I am currently recruiting graduate and undergraduate students to work on projects in my lab. I have some research projects at all levels in transcriptomics, interactomics, machine learning, cancer research and computer security. Some of the projects are listed below, but the list is not exclusive. If you have your own ideas, I will be happy to discuss them with you. Research scholarships are available for qualified students, at the PhD level only, from NSERC grants and other funding sources. For more information on my research, see my research page, or contact me at This email address is being protected from spambots. You need JavaScript enabled to view it..

 

PhD Students

At the PhD level, I am looking for students who are strong on algorithm design and analysis, lineal algebra and probability, and willingness to learn on quickly changing topics in fields of interactomics and transcriptomics, including ChIP-seq, RNA-seq data analysis, protein-protein interaction, dynamics of proteins, among others. Prospective students must demonstrate independence and creativity on conducting research.

The main topics for PhD theses are in machine learning and applications in interactomics and transcriptomics, including classification, analysis, feature extraction and selection, pattern discovery, network/pathway analysis and visualization. Knowledge of Perl, Python, Matlab and/or R is desirable. Knowledge in machine learning and bioinformatics is a requirement.

I also have some projects in computer security, more specifically on stream ciphers, latin square-based cryptographic systems, their applications and computer security protocols. Feel free to contact me for more details about those projects.

 

Master's Students

At the Master's level, I am looking for enthusiastic, self-motivated, research-oriented students to work within the areas of protein-protein interaction, ChIP-seq/RNA-seq data analysis, alternative splicing, applications on breast and prostate cancer, among others. The use of supervised and unsupervised machine learning techniques and/or visualization approaches is important.

Problems in protein-protein interaction involve discovering new domains and short-linear motifs related to the interactions between proteins and other molecules, and also those interactions and motifs related to the dynamics of the interactome. Problems in transcriptomics involve analysis of ChIP-seq and RNA-seq data for discovering biomarkers in breast and prostate cancer, and the role of alternative splicing, protein interactions and pathways.

I also have some projects in computer security, more specifically on stream ciphers, latin square-based cryptographic systems, their applications and computer security protocols. Feel free to contact me for more details about those projects.

 

Deep knowledge of Perl, Python and/or Matlab (or serious willingness to learn) is required. Knowledge or willingness to learn machine learning/bioinformatics/cryptography techniques is a requirement.

Note: At present, positions for Master's are not open for Master's students who want to take the Co-op stream.

 

Undergraduate Research Projects

I am looking for enthusiastic, self-motivated undergraduate students who have excellent analytical and programming skills, while being willing to accept challenges and engage in bioinformatics research problems in protein-protein interaction and its dynamics, analysis of transcriptomics data (RNA-seq) with applications to biomarker discovery. I also have some projects on computer security. Feel free to contact me for more details about these projects.

Knowledge of Perl, Python, Script language, HTML, PhP, Joomla and/or Matlab (or serious willingness to learn) is required. Willingness to learn machine learning techniques and fundamental concepts in bioinformatics or computer security, while improving their algorithm design and analysis skills is also a requirement. I normally have various interesting short-term and long-term projects in these fields. Feel free to contact me for more details or present your own ideas.