integrating machine learning techniques into the field. His academic trajectory began with a strong dual foundation in mathematics and physics at the University of Chicago, where he completed two bachelor's degrees with honours. His significant contribution to the field solidified during his time at the University of Washington. Between September 2016 and August 2021, he pursued advanced studies, culminating in a Doctor of Philosophy in physics. His dissertation research was conducted under the guidance of doctoral advisor Anna Goussiou. This work likely focused on the application of algorithms and statistical models—core components of machine learning—to solve complex problems in particle physics, as indicated by his declared fields of work. Following the completion of his PhD, Gasiorowski transitioned to a role as a research associate at the SLAC National Accelerator Laboratory. In this capacity, he engages with the scientific study of algorithms and high-energy physics, contributing to the laboratory's mission of exploring fundamental questions about the universe. His professional profile connects him to the broader scientific community through identifiers such as Google Scholar, Inspire HEP, and ORCID. ## FAQs ### Q: What is Sean Gasiorowski's current role? A: Sean Gasiorowski is a research associate at the SLAC National Accelerator Laboratory, a position he began in September 2021. ### Q: Where did Sean Gasiorowski receive his PhD? A: He received his Doctor of Philosophy in physics from the University of Washington in August 2021. ### Q: What are Sean Gasiorowski's primary research interests? A: His primary fields of work are particle physics, high energy physics, and machine learning. ## Why They Matter Sean Gasiorowski represents a modern generation of physicists who bridge the gap between traditional experimental physics and computational science. His academic background, holding honours degrees in both mathematics and physics, provided a rigorous foundation for his current specialization. The integration of machine learning into high energy physics is a critical advancement in the field, allowing researchers to manage and interpret the massive datasets generated by particle accelerators. By working at the SLAC National Accelerator Laboratory, Gasiorowski contributes to one of the world's premier research institutions. His work helps advance the understanding of fundamental particles and forces. His career trajectory—from a dual-major undergraduate to a doctoral graduate and now a research associate—illustrates a dedicated path of specialized expertise. His contributions matter because they aid in the development of software and algorithms that enable machines to exhibit intelligent behavior in the analysis of scientific data, pushing the boundaries of what is discoverable in particle physics. ## Notable For * **Dual Disciplinary Background:** Holding dual Bachelor of Science and Bachelor of Arts honours degrees in Mathematics and Physics from the University of Chicago. * **Advanced Application of AI:** Specializing in the intersection of machine learning and high energy physics. * **Affiliation with SLAC:** Serving as a research associate at a top-tier U.S. Department of Energy national laboratory. * **Doctoral Research:** Completing his PhD under noted physicist Anna Goussiou at the University of Washington. ## Body ### Education and Early Career Sean Joseph Gasiorowski began his higher education at the University of Chicago in September 2012. He demonstrated a broad aptitude for quantitative sciences, pursuing concurrent degrees. By June 2016, he had earned both a Bachelor of Arts (Honours) in Physics and a Bachelor of Science (Honours) in Mathematics. Following his undergraduate studies, he enrolled at the University of Washington in September 2016. He earned a Master of Science in Physics in June 2018. He continued his studies as a doctoral student in the Department of Physics, completing his Doctor of Philosophy in August 2021. His doctoral advisor was Anna Goussiou. ### Professional Affiliations Gasiorowski holds citizenship in the United States and writes and speaks English. In September 2021, he assumed the role of Research Associate at the SLAC National Accelerator Laboratory. This role involves the practical application of particle physics and computer science principles. ### Research and Digital Presence Gasiorowski's work is documented across several academic platforms. He is identified within the academic community via his Inspire HEP author ID (Sean.Gasiorowski.1) and Google Scholar profile. His research interests explicitly link the study of particle physics with "artificial intelligence" and "machine learning"—fields involving the development of software and statistical models that enable computer systems to perform tasks without explicit instructions. His professional network and research outputs are further tracked via his ORCID and LinkedIn profiles. ## References 1. WorldCat 2. ORCID Registry 3. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0002-4067-2472/employment/18282869) 4. Library of Congress Authorities