What is he best known for?
The fields of study he is best known for:
- Computer network
- The Internet
- Operating system
His scientific interests lie mostly in The Internet, Computer network, Distributed computing, Default-free zone and Computer security.
Dan Pei has researched The Internet in several fields, including Data mining and Border Gateway Protocol.
Dan Pei has included themes like Troubleshooting and Machine learning in his Data mining study.
His Computer network research is multidisciplinary, incorporating elements of Debugging and Network mapping.
The study incorporates disciplines such as Broadband networks, Cellular network, Routing protocol and CPU cache in addition to Distributed computing.
His research in Default-free zone focuses on subjects like Convergence, which are connected to Routing table and Path vector protocol.
His most cited work include:
- PHAS: a prefix hijack alert system (204 citations)
- Observation and analysis of BGP behavior under stress (155 citations)
- The (in)completeness of the observed internet AS-level structure (155 citations)
What are the main themes of his work throughout his whole career to date?
Dan Pei spends much of his time researching Computer network, The Internet, Data mining, Distributed computing and Routing protocol.
His work on Computer security expands to the thematically related Computer network.
In his work, Machine learning is strongly intertwined with Service, which is a subfield of The Internet.
His work on Anomaly detection as part of general Data mining study is frequently linked to Performance indicator and Root cause, therefore connecting diverse disciplines of science.
His research in Distributed computing intersects with topics in Routing table, Default-free zone, IP forwarding, Multiprotocol Label Switching and Router.
His Routing protocol research integrates issues from Forwarding plane, Network topology and Routing control plane.
He most often published in these fields:
- Computer network (49.33%)
- The Internet (28.00%)
- Data mining (20.00%)
What were the highlights of his more recent work (between 2018-2021)?
- Data mining (20.00%)
- Artificial intelligence (8.67%)
- Anomaly detection (12.00%)
In recent papers he was focusing on the following fields of study:
Dan Pei focuses on Data mining, Artificial intelligence, Anomaly detection, Service and Machine learning.
His studies in Data mining integrate themes in fields like The Internet, Reliability and Time series.
In his works, he performs multidisciplinary study on The Internet and Anomaly.
His work on Autoencoder and F1 score as part of general Artificial intelligence research is often related to Gaussian, thus linking different fields of science.
His Recurrent neural network research focuses on Pipeline and how it connects with Computer network.
Many of his research projects under Computer network are closely connected to Revenue with Revenue, tying the diverse disciplines of science together.
Between 2018 and 2021, his most popular works were:
- Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network (54 citations)
- LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs (40 citations)
- Dynamic TCP Initial Windows and Congestion Control Schemes Through Reinforcement Learning (30 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- The Internet
- Operating system
His main research concerns Data mining, Anomaly detection, The Internet, Reliability and Artificial intelligence.
His study in Data mining is interdisciplinary in nature, drawing from both Parsing, Service and Time series.
His studies deal with areas such as Service quality and Cluster analysis as well as Time series.
His work carried out in the field of Anomaly detection brings together such families of science as Consistency, Recurrent neural network, Overhead and Nearest neighbor search.
His work on Robustness, Artificial neural network and Autoencoder as part of general Artificial intelligence research is frequently linked to Gaussian, bridging the gap between disciplines.
His Inference study combines topics in areas such as Stability and Quality of service.
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