Data Science Institute

Professor Gouskos joined the Physics Department at Brown University in 2023, after holding the position of Experimental Physics Research Staff at the European Organization for Nuclear Research (CERN).

Professor Gouskos is an experimental particle physicist specializing in the study of the Higgs boson and the search for physics beyond the Standard Model. At the Large Hadron Collider (LHC), where scientists have to handle enormous volumes of data--far exceeding the quantity processed by global tech companies like Amazon--he has been at the forefront of developing and applying cutting-edge Artificial Intelligence (AI) and Deep Learning (DL) techniques since the early days of LHC operations. His contributions span multiple areas within high-energy physics, leveraging these advanced algorithms to tackle the extreme complexity of the data produced by particle collisions. In addition to his work in physics analysis and AI/DL, Gouskos is heavily involved in the research and development of detector upgrades and the design of future experiments, including the Future Circular Collider (FCC).

How does your research, teaching, or other work relate to data or computational science?

My research interests lie in experimental particle physics, particularly at the Large Hadron Collider (LHC) at CERN, where I am actively involved in flagship data analysis projects such as those focusing on the study of the Higgs boson and the search for Dark Matter. Given the vast volume and complexity of data generated in these experiments, I was one of the first to introduce deep learning techniques to this domain. Since then, I have been at the forefront of developing cutting-edge AI and deep learning methodologies that are crucial for significantly extending the physics outcomes from the LHC data. These concepts have been widely adopted within the particle physics community, underscoring the transformative role of data science in advancing our understanding of fundamental physics.

From 2019 to 2020, I co-coordinated the LHC inter-Experiment Machine Learning group. This group aims to unify the efforts of scientists across different LHC experiments at CERN (each experiment consists by more than 2000 scientists), facilitating the organization of machine learning initiatives, exchanging ideas, and fostering common solutions. This role serves as one example of my commitment to interdisciplinary collaboration and advancing the field through collective expertise.

Since I joined Brown, I continued playing a critical role in this area and have extended my efforts in teaching AI. Last spring, which was my first course at Brown, I designed, introduced, and taught the graduate-level course "AI in Physics", which explores various applications of AI across different domains of physics. In the upcoming Fall semester, I will be developing and introducing an upper-level undergraduate/graduate course on computational physics and data analysis, further emphasizing the integration of data science in the curriculum.

My deep interest in AI and data science extends beyond particle physics, as I am eager to explore and contribute to data-driven research and education across diverse domains.