What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Algorithm
Wai-Ki Ching spends much of his time researching Markov chain, Mathematical optimization, Algorithm, Markov model and Queueing theory.
His Markov chain study frequently intersects with other fields, such as Markov process.
His Mathematical optimization research includes themes of Closed loop, Communication channel, Dual and Operations management.
His Boolean network and Regularization study in the realm of Algorithm connects with subjects such as Vertex and Singleton.
As a member of one scientific family, Wai-Ki Ching mostly works in the field of Markov model, focusing on Artificial intelligence and, on occasion, Optimization algorithm, Pattern recognition, Systems biology and Biological network.
His research in Variable-order Markov model intersects with topics in Markov property and Hidden Markov model.
His most cited work include:
- Control of Boolean networks: hardness results and algorithms for tree structured networks. (397 citations)
- Markov Chains: Models, Algorithms and Applications (222 citations)
- An optimization algorithm for clustering using weighted dissimilarity measures (183 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Mathematical optimization, Markov chain, Artificial intelligence, Econometrics and Algorithm.
His Mathematical optimization study incorporates themes from Probability distribution and Probabilistic logic.
His work is dedicated to discovering how Probabilistic logic, Theoretical computer science are connected with Attractor and other disciplines.
His Markov chain study deals with Markov process intersecting with Operations research.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Data mining and Pattern recognition.
His research in Algorithm is mostly concerned with Boolean network.
He most often published in these fields:
- Mathematical optimization (25.54%)
- Markov chain (24.18%)
- Artificial intelligence (13.04%)
What were the highlights of his more recent work (between 2016-2021)?
- Markov chain (24.18%)
- Mathematical optimization (25.54%)
- Econometrics (13.86%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Markov chain, Mathematical optimization, Econometrics, Boolean network and Complex network.
Wai-Ki Ching interconnects Volatility, Valuation of options and Hidden Markov model in the investigation of issues within Markov chain.
Wai-Ki Ching performs integrative study on Mathematical optimization and Negotiation.
His Boolean network study integrates concerns from other disciplines, such as Upper and lower bounds, Topology, Aperiodic graph and Lyapunov function.
His Complex network study also includes fields such as
- Attractor which intersects with area such as Observability and Theoretical computer science,
- State which connect with Biological network, Approximation algorithm, Biological data and Algorithm.
His work investigates the relationship between Credit risk and topics such as Pattern recognition that intersect with problems in Artificial intelligence.
Between 2016 and 2021, his most popular works were:
- Stationary Mahalanobis kernel SVM for credit risk evaluation (19 citations)
- Identifying a Probabilistic Boolean Threshold Network From Samples (11 citations)
- Switching-based stabilization of aperiodic sampled-data Boolean control networks with all subsystems unstable (8 citations)
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