Stanford University has many formidable students and postdocs. It has been a tremendous privilege to work with them and be their mentor at least for a few years of their lives. Below, you can read more about their work.
Yosuke Tanigawa – worked on the genetics of multiple traits from the UK Biobank. His work led to the publication of manuscripts focused on biomarker genetics, glaucoma genetics, training polygenic risk scores across hundreds of traits from a population biobank, and decomposition of genetic effects in UK Biobank and across population biobanks (including Biobank Japan). He is now at MIT working with Prof. Manolis Kellis.
Jack W O’Sullivan grew up in his native Australia, where he completed medical school and clinically worked as a resident. He then pursed a PhD in computational epidemiology at the University of Oxford (UK), as a Clarendon Scholar. During his PhD, he also continued to work clinically at Oxford University Hospitals (John Radcliffe Hospital). After his PhD, he completed a NIH T32 Computational genetics and machine learning Postdoctoral Fellow with Euan Ashley at Stanford. After his postdoc, he completed internal medicine at Stanford, and fast-tracked into Cardiology also at Stanford on the Physician-Scientist pathway. His research focuses on genetics, and epidemiology/advanced statistics/machine learning. He is currently a senior postdoc building a research agenda using ML to decipher human genetic variation, how it influences disease susceptibility and can guide drug discovery.
Seth Sharp is a Larry L. Hillblom Postdoctoral Fellow co-mentored by Anna Gloyn. Seth is working on the application of polygenic risk scores for diabetes and metabolic disease to understand molecular and clinical disease heterogeneity. Additionally, he is conducting a genome-wide association study to identify polygenic drivers of insulin secretion and map variants to pathways.
Siamak Sorooshyari is currently a postdoc in the statistics department working with David Donoho on using machine learning algorithms in biology and neuroscience. Investigating methods to quantify and test for replicability in unsupervised learning techniques. By leveraging statistics we aim to formulate robust procedures that uncover structure in datasets with a quantifiable degree of confidence. This is particularly crucial for data derived from cellular biology, where inherent and experimental noise, along with artifacts, can complicate analysis. He holds a BS in Electrical Engineering and PhD in Integrative Biology.
Scott Oshiro is a postdoctoral associate working on sound as therapeutics. He recently received his PhD at the Center for Computer Research in Music and Acoustics (CCRMA) at Stanford University, where he researched the intersection between quantum computing, music, and culture.
Christopher DeBoever – extremely talented postdoc in my lab who worked on the genetics of protein-truncating variants, Global Biobank Engine, assessing digital phenotyping approaches in population biobanks, assessing protein-altering association in GPCR genes, and development of rare variant association methods. Chris is now Director of Genetics at Takeda. At Takeda, Chris led a team that collaborated with my lab to dissect the genetics of COVID19 infection and severity.
I’ve co-advised multiple students with my colleagues Prof. Rob Tibshirani and Prof. Trevor Hastie. Below are some students that graduated:
Ruilin Li – worked on a fast numerical optimization algorithm for whole genome sequencing data, an algorithms for single response time-to-event data from population biobanks, and multi-response time-to-event data from population biobanks. Ruilin is now at Citadel.
Junyang Qian – worked on fast lasso models for population-scale datasets like UK Biobank. Junyang is now at PDT Partners.
Johanne Marie Justesen – worked on assessing the genetics of disease progression. Johanne is now at Novo Nordisk.
Anna Cichonska – worked on multi-dimensional tensor analysis. Now at Harmonic Discovery.
Emily Flynn – was a PhD student that worked with my lab on sex-specific genetic effects across biomarkers – finding sex-specific genetic effects on testosterone levels. Emily is now a Senior Biological Data Scientist at UCSF.
Larissa Lauer – Stanford summer undergraduate student working on meta regression models across multiple phenotypes from population biobanks.
Visruth Kandali – Stanford summer undergraduate student working with large genomic datasets to better understand disease through meta-regression modeling.
Austin Yang – Stanford undergraduate student working on predicting small molecule activity using Kernel based regression methods and Generative AI by combining clinical trial data, CHEMBL small molecule data, and bioassay activity data. Austin has worked at Moderna and Amgen.
Matthew Aguirre – Stanford graduate student in the Biomedical Informatics program in the Department of Biomedical Data Science. He worked on Degas-risk, i.e. painting the genetic risk of individuals with components of genetic effects. He also led a study to assess the genetics of suicide attempt and ideation.
Oscar Aguilar – Stanford graduate student in Masters of Engineering program. His research focuses on multiomics dissection of disease risk, and development of a unified rare variant analysis model that incorporates constraint predictions, structural based pathogenicity predictions, and basic genetic code change predictions with applications to epilepsy.
Thomas Le Menestrel – a graduate student in ICME. He worked on multiomics model for diverse populations. Thomas worked on a computational chemistry project in the lab where he developed a Foundation Model for docking small molecules to experimentally validated and in silico proteins.
Elsa Bismuth – a graduate student focused on multiomics prediction of disease risk.
Guhan Venkataraman – a former graduate student in the lab. He worked on methods for rare variant analysis, analysis of exome sequencing data in Crohn’s disease, and analysis of population biobanks.
Oliver Bear Don’t Walk IV – a BMI student who worked with me on Clinical Note Tagger for tagging ICD codes from unstructured clinical notes. He introduced me to Pow Wow and since then I have been hooked.
Michael Wainberg – an extremely talented graduate student who is now faculty at University of Toronto. He worked on rigorously assessing transcriptome wide association studies. In addition, he worked on causal inference methods for obesity and type 2 diabetes.
Jessica Chen – a postdoctoral student in the lab who is now at Genentech. She worked on dissecting the genetics of asthma using new computational approaches.
David Amar – a postdoctoral student in the lab who is now at insitro. His work focused on causal inference methods, dissecting the contribution of exercise on human health, and other projects.
Daniel Mas Montserrat – a postdoctoral associate in the lab. Daniel’s research has focused on developing AI and ML algorithms based on neural networks for population genetics and precision medicine, with an emphasis on admixed and underrepresented populations. Some examples of developed techniques include Neural ADMIXTURE, G-Nomix, and LAI-Net. He was co-advised with Dr. Ioannidis and Prof. Bustamante. He is now the head of AI at Galatea Bio.
Alex Ioannidis – a postdoctoral associate in the lab who is now a faculty member at UC Santa Cruz. His work focused on the genetics dissection of COVID19, COVID19 genetics in admixed individuals, population history of Polynesian individuals, among others. He was co-advised with Prof. Carlos Bustamante.
I am working to build a new vision for the lab. I would love for you to join the lab. Please contact me by e-mailing me at mrivas at stanford dot edu. There are many exciting projects we are embarking on.
