Author

William J Welsh

Professor of Statistics, University of British Columbia - Cited by 25,901 - Design of experiments - computer experiments - statistical learning

Biography

Dr. Welsh’s laboratory specializes in the development and application of computational tools for pharmaceutical drug discovery, predictive toxicology, and multi-dimensional attern recognition. His laboratory’s interests extend to the molecular design and modeling of synthetic polymers, protein-material interactions, and protein-ligand interactions. In recent years, his laboratory has participated in the discovery of potential drug candidates for the treatment cancer, severe and chronic pain, and infectious diseases.
Title
Cited by
Year
Choosing the sample size of a computer experiment: A practical guide
JL Loeppky, J Sacks, WJ WelchTechnometrics 51 (4), 366-376, 2009200
673
2009
Toxic colors: the use of deep learning for predicting toxicity of compounds merely from their graphic images
M Fernandez, F Ban, G Woo, M Hsing, T Yamazaki, E LeBlanc, ...Journal of chemical information and modeling 58 (8), 1533-1543, 2018201
87
2018
Ccgan: Continuous conditional generative adversarial networks for image generation
X Ding, Y Wang, Z Xu, WJ Welch, ZJ WangInternational conference on learning representations, 2020202
49
2020
Design of computer experiments for optimization, estimation of function contours, and related objectives
D Bingham, P Ranjan, WJ WelchStatistics in Action: A Canadian Outlook 109, 109, 2014201
30
2014
ChemModLab: A web-based cheminformatics modeling laboratory
JM Hughes-Oliver, AD Brooks, WJ Welch, MG Khaledi, D Hawkins, ...In silico biology 11 (1-2), 61-81, 2011201
16
2011
Exploiting multiple descriptor sets in QSAR studies
JH Tomal, WJ Welch, RH ZamarJournal of Chemical Information and Modeling 56 (3), 501-509, 2016201
13
2016
Subsampling generative adversarial networks: Density ratio estimation in feature space with softplus loss
X Ding, ZJ Wang, WJ WelchIEEE Transactions on Signal Processing 68, 1910-1922, 2020202
11
2020
Rear mounted wash manifold retention system
K Dorshimer, WJ Welch, RM Rice, S Nordlund, W ZadrickUS Patent ,212,565, 2015201
9
2015
Using a Gaussian process as a nonparametric regression model
JR Gattiker, MS Hamada, DM Higdon, M Schonlau, WJ WelchQuality and Reliability Engineering International 32 (2), 673-60, 2016201
8
2016
Continuous conditional generative adversarial networks: Novel empirical losses and label input mechanisms
X Ding, Y Wang, Z Xu, WJ Welch, ZJ WangIEEE Transactions on Pattern Analysis and Machine Intelligence, 2022202
7
2022
PROPUESTA DE UNA FORMULACIÓN SEMISÓLIDA A PARTIR DE UN EXTRACTO HIDROALCOHÓLICO DE Talipariti elatum Sw.
YI Gutiérrez, W Welch, R Scull, V García, L LivánRevista de Ciencias Farmacéuticas y Alimentarias 3 (2), 20120
7
2017
Efficient, adaptive cross-validation for tuning and comparing models, with application to drug discovery
H Shen, WJ Welch, JM Hughes-OliverThe Annals of applied statistics, 2668-268, 2011201
7
2011
Bayesian optimization using monotonicity information and its application in machine learning hyperparameter
W Wang, WJ WelcharXiv preprint arXiv:1802.03532, 2018201
7
2018
Rear mounted wash manifold and process
K Dorshimer, WJ Welch, RM Rice, S Nordlund, W ZadrickUS Patent 9,500,098, 20120
6
2016
Continuous conditional generative adversarial networks for image generation: Novel losses and label input mechanisms
X Ding, Y Wang, Z Xu, WJ Welch, ZJ WangarXiv preprint arXiv:2011.07466, 2020202
5
2020
Flexible correlation structure for accurate prediction and uncertainty quantification in bayesian gaussian process emulation of a computer model
H Chen, JL Loeppky, WJ WelchSIAM/ASA Journal on Uncertainty Quantification (1), 98-620, 2017201
5
2017
Distilling and transferring knowledge via cGAN-generated samples for image classification and regression
X Ding, Y Wang, Z Xu, ZJ Wang, WJ WelchExpert Systems with Applications 213, 119060, 2023202
5
2023
Classification beats regression: Counting of cells from greyscale microscopic images based on annotation-free training samples
X Ding, Q Zhang, WJ WelchArtificial Intelligence: First CAAI International Conference, CICAI 2021 …, 2021202
4
2021
Identifying parametric nonlinear models for computer codes
M Schonlau, M Hamada, WJ WelchTechnical report RR-96-02, University of Waterloo Institute for Improvement …, 20120
4
2014
Efficient subsampling for generating high-quality images from conditional generative adversarial networks
X Ding, Y Wang, ZJ Wang, WJ WelcharXiv preprint arXiv:210.11166, 77, 2021202
3
2021

Referred From: https://scholar.google.com/citations?user=Bus4Xi8AAAAJ&hl=en

Cited by
Citations 25901
h-index 33
i10-index 49
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