During my Ph.D., I was part of the HopNets Lab, advised by Dr. Soudeh Ghorbani. My research focused on improving the performance and reliability of modern networks. I developed mechanisms to detect and quantify congestion events in datacenter networks, the critical infrastructure underpinning much of today’s Internet. I also proposed techniques to mitigate congestion within individual datacenters and across interconnected datacenter fabrics. Later in my Ph.D., as large-scale Artificial Intelligence (AI) training workloads rapidly expanded, my research shifted toward emerging challenges in AI datacenters, including reducing bandwidth inefficiencies in distributed training jobs and enabling scalable, high-throughput network performance.
Baltimore, MD
Baltimore, MD
Tehran, Iran
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.
During my Ph.D., I served as a TA 3 times across the following courses: “Computer Networks” and “Cloud Computing”.
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.
As an undergraduate, I served as a teaching assistant 14 times across the following courses: “Fundamentals of Programming”, “Advanced Programming”, “Data Structures”, “Operating Systems”, “Numerical Methods”, “Computer Structure”, and “Mathematical Statistics”.