What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
The scientist’s investigation covers issues in Estimator, Algorithm, Graphical model, Mathematical optimization and Applied mathematics.
His Estimator study incorporates themes from Positive definiteness, Dykstra's projection algorithm, Minimax, Optimization problem and Feature selection.
His work in the fields of Estimation theory overlaps with other areas such as Order.
He interconnects Graph, Data mining, Score, Score test and Inference in the investigation of issues within Graphical model.
His work carried out in the field of Mathematical optimization brings together such families of science as Generalized linear model, Robustness and Rank.
The various areas that Han Liu examines in his Applied mathematics study include Null hypothesis, Convex optimization, Rate of convergence, Nuisance parameter and Confidence region.
His most cited work include:
- Patterns and rates of exonic de novo mutations in autism spectrum disorders (1389 citations)
- Challenges of Big Data analysis (623 citations)
- The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs (452 citations)
What are the main themes of his work throughout his whole career to date?
Han Liu focuses on Estimator, Algorithm, Mathematical optimization, Artificial intelligence and Graphical model.
His Estimator research is multidisciplinary, incorporating perspectives in Covariance matrix, Parametric statistics, Minimax and Applied mathematics.
In general Algorithm study, his work on Estimation theory often relates to the realm of Oracle, thereby connecting several areas of interest.
His studies in Mathematical optimization integrate themes in fields like Sparse approximation and Reinforcement learning.
His study looks at the intersection of Artificial intelligence and topics like Nonparametric statistics with Additive model.
Han Liu studied Graphical model and Graph that intersect with Discrete mathematics.
He most often published in these fields:
- Estimator (26.74%)
- Algorithm (25.58%)
- Mathematical optimization (24.42%)
What were the highlights of his more recent work (between 2016-2021)?
- Algorithm (25.58%)
- Artificial intelligence (19.19%)
- Estimator (26.74%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Algorithm, Artificial intelligence, Estimator, Applied mathematics and Graphical model.
His research in Algorithm intersects with topics in Statistical hypothesis testing, Covariance, Lasso and Minimax.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Pattern recognition.
The Estimator study combines topics in areas such as Rate of convergence, Covariance matrix, Mathematical optimization and Sampling distribution.
Han Liu has included themes like Convergence, Weak convergence, Markov chain, Sufficient dimension reduction and Conditional probability distribution in his Applied mathematics study.
His study in Graphical model is interdisciplinary in nature, drawing from both Inference and Model selection.
Between 2016 and 2021, his most popular works were:
- A general theory of hypothesis tests and confidence regions for sparse high dimensional models (118 citations)
- Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions (106 citations)
- Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents (85 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Han Liu mostly deals with Algorithm, Estimator, Artificial intelligence, Reinforcement learning and Mathematical optimization.
His work deals with themes such as Graphical model, Principal component analysis and Minimax, which intersect with Algorithm.
His research in Graphical model focuses on subjects like Statistical hypothesis testing, which are connected to Null hypothesis, Model selection, Distribution free, Gumbel distribution and Inference.
His Estimator research incorporates themes from Sample size determination, Least squares, Applied mathematics and Robustness.
His research integrates issues of Machine learning and Pattern recognition in his study of Artificial intelligence.
His biological study spans a wide range of topics, including Sample and Theoretical computer science.
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.
