What is he best known for?
The fields of study he is best known for:
- Gene
- Artificial intelligence
- Algorithm
The scientist’s investigation covers issues in Constraint satisfaction, Mathematical optimization, Genome, Constraint satisfaction problem and Genetics.
Thomas Schiex works mostly in the field of Mathematical optimization, limiting it down to topics relating to Theoretical computer science and, in certain cases, Optimization problem.
The concepts of his Genome study are interwoven with issues in Evolutionary biology, Lotus japonicus and Botany.
When carried out as part of a general Constraint satisfaction problem research project, his work on Constraint satisfaction dual problem and Local consistency is frequently linked to work in Fuzzy logic, therefore connecting diverse disciplines of study.
His work on Genome evolution, Gene mapping and Gene family as part of general Genetics study is frequently connected to Software, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His study in the field of Whole genome sequencing and Chromosome also crosses realms of Moniliophthora.
His most cited work include:
- The Medicago genome provides insight into the evolution of rhizobial symbioses (939 citations)
- Genome sequence of the metazoan plant-parasitic nematode Meloidogyne incognita (762 citations)
- Genome sequence of the plant pathogen Ralstonia solanacearum (740 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Mathematical optimization, Constraint satisfaction problem, Local consistency, Constraint satisfaction and Algorithm.
His study in the fields of Branch and bound and Backtracking under the domain of Mathematical optimization overlaps with other disciplines such as Upper and lower bounds.
His Constraint satisfaction dual problem study in the realm of Constraint satisfaction problem connects with subjects such as Theoretical computer science, Bounded function and Semiring.
Many of his research projects under Constraint satisfaction are closely connected to Artificial intelligence, Decision problem, Algebra and Boolean satisfiability problem with Artificial intelligence, Decision problem, Algebra and Boolean satisfiability problem, tying the diverse disciplines of science together.
His Artificial intelligence research is multidisciplinary, relying on both Genetics and Genome.
His Algorithm study integrates concerns from other disciplines, such as Solver, Constrained optimization and Benchmark.
He most often published in these fields:
- Mathematical optimization (38.73%)
- Constraint satisfaction problem (25.35%)
- Local consistency (19.72%)
What were the highlights of his more recent work (between 2014-2021)?
- Mathematical optimization (38.73%)
- Graphical model (14.08%)
- Protein design (7.75%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Mathematical optimization, Graphical model, Protein design, Theoretical computer science and Function.
Thomas Schiex integrates Mathematical optimization with Local consistency in his research.
His Graphical model research incorporates themes from Probabilistic logic, Solver and Approximate inference.
His Protein design research integrates issues from Protein engineering and Computational problem.
Thomas Schiex combines subjects such as Bayesian network and Benchmark with his study of Function.
His Computational biology research is multidisciplinary, incorporating elements of Genome and Gene.
Between 2014 and 2021, his most popular works were:
- The sunflower genome provides insights into oil metabolism, flowering and Asterid evolution (280 citations)
- Hybridization and polyploidy enable genomic plasticity without sex in the most devastating plant-parasitic nematodes. (82 citations)
- Guaranteed Discrete Energy Optimization on Large Protein Design Problems. (42 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Artificial intelligence
- Algorithm
His primary areas of study are Mathematical optimization, Genome, Theoretical computer science, Combinatorial optimization and Optimization problem.
His research in the fields of Anytime algorithm overlaps with other disciplines such as Local consistency.
Gene and Genetics are the main areas of his Genome studies.
His Theoretical computer science research also works with subjects such as
- Speedup, which have a strong connection to Graphical model,
- Parallel computing which intersects with area such as Protein design.
His Combinatorial optimization study combines topics from a wide range of disciplines, such as Backtracking and Search algorithm.
His research in Optimization problem intersects with topics in Linear programming, Solver, Integer programming and Benchmark.
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