What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Mathematical analysis
His scientific interests lie mostly in Control theory, Model predictive control, Multi-agent system, Flocking and Computer simulation.
His Control theory research incorporates elements of Control engineering, Computational complexity theory and System identification.
His Model predictive control research integrates issues from Nonlinear control, Singular value decomposition, Network topology, Consensus and Process control.
His study in Multi-agent system is interdisciplinary in nature, drawing from both Observer and Control theory, Output feedback, Nonlinear system.
He combines subjects such as Disjoint sets, Signed graph and Bipartite graph with his study of Flocking.
His Computer simulation study deals with Nonholonomic system intersecting with Circular motion.
His most cited work include:
- An interpretable mortality prediction model for COVID-19 patients (258 citations)
- Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan (176 citations)
- A General Alignment Repulsion Algorithm for Flocking of Multi-Agent Systems (120 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Control theory, Nonlinear system, Multi-agent system, Control theory and Model predictive control.
His research investigates the connection between Control theory and topics such as Topology that intersect with issues in Convergence.
Hai-Tao Zhang works mostly in the field of Nonlinear system, limiting it down to concerns involving Laguerre polynomials and, occasionally, Series.
He has included themes like Network topology and Flocking in his Multi-agent system study.
His Control theory study combines topics in areas such as Control system, Tracking and Trajectory.
His research integrates issues of Computational complexity theory, Process control and Mathematical optimization in his study of Model predictive control.
He most often published in these fields:
- Control theory (59.71%)
- Nonlinear system (23.30%)
- Multi-agent system (18.93%)
What were the highlights of his more recent work (between 2019-2021)?
- Control theory (59.71%)
- Control theory (17.96%)
- Multi-agent system (18.93%)
In recent papers he was focusing on the following fields of study:
Hai-Tao Zhang focuses on Control theory, Control theory, Multi-agent system, Nonlinear system and Artificial intelligence.
His research in Control theory is mostly focused on Trajectory.
His Control theory research is multidisciplinary, relying on both Observer, Control and Variable.
His Multi-agent system study incorporates themes from Event triggered, Topology and Synchronization.
In his study, Fuzzy logic, Magnetic bearing, Linear matrix inequality and Lyapunov stability is inextricably linked to Control system, which falls within the broad field of Nonlinear system.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, PID controller, Unmanned surface vehicle and Computer vision.
Between 2019 and 2021, his most popular works were:
- An interpretable mortality prediction model for COVID-19 patients (258 citations)
- Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan (176 citations)
- Data-Driven Probabilistic Optimal Power Flow With Nonparametric Bayesian Modeling and Inference (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Mathematical analysis
His primary scientific interests are in Mortality rate, Machine learning, Artificial intelligence, Benchmark and Distributed generation.
His Mortality rate study integrates concerns from other disciplines, such as Stage, Cohort and Pneumonia.
Hai-Tao Zhang has researched Stage in several fields, including Decision rule and Intensive care medicine.
His work on Predictive modelling as part of general Machine learning study is frequently linked to Prognostic model, In patient and Disease severity, therefore connecting diverse disciplines of science.
His biological study spans a wide range of topics, including Mixture model and Algorithm, Interior point method.
His research in Mixture model focuses on subjects like Bayesian inference, which are connected to Statistical model, Mathematical optimization, Probabilistic logic, Inference and Data-driven.
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