What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Algorithm
The scientist’s investigation covers issues in Discrete choice, Mathematical optimization, Mixed logit, Simulation and Logistic regression.
Michel Bierlaire has included themes like Mathematical model, Estimation, Multinomial logistic regression and Logit in his Discrete choice study.
His Mathematical optimization study integrates concerns from other disciplines, such as Berth allocation problem, Container, Matrix, Path and Computation.
He regularly links together related areas like Econometrics in his Mixed logit studies.
Michel Bierlaire interconnects Mode choice, Latent class model, Latent variable and Mixed model in the investigation of issues within Econometrics.
His research integrates issues of Network topology, Microsimulation and Traffic prediction in his study of Simulation.
His most cited work include:
- BIOGEME: a free package for the estimation of discrete choice models (769 citations)
- Discrete Choice Methods and their Applications to Short Term Travel Decisions (639 citations)
- Hybrid choice models : Progress and challenges (408 citations)
What are the main themes of his work throughout his whole career to date?
Michel Bierlaire mainly focuses on Mathematical optimization, Discrete choice, Econometrics, Operations research and Transport engineering.
As part of his studies on Mathematical optimization, Michel Bierlaire often connects relevant subjects like Integer.
His Discrete choice research includes elements of Estimation and Artificial intelligence.
His study explores the link between Econometrics and topics such as Mixed logit that cross with problems in Multinomial logistic regression.
His specific area of interest is Transport engineering, where he studies Pedestrian.
His research brings together the fields of Simulation and Pedestrian.
He most often published in these fields:
- Mathematical optimization (19.09%)
- Discrete choice (18.33%)
- Econometrics (14.92%)
What were the highlights of his more recent work (between 2016-2021)?
- Mathematical optimization (19.09%)
- Discrete choice (18.33%)
- Transport engineering (9.61%)
In recent papers he was focusing on the following fields of study:
Michel Bierlaire mostly deals with Mathematical optimization, Discrete choice, Transport engineering, Pedestrian and Integer.
His work in Mathematical optimization covers topics such as Routing which are related to areas like Empirical distribution function and Limit.
His research in Discrete choice intersects with topics in Operator, Inference, Bayesian inference, Probabilistic logic and Algorithm.
His studies in Pedestrian integrate themes in fields like Flow, Control and Network planning and design.
The Integer programming study combines topics in areas such as Scheduling and Operations research.
His Relevance study combines topics in areas such as Logistic regression and Artificial intelligence.
Between 2016 and 2021, his most popular works were:
- The multi-objective railway timetable rescheduling problem (58 citations)
- Characterization of input uncertainties in strategic energy planning models (45 citations)
- Real-time management of berth allocation with stochastic arrival and handling times (33 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algorithm
His primary scientific interests are in Mathematical optimization, Operations research, Transport engineering, Public transport and Pedestrian.
His Time horizon study, which is part of a larger body of work in Mathematical optimization, is frequently linked to Energy planning, bridging the gap between disciplines.
He interconnects Assignment problem, Aggregate, Deadline-monotonic scheduling and Integer programming in the investigation of issues within Operations research.
In his research on the topic of Transport engineering, Bayesian inference, Markov chain Monte Carlo, Mixed logit and Bayes' theorem is strongly related with Probabilistic logic.
His Decomposition study frequently links to adjacent areas such as Discrete choice.
His work focuses on many connections between Discrete choice and other disciplines, such as Panel data, that overlap with his field of interest in Estimator.
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