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Computer Science

D-Index
50
Citations
13804
World Ranking
5509
National Ranking
2514

Overview

Rina Panigrahy is affiliated with Google in the United States and works primarily within the field of Computer Science. Their research production includes a significant focus on Artificial Intelligence, Computer Vision and Pattern Recognition, as well as Signal Processing and Computational Mechanics.

Their work spans a variety of specialized topics including Domain Adaptation and Few-Shot Learning, Speech Recognition and Synthesis, Topic Modeling, Advanced Image and Video Retrieval Techniques, Speech and Audio Processing, Music Technology and Sound Studies, and Algorithms and Data Compression.

Rina Panigrahy has published extensively with a notable presence on arXiv (Cornell University) and contributions to Interspeech 2022, Leibniz-Zentrum für Informatik (Schloss Dagstuhl), and the Journal of Molecular Liquids. The following are selected recent papers authored by or involving Panigrahy:

  • A Unified Cascaded Encoder ASR Model for Dynamic Model Sizes (2022), Interspeech 2022
  • Maximum Coverage in Random-Arrival Streams (2023), Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks (2021), arXiv (Cornell University)
  • For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash Functions (2021), arXiv (Cornell University)
  • Provable Hierarchical Lifelong Learning with a Sketch-based Modular Architecture (2021), arXiv (Cornell University)

Frequent collaborators in Panigrahy's research include Nishanth Dikkala, Xin Wang, Atish Agarwala, Abhimanyu Das, and Brendan Juba. These partnerships have contributed to multiple publications focusing on various intersections of machine learning and neural networks.

The main fields of study identified in Panigrahy's work are:

  • Computer Science

Subfields of study represented in their research are:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Computational Mechanics
  • Materials Chemistry

The primary topics of research encompass:

  • Domain Adaptation and Few-Shot Learning
  • Speech Recognition and Synthesis
  • Topic Modeling
  • Advanced Image and Video Retrieval Techniques
  • Speech and Audio Processing
  • Music Technology and Sound Studies
  • Algorithms and Data Compression

Best Publications

  • Consistent hashing and random trees: distributed caching protocols for relieving hot spots on the World Wide Web

    David Karger;Eric Lehman;Tom Leighton;Rina Panigrahy

  • Design tradeoffs for SSD performance

    Nitin Agrawal;Vijayan Prabhakaran;Ted Wobber;John D. Davis

  • Spamming botnets: signatures and characteristics

    Yinglian Xie;Fang Yu;Kannan Achan;Rina Panigrahy

  • Achieving anonymity via clustering

    Gagan Aggarwal;Rina Panigrahy;Tomás Feder;Dilys Thomas

  • An improved construction for counting bloom filters

    Flavio Bonomi;Michael Mitzenmacher;Rina Panigrahy;Sushil Singh

  • The smallest grammar problem

    M. Charikar;E. Lehman;Ding Liu;R. Panigrahy

  • Anonymizing tables

    Gagan Aggarwal;Tomás Feder;Krishnaram Kenthapadi;Rajeev Motwani

  • Better streaming algorithms for clustering problems

    Moses Charikar;Liadan O'Callaghan;Rina Panigrahy

  • Achieving anonymity via clustering

    Gagan Aggarwal;Tomás Feder;Krishnaram Kenthapadi;Samir Khuller

  • Approximation Algorithms for k-Anonymity

    Gagan Aggarwal;Tomas Feder;Krishnaram Kenthapadi;Rajeev Motwani

  • Estimating PageRank on graph streams

    Atish Das Sarma;Sreenivas Gollapudi;Rina Panigrahy

  • Entropy based nearest neighbor search in high dimensions

    Rina Panigrahy

  • Beyond bloom filters: from approximate membership checks to approximate state machines

    Flavio Bonomi;Michael Mitzenmacher;Rina Panigrah;Sushil Singh

  • Heuristics for Vector Bin Packing

    Rina Panigrahy;Kunal Talwar;Lincoln Uyeda;Udi Wieder

  • Method for providing social network recommended content

    Harrington Timothy;Shenoy Rajesh;Najork Marc;Panigrahy Rina

  • A sketch-based distance oracle for web-scale graphs

    Atish Das Sarma;Sreenivas Gollapudi;Marc Najork;Rina Panigrahy

  • Learning Polynomials with Neural Networks

    Alexandr Andoni;Rina Panigrahy;Gregory Valiant;Li Zhang

  • Validating Heuristics for Virtual Machines Consolidation

    Sangmin Lee;Rina Panigrahy;Vijayan Prabhakaran;Venugopalan Ramasubramanian

  • Reducing TCAM power consumption and increasing throughput

    R. Panigrahy;S. Sharma

  • Clustering to minimize the sum of cluster diameters

    Moses Charikar;Rina Panigrahy

  • Lower Bounds on Locality Sensitive Hashing

    Rajeev Motwani;Assaf Naor;Rina Panigrahy

Frequent Co-Authors

Rajeev Motwani
Rajeev Motwani Stanford University
Tomás Feder
Tomás Feder Stanford University
Marc Najork
Marc Najork Google (United States)
Kunal Talwar
Kunal Talwar Apple (United States)
Li Zhang
Li Zhang Google (United States)
Ravi Kumar
Ravi Kumar Google (United States)
Moses Charikar
Moses Charikar Stanford University
Mehrdad Nourani
Mehrdad Nourani The University of Texas at Dallas
Alexandr Andoni
Alexandr Andoni Columbia University

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