Machine Intelligence & Biocomputing (MIB) Laboratory
Welcome to the Machine Intelligence & Biocomputing (MIB) Laboratory at Memorial University! We develop new intelligent learning methods and complex network models for biomedical knowledge discovery.
- Evolutionary Computing
- Complex Networks
- Machine Learning
- Artificial Evolution
- Computational Biology
- 2018-2020: Seed, Bridge, and Multidisciplinary Grant from Memorial University
- 2016-2021: Discovery Grant from National Sciences and Engineering Research Council (NSERC) of Canada
- 2015-2018: Ignite R&D Grant from Research & Development Corporation (RDC) of Newfoundland and Labrador
- 2018-10: Our paper on "Ensemble Learning for Detecting Gene-gene Interactions in Colorectal Cancer" was accepted for publication in PeerJ
- 2018-09: Welcome medical student Michael Lee to join our lab for research!
- 2018-08: Ting Hu is selected to chair the GP track of the 2019 Genetic and Evolutionary Computation Conference (GECCO) in Prague, Czech Republic
- 2018-06: Welcome Science Undergraduate Research Award (SURA) student Ling Xu to join our lab this summer!
- 2018-05: Congratulations to Mehrzad on graduating with his MSc research on "Investigation of vertex centralities in human gene-disease networks"
- 2018-05: Welcome our new lab member Kyle Nickerson (MSc)!
- 2018-04: Ting Hu is selected to chair the 22nd European Conference on Genetic Programming (EuroGP) 2019 in Leipzig, Germany
- 2018-03: Our paper on "Measuring Evolvability and Accessibility using the Hyperlink-Induced Topic Search Algorithm" was accepted for publication in GECCO 2018
- 2018-03: Our first Master's student Fara has graduated with his thesis research on "Ensemble learning for detecting gene-gene interactions in colorectal cancer"
- 2018-02: Our paper on "Analyzing feature importance for metabolomics using genetic programming" was nominated Best Paper Award at EuroGP 2018
- 2018-01: We received new funding from Memorial University to seed our research on the discovery of genetic markers in human diseases using evolutionary learning algorithms
- 2018-01: Our paper on "An evolutionary learning and network approach to identifying key metabolites for osteoarthritis" was accepted for publication in PLoS Computational Biology
- 2018-01: Our paper on "Analyzing feature importance for metabolomics using genetic programming" was accepted for publication in EuroGP 2018
- 2018-01: Our paper on "Feature selection for detecting gene-gene interactions in genome-wide association studies" was accepted for publication in EvoApplications 2018
- 2018-01: Welcome our new lab member Shengkai Geng (MSc)!
- 2017-09: Welcome four new members to our lab, Asma (PhD), Zhendong (PhD), Arshad (MSc), and Yu(MSc)!
- 2017-04: Our paper on "Lexicase selection promotes effective search and behavioural diversity of solutions in linear genetic programming" was accepted for publication in IEEE CEC 2017
- 2016-11: Ting Hu was selected for the Best Professor Award of the Department of Computer Science by the Computer Science Graduate Society (CSGS)