Coordinator: Dr. Muhammad Faizan Khan
The Research Concentration offers motivated BIOE students a structured path to conduct supervised research and complete an undergraduate thesis. Students work closely with a faculty advisor to design and execute a research project, analyze data, and defend a written thesis. This CX is ideal for students planning to pursue MSc/PhD or research-oriented careers.
• BS in Bioengineering students
• Junior level and above
• Department approval and a BIOE faculty advisor willing to supervise the project
• Good academic standing and demonstrated interest in research
• BIOE 4XX* (3 credits): A technical elective supporting the student’s research area.
• BIOE 4XX* (3 credits): A second technical elective aligned with the research topic.
• BIOE 494 Undergraduate Thesis I (3 credits): Independent research focusing on literature review, hypothesis definition, and proposal preparation.
• BIOE 496 Undergraduate Thesis II (3 credits): Continuation focusing on research execution, data analysis, writing, and oral defense.
*The student may also take a graduate course to fulfil this requirement. A petition needs to be submitted before registering graduate classes.
Coordinator: Dr. Tanzilur Rahman
Bioinformatics is an interdisciplinary concentration that integrates biology, computer science, statistics, and computational modeling to analyze and interpret complex biological data. It equips students with essential skills in genomics, bioinformatics analytics, machine learning, and modeling of biological systems.
• BIOE students who have completed their junior-level requirements
• Students from other majors who have completed all required prerequisites
• BIOE 483: Genomics (3-0-3): Genomes, transcriptomes, proteomes; genomes mapping, sequencing, annotation and identifying gene functions; eukaryotic, prokaryotes, virus genomes; accessing genome and DNA-protein interactions; recombination, transposition, mutations and DNA repair; genome evolution and comparative genomics.
• ICS 485: Machine Learning (3-0-3): Essential foundations of machine learning; Instance-based learning; supervised learning (linear regression, logistic regression, support vector machines, decision tree, ensemble learning, neural networks, and generative classifiers); unsupervised learning (clustering, EM, mixture models, dimensionality reduction); Applications of Machine learning to real world problems.
• BIOE 487: Bioinformatics (3-0-3): DNA and protein sequence alignment, sequence motifs and pattern discovery, computational phylogenomics and evolutionary inference, protein and RNA structures: prediction, alignment, classification, transcriptomics data analysis: normalization, clustering, gene regulatory networks, metabolic/signal transduction pathways, structural bioinformatics and drug discovery applications, Bioinformatic databases and archives with information retrieval, biomedical text
• BIOE 488: Computational Modeling of Biological Systems (3-0-3): Ordinary differential equations (ODEs), numerical simulation methods, and data-driven model analysis. Growth models, single-species and multi-species population dynamics, infectious disease transmission, enzyme kinetics and biochemical reaction networks, cancer initiation and progression, gene regulatory and signalling networks, cell migration, neural and vascular system dynamics, drug interaction models, and gene expression dynamics.
Under development. It is planned to be available starting from Fall 2027 or 2028
Under development. It is planned to be available starting from Fall 2027 or 2028