What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Machine learning
His primary areas of investigation include Artificial intelligence, Object, Activity recognition, Machine learning and Wireless sensor network.
His studies deal with areas such as Radio-frequency identification, Real-time computing and Computer vision as well as Artificial intelligence.
His Object research is multidisciplinary, relying on both Smoothing, Graphical model and Abstraction.
His Machine learning study incorporates themes from Text corpus and Natural language.
His Wireless sensor network research includes themes of Ubiquitous computing and Identification.
His Data mining research includes elements of Key and Human–computer interaction.
His most cited work include:
- Inferring activities from interactions with objects (813 citations)
- Mapping and localization with RFID technology (627 citations)
- Inertially controlled switch and RFID tag (599 citations)
What are the main themes of his work throughout his whole career to date?
Matthai Philipose spends much of his time researching Artificial intelligence, Machine learning, Human–computer interaction, Data mining and Activity recognition.
He interconnects Computer vision and Pattern recognition in the investigation of issues within Artificial intelligence.
His study in the fields of Dynamic Bayesian network under the domain of Machine learning overlaps with other disciplines such as Common sense.
Many of his research projects under Human–computer interaction are closely connected to Information transfer and Activities of daily living with Information transfer and Activities of daily living, tying the diverse disciplines of science together.
His research investigates the connection between Data mining and topics such as Probabilistic logic that intersect with issues in Radio-frequency identification, Search engine indexing, Class and Data science.
His work in Activity recognition tackles topics such as Smoothing which are related to areas like WordNet, Pattern recognition, Shrinkage and Ontology.
He most often published in these fields:
- Artificial intelligence (34.92%)
- Machine learning (15.87%)
- Human–computer interaction (11.90%)
What were the highlights of his more recent work (between 2017-2020)?
- Convolutional neural network (4.76%)
- Artificial neural network (5.56%)
- Real-time computing (7.14%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Convolutional neural network, Artificial neural network, Real-time computing, Latency and Layer.
Artificial intelligence covers Matthai Philipose research in Convolutional neural network.
He combines subjects such as End-to-end principle, Voice activity detection and Pattern recognition with his study of Artificial neural network.
His studies in Real-time computing integrate themes in fields like Quantization, Detector, Speech processing and Word error rate.
His Latency research incorporates themes from Centroid, Deep neural networks, Execution model, Parallel computing and GPU cluster.
His Layer study combines topics in areas such as Field-programmable gate array, Efficient energy use and Computer engineering.
Between 2017 and 2020, his most popular works were:
- VideoEdge: Processing Camera Streams using Hierarchical Clusters (88 citations)
- Focus: querying large video datasets with low latency and low cost (76 citations)
- Nexus: a GPU cluster engine for accelerating DNN-based video analysis (34 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
Matthai Philipose spends much of his time researching Latency, Software deployment, Scheduling, Real-time computing and Deep neural networks.
Among his research on Software deployment, you can see a combination of other fields of science like Data mining, Pareto principle, Live video, Hierarchical clustering and Analytics.
In his research, Matthai Philipose undertakes multidisciplinary study on Scheduling and Query plan.
His research in Deep neural networks intersects with topics in Execution model, Parallel computing and GPU cluster.
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