What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Algorithm
His primary areas of investigation include Operations research, Vehicle routing problem, Mathematical optimization, Intelligent transportation system and Travelling salesman problem.
His research in Operations research intersects with topics in Routing, Real-time data, Transport engineering and Heuristic.
His Vehicle routing problem study combines topics from a wide range of disciplines, such as Simulation, Heuristics and Dynamic network analysis.
Mathematical optimization and Time complexity are commonly linked in his work.
His Intelligent transportation system research is multidisciplinary, incorporating perspectives in Mean absolute percentage error, Data mining, Artificial intelligence and Fleet management.
His Travelling salesman problem research is multidisciplinary, incorporating elements of Optimization problem, Probabilistic logic and Online algorithm.
His most cited work include:
- Variational inequalities and the pricing of American options (360 citations)
- Stochastic and dynamic networks and routing (301 citations)
- A priori solution of a travelling salesman problem in which a random subset of the customers are visited (271 citations)
What are the main themes of his work throughout his whole career to date?
Patrick Jaillet spends much of his time researching Mathematical optimization, Operations research, Algorithm, Artificial intelligence and Gaussian process.
The study incorporates disciplines such as Probability distribution and Regret in addition to Mathematical optimization.
The various areas that Patrick Jaillet examines in his Operations research study include Routing, Transport engineering, Fleet management, Real-time data and Scheduling.
His study of Vehicle routing problem is a part of Routing.
His work in Algorithm addresses subjects such as Inference, which are connected to disciplines such as Bayesian probability.
Patrick Jaillet has researched Artificial intelligence in several fields, including Machine learning and Intelligent transportation system.
He most often published in these fields:
- Mathematical optimization (33.47%)
- Operations research (16.53%)
- Algorithm (11.44%)
What were the highlights of his more recent work (between 2018-2021)?
- Mathematical optimization (33.47%)
- Algorithm (11.44%)
- Gaussian process (9.32%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Mathematical optimization, Algorithm, Gaussian process, Inference and Bayesian optimization.
His work on Optimization problem, Online algorithm and Flow network as part of general Mathematical optimization research is frequently linked to Convex optimization and Set, thereby connecting diverse disciplines of science.
His research on Bayesian optimization also deals with topics like
- Surrogate model which intersects with area such as Thompson sampling, Distributed computing, Ranking, Data mining and Multinomial distribution,
- Hyperparameter which connect with Convergence,
- Ranking most often made with reference to Benchmark.
The concepts of his Distributed computing study are interwoven with issues in Vehicle routing problem and Enhanced Data Rates for GSM Evolution.
His work in Artificial intelligence covers topics such as Repeated game which are related to areas like Regret.
His Operations research study incorporates themes from Scheduling, The Internet and Range.
Between 2018 and 2021, his most popular works were:
- Online Vehicle Routing: The Edge of Optimization in Large-Scale Applications (51 citations)
- Edge Weighted Online Windowed Matching (30 citations)
- On Matching and Thickness in Heterogeneous Dynamic Markets (18 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algorithm
His primary areas of investigation include Algorithm, Inference, Mathematical optimization, Bayesian probability and Gaussian process.
His Algorithm study frequently involves adjacent topics like Matching.
His Inference research incorporates elements of Divergence and Bayesian inference.
His Mathematical optimization research includes themes of Resource allocation and Combinatorics.
His studies deal with areas such as Bayesian optimization, Applied mathematics, Stochastic optimization and Kriging as well as Bayesian probability.
His Surrogate model research focuses on Key and how it relates to Distributed computing, Hyperparameter and Convergence.
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