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)
Francisco Maturana
Juncheng (Jason) Yang

Masters students:
Arvind Sai Krishnan (CMU)
Vilas Bhat (CMU)

Undergraduate students:
Kaige Liu (CMU)
Ian Chiu (CMU)

Past students:

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

Undergraduate students:
Chaitanya Mukka (BITS, India)
Eliot Robson (CMU)
Weizhong Zhang (Tsinghua University, China)
Sanya Agarwarl (CMU)

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

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• "A locality-based approach for coded computation"
  
  Michael Rudow, K.V. Rashmi, and Venkatesan Guruswami
  Available on arXiv, Feb 2020.
  


• "Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation"
  
  Jack Kosaian, K.V. Rashmi, and Shivaram Venkataraman
  Available on arXiv, June 2018.
  Code available on Github.

Conference Papers

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• "Convertible Codes: New Class of Codes for Efficient Conversion of Coded Data in Distributed Storage"
  
  Francisco Maturana and K. V. Rashmi
  Innovations in Theoretical Computer Science (ITCS), 2020.
  
  
• "Parity Models: Erasure-Coded Resilience for Prediction Serving Systems"
  
  Jack Kosaian, K.V. Rashmi, and Shivaram Venkataraman
  ACM SOSP, 2019.
  Code available on Github.
  
  
• "Vantage: Optimizing Video Upload for Time-shifted Viewing of Social Livestreams"
  
  Devdeep Ray, Jack Kosaian, K. V. Rashmi, and Srini Seshan
  ACM SIGCOMM, 2019.
  
  
• "Cluster storage systems gotta have HeART: improving storage efficiency by exploiting disk-reliability heterogeneity"
  
  Saurabh Kadekodi, K. V. Rashmi, and Greg Ganger
  USENIX FAST, 2019.
  (Slides) (Talk video)
  Press: David Rosenthal's Blog on Digital Preservation.
  
  
• "Streaming Codes for Variable-Size Arrivals"
  
  Michael Rudow and K. V. Rashmi
  Allerton Conference on Communication, Control, and Computing, 2018.
  
  
• "EC-Cache: Load-Balanced, Low-Latency Cluster Caching with Online Erasure Coding"
  
  K. V. Rashmi, Mosharaf Chowdhury, Jack Kosaian, Ion Stoica, and Kannan Ramchandran
  USENIX OSDI, 2016.
  (Slides) (Talk video)


• "Optimal Systematic Distributed Storage Codes with Fast Encoding"
  
  Preetum Nakkiran, K. V. Rashmi and Kannan Ramchandran
  IEEE International Symposium on Information Theory (ISIT), 2016.


• "Having Your Cake and Eating It Too: Jointly Optimal Erasure Codes for I/O, Storage, and Network-bandwidth"
  
  K. V. Rashmi, Preetum Nakkiran, Jingyan Wang, Nihar B. Shah, and Kannan Ramchandran
  USENIX FAST, Feb 2015.
  Picked as the best paper of USENIX FAST 2015 by StorageMojo.


• "DART: Dropouts meet Multiple Additive Regression Trees"
  
  K. V. Rashmi, and Ran Gilad-Bachrach
  International Conference on Artificial Intelligence and Statistics (AISTATS), Feb 2015.


• A "Hitchhiker's" Guide to Fast and Efficient Data Reconstruction in Erasure-coded Data Centers
  
  K. V. Rashmi, Nihar B. Shah, Dikang Gu, Hairong Kuang, Dhruba Borthakur, and Kannan Ramchandran
  ACM SIGCOMM, Aug 2014.
  (Slides)
  Press: The Morning Paper
  Press: StorageMojo


• "One Extra Bit of Download Ensures Perfectly Private Information Retrieval"
  
  Nihar B. Shah, K. V. Rashmi and Kannan Ramchandran
  IEEE International Symposium on Information Theory (ISIT), 2014.


• "Fundamental Limits on Communication for Oblivious Updates in Storage Networks"
  
  Preetum Nakkiran, Nihar B. Shah, and K. V. Rashmi
  IEEE GLOBECOM, 2014.


• "A Piggybacking Design Framework for Read-and Download-efficient Distributed Storage Codes"
  
  K. V. Rashmi*, Nihar B. Shah* and Kannan Ramchandran
  IEEE International Symposium on Information Theory (ISIT), July 2013.
  (Slides)


• "Secure network coding for distributed secret sharing with low communication cost"
  
  Nihar B. Shah, K. V. Rashmi, and Kannan Ramchandran
  IEEE International Symposium on Information Theory (ISIT), July 2013.
  (Slides)    (Poster)


• "Regenerating Codes for Errors and Erasures in Distributed Storage"
  
  K. V. Rashmi*, Nihar B. Shah*, Kannan Ramchandran, and P. Vijay Kumar
  IEEE International Symposium on Information Theory (ISIT), July 2012.
  (Slides)


• "Information-theoretically Secure Regenerating Codes for Distributed Storage"
  
  Nihar B. Shah*, K. V. Rashmi* and P. Vijay Kumar
  IEEE Globecom, Houston, Dec. 2011.


• "Enabling Node Repair in Any Erasure Code for Distributed Storage"
  
  K. V. Rashmi*, Nihar B. Shah* and P. Vijay Kumar
  IEEE International Symposium on Information Theory (ISIT), St. Petersburg, 2011.


• "A Flexible Class of Regenerating Codes for Distributed Storage"
  
  Nihar B. Shah*, K. V. Rashmi* and P. Vijay Kumar
  IEEE International Symposium on Information Theory (ISIT), Austin, June 2010.


• "Explicit and Optimal Exact-Regenerating Codes for the Minimum-Bandwidth Point in Distributed Storage"
  
  K. V. Rashmi*, Nihar B. Shah*, P. Vijay Kumar and Kannan Ramchandran
  IEEE International Symposium on Information Theory (ISIT), Austin, June 2010.


• "Explicit Codes Minimizing Repair Bandwidth for Distributed Storage"
  
  Nihar B. Shah*, K. V. Rashmi*, P. Vijay Kumar and Kannan Ramchandran
  IEEE Information Theory Workshop (ITW), Cairo, Jan.2010.


• "Explicit Construction of Optimal Exact Regenerating Codes for Distributed Storage"
  
  K. V. Rashmi*, Nihar B. Shah*, P. Vijay Kumar and Kannan Ramchandran
  Allerton Conf., Urbana-Champaign, Sep.2009.

Workshop Papers

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• "A Solution to the Network Challenges of Data Recovery in Erasure-coded Distributed Storage Systems: A Study on the Facebook Warehouse Cluster"
  
  K. V. Rashmi, Nihar B. Shah, Dikang Gu, Hairong Kuang, Dhruba Borthakur, and Kannan Ramchandran
  USENIX HotStorage, June 2013.
  (Slides)    (Video)

Teaching

Spring 2020: 15-440 Distributed Systems
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 NSF CAREER Award 2020, Tata Institute of Fundamental Research Memorial Lecture Award 2020, 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.