What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Artificial intelligence, Machine learning, Data science, Data mining and Topic model.
His research on Artificial intelligence often connects related topics like Computer graphics.
His Machine learning research is multidisciplinary, incorporating elements of Proportional hazards model, Encoder, Survival analysis and Censoring.
In his work, Resource, Key, Pseudocode and Analytics is strongly intertwined with Big data, which is a subfield of Data science.
His work deals with themes such as Transfer of learning, AdaBoost and Regression, which intersect with Data mining.
Chandan K. Reddy combines subjects such as Matrix decomposition, Non-negative matrix factorization, Joint and Quantitative Evaluations with his study of Topic model.
His most cited work include:
- Data Clustering: Algorithms and Applications (662 citations)
- A survey on platforms for big data analytics (250 citations)
- UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization (183 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Machine learning, Data mining, Cluster analysis and Pattern recognition are his primary areas of study.
As a member of one scientific family, Chandan K. Reddy mostly works in the field of Artificial intelligence, focusing on Task and, on occasion, Information retrieval.
In his study, Survival analysis is strongly linked to Regression, which falls under the umbrella field of Machine learning.
His Data mining research integrates issues from Regularization, Biclustering and Clustering high-dimensional data.
The concepts of his Cluster analysis study are interwoven with issues in Algorithm and Mathematical optimization.
As part of the same scientific family, he usually focuses on Artificial neural network, concentrating on Automatic summarization and intersecting with Reinforcement learning.
He most often published in these fields:
- Artificial intelligence (52.08%)
- Machine learning (31.77%)
- Data mining (18.75%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (52.08%)
- Machine learning (31.77%)
- Deep learning (7.29%)
In recent papers he was focusing on the following fields of study:
Chandan K. Reddy focuses on Artificial intelligence, Machine learning, Deep learning, Domain and Task.
His Artificial intelligence study which covers Natural language processing that intersects with Ontology and Entity linking.
His Interpretability study in the realm of Machine learning interacts with subjects such as Process.
His research in Deep learning intersects with topics in Product, Convolutional neural network, Data mining and Benchmark.
His Data mining research includes themes of Semi-supervised learning, Segmentation, Unsupervised learning, Supervised learning and Feature extraction.
His Task study deals with Interpretation intersecting with Visualization, Simple and Topic model.
Between 2019 and 2021, his most popular works were:
- Deep Reinforcement Learning for Sequence-to-Sequence Models (51 citations)
- Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data (16 citations)
- LATTE: Latent Type Modeling for Biomedical Entity Linking (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Chandan K. Reddy mainly investigates Artificial intelligence, Deep learning, Feature learning, Machine learning and Embedding.
Chandan K. Reddy interconnects Task and Natural language processing in the investigation of issues within Artificial intelligence.
His Deep learning research is multidisciplinary, relying on both Data quality, Convolutional neural network and Data mining.
His biological study spans a wide range of topics, including Semi-supervised learning, Segmentation, Autoencoder, Unsupervised learning and Supervised learning.
His Feature learning study incorporates themes from E-commerce, Service, Product, Information retrieval and Multi-task learning.
His research on Machine learning focuses in particular on Feature.
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