Min Liang

Min Liang

 

 

Research Experience:

 

2017 - McGill University

- Experimenting with sequential application of sparse multivariate factor analysis models (SMFA), Bayesian additive regression and classification    tree model (BART) in supervised and semi-supervised scenario.

- Designing novel learning algorithms to train SMFA and BART as an integral unit. 

2017 - MITACS Accelerate Research Internship

Developed non-intrusive techniques for assessing the state of vigilance (alert, drowsy) of drivers based on eye-movements and blinking data

       2015 - Tianjin University, Harvard University   

- Analyzed fMRI brain scans from patients with Alzheimer's disearse using probabilistic models including latent space model (LSM).

- Proposed an extended dynamic LSM to track changes occurring in the brain of patients with Alzheimer’s disease.

 

Courses:

                   - COMP 598 Topics in Computer Science 1 (Applied Machine Learning)

                   - COMP 690 Probabilistic Analysis of Algorithms

                   - ECSE 509 Probability and Random Signal 2

                   - ECSE 608 Machine Learning

                   - ECSE 621 Stat Detection and Estimation

                   - math 523 Generalized Linear Models

 

Contact Information

E-mail: min.liang3@mail.mcgill.ca

Supervisor: Prof. Mark Coates

Address: 

Room: ENGMC 702
Dept. of Electrical & Computer Engineering
McGill University
3480 University Street
Montreal, Quebec
Canada
H3A 2A7