Christopher Carothers

Christopher Carothers is a Professor in the Computer Science Department at Rensselaer Polytechnic Institute and serves as Chief Scientist and Center Director of the Center for Computational Innovations (CCI). His work focuses on advancing computational science through high-performance computing infrastructure and simulation methodologies.

Carothers' research centers on massively parallel systems with expertise in modeling and simulation of complex networks and storage architectures. He actively contributes to national initiatives including the DOE Exascale Co-Design Program for next-generation storage systems, NSF PetaApps Program, and Army Research Center's Mobile Network Modeling Institute. His interdisciplinary work bridges computational theory with practical applications in HPC network design and medical diagnostics.

Recent publications demonstrate strong trends in Hybrid Parallel Discrete-Event Simulation (PDES) frameworks, multi-fidelity network modeling for Dragonfly systems, and integration of surrogate models in simulation workflows. His research extends into medical AI with foundation models for lung cancer screening, highlighting cross-domain applications of computational methods. Key thematic areas include performance optimization of HPC networks, spintronic neural networks, and efficient expert pruning in sparse AI architectures.

Scientific Awards:

• NSF CAREER award

Carothers leads major collaborative research through the DOE Exascale Co-Design Program, NSF PetaApps, and Army Research Center initiatives, which involve substantial grant funding and cross-institutional teams. While specific advisees aren't listed, his leadership roles in simulation conferences and center direction indicate active mentorship of researchers and students in computational science.

As head of the Center for Computational Innovations, Carothers directs a multidisciplinary team focused on developing advanced simulation frameworks and high-performance computing solutions. The CCI serves as RPI's hub for computational research, providing critical infrastructure for large-scale modeling projects across scientific domains.