What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer network
His scientific interests lie mostly in Artificial intelligence, Machine learning, Data mining, Pattern recognition and Online machine learning.
His study in Constrained clustering, Active learning, Correlation clustering, Data stream clustering and Cluster analysis are all subfields of Artificial intelligence.
His research in Machine learning intersects with topics in Bridge, Adversarial machine learning, Dynamic network analysis and Network simulation.
His Data mining research is multidisciplinary, relying on both Software, File system, Parsing and Source code.
His work on Anomaly detection as part of general Pattern recognition study is frequently connected to Anomaly, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
The Online machine learning study combines topics in areas such as Adversary model, Instance-based learning, Computational learning theory and Algorithmic learning theory.
His most cited work include:
- Tapestry: a resilient global-scale overlay for service deployment (1666 citations)
- Detecting large-scale system problems by mining console logs (587 citations)
- Fast approximate spectral clustering (380 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Machine learning, Data mining, Anomaly detection and Classifier are his primary areas of study.
The study incorporates disciplines such as Malware and Pattern recognition in addition to Artificial intelligence.
His research in the fields of Instance-based learning overlaps with other disciplines such as Generalization, Noise and Sample.
His work on Decision tree is typically connected to Anomaly and Detector as part of general Data mining study, connecting several disciplines of science.
He combines subjects such as Volume, Denial-of-service attack, Real-time computing and Principal component analysis with his study of Anomaly detection.
In the subject of general Classifier, his work in Decision boundary is often linked to Regular polygon, thereby combining diverse domains of study.
He most often published in these fields:
- Artificial intelligence (42.03%)
- Machine learning (34.78%)
- Data mining (27.54%)
What were the highlights of his more recent work (between 2013-2020)?
- Artificial intelligence (42.03%)
- Machine learning (34.78%)
- Data mining (27.54%)
In recent papers he was focusing on the following fields of study:
Ling Huang spends much of his time researching Artificial intelligence, Machine learning, Data mining, Anomaly detection and Adversarial system.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both The Internet, Malware and Pattern recognition.
His Machine learning research is multidisciplinary, incorporating perspectives in Relationship extraction and Relation.
Data mining and Homophily are commonly linked in his work.
His Anomaly detection research incorporates elements of Generative grammar, Clustering high-dimensional data and Dimension.
His Instance-based learning research integrates issues from Software architecture, Human–computer interaction and Security domain.
Between 2013 and 2020, his most popular works were:
- Adversarial Machine Learning (172 citations)
- Joint Link Prediction and Attribute Inference Using a Social-Attribute Network (143 citations)
- I Know Why You Went to the Clinic: Risks and Realization of HTTPS Traffic Analysis (51 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer network
His main research concerns Artificial intelligence, Machine learning, Instance-based learning, Data mining and Software architecture.
His Artificial intelligence research is multidisciplinary, relying on both Adversary model and Hash function.
The concepts of his Machine learning study are interwoven with issues in Node, Inference and Homophily.
His studies in Instance-based learning integrate themes in fields like Online machine learning, Adversarial machine learning and Algorithmic learning theory.
His biological study spans a wide range of topics, including Malware and False positive rate.
He has researched Software architecture in several fields, including Adversarial system, Human–computer interaction and Security domain.
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