Deepti Raghavan

I am an Assistant Professor in the Department of Computer Science at Brown University. My research interests focus on operating systems, networking, and machine learning systems.

Previously, I received my PhD in computer science from Stanford. I was lucky to be advised by Matei Zaharia and Phil Levis. I also closely collaborated with Irene Zhang. I was supported by a National Science Foundation Graduate Research Fellowship (2019) and a Stanford School of Engineering Fellowship (2018-2019). Even earlier, I received both my Bachelor's (2017) and my M.Eng (2018) degrees at MIT, where I researched in the Networks and Mobile Systems group in CSAIL, under Hari Balakrishnan.

I am actively recruiting PhD students to join my lab. If you are interested, apply to the Brown CS PhD program and mention my name. If you are a current Brown masters or undergraduate student interested in independent research in systems, email me!

Selected Publications

ALTO: An Efficient Network Orchestrator for Compound AI Systems.

Keshav Santhanam*, Deepti Raghavan*, Muhammad Shahir Rahman, Thejas Venkatesh, Neha Kunjal, Pratiksha Thaker, Philip Levis, Matei Zaharia.

EuroMLSys 2024, Athens, Greece.

Cornflakes: Zero-Copy Serialization for Microsecond-Scale Networking.

Deepti Raghavan, Shreya Ravi, Gina Yuan, Pratiksha Thaker, Sanjari Srivastava, Micah Murray, Pedro Henrique Penna, Amy Ousterhout, Philip Levis, Matei Zaharia, Irene Zhang.

SOSP 2023, Koblenz, Germany. Awarded Distinguished Artifact.

Clamor: Extending Functional Cluster Computing Frameworks with Fine-Grained Remote Memory Access.

Pratiksha Thaker, Hudson Ayers, Deepti Raghavan, Ning Niu, Philip Levis, Matei Zaharia.

SoCC 2021, Hybrid.

Breakfast of Champions: Towards Zero-Copy Serialization with NIC Scatter-Gather.

Deepti Raghavan, Philip Levis, Matei Zaharia, Irene Zhang.

HotOS 2021, Virtual.

POSH: A Data-Aware Shell.

Deepti Raghavan, Sadjad Fouladi, Philip Levis, Matei Zaharia.

Usenix ATC 2020, Boston, USA.

Model Assertions for Monitoring and Improving ML Models.

Daniel Kang*, Deepti Raghavan*, Peter Bailis, Matei Zaharia.

MLSys 2020, Austin, Texas.

Model Assertions for Debugging Machine Learning.

Daniel Kang*, Deepti Raghavan*, Peter Bailis, Matei Zaharia.

NeurIPS MLSys Workshop 2018, Montreal, Canada.

ICLR DebugML Workshop 2019, New Orleans, Louisiana.

Restructuring Endpoint Congestion Control.

Akshay Narayan, Frank Cangialosi, Deepti Raghavan, Prateesh Goyal, Srinivas Narayana, Radhika Mittal, Mohammad Alizadeh, Hari Balakrishnan.

ACM SIGCOMM 2018, Budapest, Hungary.

Pantheon: the training ground for Internet congestion-control research.

Francis Y. Yan, Jestin Ma, Greg Hill, Deepti Raghavan, Riad S. Wahby, Philip Levis, Keith Winstein.

Usenix ATC 2018, Boston, USA. Awarded Best Paper.

Misc.

I am always on the lookout for a good scone, and I enjoy making lattes. I have been playing violin since I was small, and I was lucky to continue playing chamber music throughout college and graduate school. Once I contributed a small Linux kernel commit :P.