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Chulan Kwon

dblp:81/541 · DBLP profile ↗
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3ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0002-4924-2778ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Kernel, tree and ensemble methods · 50% Deep learning architectures and training · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Kernel, tree and ensemble methods › model ensemble
committee machine
0.011997
The Storage Capacity of a Fully-Connected Committee Machine · NIPS 1997
Machine learning › Deep learning architectures and training › feedforward neural network
multilayer perceptron
0.011997
The Storage Capacity of a Fully-Connected Committee Machine · NIPS 1997

Methods — techniques the papers use, named apart from their topics

statistical mechanics analysis · 0.0
YearPublicationVenuePosition
2021 Transcriptomic entropy benchmarks stem cell-derived cardiomyocyte maturation against endogenous tissue at single cell level
abstract
The immaturity of pluripotent stem cell (PSC)-derived tissues has emerged as a universal problem for their biomedical applications. While efforts have been made to generate adult-like cells from PSCs, direct benchmarking of PSC-derived tissues against in vivo development has not been established. Thus, maturation status is often assessed on an ad-hoc basis. Single cell RNA-sequencing (scRNA-seq) offers a promising solution, though cross-study comparison is limited by dataset-specific batch effects. Here, we developed a novel approach to quantify PSC-derived cardiomyocyte (CM) maturation through transcriptomic entropy. Transcriptomic entropy is robust across datasets regardless of differences in isolation protocols, library preparation, and other potential batch effects. With this new model, we analyzed over 45 scRNA-seq datasets and over 52,000 CMs, and established a cross-study, cross-species CM maturation reference. This reference enabled us to directly compare PSC-CMs with the in vivo developmental trajectory and thereby to quantify PSC-CM maturation status. We further found that our entropy-based approach can be used for other cell types, including pancreatic beta cells and hepatocytes. Our study presents a biologically relevant and interpretable metric for quantifying PSC-derived tissue maturation, and is extensible to numerous tissue engineering contexts.
Suraj Kannan, Michael Farid, Brian L. Lin, Matthew Miyamoto, Chulan Kwon
PLoS Comput. Biol.5
1997 Exact asymptotic estimates of the storage capacities of the committee machines with overlapping and non-overlapping receptive fields
Chulan Kwon, Jong-Hoon Oh
ESANN1
1997 The Storage Capacity of a Fully-Connected Committee Machine
Yuansheng Xiong, Chulan Kwon, Jong-Hoon Oh
NIPS2