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.
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Posted ContentDOI
Abnormalities in cortical pattern of coherence in interictal migraine detected using ultra high-density EEG
TL;DR: Changes in cortical patterns of coherence (connectivity) in interictal migraineurs during the presentation of visual and auditory stimuli, as well as at rest, are uncovered to capitalize on the sensory sensitivities that are characteristic of migraine, and to reconcile the conflicting literature on migraine cortical connectivity.
Book ChapterDOI
3D Coded SUMMA: Communication-Efficient and Robust Parallel Matrix Multiplication
Haewon Jeong,Yaoqing Yang,Vipul Gupta,Christian Engelmann,Tze Meng Low,Viveck R. Cadambe,Kannan Ramchandran,Pulkit Grover +7 more
TL;DR: It is shown that MatDot codes, an innovative code construction for parallel matrix multiplications, can be integrated into three-dimensional SUMMA (Scalable Universal Matrix Multiplication Algorithm) in a communication-avoiding manner.
Posted Content
Energy-efficient Decoders for Compressive Sensing: Fundamental Limits and Implementations
TL;DR: The smallest amount of bit-meters is examined as a measure for the energy consumed by a circuit to derive a fundamental lower bound for the implementation of compressive sensing decoding algorithms on a circuit.
Journal ArticleDOI
F123. Ultra-high-density scalp EEG outperforms localized invasive ECoG grids in inferring depth of seizure foci
Ritesh Kumar,Praveen Venkatesh,Rui Sun,Gayathri Mohankumar,Arun Antony,Mark P. Richardson,Pulkit Grover +6 more
TL;DR: UHD-EEG can complement other modalities used during pre-surgical evaluation for epilepsy, before and after intracranial electrode implantation, particularly for inferring the presence of a deep seizure focus.
Proceedings ArticleDOI
Robust Molecular Dynamics Simulations Using Coded FFT Algorithm
TL;DR: This work applied "coded computing" to protein folding simulations in an error-prone environment, and implemented the fast Fourier Poisson method for solving electrostatic equations at each time step of the simulation, and utilized coded FFT algorithm to protect the compute-intensive FFT algorithms from soft errors.