Cornell University Registrar

This class is designed to inspire students in the life sciences to see the power of computational biology in advancing the research frontier. In addition to providing students with foundational concepts in modern biology, the course will introduce core principles in computer science, mathematics, and statistics, including AI and machine learning. The course will also teach many fundamental skills in manipulating large data sets, including genome sequences, functional genomic data, protein structures, etc. The course will consist of two lectures and one practical session each week. In the practical's, students will learn the use of many of the latest software tools and will develop some basic programming skills. Students will be able to explore their own interests in greater depth in a term project.

Life is dynamic and ever-changing. Because of this, a central tool used to study living systems are dynamic models. This course provides an introductory survey of the development, computer implementation, and applications of dynamic models in biology, as well as statistical and machine learning methods for building such models from biological data. Case-study format covering broad range of biological applications including gene regulation, neurobiology, physiology, behavior, epidemiology and ecology. Students learn mathematical methods for interpreting and building biological systems models, as well as computational methods for simulating models on the computer using a scripting and graphics environment.

This course integrates technical approaches to biodiversity conservation, with a focus on biological analysis of species facing extinction risk. Students will learn quantitative tools for analyzing variation at genetic, population, and landscape levels. The curriculum covers stage-structured population dynamics, predation, gene flow, inbreeding, extinction processes, and harvesting effects. Analytical methods include population-projection models, perturbation analysis, metapopulation models, population viability analysis, and various genetic diversity assessments. Special attention is given to evaluating extinction risk in data-deficient species such as insects. By developing these quantitative skills, students will critically evaluate assumptions underlying conservation plans and assessments, including IUCN criteria and endangered species classification. The course is suitable for all biology and conservation-related majors.

This course will provide a rigorous treatment of computational statistics and machine learning methods used to analyze big biological data types. Analysis methods covered will include: generalized linear models, support vector machines, regularized linear models, kernel methods, random forests, neural networks, large language models. While the course will be focused on analysis methods and connections among methods, applications making use of specific big biological data types will be covered. An understanding of method limitations will be prioritized, as well as how to critically assess when a desired conclusion can be justified. Methods will be implemented in the computer lab in Python, where some previous exposure to programming will be assumed.

A biomedical data science course using Python and available bioinformatics tools and techniques for the analysis of molecular biological data, including biosequences, microarrays, and networks. This course emphasizes practical skills rather than theory. Topics include advanced Python programming, R and Bioconductor, sequence alignment, MySQL database (DBI), web programming and services (CGI), genomics and proteomics data mining and analysis, machine learning, and methods for inferring and analyzing regulatory, protein-protein interaction, and metabolite networks.

Population genetics studies the genetic composition of biological populations and how allele frequencies change over time and space. It serves as the theoretical foundation for understanding evolution. This course offers a comprehensive introduction to the core concepts and methods of population genetics, with a focus on linking observed patterns of genetic variation to the underlying evolutionary processes that generate them. Throughout the course, we emphasize the interplay between analytical theory, computer simulations, and the analysis of genetic data from both natural and experimental populations. We will also explore current efforts to connect genotype to phenotype and, ultimately, to fitness. Case studies will include the evolution of drug resistance, genetic ancestry inference, experimental evolution, and the genetic structure and demographic history of human populations.

A rigorous treatment of analysis techniques used to understand complex genetic systems. This course covers both the fundamentals and advances in statistical methodology used to analyze disease and agriculturally relevant and evolutionarily important phenotypes. Topics include mapping quantitative trait loci (QTLs), application of microarray and related genomic data to gene mapping, and evolutionary quantitative genetics. Analysis techniques include association mapping, interval mapping, and analysis of pedigrees for both single and multiple QTL models. Application of classical inference and Bayesian analysis approaches is covered and there is an emphasis on computational methods.

Computational methods for analyzing genetic and genomic data. Topics include sequence alignment, hidden Markov Models for discovering sequence features, motif finding using Gibbs sampling, phylogenetic tree reconstruction, inferring haplotypes, and local and global ancestry inference. Prior knowledge of biology is not necessary to complete this course.Grad students must do a final project that involves original research and that in most circumstances will involve programming and real data.Undergrads will not need to do research but will do a final project that involves a sizeable amount of programming, comprehensive literature review of a topic, or similar.

This course covers the latest development and cutting-edge research topics in population genetics, aiming to enable students to perform research in population genetics. The first part will cover coalescent theory and inference involving complex demography. The second part will discuss natural selection and methods for inferring selection. We will allude to the complexity of demographic history and natural selection and their importance in explaining genomic patterns. The third part will introduce new data types and the challenges and opportunities with these data. We will dive into genotype likelihood and will emphasize the importance of simulation in population genetics. The course will be mostly delivered through lecturing, each interspersed with short conversations about reading assignments. Coursework involves reading literature, solving problem sets, and a course project.

This course integrates technical approaches to biodiversity conservation, with a focus on biological analysis of species facing extinction risk. Students will learn quantitative tools for analyzing variation at genetic, population, and landscape levels. The curriculum covers stage-structured population dynamics, predation, gene flow, inbreeding, extinction processes, and harvesting effects. Analytical methods include population-projection models, perturbation analysis, metapopulation models, population viability analysis, and various genetic diversity assessments. Special attention is given to evaluating extinction risk in data-deficient species such as insects. By developing these quantitative skills, students will critically evaluate assumptions underlying conservation plans and assessments, including IUCN criteria and endangered species classification. The course is suitable for all biology and conservation-related majors.

