Machine learning

This subclass comprises research and experimental development in machine learning.
Parent Category
 
 
 
 

UBC Researchers conducting research in Machine learning

A

Aamodt, Tor

Department of Electrical & Computer Engineering, Faculty of Applied Science

Faculty (G+PS eligible/member)

Computer architecture; Machine learning; Computer Architecture; graphics processor units (GPUs); Machine Learning

Alacaoglu, Ahmet

Department of Mathematics, Faculty of Science

Faculty (G+PS eligible/member)

Mathematical optimization; Machine learning; Mathematical operations research; Numerical optimization; Machine Learning; Stochastic algorithms; Min-max games; Monotone operator theory

H

Heagy, Lindsey

Department of Earth, Ocean & Atmospheric Sciences, Faculty of Science

Faculty (G+PS eligible/member)

Geoelectrics and geoelectromagnetics; Magnetism and paleomagnetism; Gravimetry; Environmental monitoring; Numerical computation; Machine learning; Geophysics; Inverse theory; data science; Machine Learning; Electromagnetics; Carbon sequestration; Groundwater; Environmental geophysics; Resource exploration

M

Mallik, Wrik

Faculty of Applied Science

Postdoctoral Fellow

Fluid-structure interaction; Acoustics and noise control (except architectural acoustics); Machine learning; Reduced-order modelling; Machine Learning; Underwater wave propagation; Multidisciplinary design optimization

McDonald, Daniel

Department of Statistics, Faculty of Science

Faculty (G+PS eligible/member)

High dimensional data analysis; Computational methods in statistics; Statistical theory and modeling; Machine learning; Epidemiology (except nutritional and veterinary epidemiology); Methods and models for epidemiological forecasting; Estimation and quantification of prediction risk; Evaluating the predictive abilities of complex dependent data; Application of statistical learning techniques to time series prediction problems; Investigations of cross-validation and the bootstrap for risk estimation

P

Pleiss, Geoffrey

Department of Statistics, Faculty of Science

Faculty (G+PS eligible/member)

Statistical theory and modeling; Machine learning; Computational methods in statistics; Spatial statistics; Numerical analysis; Machine Learning; neural networks; Gaussian processes; Bayesian optimization; reliable deep learning

R

Robeva, Elina

Department of Mathematics, Faculty of Science

Faculty (G+PS eligible/member)

Statistical theory and modeling; Machine learning; Algebra; Algebraic statistics; Graphical Models; Tensor decomposition; Causality; Applied algebraic geometry; Shape-constrained density estimation; Tensor networks

S

Shelhamer, Evan

Department of Computer Science, Faculty of Science

Faculty (G+PS eligible/member)

Machine learning; Computer vision in artificial intelligence; Artificial intelligence (AI); Machine Learning; Computer Vision / Visual Recognition; Robustness and Adaptation; Self-Supervised / Unsupervised Learning; Applications to Remote Sensing and Sustainability

T

Tam, Roger

School of Biomedical Engineering, Faculty of Medicine

Faculty (G+PS eligible/member)

Machine learning; Biomedical signal processing; Biomedical Design and Innovation; Biomedical Technologies; Computer Science and Statistics; Data Analytics; Medical Imaging; Machine Learning; Neurodegenerative diseases; Precision Medicine; Radiology

Traboulsee, Tony

Division of Neurology, Faculty of Medicine

Faculty (G+PS eligible/member)

Neurology; Central nervous system; Biomedical signal processing; Machine learning; magnetic resonance imaging; multiple sclerosis; Neuromyelitls optica spectrum disorder (NMOSD); Myelin Oligodendrocyte Antibody Disorder (MOGAD); Brain MRI; Spinal Cord MRI
 
 
 
 
 
French Name
Apprentissage machine
French Description
Cette sous-classe comprend la recherche et le développement expérimental en apprentissage machine.
 
 

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