Talks and teaching materials

Selected talks and posters

Teaching materials

PowerPoint slides. Free to use with credit. Please email me for errors, suggestions, or missing credits.


Intended audience: Students in the biomedical sciences.
Philosophy: The material emphasizes intuition and makes a special effort to provide motivation and examples. Yet derivations are also provided where possible and the material is mathematically rigorous. Particular emphasis is placed on probability theory and its applications.
Prerequisites: High-school math.
When/where was it used: The material was collected from five classes that were taught between 2017-2020 at the Hebrew University faculty of medicine (statistics for medical students, research methods for medical students, statistics for public health, statistics for biomedical sciences, advanced statistics for biomedical sciences).
Recommended use: The materials in part I are appropriate for a first class in statistics (usually first year medical/life-sciences students). The material in part II can be taught in the first class if time permits, or in a more advanced undergraduate class. The advanced topics cover more modern material, and are appropriate for advanced undergraduates or starting graduate students.

Basic statistics part I (Hebrew)

Introduction, Descriptive statistics, Correlation, Regression, Introduction to probability theory, Conditional probability, Random variables, Normal variable, Estimation 1, Estimation 2, Hypothesis testing, Discussion.

Basic statistics part II (Hebrew)

Review of hypothesis testing and paired samples, Inference errors and statistical power, Beyond normality: detecting non-normality, transformations, non-parametric tests, and permutation tests, Multiple testing, Maximum likelihood estimation, Bayesian inference, and the likelihood ratio test, Two categorical variables, The chi-squared test, Survival analysis

Topics in advanced statistics (English)

Generating random numbers, Resampling (bootstrap), Introduction to machine learning, Logistic regression, Neural networks, Unsupervised learning (clustering and dimension reduction).

Genomics (English)

Intended audience: biomedical students – advanced undergraduates or graduate students.
Prerequisites: Basic genetics, basic statistics.
When/where was it used: Most of the material was taught in 2018 and 2019 in a class on personal genomics at the Hebrew University faculty of medicine. Some material was not taught in class.
What is included: The class is an arbitrary collection of topics in genomics, applied genetic analysis, genetic genealogy, population genetics, and demographic inference. The depth of coverage can vary substantially between subjects. When possible, the most recent relevant research was described.

Introduction to genomics: basic genetics, the components of the genome, genetic variation, mutation and recombination
Population genetics: Hardy-Weinberg equilibrium, the Wright-Fisher model, genetic drift and inbreeding, the coalescent, estimators of the scaled mutation rate, estimating population size changes
Recombination and linkage disequilibrium: demographic inference with haplotypes, measures of LD, crossover interference, the coalescent with recombination
Selection: basic theory, negative selection, negative selection, inbreeding depression, detecting positive selection, selection in response to dietary changes, selection against pathogens, selection in extreme environments, adaptive introgression
Genotyping methods: Sanger, whole-genome and whole-exome sequencing, variant calling, microarrays, QC
Phasing and imputation: basic principles of statistical phasing, trio and experimental phasing, basic ideas of imputation, hidden Markov models, the Li and Stephens model
Genomic projects and new technologies: major sequencing and genotyping projects, national and global genetic surveys, long-read sequencing, nanopore sequencing, ancient DNA
Ancestry inference: PCA, ADMIXTURE, local ancestry inference, estimating admixture time, real-world examples
Relatedness inference: kinship and IBD coefficients across relationships, Plink’s method for estimating kinship, detecting relatives with IBD sharing, genealogical vs genetic ancestors/relatives, the collapse of genealogical ancestors back in time, relatives in founder populations