This class is designed to inspire students in the life sciences to see the power of computational biology in advancing the research frontier. In addition to providing students with foundational concepts in modern biology, the course will introduce core principles in computer science, mathematics, and statistics, including AI and machine learning. The course will also teach many fundamental skills in manipulating large data sets, including genome sequences, functional genomic data, protein structures, etc. The course will consist of two lectures and one practical session each week. In the practicals, students will learn the use of many of the latest software tools and will develop some basic programming skills. Students will be able to explore their own interests in greater depth in a term project.

This course will provide a rigorous treatment of computational statistics and machine learning methods used to analyze big biological data types. Analysis methods covered will include: generalized linear models, support vector machines, regularized linear models, kernel methods, random forests, neural networks, large language models. While the course will be focused on analysis methods and connections among methods, applications making use of specific big biological data types will be covered. An understanding of method limitations will be prioritized, as well as how to critically assess when a desired conclusion can be justified. Methods will be implemented in the computer lab in Python, where some previous exposure to programming will be assumed.

A biomedical data science course using Python and available bioinformatics tools and techniques for the analysis of molecular biological data, including biosequences, microarrays, and networks. This course emphasizes practical skills rather than theory. Topics include advanced Python programming, R and Bioconductor, sequence alignment, MySQL database (DBI), web programming and services (CGI), genomics and proteomics data mining and analysis, machine learning, and methods for inferring and analyzing regulatory, protein-protein interaction, and metabolite networks.

Life is dynamic and ever-changing. Because of this, a central tool used to study living systems are dynamic models. This course provides an introductory survey of the development, computer implementation, and applications of dynamic models in biology, as well as statistical and machine learning methods for building such models from biological data. Case-study format covering broad range of biological applications including gene regulation, neurobiology, physiology, behavior, epidemiology and ecology. Students learn mathematical methods for interpreting and building biological systems models, as well as computational methods for simulating models on the computer using a scripting and graphics environment.

Population genetics studies the genetic composition of biological populations and how allele frequencies change over time and space. It serves as the theoretical foundation for understanding evolution. This course offers a comprehensive introduction to the core concepts and methods of population genetics, with a focus on linking observed patterns of genetic variation to the underlying evolutionary processes that generate them. Throughout the course, we emphasize the interplay between analytical theory, computer simulations, and the analysis of genetic data from both natural and experimental populations. We will also explore current efforts to connect genotype to phenotype and, ultimately, to fitness. Case studies will include the evolution of drug resistance, genetic ancestry inference, experimental evolution, and the genetic structure and demographic history of human populations.

A rigorous treatment of analysis techniques used to understand complex genetic systems. This course covers both the fundamentals and advances in statistical methodology used to analyze disease and agriculturally relevant and evolutionarily important phenotypes. Topics include mapping quantitative trait loci (QTLs), application of microarray and related genomic data to gene mapping, and evolutionary quantitative genetics. Analysis techniques include association mapping, interval mapping, and analysis of pedigrees for both single and multiple QTL models. Application of classical inference and Bayesian analysis approaches is covered and there is an emphasis on computational methods.

Computational methods for analyzing genetic and genomic data. Topics include sequence alignment, hidden Markov Models for discovering sequence features, motif finding using Gibbs sampling, phylogenetic tree reconstruction, inferring haplotypes, and local and global ancestry inference. Prior knowledge of biology is not necessary to complete this course.Grad students must do a final project that involves original research and that in most circumstances will involve programming and real data.

Graduate seminar on current topics in population genetics. Readings are chosen primarily from current scientific literature. Participation in discussion and presentation of at least one paper required for course credit.

This course covers the latest development and cutting-edge research topics in population genetics, aiming to enable students to perform research in population genetics. The first part will cover coalescent theory and inference involving complex demography. The second part will discuss natural selection and methods for inferring selection. We will allude to the complexity of demographic history and natural selection and their importance in explaining genomic patterns. The third part will introduce new data types and the challenges and opportunities with these data. We will dive into genotype likelihood and will emphasize the importance of simulation in population genetics. The course will be mostly delivered through lecturing, each interspersed with short conversations about reading assignments. Coursework involves reading literature, solving problem sets, and a course project.

Weekly seminar series on recent advances in computational genomics. A selection of the latest papers in the field are read and discussed. Methods are stressed, but biological results and their significance are also addressed.

Weekly seminar series on recent advances in quantitative genomics. A selection of the latest papers in the field is read and discussed. Methods are stressed, but biological results and their significance are also addressed.

Graduate seminar on methods for building models of biological systems using data, with an emphasis on recent methods including machine learning tools. Students will read and discuss recent literature in this area and, through group discussions, develop strategies for applying methods within their own research domains. Participation in discussion and presentation of at least one paper required for course credit.

Weekly seminar series on recent advances in statistical genetics. A selection of the latest papers in the field are read and discussed. Methods are stressed, but biological results and their significance are also addressed.

This class is aimed at students interested in designing experiments in evolutionary genomics. Evolutionary biology is at an exciting moment where “any organism…any genome” is possible. However, the pace of sequencing technology and computational methods development is rapid and before carrying out a research project, it is critical to use the appropriate methods for the focal research question. Specific topics will include laboratory methods, sample selection and collection, and computational approaches to data analysis. Students will read and discuss recent literature where genomics have been applied to fundamental questions in ecology, evolution, and conservation and, through discussions, develop strategies for applying these tools to their own research domains. Students are expected to present at least one paper and participate in course discussion for class credit.

This class will help graduate students develop skills for disseminating their research in computational biology. This will include five topics. First, students will strengthen their writing skills, with a particular emphasis on writing compelling scientific proposals in the field of computational biology, which is a requirement for the A-exam. Second, students will learn how to design effective figures to communicate findings. Third, students will learn how to give scientific presentations, which Computational Biology Ph.D. students are required to do annually. Fourth, students will learn techniques for making and presenting posters. Fifth, students will develop strategies for communicating their research to non-specialists, including the public and stakeholders who might apply their research.