What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Database
- Algorithm
His main research concerns Data mining, Computer security, Location-based service, Theoretical computer science and Nearest neighbor search.
In general Data mining, his work in Relational database is often linked to Spatial database linking many areas of study.
In general Computer security study, his work on Attack model, Anonymity and Privacy software often relates to the realm of Association, thereby connecting several areas of interest.
His Location-based service research includes themes of Overhead and Mobile device.
His research in Theoretical computer science tackles topics such as Heuristic which are related to areas like Semantics, Matching, Graph and Friend of a friend.
His work carried out in the field of Nearest neighbor search brings together such families of science as Ranking, Similarity measure and Edit distance.
His most cited work include:
- Private queries in location based services: anonymizers are not necessary (663 citations)
- Preventing Location-Based Identity Inference in Anonymous Spatial Queries (573 citations)
- PRIVE: anonymous location-based queries in distributed mobile systems (360 citations)
What are the main themes of his work throughout his whole career to date?
Panos Kalnis mostly deals with Data mining, Theoretical computer science, Scalability, Database and Information retrieval.
His Data mining study combines topics in areas such as Cluster analysis and Anonymity.
Panos Kalnis has included themes like SPARQL, RDF, Approximation algorithm, Query optimization and Graph in his Theoretical computer science study.
His Scalability research is multidisciplinary, relying on both Parallel computing, Distributed computing, Peer-to-peer and Server.
His Database research integrates issues from Private information retrieval and Encryption.
In his research, Overhead is intimately related to Mobile device, which falls under the overarching field of Encryption.
He most often published in these fields:
- Data mining (31.94%)
- Theoretical computer science (25.69%)
- Scalability (15.97%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (7.64%)
- Deep learning (4.86%)
- Algorithm (9.03%)
In recent papers he was focusing on the following fields of study:
Panos Kalnis mainly focuses on Artificial intelligence, Deep learning, Algorithm, Data science and Machine learning.
His Artificial intelligence study incorporates themes from Graph and Linear model.
His Deep learning research includes themes of Technical report and Multimedia.
His work in the fields of Computation overlaps with other areas such as Macromolecular docking.
His work on Analytics as part of general Data science research is frequently linked to Smart city and Self driving, bridging the gap between disciplines.
His Machine learning study integrates concerns from other disciplines, such as Key and Benchmark.
Between 2018 and 2021, his most popular works were:
- Scaling Distributed Machine Learning with In-Network Aggregation (39 citations)
- Parallel Trajectory-to-Location Join (36 citations)
- GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Database
The scientist’s investigation covers issues in Artificial intelligence, Deep learning, Algorithm, Machine learning and Technical report.
Workload and Process are fields of study that overlap with his Artificial intelligence research.
His studies in Deep learning integrate themes in fields like Graph, Linear model and Graph.
His Computation and Quantization study in the realm of Algorithm connects with subjects such as Implementation.
His research in Machine learning intersects with topics in Key and Benchmark.
The study of Technical report is intertwined with the study of Multimedia in a number of ways.
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