What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Nikos Vlassis mainly investigates Artificial intelligence, Mathematical optimization, Reinforcement learning, Cluster analysis and Markov decision process.
Nikos Vlassis combines subjects such as Expectation–maximization algorithm, Management science and Pattern recognition with his study of Artificial intelligence.
His Mathematical optimization research is multidisciplinary, incorporating perspectives in Mixture model, Partially observable Markov decision process, Value and Point.
His study in Reinforcement learning is interdisciplinary in nature, drawing from both Multi-agent system and Nash equilibrium.
The study incorporates disciplines such as Applied mathematics, Data mining, Identification and Bioinformatics in addition to Cluster analysis.
His work carried out in the field of Markov decision process brings together such families of science as Scale, Finite set and Bellman equation.
His most cited work include:
- The global k-means clustering algorithm (1257 citations)
- Perseus: randomized point-based value iteration for POMDPs (505 citations)
- VizBin - an application for reference-independent visualization and human-augmented binning of metagenomic data. (341 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Mathematical optimization, Algorithm, Markov decision process and Partially observable Markov decision process.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Computer vision and Pattern recognition.
His work on Bellman equation as part of general Mathematical optimization research is frequently linked to Set, bridging the gap between disciplines.
Nikos Vlassis interconnects Mixture model, Variable elimination and Expectation–maximization algorithm in the investigation of issues within Algorithm.
His Markov decision process research is multidisciplinary, relying on both Computational complexity theory and State.
His studies in Partially observable Markov decision process integrate themes in fields like Value, Function and Dynamic programming.
He most often published in these fields:
- Artificial intelligence (32.82%)
- Mathematical optimization (30.53%)
- Algorithm (18.32%)
What were the highlights of his more recent work (between 2013-2021)?
- Data mining (8.40%)
- Mathematical optimization (30.53%)
- Estimator (4.58%)
In recent papers he was focusing on the following fields of study:
His main research concerns Data mining, Mathematical optimization, Estimator, Set and Artificial intelligence.
His work in the fields of Data mining, such as Identification and Visualization, overlaps with other areas such as Motif and Spectral sequence.
His biological study spans a wide range of topics, including Sampling, Regret, Bayesian probability, Sample and Reinforcement learning.
His study explores the link between Estimator and topics such as Causal inference that cross with problems in Estimation, Asymptotic distribution and Outcome.
He studied Artificial intelligence and Machine learning that intersect with Human–computer interaction and Data set.
His research in Algorithm intersects with topics in Image segmentation, Correspondence problem, Key and Active shape model.
Between 2013 and 2021, his most popular works were:
- VizBin - an application for reference-independent visualization and human-augmented binning of metagenomic data. (341 citations)
- Fast reconstruction of compact context-specific metabolic network models. (135 citations)
- fastGapFill: efficient gap filling in metabolic networks (80 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Data mining, Theoretical computer science, Metabolomics, Identification and Range.
His work on Visualization as part of general Data mining research is often related to Metagenomics, thus linking different fields of science.
His Visualization study integrates concerns from other disciplines, such as Sequence, Computational biology, Identification and Bioinformatics.
Along with Theoretical computer science, other disciplines of study including Gap filling, Organism, Consistency, KEGG and Network analysis are integrated into his research.
His Identification research incorporates elements of Mac OS, Software implementation, Cluster analysis and Pattern recognition.
Nikos Vlassis has included themes like Disjoint sets, Biological system, Mathematical structure and Computational model in his Range study.
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