What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Her primary scientific interests are in Artificial intelligence, Human–robot interaction, Motion, Robot and Pattern recognition.
Her study in Artificial intelligence is interdisciplinary in nature, drawing from both Wearable computer and Computer vision.
Her Human–robot interaction research incorporates elements of Semantic differential, Social psychology, Simulation and Human–computer interaction.
Her studies in Motion integrate themes in fields like Motion estimation, Tree structure and Humanoid robot.
Dana Kulic combines subjects such as Applied psychology and Animacy with her study of Robot.
Dana Kulic studied Pattern recognition and Cluster analysis that intersect with Tree.
Her most cited work include:
- Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (1000 citations)
- Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains (181 citations)
- Incremental learning of full body motion primitives and their sequencing through human motion observation (162 citations)
What are the main themes of her work throughout her whole career to date?
Her main research concerns Artificial intelligence, Robot, Computer vision, Motion and Human–computer interaction.
Her Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition.
Her research in the fields of Human–robot interaction, Robotics and Motion planning overlaps with other disciplines such as Preference learning.
The study incorporates disciplines such as Robot control and Articulated robot in addition to Human–robot interaction.
Many of her research projects under Computer vision are closely connected to Accelerometer with Accelerometer, tying the diverse disciplines of science together.
The concepts of her Motion study are interwoven with issues in Humanoid robot, Tree structure, Representation and Cluster analysis.
She most often published in these fields:
- Artificial intelligence (56.09%)
- Robot (30.00%)
- Computer vision (24.78%)
What were the highlights of her more recent work (between 2019-2021)?
- Human–computer interaction (19.13%)
- Robot (30.00%)
- Artificial intelligence (56.09%)
In recent papers she was focusing on the following fields of study:
Her scientific interests lie mostly in Human–computer interaction, Robot, Artificial intelligence, Human–robot interaction and Reinforcement learning.
Her study on Behavior-based robotics, Motion planning and Social robot is often connected to Preference learning as part of broader study in Robot.
Her studies examine the connections between Motion planning and genetics, as well as such issues in Motion, with regards to Mobile robot, Hidden Markov model and Affect.
As a part of the same scientific study, Dana Kulic usually deals with the Artificial intelligence, concentrating on Pattern recognition and frequently concerns with Object detection.
Human–robot interaction combines with fields such as Context and Field in her work.
Her study looks at the relationship between Robotic arm and fields such as Maximum principle, as well as how they intersect with chemical problems.
Between 2019 and 2021, her most popular works were:
- Active Preference Learning using Maximum Regret (9 citations)
- Object Handovers: a Review for Robotics. (7 citations)
- Improving user specifications for robot behavior through active preference learning: Framework and evaluation: (7 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Dana Kulic focuses on Robot, Human–computer interaction, Motion planning, Artificial intelligence and Behavior-based robotics.
Her Robot research includes elements of Structure and User expectations.
Her Human–computer interaction study integrates concerns from other disciplines, such as Wearable technology, Salient and Human–robot interaction.
Her work carried out in the field of Human–robot interaction brings together such families of science as Domain and Table.
Her Convolutional neural network study, which is part of a larger body of work in Artificial intelligence, is frequently linked to High resolution, bridging the gap between disciplines.
Her Set research includes themes of Motion and Robotics.
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