Erik Pautsch

Bio

Erik is a first-year PhD student at Loyola University Chicago, specializing in high-performance computing and artificial intelligence. With a Master’s in Computer Science and a focus on uncertainty quantification in deep learning models, Erik has contributed to multiple research projects at Argonne National Laboratory, including porting the marching cubes algorithm from CUDA to SYCL and benchmarking state-of-the-art AI accelerators. Erik’s work has been featured in leading conferences like last year’s SC23 and CARLA2023. He is now exploring the integration of robotics with AI, investigating how human data can enhance robotic learning. As an active member of ACM’s SIGHPC, Erik is passionate about democratizing access to HPC and AI technologies, making them accessible to diverse communities. Erik’s long-term goal is to contribute to the development of innovative, scalable solutions that advance the field of HPC and AI.