All InQuBATE trainees will be required to take the following courses where ‘Year 1’ denotes their year of matriculation into their PhD and Year 2 denotes their 2nd year (coincident with the first year of trainee support):
- Foundations of Quantitative Biosciences, Year 1, Fall
- Machine Learning in Biosciences, Year 1, Spring
- InQuBATE Project Laboratory, Year 2, Fall
- Advances in InQuBATE Research, Year 2, Spring
- Foundations of Quantitative Biosciences (developed by PI Weitz): This course is organized around key advances in the biosciences across scales from cells to ecosystems in which the advances depend critically on quantitative methods and logic. Each week, students learn methods for developing and analyzing quantitative models, logic for how to reason given uncertainty in the biosciences, and computational skills of stochastic and dynamic modeling at the interface between quantitative reasoning and biological data. The course spans cell/molecular scales, organismal scales, and population scales.
- Machine Learning in Biosciences (developed by PI Qiu): This Machine Learning course provides an introduction to the basic principles and techniques of machine learning, biostatistics, and their applications in biological data analysis drawn from problems across the scope of research areas relevant to the InQuBATE program. Machine learning methods taught in this course include supervised learning, unsupervised learning, dimension reduction, and visualization.
- InQuBATE Project Laboratory (first offering planned for Fall 2022): This new team-science and team-taught course will be initiated in Fall 2022, enabled by T32 support. The objective of the course is to act as a bridge between core curricula and thesis development of InQuBATE trainees. Students in this course will work in groups of two or three to identify problem areas of interest around a common disciplinary theme, conduct a literature review, and propose a term-length independent research project involving the analysis of high-dimensional biological datasets.
- Advances in InQuBATE Research (first offering planned for Spring 2023): This new seminar class will be initiated in Spring 2023, enabled by T32, and support will center on weekly discussions of advanced research methods in the disciplinary expertise of training program faculty. These research seminars will also provide an opportunity to facilitate new collaborations, identify cross-disciplinary mentors, and will highlight ongoing collaborations within the program as examples of integrative and quantitative research in practice.
We will offer a series of short-form Statistics Bootcamps during the Summer term, (check back here for more information). These boot camps will be offered for course credit and serve to ensure that trainees from life science backgrounds have rigorous and relevant statistics training, while trainees from data science backgrounds have the opportunity to combine advanced statistical methods in the context of biological data. The course format will include the equivalent contact time as a long-form course, albeit in mini-mesters in the first two weeks of summer. The objective will be to train students in foundational and advanced topic areas that are critical to harness large-scale biological datasets. Examples of planned offerings include:
- Introductory: ‘Model-based statistics’; ‘Intro to regression’; ‘Introduction to neural networks’
- Intermediate: ‘Statistical learning’; ‘Forecasting’; ‘Image analysis’; ‘Clustering methods in biology’
- Advanced: ‘Signal reconstruction’; ‘Matrix factorization’; ‘Fundamentals of deep learning’
All bootcamps will involve hands-on implementation including data and code, and will be cross-listed across participating Colleges, ensuring long-term sustainability and participation by both InQuBATE trainees and the broader community. Training materials, associated code, and data from bootcamps will be released via the InQuBATE software/data repository.