Dylan Rankin

Dylan Rankin is an Assistant Professor in the Department of Physics and Astronomy at the University of Pennsylvania’s School of Arts & Sciences. His research focuses on particle physics experiments at the Large Hadron Collider (LHC), leveraging machine learning (ML) for data analysis and optimizing high-speed trigger systems. He is a key contributor to the FastML collaboration, advancing FPGA-based ML inference for low-latency applications in physics and astronomy.

Education: Ph.D. in Physics from Boston University (2018), Sc.B. in Physics from MIT (2012).

Research Interests:

• Probing the Standard Model through LHC proton-proton collision data
• Machine learning applications in jet classification, mass regression, and event reconstruction
• Optimizing trigger systems for real-time data selection at the LHC
• Hardware acceleration (FPGA/GPU) for scientific computing challenges

Recent work emphasizes ML deployment in latency-constrained environments, including gravitational wave astronomy and FPGA-as-a-service frameworks. His collaborative projects include hls4ml for low-latency inference and AIgean for heterogeneous cluster ML workflows.

Advising/Grants: Active in training next-generation researchers in ML-driven particle physics methodologies. Involved in multi-institutional initiatives for computational infrastructure development.

Labs/Teams: Core member of the FastML collaboration, leading FPGA-based ML solutions for physics experiments. Associated with the Penn High Energy Physics group.