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.
As an intern on the UCLA REMAP team under the supervision of Prof. Jeff Burke, I integrated Named Data Networking (NDN) APIs into the Chromium codebase.
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.