What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Julie A. Shah focuses on Robot, Human–robot interaction, Artificial intelligence, Human–computer interaction and Task.
Her study in the field of Mobile robot and Robot kinematics also crosses realms of Foundation and Graduate research.
Julie A. Shah has included themes like Robot learning and Computation in her Human–robot interaction study.
Her research integrates issues of Social robot, Emerging technologies and Reinforcement learning in her study of Robot learning.
Her work in Artificial intelligence covers topics such as Machine learning which are related to areas like Generative grammar.
Her Human–computer interaction study integrates concerns from other disciplines, such as Data modeling, Model learning, Robotics and Task analysis.
Her most cited work include:
- The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification (154 citations)
- Improved human-robot team performance using chaski, a human-inspired plan execution system (146 citations)
- A Survey of Methods for Safe Human-Robot Interaction (117 citations)
What are the main themes of her work throughout her whole career to date?
Artificial intelligence, Robot, Human–robot interaction, Task and Human–computer interaction are her primary areas of study.
Her Artificial intelligence research incorporates themes from Machine learning and Computer vision.
Her studies deal with areas such as Motion, Set and Reinforcement learning as well as Robot.
Julie A. Shah combines subjects such as Robot learning, Knowledge management and Social robot with her study of Human–robot interaction.
The study incorporates disciplines such as Control and Distributed computing in addition to Task.
Her study on Adaptation and Modality is often connected to Stimulus modality as part of broader study in Human–computer interaction.
She most often published in these fields:
- Artificial intelligence (46.10%)
- Robot (40.26%)
- Human–robot interaction (26.62%)
What were the highlights of her more recent work (between 2019-2021)?
- Artificial intelligence (46.10%)
- Robot (40.26%)
- Task (25.32%)
In recent papers she was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Robot, Task, Human–computer interaction and Machine learning.
Her Artificial intelligence research is mostly focused on the topic Robotics.
Julie A. Shah interconnects Object, Imitation, Saucer and Set in the investigation of issues within Robot.
Her Task research is multidisciplinary, incorporating elements of Domain and Distributed computing.
Her Human–computer interaction research is multidisciplinary, incorporating perspectives in Adversarial system and Human–robot interaction.
In her research on the topic of Machine learning, Generative model, Generative grammar and Root cause is strongly related with Bayesian probability.
Between 2019 and 2021, her most popular works were:
- Planning With Uncertain Specifications (PUnS) (6 citations)
- Blind Spot Detection for Safe Sim-to-Real Transfer (5 citations)
- Pose consensus based on dual quaternion algebra with application to decentralized formation control of mobile manipulators (4 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Julie A. Shah mostly deals with Artificial intelligence, Robot, Human–computer interaction, Reinforcement learning and Task.
Her Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Transfer and Computer vision.
Her work on Human–robot interaction as part of general Robot research is frequently linked to Partially observable Markov decision process, bridging the gap between disciplines.
Her Human–computer interaction research includes elements of Robotics, Behavior-based robotics and Process.
The Reinforcement learning study combines topics in areas such as Quality, Control theory and Domain.
Her work on Task analysis as part of general Task study is frequently linked to Semantics, therefore connecting diverse disciplines of science.
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