Google Scholar page
Gates 9011


• Our paper on Segcache wins USENIX NSDI 2021 Community (Best Paper) Award ! Congratulations to Juncheng!
• Invited talk at Stanford Compression Workshop 2021.
• "Segcache", a memory-efficient and high-throughput in-memory cache, accepted for publication at NSDI 2021. Congratulations to Juncheng!
• Our OSDI 2020 paper recognized by the PC chairs as one of the best storage-related papers of OSDI 2020 and invited for submission to ACM Transactions on Storage. Congratulations to Juncheng!
• Saurabh successfully defended his Ph.D. thesis! Congratulations to Saurabh!
• Two papers from our group accepted for publication at OSDI 2020 (on resource efficiency in storage and in-memory caching systems). Congratulations to Jason, Saurabh, Francisco and Suhas!
• Excited to be participating in the live panel sesssion on Machine-learning based approaches to coding" at IEEE ISIT 2020.
• Honored to receive NSF CAREER Award 2020.
• Juncheng (Jason) Yang receives Facebook PhD fellowship. Congratulations, Jason!
• Honored to receive "Prof. Narasimhan Memorial Lecture Award 2020" from Tata Institute of Fundamental Research.
• Our work on a new class of storage codes called Convertible codes to appear in ITCS (Innovations in Theoretical Computer Science) 2020.
• Invited talk at AISystems workshop at ACM SOSP 2019.
• Our work on learning-based coded computation for ML inference systems 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 both the Systems group and the Theory group. I lead the CMU TheSys research group, and also a part of the Parallel Data Lab (PDL). My research interests broadly lie in computer/networked systems and information/coding theory, and the wide spectrum of intersection between the two areas.

My current focus is on robustness and resource efficiency in data systems spanning across the system stack including storage, communication, and computation. The key thrusts include storage and caching systems, systems for machine learning, and real-time video communication.

My CV is available here.

A note to prospective students:
Due to the high volume of emails that I receive from prospective students, unfortunately, I am unable to respond to them. Apologies in advance for this. But, I do catch up with these emails eventually, if there is a good fit and an opening in my group. If you are interested in joining my group as a PhD student, please apply to either CSD or ECE departments at CMU and write my name in your application.

TheSys ("Theory + Systems") Lab

Overarching goal: The amount of data stored, communicated, and processed is increasing exponentially, making data systems indispensable to our society. Data systems have grown to a humongous scale where non-ideal conditions such as failures and unavaiblabilities are the norm. Safety-critical applications are increasingly becoming data driven. The frontiers of computing are expanding into operating regimes which are inherently unreliable. Due to these trends, it is imperative to make data systems robust by design, while being resource efficient and performant. The overarching goal of our research is to design and build next-generation data systems that are robust, efficient and performant, spanning across the system stack including storage, computation, and communication.

We take a multi-disciplinary approach, spanning computer systems and networking, information and coding theory, and machine learning. We design solutions that are rooted in fundamental theory and enhanced with machine learning, and build systems that employ the resulting solutions to advance the state-of-the-art in data systems. We also collaborate extensively with industry which enables us to (1) base our solutions on data from real-world production systems, and (2) make an impact on real-world practice. Our industry collaborators (past and present) include Facebook, Google, Microsoft, NetApp, Cisco, Intel and Cloudera.

Here is our group's Github.

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

Current students:

PhD students:

Masters students:
  • Shaobo Guan

Undergraduate students:
  • Tianyu Zhang

Past students:

PhD students:
  • Saurabh Kadekodi (co-advised with Greg Ganger) (CMU -> Visiting Researcher at Google)

Masters students:
  • Arvind Sai Krishnan (CMU)
  • Vilas Bhat (CMU)
  • Jiaan Dai (CMU)
  • Jiaqi Zuo (CMU)
  • Jiongtao Ye (CMU)
  • Xuren Zhou (CMU)
  • Sai Kiriti Badam (CMU)

Undergraduate students:
  • Kaige Liu (CMU -> Facebook)
  • Ian Chiu (CMU -> Georgia Tech Ph.D. student)
  • Eliot Robson (CMU -> UIUC Ph. D. student)
  • Chaitanya Mukka (BITS, India)
  • Weizhong Zhang (Tsinghua University, China)
  • Sanya Agarwarl (CMU)

My group's research has been generously supported by NSF, Facebook, Google, and Amazon Web Services. Support gratefully acknowledged.


(On Google Scholar)


Conference Papers

Workshop Papers

Journal Papers


* indicates equal contribution


Rashmi Vinayak is an assistant professor in the Computer Science department at Carnegie Mellon University. Rashmi is a recipient of NSF CAREER Award, Tata Institute of Fundamental Research Memorial Lecture Award 2020, Facebook Distributed Systems Research Award 2019, Google Faculty Research Award 2018, Facebook Communications and Networking Research Award 2017, UC Berkeley Eli Jury Award 2016 for "outstanding achievement in the area of systems, communications, control, or signal processing". Her work has received USENIX NSDI 2021 Community (Best Paper) Award, and IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012. Her research interests broadly lie in computer/networked systems and information/coding theory, and the wide spectrum of intersection between the two areas. Her current focus is on fault tolerance and resource efficiency in data systems. Key thrusts include storage and caching systems, systems for machine learning, and live streaming communication.

Rashmi received her Ph.D. from UC Berkeley in 2016 where she worked on resource-efficient fault tolerance for big-data systems, and was a postdoctoral scholar at UC Berkeley's AMPLab/RISELab from 2016-17. During her Ph.D. studies, Rashmi was a recipient of Facebook Fellowship 2012-13, the Microsoft Research PhD Fellowship 2013-15, and the Google Anita Borg Memorial Scholarship 2015-16.