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• Invited attendee at Microsoft Systems Faculty Summit 2018.
• Program committee member for USENIX OSDI 2018.
DART used in Top 5 WSDM 2018 Music Recommendation Challenge winner.
• Program committee member for SysML 2018.
• Joined CMU CSD as an assistant professor.
• Talk at Stanford Information Theory Forum.
• 'EC-Cache' accepted at USENIX OSDI 2016.
• Received Eli Jury Award 2016 for best thesis in the area of Systems, Communications, Control, or Signal Processing at EECS, UC Berkeley.
• 'Piggybacking framework' accepted to IEEE Transactions on Information Theory.
• 'Sparsifying storage codes for fast encoding' accepted at IEEE ISIT 2016.
• Invited talk at ITA Graduation Day 2016.
• Invited talk at Allerton 2015.
• Awarded Google Anita Borg Memorial Scholarsihp 2015. Thanks, Google!
• 'Distributed secret sharing' accepted to IEEE Journal of Selected Topics in Signal Processing.
• 'Reducing I/O cost in distributed storage codes' at USENIX FAST 2015. Chosen as the best paper of USENIX FAST 2015 by StorageMojo.

I am an Assistant Professor in the Computer Science Department at Carnegie Mellon University, with a courtesy appointment in the ECE department. I lead the TheSys research group at CMU. I am a part of the Parallel Data Lab (PDL). My research interests lie in the broad area of computer and networked systems with a current focus on data analytics systems and live video streaming. I am interested in the fault tolerance, scalability, and performance challenges that arise in all layers of the big data stack -- storage/caching, distributed computation, networking, and applications.

Research Overview

A bulk of my past research has focussed on the storage/caching layer and in part on the application (specifically, machine learning) layer:

  • Storage/caching: My research focus here has been on fault tolerance, scalability, load balancing, and reducing latency in large-scale distributed data storage and caching systems. We designed coding theory based solutions that we showed are provably optimal. We also built systems and evaluated them on Facebook's data-analytics cluster and on Amazon EC2 showing significant benefits over the state-of-the-art. Our solutions are now a part of Apache Hadoop 3.0 and are also being considered by several companies such as NetApp and Cisco.

  • Machine learning: My research focus here has been on the generalization performance of a class of learning algorithms that are widely used for ranking. We designed an algorithm building on top of Multiple Additive Regression Trees, and through empirical evaluation on real-world datasets showed significant improvement over classification, regression, and ranking tasks. The new algorithm that we proposed is now deployed in production in Microsoft's data-analysis toolbox which powers the Azure Machine Learning product.

TheSys (theory + systems) research group

My research group is called "TheSys (Theory + Systems)" since we take a principled and holistic approach towards solving real-world problems considering both theoretical and systems perspectives. We design solutions rooted in fundamental theory as well as build systems that employ the resulting insights and solutions to advance the state-of-the-art.

Here is our group's Github.

I am fortunate to be advising and working with the following amazing students at CMU.

PhD advisees:
Jack Kosaian
Michael Rudow
Saurabh Kadekodi (co-advised with Prof. Greg Ganger)
Juncheng (Jason) Yang

Working with:
Devdeep Ray (Prof. Srini Seshan's PhD student)

Undergraduate advisees:
Amadou Ngom
Eliot Robson


(On Google Scholar)


Conference Papers

Workshop Papers

Journal Papers

* indicates equal contribution


Rashmi K. Vinayak is an assistant professor in the Computer Science department at Carnegie Mellon University. She recieved her PhD in the EECS department at UC Berkeley in 2016, and was a postdoctoral researcher at AMPLab/RISELab and BLISS. Her dissertation received the Eli Jury Award 2016 from the EECS department at UC Berkeley for outstanding achievement in the area of systems, communications, control, or signal processing. Rashmi is the recipient of the IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012. She is also a recipient of the Facebook Fellowship 2012-13, the Microsoft Research PhD Fellowship 2013-15, and the Google Anita Borg Memorial Scholarship 2015-16. Her research interests lie in building high performance and resource-efficient big data systems based on theoretical foundations.