Bass Connections Awards Grants for Three Interdisciplinary Courses
May 4, 2017
Bass Connections has awarded course development grants to three groups of Duke faculty members whose pedagogical ideas will expand interdisciplinary curricular options for undergraduates as well as graduate and professional students.
In addition to more than 40 project teams each year, Bass Connections offers curricular pathways in each theme to complement students’ majors or programs of study. This spring an RFP invited faculty, departments or schools to organize new courses or modify existing ones that are multidisciplinary, open to students at different levels and/or ask questions of societal importance.
Introductory Machine Learning for Data Science
- Faculty: Kyle Bradbury (Energy Initiative and Electrical & Computer Engineering) and Leslie Collins (Electrical & Computer Engineering and Biomedical Engineering)
- Themes: Information, Society & Culture and Energy
In almost every field, there is a need to draw inference from or make decisions based on data. The goal of this course is to provide an introduction to machine learning that is approachable to students of diverse disciplines and empowers those students to become proficient in the foundational concepts and tools in order to work with interdisciplinary real-world data. Students will learn to structure a machine learning problem, determine which algorithmic tools are applicable to a given problem, apply those tools to diverse data examples, evaluate the performance of their solution and learn how to accurately interpret and communicate their results. This highly-applied introduction to machine learning will arm students with the basic skills they will need in practice.
Women’s Health and Technologies
- Faculty: Nimmi Ramanujam (Biomedical Engineering) and Rae Jean Proeschold-Bell (Duke Global Health Institute)
- Theme: Global Health
This course will give students an opportunity to learn about global poverty and how it disproportionately affects women. Working in teams, students will investigate how design thinking can be used to explore, understand and generate solutions to global challenges. The course will focus on the human element of human-centered design. By experiencing and reflecting on a collaborative design process, each student will develop a unique set of answers to difficult but meaningful questions. What is the role of engineering design in our attempts to solve global challenges? Why is it important to have multiple perspectives represented and what are the dangers of a design team with few viewpoints? Why is it important to partner with community members when you seek to solve a problem in that community? What are the challenges with working with community partners and what are strategies to overcome these challenges? How can design thinking be applied to problems that cannot be solved with engineering?
Applications of Genome Sciences and Medicine
- Faculty: Greg Wray (Biology and Center for Genomic and Computational Biology) and Susanne Haga (Medicine, Public Policy, Center for Genomic and Computational Biology, Center for Applied Genomics and Precision Medicine)
- Theme: Information, Society & Culture
The landmark sequencing of the human genome in 2003 heralded a new era in biomedical research. A key result has been the development of genomics-based tools to diagnose diseases, predict disease onset or recurrence, tailor treatment options and assess treatment response. However, translating these discoveries into actionable diagnostics and therapies remains a substantial challenge. This course aims to provide a comprehensive overview of genome science technologies, clinical applications and policy and ethical issues related to the conduct of genome sciences research and clinical implementation. The multidisciplinary course will offer students a 360-degree view of genome sciences from the perspectives of biology, computational biology, statistics, public policy and medicine. Students will gain firsthand experience in working with actual genome datasets to become familiar with data analysis for variant interpretation.