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- Dimitris Bertsimas

Discipline name
H-index
Citations
Publications
World Ranking
National Ranking

Mathematics
H-index
85
Citations
35,934
236
World Ranking
49
National Ranking
30

Engineering and Technology
H-index
90
Citations
39,129
286
World Ranking
60
National Ranking
33

2019 - INFORMS John von Neumann Theory Prize

2007 - Fellow of the Institute for Operations Research and the Management Sciences (INFORMS)

2005 - Member of the National Academy of Engineering For contributions to optimization theory and stochastic systems and innovative applications in financial engineering and transportation.

- Statistics
- Mathematical optimization
- Artificial intelligence

His main research concerns Mathematical optimization, Robust optimization, Linear programming, Optimization problem and Operations research. His work is connected to Stochastic programming, Stochastic optimization, Combinatorial optimization, Dynamic programming and Travelling salesman problem, as a part of Mathematical optimization. His Robust optimization research is multidisciplinary, relying on both Independent and identically distributed random variables, Semidefinite programming, Probabilistic-based design optimization and Robustness.

His biological study spans a wide range of topics, including Structure, Stochastic game, Stock exchange, Risk aversion and Relaxation. The Optimization problem study combines topics in areas such as Time complexity, Stochastic control and Replicating portfolio, Portfolio optimization. His Operations research research incorporates elements of Air traffic flow management, Order, Integer programming and Dynamic network analysis.

- The Price of Robustness (2661 citations)
- Introduction to linear optimization (1933 citations)
- Theory and Applications of Robust Optimization (1484 citations)

His scientific interests lie mostly in Mathematical optimization, Robust optimization, Linear programming, Optimization problem and Algorithm. His studies in Mathematical optimization integrate themes in fields like Upper and lower bounds and Queueing theory. His research is interdisciplinary, bridging the disciplines of Queue and Queueing theory.

The concepts of his Robust optimization study are interwoven with issues in Probabilistic-based design optimization and Robustness.

- Mathematical optimization (47.92%)
- Robust optimization (16.41%)
- Linear programming (12.04%)

- Mathematical optimization (47.92%)
- Artificial intelligence (7.44%)
- Machine learning (6.13%)

Dimitris Bertsimas mostly deals with Mathematical optimization, Artificial intelligence, Machine learning, Optimization problem and Robust optimization. His Mathematical optimization study incorporates themes from Regularization and Sample. In the field of Artificial intelligence, his study on Deep learning overlaps with subjects such as Vulnerability.

His work in the fields of Machine learning, such as Interpretability, overlaps with other areas such as Multi dimensional. The various areas that Dimitris Bertsimas examines in his Optimization problem study include Sparse PCA, Matrix, Matrix completion, Covariate and Asymptotically optimal algorithm. His study explores the link between Robust optimization and topics such as Probabilistic logic that cross with problems in Operations research.

- Data-driven robust optimization (227 citations)
- From Predictive to Prescriptive Analytics (69 citations)
- Robust sample average approximation (65 citations)

- Statistics
- Mathematical optimization
- Artificial intelligence

His primary areas of investigation include Mathematical optimization, Robust optimization, Optimization problem, Algorithm and Prescriptive analytics. His work on Facility location problem as part of general Mathematical optimization study is frequently linked to Product line, bridging the gap between disciplines. His research in Robust optimization intersects with topics in Statistical hypothesis testing, Probabilistic logic, Linear programming, Numerical analysis and Process management.

His Optimization problem research includes themes of Function, Black box and Range. In Algorithm, he works on issues like Lasso, which are connected to Convergence, Code, Discrete optimization and Feature selection. Dimitris Bertsimas combines subjects such as Disease and Function with his study of Prescriptive analytics.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

The Price of Robustness

Dimitris Bertsimas;Melvyn Sim.

Operations Research **(2004)**

3709 Citations

Introduction to linear optimization

Dimitris Bertsimas;John Tsitsiklis.

**(1997)**

3532 Citations

Theory and Applications of Robust Optimization

Dimitris J. Bertsimas;David B. Brown;Constantine Caramanis.

Siam Review **(2011)**

1933 Citations

Robust discrete optimization and network flows

Dimitris Bertsimas;Melvyn Sim.

Mathematical Programming **(2003)**

1854 Citations

Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem

D. Bertsimas;E. Litvinov;Xu Andy Sun;Jinye Zhao.

IEEE Transactions on Power Systems **(2013)**

1215 Citations

Optimal control of execution costs

Dimitris Bertsimas;Andrew W. Lo.

Journal of Financial Markets **(1998)**

1097 Citations

The Air Traffic Flow Management Problem with Enroute Capacities

Dimitris Bertsimas;Sarah Stock Patterson.

Operations Research **(1998)**

652 Citations

A Robust Optimization Approach to Inventory Theory

Dimitris Bertsimas;Aurlie Thiele.

Operations Research **(2006)**

603 Citations

A stochastic and dynamic vehicle routing problem in the Euclidean plane

Dimitris J. Bertsimas;Garrett J. van Ryzin.

Operations Research **(1991)**

573 Citations

Robust linear optimization under general norms

Dimitris Bertsimas;Dessislava Pachamanova;Melvyn Sim.

Operations Research Letters **(2004)**

525 Citations

Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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