What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Artificial intelligence, Control theory, Dynamic programming, Artificial neural network and Machine learning.
He has researched Artificial intelligence in several fields, including Data mining and Pattern recognition.
His Control theory study combines topics from a wide range of disciplines, such as Control engineering and Electric power system.
His Dynamic programming research incorporates elements of Stability, Adaptive system and Adaptive learning.
His Machine learning research incorporates themes from Probability distribution and Raw data.
His Raw data research is multidisciplinary, relying on both Boosting methods for object categorization and Knowledge representation and reasoning.
His most cited work include:
- Learning from Imbalanced Data (4443 citations)
- ADASYN: Adaptive synthetic sampling approach for imbalanced learning (1265 citations)
- Imbalanced Learning: Foundations, Algorithms, and Applications (325 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Control theory, Artificial neural network, Mathematical optimization and Machine learning.
Haibo He interconnects Data mining and Pattern recognition in the investigation of issues within Artificial intelligence.
His Pattern recognition study combines topics in areas such as Feature and Data set.
His Control theory research is multidisciplinary, incorporating elements of Dynamic programming and Electric power system.
His work deals with themes such as Stability, Algorithm and Reinforcement learning, which intersect with Artificial neural network.
Haibo He has researched Mathematical optimization in several fields, including Algorithm design, Microgrid and Energy management.
He most often published in these fields:
- Artificial intelligence (32.79%)
- Control theory (26.48%)
- Artificial neural network (22.20%)
What were the highlights of his more recent work (between 2019-2021)?
- Control theory (26.48%)
- Mathematical optimization (15.27%)
- Artificial neural network (22.20%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Control theory, Mathematical optimization, Artificial neural network, Artificial intelligence and Reinforcement learning.
The Control theory study combines topics in areas such as Multi-agent system and Microgrid.
He has included themes like Energy management and Job shop scheduling in his Mathematical optimization study.
His Artificial neural network study integrates concerns from other disciplines, such as Discrete time and continuous time, Bounded function, Nash equilibrium, Fuzzy logic and Fault.
His Artificial intelligence study typically links adjacent topics like Pattern recognition.
His work carried out in the field of Reinforcement learning brings together such families of science as Scheduling, Electric power transmission, Smart grid and Trust region.
Between 2019 and 2021, his most popular works were:
- Impact of Power Grid Strength and PLL Parameters on Stability of Grid-Connected DFIG Wind Farm (48 citations)
- Dimensionality Reduction of Hyperspectral Imagery Based on Spatial–Spectral Manifold Learning (23 citations)
- Automated Demand Response Framework in ELNs: Decentralized Scheduling and Smart Contract (23 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Haibo He spends much of his time researching Control theory, Artificial neural network, Mathematical optimization, Reinforcement learning and Nonlinear system.
As a part of the same scientific study, Haibo He usually deals with the Control theory, concentrating on Microgrid and frequently concerns with Power engineering.
His research in the fields of Long short term memory overlaps with other disciplines such as Visual perception.
His Reinforcement learning research integrates issues from Scheduling, Smart grid and Demand response.
In his work, Lyapunov function is strongly intertwined with Optimal control, which is a subfield of Nonlinear system.
His Iterative reconstruction study introduces a deeper knowledge of Artificial intelligence.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
