Students will learn theoretical and practical foundations of computational methods for analysis of experimental data from various biological data types. Course will cover algorithms, statistical and computational methods, and application areas in computational biology, and will include both classical methods as well as recent advances. Prior coursework/experience in linear algebra, UNIX, and R & Python programming required.

Students will learn theoretical and practical foundations of computational methods for analysis of experimental data from various biological data types. Course will cover algorithms, statistical and computational methods, and application areas in computational biology, and will include both classical methods as well as recent advances. Prior coursework/experience in linear algebra, UNIX, and R & Python programming required.

Networks provide a unifying framework to study the structure hidden within complex data. This graduate-level course focuses on the fundamental concepts and statistics as well as recent advancements and applications of network science. Topics include: graph theory, structural paradoxes, measures and algorithms for quantifying importance, community detection, network inference, recommendation systems, and link prediction.

Where do our educational practices come from? This seminar invites graduate students to explore theories and philosophies of education in order to guide their own professional development and practices as educators. We will examine topics such as psychological and social development, human rights, social justice, and civic engagement in order to determine what it means to be effective educators in 21st century institutions of higher education.

Students will learn algorithms and applications of computational methods for analysis of experimental data from genome, epigenome, and transcriptome sequencing experiments. The course will cover various biological data types (whole genome sequencing, ChIP-seq, ATAC-seq, RNA-seq, and DNA methylation profiling/bisulfite sequencing), algorithms, statistical and computational methods, and application areas in genomics and systems biology. 

This course will cover fundamental topics in statistical genetics with a focus on concepts and methods critical to a concrete understanding of the application of statistical genetics to public health genomics. Major topics covered in this course include modes of genetic inheritance, heritability analysis, linkage and association mapping, integrative analysis leveraging molecular 'omics' data, and genetic risk prediction modeling.

This course provides the opportunity to offer a new topic in the subject area of academic, professional, and creative writing. For more details on this class, please visit the department website at http://www.engl.virginia.edu/courses.

This course will use a student-centered approach to analyze the benefits of experiential learning for PhD students. Through completing course assignments and participating in an internship, students will gain professional experience, obtain knowledge of a career field, gain access to professional networks, and develop career-specific skills to increase their career preparedness.

This course introduces students to the underlying histories and theoretical dimensions of urban design as a creative spatial practice. By exploring a wide range of urban interventions at multiple scales, the course contextualizes contemporary design practice within the social, environmental, and political forces acting on the urban environment.

While urbanization processes are expected to increase until 2050, shrinkage and growth are occurring simultaneously asking for new approaches to urban regeneration addressing social justice and sustainability. Discussions, lectures, readings, and workshops combined with a design assignment will allow students to engage with urban design strategies and emerging methodologies ranging from regional frameworks to tactical urbanism.