Skip to content Skip to navigation

Research Scientists, Consulting Professors, and Staff

Francisco de la Vega photo

Francisco M. De La VegaConsulting Professor (Genetics)

francisco.delavega at stanford dot edu

The availability of human genome sequences is enabling studies in disease and evolution not possible before, and could lead to a revolution in personalized genomics. Challenges include the massive amounts of data, the complex relationships among the types of relevant data, and the need to make the data accessible from different perspectives. The variation in the DNA sequence among the billions of separate copies of extant human genomes can occasionally be of medical significance because it can alter disease susceptibility and reactions to drugs and pathogens. Aspects of the effective collection, representation, and use of high throughput genomics data in the elucidation of the etiology of common disease have been a major theme of  Dr. De La Vega's work. He is currently interested in: the application of ultra-high throughput sequencing technologies in genetic epidemiology and population studies, aiming to identify the role of rare and structural variants in complex diseases; the study of genetic variation of populations of mixed ancestry and personal genome sequence analysis and annotation; and the development of ancestry deconvolution methods, panels of ancestry informative markers, and annotations of genetic variants of medical significance and their prevalence based on ethnic groups and ancestral origin.


Nilah Ioannidis headshot

Nilah Ioannidis, Research Engineer

nilah at stanford dot edu

As part of the Clinical Genomics project, Nilah is developing new methods to interpret the pathogenicity of rare genetic variants from whole exome and whole genome sequencing studies. She received her PhD in Biophysics from Harvard University while working in the Biological Engineering department at MIT, where she developed methods for analyzing intracellular particle trajectories using Bayesian inference and hidden Markov models. Prior to her PhD, she worked as research director at the Jain Foundation, a private foundation focused on the rare genetic disease of dysferlinopathy, and held internships at the National Academy of Sciences and the journal Science. She also has an MPhil in Chemistry from the University of Cambridge and BA in Biochemical Sciences from Harvard College.


Snehit Prabhu photo

Snehit PrabhuSenior Research Scientist

snehit at stanford dot edu

Snehit is a computer scientist and statistician by training with an abiding interest in quantitative and clinical genetics. As part of the Clinical Genomics (ClinGen) initiative, he works on several projects that are trying to bring rigor, standardization, and reproducibility to the burgeoning field of next-generation genetic testing. His areas of research include statistical methodologies for genetic tests, interpretation and actionability of test results, data sharing modalities, and lastly, regulatory and privacy frameworks surrounding all aspects of such testing. He received his PhD at Columbia University under the supervision of Itsik Pe’er, where he worked on a variety of problems like cost-efficient large scale sequencing, gene-gene interaction mapping and statistical inference techniques. 


Gen Wojcik headshot

Genevieve Wojcik, Senior Research Engineer

gwojcik at stanford dot edu

Gen is a genetic epidemiologist interested in human-pathogen co-evolution. Specifically, she is interested in how host-pathogen interactions have shaped human genetics and how it can inform better treatment for infectious diseases, as well as vaccine development. She received an MHS in Human Genetics/Genetic Epidemiology, followed by a PhD in Epidemiology from Johns Hopkins University Bloomberg School of Public Health. Her dissertation work focused on the evaluation of secondary methods for genome-wide association studies, as well as the genetics underlying the infant response to oral poliovirus vaccine. She joined the Bustamante lab in January 2014, where she is focusing on genetic epidemiology across diverse populations as part of a large genetics consortium while continuing to examine the consequences of selective pressures that pathogens exert upon human populations.