Google Scholar page
rvinayak[at]cs.cmu[dot]edu
Gates 9011

News

• Our work on learning-based coded computation to appear in ACM SOSP 2019.
• Invited talk at Facebook Networking and Communications Faculty Summit 2019.
• Invited talk at ICML 2019 CodML Workshop on "Reslient ML Inference via Coded Computation: A learning-based Approach" (video).
• Our work on social live video streaming to appear in ACM SIGCOMM 2019.
• Google Faculty Research Award 2018.
• NSF CRII 2018.
• Program committee member for USENIX NSDI 2020.
• Invited talk at ITA 2019.
• 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 CS as an assistant professor.
• Received Eli Jury Award 2016 for best thesis in the area of Systems, Communications, Control, or Signal Processing at EECS, UC Berkeley.
• Invited talk at ITA Graduation Day 2016.
• Invited talk at Allerton 2015.
• Awarded Google Anita Borg Memorial Scholarsihp 2015.
• '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, where I am a part of the Systems group and the Theory group. I lead the CMU TheSys research group, which is 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 reliability, availability, scalability, and performance challenges in data storage and caching systems, in systems for machine learning and in live video streaming.

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

Masters students:
Jiaan Dai
Jiaqi Zuo
Jiongtao Ye
Sai Kiriti Badam
Xuren Zhou

Undergraduate students:
Ian Chiu (CMU)
Sanya Agarwarl (CMU)
Chaitanya Mukka (BITS)

Support:
Our research is generously supported by NSF, Facebook, Google, Amazon, and all members of the PDL Consortium. Support gratefully acknowledged.


Publications

(On Google Scholar)

Preprints


Conference Papers


Workshop Papers


Journal Papers


* indicates equal contribution

Teaching

Fall 2019: 15-853 Algorithms in the real world
Fall 2018: 15-848 Practical information and coding theory for computer systems
Spring 2018: 15-359/659 Probability and computing

Bio

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 Facebook Communications and Networking Research Award 2018, Google Faculty Research Award 2018, IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012. She was 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 broadly in the areas of computer and networked systems, and information and coding theory. Her current research focus is on addressing reliability, availability, scalability, and performance challenges in data storage and caching systems, in systems for machine learning, and in live video streaming based on theoretical foundations.