What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Programming language
His primary areas of study are Engineering design process, Simulated annealing, Artificial intelligence, Computer Aided Design and Mathematical optimization.
His work on Probabilistic design as part of general Engineering design process study is frequently linked to Fixation, bridging the gap between disciplines.
Jonathan Cagan has included themes like Mechanical engineering and Very-large-scale integration in his Simulated annealing study.
His work deals with themes such as Cognitive psychology, Creativity, Function, Product design and Novelty, which intersect with Artificial intelligence.
In Computer Aided Design, Jonathan Cagan works on issues like Engineering drawing, which are connected to Grammar, Shape grammar, Brand identity and Representation.
His Mathematical optimization study combines topics from a wide range of disciplines, such as Turbine, Turbine blade and Component.
His most cited work include:
- Creating Breakthrough Products: Innovation from Product Planning to Program Approval (303 citations)
- A Study of Design Fixation, Its Mitigation and Perception in Engineering Design Faculty (254 citations)
- Speaking the Buick Language: Capturing, Understanding, and Exploring Brand Identity With Shape Grammars (190 citations)
What are the main themes of his work throughout his whole career to date?
Jonathan Cagan focuses on Engineering design process, Artificial intelligence, Mathematical optimization, Shape grammar and Simulated annealing.
The concepts of his Engineering design process study are interwoven with issues in Management science, Conceptual design, Human–computer interaction, Systems engineering and Process.
His Artificial intelligence study combines topics in areas such as Machine learning, Creativity, Function and Natural language processing.
His Pattern search and Stochastic optimization study, which is part of a larger body of work in Mathematical optimization, is frequently linked to Numerical analysis, bridging the gap between disciplines.
His Shape grammar research incorporates elements of Interpreter and Engineering drawing.
His Simulated annealing research includes themes of Computer Aided Design and Annealing.
He most often published in these fields:
- Engineering design process (24.75%)
- Artificial intelligence (17.49%)
- Mathematical optimization (12.87%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (17.49%)
- Human–computer interaction (8.25%)
- Engineering design process (24.75%)
In recent papers he was focusing on the following fields of study:
Jonathan Cagan mainly investigates Artificial intelligence, Human–computer interaction, Engineering design process, Mathematical optimization and Heuristic.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Grammar.
His Human–computer interaction research incorporates themes from Test, Differential game, Task and Mechanism.
His study in Engineering design process is interdisciplinary in nature, drawing from both Design strategy, Engineering management, Configuration design, Transfer of learning and Conceptual design.
His Mathematical optimization research focuses on Stackelberg competition and how it relates to Adversarial system.
His work carried out in the field of Learning curve brings together such families of science as Shape grammar and Rule-based machine translation.
Between 2017 and 2021, his most popular works were:
- A neuroimaging investigation of design ideation with and without inspirational stimuli—understanding the meaning of near and far stimuli (27 citations)
- Crowdsourcing inspiration: Using crowd generated inspirational stimuli to support designer ideation (23 citations)
- Learning to Design From Humans: Imitating Human Designers Through Deep Learning (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Programming language
The scientist’s investigation covers issues in Engineering design process, Human–computer interaction, Artificial intelligence, Neuroimaging and Conceptual design.
His biological study spans a wide range of topics, including Domain, Design strategy, Engineering management, Transfer of learning and Process management.
The Human–computer interaction study combines topics in areas such as Crowdsourcing, Test, Interface and Mechanism.
When carried out as part of a general Artificial intelligence research project, his work on Learning classifier system is frequently linked to work in Computational design, therefore connecting diverse disciplines of study.
His Neuroimaging research spans across into subjects like Cognitive science, Cognitive psychology, Meaning, Problem space and Brain activation.
His research in Conceptual design intersects with topics in Configuration design, Management science, Process and Diversity.
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