Professor Gleyzer's research is in the area of intelligent systems to extract meaningful information from big science experiments such as the Large Hadron Collider (LHC) at CERN. He is the founder of several major machine learning initiatives such as the Inter-experimental Machine Learning Working Group and Compact Muon Solenoid experimental Machine Learning Forum. Professor Gleyzer is working on applying advanced machine learning methods to searches for new physics, such as dark matter.

In the present era of exploration and discovery, machine learning has become instrumental in advancing experimental science. Driven by significant algorithmic, computational and data-intensive advances, data science methods have opened new avenues of research in many fields: from physics and astronomy to computational biology, economics, computer vision, health, engineering, robotics and many others. Big scientific experiments, such as the Large Hadron Collider (LHC), the Laser Interferometer Gravitational-Wave Observatory (LIGO), the Large Synoptic Survey Telescope and the Event Horizon Telescope (EHT) have opened or will soon open new windows into our understanding of the Universe. Advanced data science methods are at the forefront of discovery-driven applications in these Big Science experiments. I will discuss both current and possible future applications and opportunities for data science in the context of Big Science experiments and their impact on discovery-driven data intensive science.