What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Programming language
Stephan Winter spends much of his time researching Artificial intelligence, Data science, Spatial analysis, Information retrieval and Salience.
His work on Probabilistic logic as part of general Artificial intelligence study is frequently connected to Time geography, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His studies deal with areas such as Group method of data handling, Position paper, Geospatial analysis and Big data as well as Data science.
His Spatial analysis research integrates issues from Cartography, Geographic information system, Spatial relation, Toponymy and Natural language.
Stephan Winter focuses mostly in the field of Information retrieval, narrowing it down to topics relating to Landmark and, in certain cases, Key and Routing.
His research investigates the connection with Salience and areas like Salient which intersect with concerns in Formal language, User assistance and Conceptualization.
His most cited work include:
- Spatial Information Theory (507 citations)
- Enriching Wayfinding Instructions with Local Landmarks (380 citations)
- Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges (217 citations)
What are the main themes of his work throughout his whole career to date?
Stephan Winter mainly investigates Artificial intelligence, Data science, Human–computer interaction, Spatial analysis and Information retrieval.
His biological study spans a wide range of topics, including Machine learning, Computer vision and Natural language processing.
His Data science research is multidisciplinary, incorporating perspectives in Space and Geographic information system.
His Human–computer interaction study frequently involves adjacent topics like Service.
He most often published in these fields:
- Artificial intelligence (17.59%)
- Data science (13.03%)
- Human–computer interaction (11.07%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (17.59%)
- Information retrieval (9.77%)
- Data science (13.03%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Information retrieval, Data science, Transport engineering and Pedestrian.
The concepts of his Artificial intelligence study are interwoven with issues in Natural language processing, Heuristics, Computer vision, Toponymy and Machine learning.
While the research belongs to areas of Information retrieval, he spends his time largely on the problem of Spatial analysis, intersecting his research to questions surrounding Mobile computing.
In his research, Sentiment analysis is intimately related to Social media, which falls under the overarching field of Data science.
His Transport engineering research includes elements of Empirical research and Operations research.
His study focuses on the intersection of Pedestrian and fields such as Urban road with connections in the field of Cartography.
Between 2016 and 2021, his most popular works were:
- Similarity matching for integrating spatial information extracted from place descriptions (43 citations)
- A conceptualization of vehicle platoons and platoon operations (34 citations)
- Predicting susceptibility to use demand responsive transport using demographic and trip characteristics of the population (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Programming language
Stephan Winter mainly investigates Information retrieval, Artificial intelligence, Spatial analysis, Data science and Transport engineering.
His Information retrieval study integrates concerns from other disciplines, such as Event, Dempster–Shafer theory, Transferable belief model and Social research.
His studies in Artificial intelligence integrate themes in fields like Machine learning, Computer vision and Toponymy.
His research in Spatial analysis tackles topics such as Mobile computing which are related to areas like Internet access, Location-based service, Human-centered computing, Global Positioning System and Human–computer interaction.
His Data science study incorporates themes from Intelligent agent, Space, Probabilistic logic and Heuristic.
His Transport engineering research is multidisciplinary, relying on both Negotiation and Interaction design.
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