Vahab Jabrayilov

I am a Ph.D. candidate in Computer Science at Columbia University advised by Kostis Kaffes, focusing on systems performance and low-latency computing.

My work spans networking, operating systems, and machine learning runtimes, with an emphasis on CPU and GPU virtualization. I have built high-performance software such as Machnet, a kernel-bypass networking framework, and interned at Microsoft Research, where I worked on large-scale infrastructure for data and networking.

I have expertise in C/C++, Rust, CUDA, DPDK, eBPF and Linux kernel programming, and my interests lie at high-frequency trading systems and large-scale AI platforms, where reliability and nano/microsecond-scale performance are critical.

Before starting my Ph.D., I built Replicant, a MultiPaxos-based key-value store for strongly consistent cloud replication during a research internship at Penn State, and also gained experience at EPFL, where I worked on distributed systems reliability and failure analysis.

Education

	Columbia University PhD, Computer Science, 2023-2027 (Expected)
	Columbia University MSc, Computer Science, 2023-2025
	Middle East Technical University BSc, Computer Engineering Engineering, 2019-2023