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Bioinformatics and Genomics: Computational and experimental methods in bioinformatics and genomics, and how these methods provide insights into protein structure and function, molecular evolution, biological diversity, cell biology and human disease. Topics include database searching, multiple sequence alignment, molecular phylogeny, microarrays, proteomics and pharmacogenomics.

Quantitative Optical Imaging:��Explores the fundamentals of optical imaging in biology. Covered topics include an introduction to optics and microscopes, fluorescence microscopy and image analysis. MATLAB will be taught throughout the course and used for image processing.

Prepares biological and biomedical students to integrate predictive models into their research. Covers the underlying technologies needed to understand how predictions are made, along with a framework for confronting the ethical questions they pose (including data sourcing, representation, and costs). Focuses on quantifying when and to what extent we can trust a model's output and how that trust defines its role in scientific discovery, without eroding rigor.

Training in handling issues such as conflicts of interest, mentor/mentee relationships, peer review, research misconduct and authorship and publication. Reviewing policies regarding human and animal subjects, safe laboratory practices. Instruction on conducting quality research including collaborative research, the scientist as a responsible member of society, ethical issues, rigor and reproducibility which will include effective design, execution, analysis, interpretation and communication.