Deepti Raghavan

Deepti Raghavan serves as Assistant Professor in Brown University's Department of Computer Science, specializing in operating systems, networking, and machine learning systems. Her research develops novel networked abstractions to optimize data movement in distributed applications through API redesign.

Her educational background includes:

• PhD in Computer Science from Stanford University (2024)
• MEng in Computer Science from Massachusetts Institute of Technology (2018)
• BS in Computer Science from Massachusetts Institute of Technology (2017)

Her research integrates systems infrastructure with machine learning workloads, focusing on performance-critical communication patterns. Key innovations include zero-copy serialization techniques and network orchestrators for compound AI systems, bridging theoretical advances with practical deployment in cloud environments.

Publication trends (2018-2024) reveal consistent contributions to high-performance networking and ML systems, with increasing focus on AI infrastructure. Her work shows strong collaboration patterns with Stanford/MIT researchers and addresses fundamental challenges in data movement efficiency across distributed systems.

Scientific recognition includes:

• National Science Foundation Graduate Research Fellowship (2019)
• Stanford School of Engineering Fellowship (2018-2019)
• Distinguished Artifact Award at SOSP 2023
• Best Paper Award at Usenix ATC 2018

She actively recruits PhD students for her research group and teaches advanced systems courses including Cloud and Datacenter Operating Systems. Her research is supported by prestigious fellowships and likely NSF grants, with teaching responsibilities spanning graduate seminars and core undergraduate systems education.

Her research group collaborates with Stanford's Systems Lab and MIT's CSAIL Networks group, maintaining strong ties to the Networks and Mobile Systems research community. Current projects focus on next-generation abstractions for AI-driven networked applications.