What is he best known for?
The fields of study he is best known for:
- Statistics
- Topology
- Mathematical optimization
The scientist’s investigation covers issues in Mathematical optimization, Robust optimization, Probability distribution, Decision rule and Portfolio optimization.
He regularly ties together related areas like Moment in his Mathematical optimization studies.
Daniel Kuhn combines subjects such as Linear programming, Stochastic programming, Uncertainty quantification and Optimal decision with his study of Robust optimization.
His Uncertainty quantification research incorporates elements of Independence, Global optimization and Wasserstein metric.
His Probability distribution study frequently intersects with other fields, such as Convex optimization.
His Portfolio optimization research includes themes of Statistics and Statistical dispersion.
His most cited work include:
- Data-Driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations (485 citations)
- Distributionally Robust Convex Optimization (377 citations)
- Distributionally robust joint chance constraints with second-order moment information (292 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Mathematical optimization, Robust optimization, Stochastic programming, Decision rule and Linear programming.
The concepts of his Mathematical optimization study are interwoven with issues in Portfolio optimization, Portfolio, Probability distribution and Convex optimization.
Daniel Kuhn works mostly in the field of Robust optimization, limiting it down to topics relating to Empirical distribution function and, in certain cases, Probabilistic logic, as a part of the same area of interest.
His Stochastic programming study combines topics from a wide range of disciplines, such as Hedge, Computational complexity theory, Stochastic process, Stochastic optimization and Bounding overwatch.
His biological study spans a wide range of topics, including Upper and lower bounds and Optimal decision.
His research in Linear programming intersects with topics in Optimal control, Conic optimization and Integer programming.
He most often published in these fields:
- Mathematical optimization (60.69%)
- Robust optimization (32.41%)
- Stochastic programming (22.76%)
What were the highlights of his more recent work (between 2018-2021)?
- Mathematical optimization (60.69%)
- Robust optimization (32.41%)
- Convex optimization (10.34%)
In recent papers he was focusing on the following fields of study:
Daniel Kuhn mostly deals with Mathematical optimization, Robust optimization, Convex optimization, Artificial intelligence and Applied mathematics.
His Mathematical optimization research is multidisciplinary, relying on both Probability distribution, Empirical distribution function and Decision rule.
His studies deal with areas such as Kullback–Leibler divergence, Stochastic process, Probability measure, Function and Optimization problem as well as Robust optimization.
In his research, Algorithm, Likelihood function and Moment is intimately related to Divergence, which falls under the overarching field of Convex optimization.
The study incorporates disciplines such as Machine learning and Decision problem in addition to Artificial intelligence.
His work on Wasserstein metric as part of general Applied mathematics research is frequently linked to Approximation error and Zeroth order, bridging the gap between disciplines.
Between 2018 and 2021, his most popular works were:
- Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning (46 citations)
- Regularization via Mass Transportation (42 citations)
- The decision rule approach to optimization under uncertainty: methodology and applications (15 citations)
In his most recent research, the most cited papers focused on:
- Topology
- Statistics
- Mathematical optimization
Daniel Kuhn mainly investigates Mathematical optimization, Robust optimization, Convex optimization, Decision problem and Artificial intelligence.
His Mathematical optimization study combines topics from a wide range of disciplines, such as Spillage and Empirical distribution function.
His Robust optimization research incorporates elements of Stochastic programming and Regularization.
The Stochastic programming study combines topics in areas such as Decision rule, Dice and Risk measure.
His work in Convex optimization addresses issues such as Machine learning, which are connected to fields such as Maximum likelihood.
His work carried out in the field of Decision problem brings together such families of science as Stochastic process, Feasible region, Partition and Curse of dimensionality.
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