About
I’m Babatunde Ololade, a Research Assistant at the Parallel Systems (PARS) Research Group at İzmir Institute of Technology (İYTE), Turkey.
My work focuses on GPU-accelerated and heterogeneous parallel computing, CUDA kernel optimization, and scalable deep learning systems.
I earned my B.Sc. in Software Engineering from Haliç University, Istanbul, where my thesis introduced a Git-enabled assignment submission system that reduced grading time by 38% and storage costs by 43%. The work later evolved into a peer-reviewed publication and a preprint on arXiv.
At PARS, I work on CUDA-based performance benchmarking for both discrete GPUs and Jetson embedded devices, profiling kernels with NVIDIA Nsight Systems/Compute, and building Dockerized GPU environments to support reproducible research.
I’m currently preparing to pursue a Ph.D. in Computer Science in the United States, beginning in Fall 2026, with research interests centered on high-performance computing, parallel runtime systems, and scalable machine learning frameworks.
My goal is to contribute to the design of efficient, transparent, and fair systems that bridge the gap between software performance and real-world scalability.
Outside research, I enjoy teaching, mentoring students in web and systems programming, and collaborating on open-source projects that make advanced computing more accessible.