P
Pulkit Grover
Researcher at Carnegie Mellon University
Publications - 191
Citations - 5602
Pulkit Grover is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Decoding methods & Counterexample. The author has an hindex of 27, co-authored 176 publications receiving 4874 citations. Previous affiliations of Pulkit Grover include Stanford University & University of California, Berkeley.
Papers
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Proceedings ArticleDOI
Shannon meets Tesla: Wireless information and power transfer
Pulkit Grover,Anant Sahai +1 more
TL;DR: The problem considered here is that of wireless information and power transfer across a noisy coupled-inductor circuit, which is a frequency-selective channel with additive white Gaussian noise, and the optimal tradeoff is characterized given the total power available.
Journal ArticleDOI
Energy Harvesting Wireless Communications: A Review of Recent Advances
Sennur Ulukus,Aylin Yener,Elza Erkip,Osvaldo Simeone,Michele Zorzi,Pulkit Grover,Kaibin Huang +6 more
TL;DR: The current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access, and networking issues are provided.
Proceedings Article
Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products
TL;DR: In this article, the authors propose a technique called Short-Dot to reduce the number of redundant computations in a coding theory-inspired fashion for computing linear transforms of long vectors.
Journal ArticleDOI
Very high density EEG elucidates spatiotemporal aspects of early visual processing
Amanda K. Robinson,Praveen Venkatesh,Matthew J. Boring,Matthew J. Boring,Michael J. Tarr,Pulkit Grover,Marlene Behrmann +6 more
TL;DR: “super-Nyquist” density EEG (“SND”) with Nyquist density arrays for assessing the spatiotemporal aspects of early visual processing argued for increased development of this approach in basic and translational neuroscience.
Journal ArticleDOI
On the Optimal Recovery Threshold of Coded Matrix Multiplication
Sanghamitra Dutta,Mohammad Fahim,Farzin Haddadpour,Haewon Jeong,Viveck R. Cadambe,Pulkit Grover +5 more
TL;DR: Novel coded computation strategies for distributed matrix–matrix products that outperform the recent “Polynomial code” constructions in recovery threshold, i.e., the required number of successful workers are provided.