K. V. Rashmi (Rashmi Vinayak)

About K. V. Rashmi (Rashmi Vinayak)

K. V. Rashmi (Rashmi Vinayak), With an exceptional h-index of 26 and a recent h-index of 21 (since 2020), a distinguished researcher at Carnegie Mellon University, specializes in the field of Computer systems, Storage systems, Big-data systems, Coding Theory, Information Theory.

His recent articles reflect a diverse array of research interests and contributions to the field:

Communication-efficient, Fault Tolerant PIR over Erasure Coded Storage

Code Conversions in Storage Systems

Sieve is simpler than lru: an efficient turn-key eviction algorithm for web caches

FIFO queues are all you need for cache eviction

Efficient Constructions of Streaming Codes

Efficient Fault Tolerance for Recommendation Model Training via Erasure Coding

On expanding the toolkit of locality-based coded computation to the coordinates of inputs

Learning-augmented streaming codes for variable-size messages under partial burst losses

K. V. Rashmi (Rashmi Vinayak) Information

University

Position

Assistant Professor CS

Citations(all)

4679

Citations(since 2020)

2230

Cited By

3379

hIndex(all)

26

hIndex(since 2020)

21

i10Index(all)

42

i10Index(since 2020)

37

Email

University Profile Page

Google Scholar

K. V. Rashmi (Rashmi Vinayak) Skills & Research Interests

Computer systems

Storage systems

Big-data systems

Coding Theory

Information Theory

Top articles of K. V. Rashmi (Rashmi Vinayak)

Communication-efficient, Fault Tolerant PIR over Erasure Coded Storage

2024/2/1

Code Conversions in Storage Systems

IEEE BITS the Information Theory Magazine

2024/1/10

Sieve is simpler than lru: an efficient turn-key eviction algorithm for web caches

21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24). USENIX Association

2024

FIFO queues are all you need for cache eviction

2023/10/23

Efficient Constructions of Streaming Codes

2023/8/21

Efficient Fault Tolerance for Recommendation Model Training via Erasure Coding

Proceedings of the VLDB Endowment

2023/7/1

On expanding the toolkit of locality-based coded computation to the coordinates of inputs

2023/6/25

Learning-augmented streaming codes for variable-size messages under partial burst losses

2023/6/25

Locally repairable convertible codes: Erasure codes for efficient repair and conversion

2023/6/25

Compression-Informed Coded Computing

2023/6/25

FIFO can be Better than LRU: the Power of Lazy Promotion and Quick Demotion

2023/6/22

Bandwidth cost of code conversions in distributed storage: Fundamental limits and optimal constructions

IEEE Transactions on Information Theory

2023/4/7

Online versus offline rate in streaming codes for variable-size messages

IEEE Transactions on Information Theory

2023/2/14

Tambur: Efficient loss recovery for videoconferencing via streaming codes

2023

{GL-Cache}: Group-level learning for efficient and high-performance caching

2023

Bandwidth cost of code conversions in the split regime

2022/6/26

Learning-augmented streaming codes are approximately optimal for variable-size messages

2022/6/26

Streaming codes for variable-size messages

IEEE Transactions on Information Theory

2022/4/28

Convertible codes: enabling efficient conversion of coded data in distributed storage

IEEE Transactions on Information Theory

2022/3/2

Matrix Approxiations for Recommender Systems on TPUs

2022

See List of Professors in K. V. Rashmi (Rashmi Vinayak) University(Carnegie Mellon University)

Co-Authors

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