Print Settings

BIOS 206: Matrix Methods for Dynamic Models and Data Analysis (BIO 329)

Types of matrices in dynamic & stochastic models, covariances, rectangular data, networks. Spectral theorem, asymptotics, stability theory, Nonnegative matrices, ergodicity, Markov chains. Hermitian, covariance, SVD. Perturbation theory. Random matrix products, Lyapunov exponents. Open to Ph.D. students in Biology. Prerequisites: Calculus (AP level) required. Some knowledge of linear algebra, R, preferred.
Terms: Win | Units: 1
Instructors: ; Tuljapurkar, S. (PI)

BIOS 231: Public Speaking Bootcamp: How to Give a Stronger Presentation

Everyone has fears presenting in front of a crowd. But with practice, self-awareness and preparation you can put those fears aside and make a real impact with your message. Utilizing professional theater practices and tricks, this course is a deep dive into what makes a presentation work. Get a chance to explore your own presentation style and address your questions and challenges with public speaking in a safe and fun space. The course is taught by Michileen Marie Oberst, a Professional director and actor in the Bay Area whose background includes teaching at the Tony Award winning TheatreWorks Silicon Valley.
Terms: Win | Units: 1 | Repeatable 2 times (up to 2 units total)

BIOS 266: Mini Proposal Bootcamp

In an intensive 1-day format, students learn the fundamentals for writing competitive fellowships, i.e. NIH NRSA fellowships (F30, F31, F32). Topics include developing specific aims; outlining research and career development plans; and using the review criteria to inform writing. Students develop early drafts of the 1-page specific aims, NIH biosketch, and training plan, and receive feedback from instructor. Students are expected to be in the early stages of writing a fellowship proposal.
Terms: Win | Units: 1

BIOS 274: Introductory Python Programming for Genomics

An inherent part of genomics research is the creation and then analysis of large quantities of data. A variety of useful tools are available for data analysis; however, research often requires the skill to create software scripts to extend the analysis. You will learn the basics of the Python programming language. Lectures will foster developing the basics through the process of writing code. Discussion sections will build on the skills from lectures by applying them to complete assigned problem sets. Problem sets are designed to learn good coding style, logic, and the use Python libraries. No programming experience is required.
Terms: Win | Units: 3
Instructors: ; Cherry, J. (PI)

BIOS 287: Proteostatis: guarding the proteome in health and disease

The control of cellular protein homeostasis, also called Proteostasis, is emerging as the central cellular process controlling the stability, function and quality control of the proteome and central to our understanding of a vast range of diseases. The proteostasis machinery maintains the function of destabilized and mutant proteins; assists the degradation of damaged and aggregated proteins and monitors the health of the proteome, adjusting it in response to environmental or metabolic stresses. This class will introduce students to the exciting cutting edge discoveries in this field, and will relate them to medical and biotechnology applications, as well as how a better understanding of proteostasis can be leveraged to understand fundamental biological processes, such as evolution and aging and to ameliorate a wide range of diseases. Given the increasingly close links between aging, protein misfolding, and neurodegenerative disease, understanding proteostasis networksis of critical fundamental and practical importance. These insights are particularly relevant in view of the increased prevalence of late-onset neurodegenerative aggregation diseases caused by an increasingly elderly population.
Terms: Aut, Win | Units: 3

BIOS 289: Preparation & Practice: Finance of Biotechnology

Tailored lectures and case studies lead to a practical final project. Leaders from local firms and companies will help you gain insight into the biotechnology industry, the skills and experiences necessary to succeed, and the various roles and responsibilities within the industry. Coursework is divided into 4 sections: Introductory Material: The first segment consists of two lectures and introduces the biotechnology company life cycle along with introductory concepts in finance. Venture Capital and Private Equity: The second segment consists of three lectures devoted to venture capital finance and private equity where students will learn the basic mechanics of raising capital. nPublic Finance: The third segment consists of the interpretation of financial statements, construction of company forecasts, and evaluating business value from such projections. Final Project: The final lecture will conclude with student presentations on their final projects.
Terms: Win | Units: 1
Instructors: ; Eberle, S. (PI)
© Stanford University | Terms of Use | Copyright Complaints