What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- The Internet
His scientific interests lie mostly in Artificial intelligence, Polynomial, Algorithm, Mathematical optimization and Combinatorics.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Computer vision.
His work deals with themes such as Matrix and Applied mathematics, which intersect with Polynomial.
His studies deal with areas such as Impulse, Factorization, Upper and lower bounds and Perplexity as well as Algorithm.
John Canny combines subjects such as Time complexity, Motion planning, Trajectory and Collision detection with his study of Mathematical optimization.
In his study, which falls under the umbrella issue of Combinatorics, Detector, Second derivative, Bandwidth, Heuristics and Edge detection is strongly linked to Discrete mathematics.
His most cited work include:
- A Computational Approach to Edge Detection (22019 citations)
- The complexity of robot motion planning (1180 citations)
- Planning optimal grasps (728 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Human–computer interaction, Algorithm, Machine learning and Multimedia.
John Canny combines topics linked to Computer vision with his work on Artificial intelligence.
His research ties Natural language and Human–computer interaction together.
In his study, Combinatorics is strongly linked to Polynomial, which falls under the umbrella field of Algorithm.
Machine learning and Domain are two areas of study in which John Canny engages in interdisciplinary work.
John Canny interconnects Mathematical optimization and Mobile robot in the investigation of issues within Motion planning.
He most often published in these fields:
- Artificial intelligence (27.78%)
- Human–computer interaction (12.04%)
- Algorithm (11.42%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (27.78%)
- Machine learning (11.42%)
- Human–computer interaction (12.04%)
In recent papers he was focusing on the following fields of study:
John Canny focuses on Artificial intelligence, Machine learning, Human–computer interaction, Key and Set.
In his work, Robot is strongly intertwined with Computer vision, which is a subfield of Artificial intelligence.
His study in the field of Reinforcement learning also crosses realms of Domain.
John Canny has researched Human–computer interaction in several fields, including Generalization, Deep learning, Task and Natural language.
His studies in Key integrate themes in fields like Control, Image, World Wide Web and Usability.
He has included themes like Conversation and Chatbot in his Set study.
Between 2017 and 2021, his most popular works were:
- Evaluating Protein Transfer Learning with TAPE. (73 citations)
- Textual Explanations for Self-Driving Vehicles (71 citations)
- Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedure (37 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Artificial intelligence, Machine learning, Set, Feature learning and Generalization.
His Artificial intelligence research is multidisciplinary, incorporating elements of Task, Folding and Computer vision.
His work on Edge detection is typically connected to Baseline as part of general Computer vision study, connecting several disciplines of science.
His Machine learning research is multidisciplinary, incorporating perspectives in Adversarial system, Adversary, Task analysis and Robustness.
His Task research focuses on Code and how it relates to Image, Introspection and Key.
His study focuses on the intersection of Deep learning and fields such as Sketch with connections in the field of Human–computer interaction.
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.
