What is he best known for?
The fields of study he is best known for:
- Computer network
- Statistics
- Telecommunications
His scientific interests lie mostly in Communication channel, Channel capacity, Computer network, Fading and Topology.
His Communication channel study focuses on MIMO in particular.
David Tse has included themes like Markov process, Power control, Signal-to-noise ratio, Mathematical optimization and Algorithm in his Channel capacity study.
The study incorporates disciplines such as Wireless, Wireless ad hoc network, Throughput and Wireless network in addition to Computer network.
His Fading research integrates issues from Spatial correlation, Antenna diversity, Code division multiple access, Multipath propagation and Channel state information.
His work in the fields of Topology, such as Upper and lower bounds, overlaps with other areas such as Generalization.
His most cited work include:
- Cooperative diversity in wireless networks: Efficient protocols and outage behavior (11586 citations)
- Fundamentals of Wireless Communication (7044 citations)
- Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels (4029 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Communication channel, Computer network, Topology, Algorithm and Channel capacity.
His Communication channel research is mostly focused on the topic Fading.
His studies in Computer network integrate themes in fields like Wireless, Wireless ad hoc network, Throughput and Wireless network.
His work in Wireless addresses issues such as MIMO, which are connected to fields such as Multiplexing and Antenna.
His biological study deals with issues like Interference, which deal with fields such as Transmission.
The Channel capacity study combines topics in areas such as Signal-to-noise ratio, Noise and Decoding methods.
He most often published in these fields:
- Communication channel (27.46%)
- Computer network (25.22%)
- Topology (20.31%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (4.46%)
- Algorithm (17.19%)
- Computer network (25.22%)
In recent papers he was focusing on the following fields of study:
David Tse mainly focuses on Artificial intelligence, Algorithm, Computer network, Machine learning and Cluster analysis.
His research in Artificial intelligence tackles topics such as Pattern recognition which are related to areas like Pairwise comparison.
His Algorithm study incorporates themes from Sampling and Gradient descent.
Many of his research projects under Computer network are closely connected to Ledger with Ledger, tying the diverse disciplines of science together.
He undertakes multidisciplinary investigations into Market liquidity and Communication channel in his work.
His Gaussian channels study in the realm of Communication channel interacts with subjects such as Extension.
Between 2016 and 2021, his most popular works were:
- Longitudinal multi-omics of host–microbe dynamics in prediabetes (133 citations)
- HINGE: long-read assembly achieves optimal repeat resolution (63 citations)
- HINGE: long-read assembly achieves optimal repeat resolution (63 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Computer network
- Artificial intelligence
David Tse mostly deals with Algorithm, Machine learning, Artificial intelligence, Software and Blockchain.
The concepts of his Algorithm study are interwoven with issues in Sampling, Molecule and Code.
His work on Artificial neural network and Supervised learning as part of general Machine learning study is frequently linked to Matrix norm, therefore connecting diverse disciplines of science.
His Artificial intelligence study integrates concerns from other disciplines, such as Mathematical optimization, Linear-quadratic-Gaussian control and Generalizability theory.
His work deals with themes such as Database transaction, Computer network and Scaling, which intersect with Blockchain.
His work focuses on many connections between Scaling and other disciplines, such as Adversary, that overlap with his field of interest in Security analysis and Network delay.
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.
