What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- Database
The scientist’s investigation covers issues in Data mining, Artificial intelligence, k-nearest neighbors algorithm, Machine learning and Mobile device.
His Data mining study combines topics from a wide range of disciplines, such as Spatial database, Spatial analysis, Geospatial analysis, Personalization and Data set.
His Artificial intelligence research integrates issues from Graph and Pattern recognition.
His work carried out in the field of k-nearest neighbors algorithm brings together such families of science as Spatial network and Nearest neighbor search.
His Machine learning study combines topics from a wide range of disciplines, such as Intelligent transportation system and Scalability.
His Mobile device research is multidisciplinary, relying on both Crowdsourcing, Focus and Computer security.
His most cited work include:
- Private queries in location based services: anonymizers are not necessary (663 citations)
- Big data and its technical challenges (560 citations)
- Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting (446 citations)
What are the main themes of his work throughout his whole career to date?
Cyrus Shahabi mostly deals with Data mining, Artificial intelligence, Information retrieval, Scalability and Crowdsourcing.
The various areas that Cyrus Shahabi examines in his Data mining study include Spatial query, Spatial analysis, Geospatial analysis and k-nearest neighbors algorithm.
His study in Spatial query is interdisciplinary in nature, drawing from both Query optimization and Database.
His Artificial intelligence study incorporates themes from Machine learning, Computer vision and Pattern recognition.
He combines subjects such as Mobile device and Data science with his study of Crowdsourcing.
His study connects Computer security and Mobile device.
He most often published in these fields:
- Data mining (25.46%)
- Artificial intelligence (20.16%)
- Information retrieval (10.18%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (20.16%)
- Data mining (25.46%)
- Crowdsourcing (8.76%)
In recent papers he was focusing on the following fields of study:
Cyrus Shahabi mainly focuses on Artificial intelligence, Data mining, Crowdsourcing, Machine learning and Data science.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Graph, Computer vision and Pattern recognition.
His Data mining research includes themes of Entropy and Baseline.
His Crowdsourcing study integrates concerns from other disciplines, such as Budget constraint, Heuristics and Mobile device.
The concepts of his Data science study are interwoven with issues in World Wide Web and Crowdsource.
His biological study spans a wide range of topics, including Recurrent neural network and Traffic flow.
Between 2015 and 2021, his most popular works were:
- Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting (446 citations)
- Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting. (146 citations)
- Differentially Private Location Protection for Worker Datasets in Spatial Crowdsourcing (82 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Machine learning
His primary areas of study are Artificial intelligence, Data mining, Machine learning, Crowdsourcing and Deep learning.
As part of one scientific family, he deals mainly with the area of Artificial intelligence, narrowing it down to issues related to the Graph, and often Road networks.
His Data mining research includes elements of Entropy, Boundary and Degree.
His work on Multivariate statistics as part of his general Machine learning study is frequently connected to Multi-task learning, thereby bridging the divide between different branches of science.
Cyrus Shahabi has included themes like Budget constraint, Overhead, Heuristics, Mobile device and Data science in his Crowdsourcing study.
His research integrates issues of Recurrent neural network, Moving average, Computer vision and Time series in his study of Deep learning.
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