What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- The Internet
Jordi Torres mainly investigates Distributed computing, Scheduling, Workload, Resource allocation and Artificial intelligence.
His studies in Distributed computing integrate themes in fields like Grid computing, Real-time computing and Virtual machine.
His Scheduling research includes themes of Batch processing, Schedule, Electrical grid and Backup.
He works mostly in the field of Workload, limiting it down to topics relating to Data center and, in certain cases, Energy aware scheduling, Power consumption, Electrical efficiency and Computer security.
His work is dedicated to discovering how Resource allocation, Quality of service are connected with Job scheduler and Dynamic priority scheduling and other disciplines.
His work in Artificial intelligence tackles topics such as Machine learning which are related to areas like Pattern recognition.
His most cited work include:
- GreenHadoop: leveraging green energy in data-processing frameworks (264 citations)
- GreenSlot: scheduling energy consumption in green datacenters (243 citations)
- Towards energy-aware scheduling in data centers using machine learning (201 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Distributed computing, Artificial intelligence, Application server, Server and Operating system.
His Distributed computing research integrates issues from Virtualization, Workload, Grid computing, Middleware and Scheduling.
His studies deal with areas such as Real-time computing and Cloud computing as well as Workload.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Pattern recognition.
His Application server research is multidisciplinary, incorporating elements of Java, Web application and Web server, The Internet.
The study incorporates disciplines such as File server and Scalability in addition to Server.
He most often published in these fields:
- Distributed computing (34.10%)
- Artificial intelligence (23.12%)
- Application server (14.45%)
What were the highlights of his more recent work (between 2016-2020)?
- Artificial intelligence (23.12%)
- Deep learning (9.83%)
- Convolutional neural network (11.56%)
In recent papers he was focusing on the following fields of study:
Jordi Torres mostly deals with Artificial intelligence, Deep learning, Convolutional neural network, Machine learning and Segmentation.
He has researched Artificial intelligence in several fields, including Natural language processing, Computer vision and Pattern recognition.
His Machine learning research is multidisciplinary, relying on both Contextual image classification, Document classification, Pipeline and Process.
His Segmentation research includes themes of Pascal and Leverage.
Much of his study explores Data visualization relationship to Distributed computing.
His research in Distributed computing tackles topics such as Analytics which are related to areas like Parallelism, Dependency and Server.
Between 2016 and 2020, his most popular works were:
- SalGAN: visual saliency prediction with generative adversarial networks (177 citations)
- Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks (82 citations)
- PyCOMPSs: Parallel computational workflows in Python: (45 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Software
Jordi Torres spends much of his time researching Artificial intelligence, Deep learning, Convolutional neural network, Machine learning and Pattern recognition.
His study involves Recurrent neural network, Image processing and Face, a branch of Artificial intelligence.
His study focuses on the intersection of Recurrent neural network and fields such as State with connections in the field of Technical support and Library science.
Within one scientific family, Jordi Torres focuses on topics pertaining to Artificial neural network under Deep learning, and may sometimes address concerns connected to Throughput, Computer vision, Distributed algorithm and Algorithm.
His studies deal with areas such as Scalability and Distributed design patterns as well as Convolutional neural network.
In the field of Pattern recognition, his study on Segmentation overlaps with subjects such as Cross entropy.
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