What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Mechanical engineering
- Electrical engineering
Robot, Artificial intelligence, Spinal cord injury, Control engineering and Spinal cord are his primary areas of study.
His Robot study incorporates themes from Motion, Simulation, Control theory and Topology.
Joel W. Burdick has researched Artificial intelligence in several fields, including Algorithm, Machine learning and Computer vision.
His research in Spinal cord injury intersects with topics in Rehabilitation, Assist as needed, Proprioception, Motor learning and Stimulation.
His Control engineering research incorporates themes from Fixture, Planetary exploration, Software deployment, Stiffness and Robustness.
As part of the same scientific family, he usually focuses on Spinal cord, concentrating on Physical medicine and rehabilitation and intersecting with Sensory system, Tonic and Paraplegia.
His most cited work include:
- Effect of epidural stimulation of the lumbosacral spinal cord on voluntary movement, standing, and assisted stepping after motor complete paraplegia: a case study (642 citations)
- Implications of Assist-As-Needed Robotic Step Training after a Complete Spinal Cord Injury on Intrinsic Strategies of Motor Learning (281 citations)
- The Geometric Mechanics of Undulatory Robotic Locomotion (274 citations)
What are the main themes of his work throughout his whole career to date?
Joel W. Burdick mainly investigates Artificial intelligence, Robot, Control theory, Mathematical optimization and Computer vision.
Joel W. Burdick interconnects Algorithm, Machine learning and Human–computer interaction in the investigation of issues within Artificial intelligence.
His Robot research is multidisciplinary, incorporating elements of Control engineering, Kinematics and Simulation.
His research integrates issues of Robot kinematics and Topology in his study of Kinematics.
His Control theory research is multidisciplinary, incorporating perspectives in Mechanism and Model predictive control.
His studies in Mathematical optimization integrate themes in fields like Function and Reinforcement learning.
He most often published in these fields:
- Artificial intelligence (26.85%)
- Robot (26.51%)
- Control theory (16.44%)
What were the highlights of his more recent work (between 2017-2021)?
- Robot (26.51%)
- Artificial intelligence (26.85%)
- Mathematical optimization (13.76%)
In recent papers he was focusing on the following fields of study:
Joel W. Burdick focuses on Robot, Artificial intelligence, Mathematical optimization, Control theory and Control theory.
Joel W. Burdick focuses mostly in the field of Robot, narrowing it down to topics relating to Kinematics and, in certain cases, Robot kinematics.
The various areas that Joel W. Burdick examines in his Artificial intelligence study include Machine learning, State and Computer vision.
His Mathematical optimization research is multidisciplinary, relying on both Sequence and Reinforcement learning.
His research integrates issues of Quadratic programming, Invariant and Model predictive control in his study of Control theory.
His Motion planning research focuses on subjects like Trajectory, which are linked to Exoskeleton.
Between 2017 and 2021, his most popular works were:
- End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks (121 citations)
- Self-Assisted Standing Enabled by Non-Invasive Spinal Stimulation after Spinal Cord Injury (39 citations)
- Stagewise Safe Bayesian Optimization with Gaussian Processes (34 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mechanical engineering
- Electrical engineering
His primary scientific interests are in Mathematical optimization, Robot, Control theory, Reinforcement learning and Gaussian process.
His Mathematical optimization study incorporates themes from Cardinality and Sequence.
Robot is a subfield of Artificial intelligence that Joel W. Burdick studies.
His research in Control theory intersects with topics in Applied mathematics and Dynamic mode decomposition.
His Reinforcement learning research incorporates themes from Function space, End-to-end principle, Regularization, Inverted pendulum and Control engineering.
His research in Quadratic programming focuses on subjects like Control theory, which are connected to Work.
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