I am a Computer Science Ph.D. student at Johns Hopkins University advised by Dr. Soudeh Ghrobani. My research focuses on improving the speed and reliability of modern networks. I have developed mechanisms to detect and quantify congestion events in datacenter networks — critical infrastructure at the core of the Internet. In addition, I have proposed techniques to mitigate congestion both within individual datacenters and across interconnected datacenter fabrics. With the rapid expansion of large-scale Artificial Intelligence (AI) training workloads, my current work targets emerging challenges in AI datacenters, including addressing bandwidth inefficiencies and ensuring scalable, high-throughput network performance.
During my time at Microsoft, we designed Uno, a unified congestion control scheme that operates both within and across large-scale datacenter networks.
As an intern on the AI Enablement team, I designed and implemented a high-performance data migration technique that significantly improved the speed of transfers between the company's remote storage servers.
I was responsible for automating the system update process and performing system-level FIO benchmarking tests.
Darmaneh was a startup focused on building a platform for online medical consultations. I was responsible for developing and maintaining the Android and iOS applications.