Prospective Students

I currently have opening positions for undergraduate, Master's and PhD students. Partial financial support is available from the School of Computer Science and the Faculty of Science, in terms of scholarships and teaching assistatnships. Research assistatnships are currently available for outstanding PhD students only. Prospective students at all levels are encouraged to contact me about my short and long-term research projects in shallow and deep machine learning at the fundamental level and with applications to protein-protein interaction and dynamics, transcriptomics, microRNA prediction, integrative genome-wide analysis, and identification of cancer biomarkers.

The main topics for theses and projects are in foundations of shallow and deep machine learning and applications in genomics, interactomics, and transcriptomics, mostly in cancer biomarkers. My projects involve mostly finding meaningful biomarkers in prostate cancer, breast cancer and bladder cancer. We mainly focus on stages of progression, location of the tumor, survival/response to therapies, and others. The main tasks include classification, analysis, feature extraction and selection, pattern discovery, network/pathway analysis and visualization.

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, as well as Calmodulin-binding proteins. Problems in transcriptomics involve analysis of next generation sequencing data, including ChIP-seq and RNA-seq data for discovering biomarkers in different types of cancer, and the role of alternative splicing, protein interactions and pathways.

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 the foundations of machine learning, and applications in interactomics and transcriptomics data analysis, including ChIP-seq, RNA-seq data analysis, big genomic data analysis, protein-protein interaction, dynamics of proteins, and Calmodulin-binding proteins, among others. Prospective students must demonstrate independence and creativity in conducting research. Knowledge of Python, Matlab and/or R, and machine learning tools like Scikit-learn/TensorFlow is desirable. Knowledge of foundations of machine learning is a requirement, while bioinformatics is optional.

Notes: Please consider the following guidelines about funding. Note that these numbers are given as a guideline and for reference purposes only, and may change over time and/or be outdated by the time you read this page. Please consider the following notes before contacting me for a position.

Master's Students

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

Deep knowledge of Python, R and/or Matlab, and machine learning tools like Scikit-learn/TensorFlow (or serious willingness to learn) is required. Knowledge or willingness to learn machine learning/bioinformatics techniques is a requirement.

Notes: At present, positions for Master's are not open for Master's students who want to pursue the Co-op stream. Currently, only exceptional prospective students are being considered at the Master's level.

Undergraduate Research Projects

I am looking for enthusiastic, self-motivated undergraduate students who possess excellent analytical and programming skills, while being willing to accept challenges and engage in machine learning research problems in data analytics for genomics, proteomics and interactomics data, with applications to biomarker discovery in cancer. Feel free to contact me for more details about these projects.

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