Juan Carlos Pichel

About Juan Carlos Pichel

Juan Carlos Pichel, With an exceptional h-index of 16 and a recent h-index of 12 (since 2020), a distinguished researcher at Universidad de Santiago de Compostela, specializes in the field of Parallel Computing, HPC, Big Data.

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

Review of Distributed Quantum Computing. From single QPU to High Performance Quantum Computing

QPU integration in OpenCL for heterogeneous programming

An unsupervised perplexity-based method for boilerplate removal

An accurate machine learning model to study the impact of realistic metal grain granularity on Nanosheet FETs

A machine learning approach to model the impact of line edge roughness on gate-all-around nanowire FETs while reducing the carbon footprint

BigSeqKit: a parallel Big Data toolkit to process FASTA and FASTQ files at scale

A multistage retrieval system for health-related misinformation detection

A unified framework to improve the interoperability between HPC and Big Data languages and programming models

Juan Carlos Pichel Information

University

Position

CiTIUS

Citations(all)

1014

Citations(since 2020)

531

Cited By

641

hIndex(all)

16

hIndex(since 2020)

12

i10Index(all)

25

i10Index(since 2020)

12

Email

University Profile Page

Google Scholar

Juan Carlos Pichel Skills & Research Interests

Parallel Computing

HPC

Big Data

Top articles of Juan Carlos Pichel

Review of Distributed Quantum Computing. From single QPU to High Performance Quantum Computing

arXiv preprint arXiv:2404.01265

2024/4/1

QPU integration in OpenCL for heterogeneous programming

The Journal of Supercomputing

2024/1/31

An unsupervised perplexity-based method for boilerplate removal

Natural Language Engineering

2024/1

An accurate machine learning model to study the impact of realistic metal grain granularity on Nanosheet FETs

Solid-State Electronics

2023/9/1

A machine learning approach to model the impact of line edge roughness on gate-all-around nanowire FETs while reducing the carbon footprint

Plos one

2023/7/24

BigSeqKit: a parallel Big Data toolkit to process FASTA and FASTQ files at scale

GigaScience

2023

A multistage retrieval system for health-related misinformation detection

Engineering Applications of Artificial Intelligence

2022/10/1

A unified framework to improve the interoperability between HPC and Big Data languages and programming models

Future Generation Computer Systems

2022/9/1

Real-Time Focused Extraction of Social Media Users

IEEE Access

2022/4/20

Social Minder: a Tool for Social Media Monitoring and its Use for Detecting COVID-19 Misinformation.

2022

Marcos Fernández-Pichel
Marcos Fernández-Pichel

H-Index: 0

Juan Carlos Pichel
Juan Carlos Pichel

H-Index: 9

Comparing traditional and neural approaches for detecting health-related misinformation

2021

Colaboración entre docentes de una universidad alemana y una española para el desarrollo de seminarios prácticos acerca de la credibilidad de la información

2021

Reliability prediction for health-related content: a replicability study

2021

A big data approach to metagenomics for all-food-sequencing

BMC bioinformatics

2020/12

VeryFastTree: speeding up the estimation of phylogenies for large alignments through parallelization and vectorization strategies

Bioinformatics

2020/9/1

A big data platform for real time analysis of signs of depression in social media

International Journal of Environmental Research and Public Health

2020/7

Ignis: An efficient and scalable multi-language Big Data framework

Future Generation Computer Systems

2020/4/1

CiTIUS at the TREC 2020 Health Misinformation Track.

2020

Marcos Fernández-Pichel
Marcos Fernández-Pichel

H-Index: 0

Juan Carlos Pichel
Juan Carlos Pichel

H-Index: 9

eXtream: a System for Real-time Monitoring of Dynamic Web Sources.

2020

See List of Professors in Juan Carlos Pichel University(Universidad de Santiago de Compostela)

Co-Authors

academic-engine