I teach “Quantitative Methods in Human Genetics” for undergraduates every Spring semester.  You can access the syllabus for this 28 lecture class here.

I am writing a text book for this class that integrates instruction in statistics, genetics, and R programming.  To make suggestions for this book clone the git repo and issue a pull request.

About this course: Deciphering the information encoded in the human genome is one of the greatest (and most exciting) challenges of the 21st century.  This course will provide an introduction to studying and interpreting the human genome with a focus on the statistical methods required for its study.  Fundamental concepts in human genetics will be introduced including inheritance of mendelian disease, population genetics, multifactorial disease and functional genomics.  Accompanying each topic will be an introduction to the statistical concepts and tools that are required to study inheritance, genes and gene function.  These include probability, hypothesis testing, ANOVA, regression, correlation and likelihood.  Hands on experience will be provided through weekly assignments using the statistical programming language, R.  Prior experience with statistics and genetics is not required.

Other Undergraduate level teaching:

Microbiology and Microbial Genomics Guest lecture: “Microbial metabolism”

Molecular and Cell Biology I  Guest lectures: “Sequencing the Human Genome I” and “Sequencing the Human Genome II”


I co-direct the Quantitative Biological Systems Training (QBIST) Program with Christine Vogel.

I teach Applied Genomics for graduate students  Manny Katari. You can access the class syllabus here.

We use many of the NYU learn resources for this course, which are here.

About this course: This course provides a comprehensive introduction to the analysis of next generation DNA sequence (NGS) data. Through a combination of lectures, hands-on computational training, discussions of scientific papers, and assignments using real data, students will learn the foundations of analytical methods, the computational skills to implement those methods, and the reasoning skills to critically assess the primary literature in genomics. The course will cover all commonly used NGS methods including genome sequence analysis, gene expression analysis and protein-nucleic acid interactions. To gain practical expertise in executing bioinformatic analyses, students will undertake a series of assignments using real data. Students will also complete an individual project that integrates skills and concepts covered during the class and that is tailored to meet their background and training.

I have also taught “Current Topics in Biology II: The World of RNA” for graduate students. You can access the class syllabus here.

This course aims to provide students with the skills to critically read and evaluate primary literature articles in a small class setting. The course will guide students through papers by starting with a brief overview of the weekly topic and an introduction to the terminology used. In addition, it provides students with an in-depth look into a current area of biology including recent discoveries relevant to human health. The course is designed to improve the ability of students to critically evaluate scientific discoveries and ultimately to design experiments of their own.

Other Graduate level teaching:

Biocore I Genomics and systems biology I: methods.

Biocore II Paper-based discussion.

Biocore III Student paper assessment.

Genome Biology eQTL mapping

Principles of Evolution Experimental evolution

Systems Biology Towards quantitative reverse genetics”

Additional Resources

My lab uses Datacamp to enhance our computational skills.