What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- Machine learning
Data mining, Artificial intelligence, Collaborative filtering, Recommender system and Wireless sensor network are his primary areas of study.
His study on Data mining also encompasses disciplines like
- Feature which connect with Computational biology, Hierarchical clustering, Gene and DNA microarray,
- Cluster analysis which is related to area like Set and Phenotype.
His research in Artificial intelligence intersects with topics in Algorithm, Receiver operating characteristic and Pattern recognition.
His Pattern recognition study integrates concerns from other disciplines, such as Metadata and Spatial relation.
The concepts of his Collaborative filtering study are interwoven with issues in Anomaly detection, Private information retrieval, Linear model and Singular value decomposition.
Fillia Makedon interconnects Ambient intelligence, Software and Embedded system in the investigation of issues within Wireless sensor network.
His most cited work include:
- Learning from incomplete ratings using non-negative matrix factorization (247 citations)
- Fast approximation algorithms for multicommodity flow problems (205 citations)
- HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data (160 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Human–computer interaction, Computer vision, Multimedia and Robot.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Pattern recognition.
His studies deal with areas such as Rehabilitation, Cognition and Human–robot interaction as well as Human–computer interaction.
The study incorporates disciplines such as Simulation and Physical medicine and rehabilitation in addition to Rehabilitation.
His Computer vision research focuses on Motion in particular.
Multimedia and World Wide Web are commonly linked in his work.
He most often published in these fields:
- Artificial intelligence (31.25%)
- Human–computer interaction (17.07%)
- Computer vision (12.74%)
What were the highlights of his more recent work (between 2014-2021)?
- Human–computer interaction (17.07%)
- Artificial intelligence (31.25%)
- Robot (10.34%)
In recent papers he was focusing on the following fields of study:
Fillia Makedon mostly deals with Human–computer interaction, Artificial intelligence, Robot, Cognition and Rehabilitation.
His research integrates issues of Human–robot interaction, Personalization, Wearable technology, Facial expression and Session in his study of Human–computer interaction.
His Artificial intelligence research includes themes of Machine learning and Computer vision.
Fillia Makedon has researched Robot in several fields, including Robotic arm and Reinforcement learning.
His work on Cognitive test, Cognitive flexibility and Cognitive load as part of general Cognition research is frequently linked to Cognitive Assessment System, thereby connecting diverse disciplines of science.
His work investigates the relationship between Rehabilitation and topics such as Physical medicine and rehabilitation that intersect with problems in Behavioral pattern and Robotic rehabilitation.
Between 2014 and 2021, his most popular works were:
- Brain-EE: Brain Enjoyment Evaluation using Commercial EEG Headband (25 citations)
- Task Engagement as Personalization Feedback for Socially-Assistive Robots and Cognitive Training (23 citations)
- Deep Visual Attributes vs. Hand-Crafted Audio Features on Multidomain Speech Emotion Recognition (23 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Machine learning
Fillia Makedon spends much of his time researching Human–computer interaction, Robot, Artificial intelligence, Simulation and Cognition.
His Human–computer interaction research is multidisciplinary, incorporating elements of Personalization, Cognitive skill, Sequence learning, Embodied cognition and Session.
The study incorporates disciplines such as Robotic arm and Reinforcement learning in addition to Robot.
His Artificial intelligence study frequently involves adjacent topics like Machine learning.
Fillia Makedon interconnects Physical medicine and rehabilitation, Virtual reality, Human–robot interaction and Rehabilitation robotics in the investigation of issues within Simulation.
His Cognition research includes elements of Neurofeedback, Brain–computer interface, Electroencephalography and Usability.
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