What is he best known for?
The fields of study he is best known for:
- Geometry
- Artificial intelligence
- Algorithm
Dan Halperin mainly investigates Motion planning, Algorithm, Theoretical computer science, Robot and Combinatorics.
The concepts of his Motion planning study are interwoven with issues in Plane, Mathematical optimization, Combinatorial complexity and Haystack.
His Algorithm research is multidisciplinary, incorporating perspectives in Software and Data structure.
His Theoretical computer science research incorporates themes from Computer program, Task and Robustness.
His research in Robot intersects with topics in Pathfinding, Degrees of freedom, Tensor product and Configuration space.
His Combinatorics research includes elements of Discrete mathematics, Regular polygon, Algebraic number and Constant.
His most cited work include:
- Computational characterization of B-cell epitopes. (150 citations)
- A General Framework for Assembly Planning: The Motion Space Approach (110 citations)
- Quasi-symmetry in the cryo-EM structure of EmrE provides the key to modeling its transmembrane domain. (109 citations)
What are the main themes of his work throughout his whole career to date?
Dan Halperin mainly focuses on Combinatorics, Motion planning, Algorithm, Discrete mathematics and Robot.
His Combinatorics research integrates issues from Upper and lower bounds, Minkowski addition, Regular polygon and Set.
Dan Halperin combines subjects such as Workspace, Mathematical optimization and Configuration space with his study of Motion planning.
The various areas that he examines in his Algorithm study include Collision detection, Plane, Representation and Data structure.
His Discrete mathematics research is multidisciplinary, relying on both Algebraic surface and Bounded function.
His work carried out in the field of Robot brings together such families of science as Theoretical computer science and Tensor product.
He most often published in these fields:
- Combinatorics (32.54%)
- Motion planning (32.14%)
- Algorithm (21.83%)
What were the highlights of his more recent work (between 2014-2021)?
- Motion planning (32.14%)
- Robot (16.27%)
- Mathematical optimization (13.10%)
In recent papers he was focusing on the following fields of study:
Dan Halperin focuses on Motion planning, Robot, Mathematical optimization, Combinatorics and Sampling.
The study incorporates disciplines such as Asymptotically optimal algorithm, Algorithm, Computer vision and Configuration space in addition to Motion planning.
His Algorithm study integrates concerns from other disciplines, such as Artificial neural network, Representation, Bounded function, Convolutional neural network and Robustness.
In the subject of general Robot, his work in Workspace is often linked to Multi unit, thereby combining diverse domains of study.
The Mathematical optimization study combines topics in areas such as Curse of dimensionality, Tree, Probabilistic logic, Function and Trajectory.
His Combinatorics research includes themes of Discrete mathematics, Unit and Regular polygon.
Between 2014 and 2021, his most popular works were:
- Asymptotically Near-Optimal RRT for Fast, High-Quality Motion Planning (64 citations)
- Motion Planning for Unlabeled Discs with Optimality Guarantees (37 citations)
- Efficient Multi-Robot Motion Planning for Unlabeled Discs in Simple Polygons (36 citations)
In his most recent research, the most cited papers focused on:
- Geometry
- Artificial intelligence
- Algorithm
His main research concerns Motion planning, Robot, Mathematical optimization, Workspace and Sampling.
His research integrates issues of Algorithm, Position and Configuration space in his study of Motion planning.
His studies deal with areas such as Visibility graph, Mathematical proof and Trajectory as well as Robot.
The Asymptotically optimal algorithm research he does as part of his general Mathematical optimization study is frequently linked to other disciplines of science, such as Scalability, therefore creating a link between diverse domains of science.
His Workspace study combines topics from a wide range of disciplines, such as Euclidean shortest path, Shortest path problem, K shortest path routing, Planar and Path length.
His study explores the link between Planar and topics such as Discrete mathematics that cross with problems in Combinatorics.
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