What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Computer vision
His primary areas of investigation include Algorithm, Artificial intelligence, Collision detection, Computer vision and Computation.
His Algorithm research is multidisciplinary, relying on both Polyhedron, Graphics hardware, Polytope, Polygon and Voronoi diagram.
His studies examine the connections between Artificial intelligence and genetics, as well as such issues in Machine learning, with regards to Variety.
The Collision detection study combines topics in areas such as Theoretical computer science, Feature and CUDA, Parallel computing.
His studies in Computer vision integrate themes in fields like Computer graphics, Virtual reality and Speedup.
His Computation research incorporates themes from Point and Geometric modeling.
His most cited work include:
- OBBTree: a hierarchical structure for rapid interference detection (1874 citations)
- Reciprocal Velocity Obstacles for real-time multi-agent navigation (856 citations)
- Reciprocal n-Body Collision Avoidance (836 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Algorithm, Computer vision, Robot and Computation.
His research in Artificial intelligence intersects with topics in Computer graphics and Trajectory.
Dinesh Manocha combines subjects such as Medial axis, Mathematical optimization, Collision detection, Point and Speedup with his study of Algorithm.
His Collision detection research integrates issues from Graphics hardware, Computational science and Parallel computing.
He has researched Robot in several fields, including Control theory, Collision avoidance and Human–computer interaction.
In most of his Computation studies, his work intersects topics such as Configuration space.
He most often published in these fields:
- Artificial intelligence (29.49%)
- Algorithm (23.16%)
- Computer vision (19.10%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (29.49%)
- Computer vision (19.10%)
- Robot (13.56%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Computer vision, Robot, Algorithm and Collision avoidance.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Trajectory.
He studied Computer vision and GRASP that intersect with Object.
Many of his research projects under Robot are closely connected to Obstacle with Obstacle, tying the diverse disciplines of science together.
His work carried out in the field of Algorithm brings together such families of science as Probabilistic logic, Collision detection, Graph and Random access.
Within one scientific family, Dinesh Manocha focuses on topics pertaining to Real-time computing under Collision avoidance, and may sometimes address concerns connected to Path.
Between 2018 and 2021, his most popular works were:
- TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents (141 citations)
- TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions (98 citations)
- VV-Net: Voxel VAE Net With Group Convolutions for Point Cloud Segmentation (58 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Computer vision
His scientific interests lie mostly in Artificial intelligence, Robot, Computer vision, Trajectory and Collision avoidance.
His research on Artificial intelligence frequently connects to adjacent areas such as Algorithm.
His Robot research is multidisciplinary, incorporating elements of Proxemics, Human–computer interaction, Real-time computing and Reinforcement learning.
His Computer vision study combines topics in areas such as Manipulator, Optimal control and k-nearest neighbors algorithm.
His studies deal with areas such as Relation, Layer and Mathematical optimization as well as Trajectory.
He has included themes like Distributed computing, Solid modeling, Data-driven, Swarm behaviour and Multi-agent system in his Collision avoidance study.
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