Dr. Alexander Ioannidis (PhD, MPhil) earned his Ph.D. from Stanford University in Computational and Mathematical Engineering, where he teaches machine learning and data science as an Adjunct Professor in the School of Engineering. He also has an M.S. in Mgmt. Sci. and Eng. (Optimization) from Stanford. Prior to Stanford, he worked in superconducting computing logic and quantum computing at Northrop Grumman. He graduated summa cum laude from Harvard University in Chemistry and Physics and earned an M.Phil in Computational Biology and Diploma in Greek from the University of Cambridge. As a current research fellow in the Stanford School of Medicine (Department of Biomedical Data Science), his work focuses on the design of algorithms and application of computational methods for problems in genomics, clinical data science, and precision health with a particular focus on underrepresented populations in Oceania and Latin America.

Professional Education

  • Doctor of Philosophy, Stanford University, CME-PHD (2018)
  • Master of Science, Stanford University, MGTSC-MS (2018)
  • Master of Philosophy, University of Cambridge, Computational Biology (2005)
  • Bachelor of Arts, Harvard University, Chemistry and Physics (2003)

2021-22 Courses

All Publications

  • Paths and timings of the peopling of Polynesia inferred from genomic networks. Nature Ioannidis, A. G., Blanco-Portillo, J., Sandoval, K., Hagelberg, E., Barberena-Jonas, C., Hill, A. V., Rodriguez-Rodriguez, J. E., Fox, K., Robson, K., Haoa-Cardinali, S., Quinto-Cortes, C. D., Miquel-Poblete, J. F., Auckland, K., Parks, T., Sofro, A. S., Avila-Arcos, M. C., Sockell, A., Homburger, J. R., Eng, C., Huntsman, S., Burchard, E. G., Gignoux, C. R., Verdugo, R. A., Moraga, M., Bustamante, C. D., Mentzer, A. J., Moreno-Estrada, A. 2021; 597 (7877): 522-526


    Polynesia was settled in a series of extraordinary voyages across an ocean spanning one third of the Earth1, but the sequences of islands settled remain unknown and their timings disputed. Currently, several centuries separate the dates suggested by different archaeological surveys2-4. Here, using genome-wide data frommerely 430 modern individuals from 21 key Pacific island populations and novel ancestry-specific computational analyses, we unravel the detailed genetic history of this vast, dispersed island network. Our reconstruction of the branching Polynesian migration sequence reveals a serial founder expansion, characterized by directional loss of variants, that originated in Samoa and spread first through the Cook Islands (Rarotonga), then to the Society (Totaiete ma) Islands (11th century), the western Austral (Tuha'a Pae) Islands and Tuamotu Archipelago (12th century), and finally to the widely separated, but genetically connected, megalithic statue-building cultures of the Marquesas (Te Henua 'Enana) Islands in the north, Raivavae in the south, and Easter Island (Rapa Nui), the easternmost of the Polynesian islands, settled in approximately AD 1200 via Mangareva.

    View details for DOI 10.1038/s41586-021-03902-8

    View details for PubMedID 34552258

  • Deconvoluting complex correlates of COVID19 severity with local ancestry inference and viral phylodynamics: Results of a multiomic pandemic tracking strategy bioRxiv Parikh, V. N., Ioannidis, A. G., et al 2021
  • High Resolution Ancestry Deconvolution for Next Generation Genomic Data bioRxiv Hilmarsson, H., Kumar, A. S., Rastogi, R., Bustamante, C. D., Mas Montserrat, D., Ioannidis, A. G. 2021
  • Native American gene flow into Polynesia predating Easter Island settlement. Nature Ioannidis, A. G., Blanco-Portillo, J., Sandoval, K., Hagelberg, E., Miquel-Poblete, J. F., Moreno-Mayar, J. V., Rodriguez-Rodriguez, J. E., Quinto-Cortes, C. D., Auckland, K., Parks, T., Robson, K., Hill, A. V., Avila-Arcos, M. C., Sockell, A., Homburger, J. R., Wojcik, G. L., Barnes, K. C., Herrera, L., Berrios, S., Acuna, M., Llop, E., Eng, C., Huntsman, S., Burchard, E. G., Gignoux, C. R., Cifuentes, L., Verdugo, R. A., Moraga, M., Mentzer, A. J., Bustamante, C. D., Moreno-Estrada, A. 2020


    The possibility of voyaging contact between prehistoric Polynesian and Native Americanpopulations has long intrigued researchers. Proponents have pointed to the existence of New World crops, such as the sweet potato and bottle gourd, in the Polynesian archaeological record, but nowhere else outside the pre-Columbian Americas1-6, while critics have argued that these botanical dispersals need not have been human mediated7. The Norwegian explorer Thor Heyerdahl controversially suggested that prehistoric South Americanpopulations had an important role in the settlement of east Polynesia and particularly of Easter Island (Rapa Nui)2. Several limited molecular genetic studies have reached opposing conclusions, and the possibility continues to be as hotly contested today as it was when first suggested8-12. Here we analyse genome-wide variation in individuals from islands across Polynesia for signs of Native American admixture, analysing 807 individuals from 17 island populations and 15 Pacific coast Native American groups. We find conclusive evidence for prehistoric contact of Polynesianindividuals with Native Americanindividuals (around AD 1200) contemporaneouswith the settlement of remote Oceania13-15. Our analyses suggest strongly that a single contact event occurred in eastern Polynesia, before the settlement of Rapa Nui, between Polynesianindividuals and a Native American group most closely related to the indigenous inhabitants of present-day Colombia.

    View details for DOI 10.1038/s41586-020-2487-2

    View details for PubMedID 32641827

  • Ultra-low-power superconductor logic JOURNAL OF APPLIED PHYSICS Herr, Q. P., Herr, A. Y., Oberg, O. T., Ioannidis, A. G. 2011; 109 (10)

    View details for DOI 10.1063/1.3585849

    View details for Web of Science ID 000292115900092

  • The genetic legacy of the Manila galleon trade in Mexico. Philosophical transactions of the Royal Society of London. Series B, Biological sciences Rodriguez-Rodriguez, J. E., Ioannidis, A. G., Medina-Munoz, S. G., Barberena-Jonas, C., Blanco-Portillo, J., Quinto-Cortes, C. D., Moreno-Estrada, A. 2022; 377 (1852): 20200419


    The population of Mexico has a considerable genetic substructure due to both its pre-Columbian diversity and due to genetic admixture from post-Columbian trans-oceanic migrations. The latter primarily originated in Europe and Africa, but also, to a lesser extent, in Asia. We analyze previously understudied genetic connections between Asia and Mexico to infer the timing and source of this genetic ancestry in Mexico. We identify the predominant origin within Southeast Asia-specifically western Indonesian and non-Negrito Filipino sources-and we date its arrival in Mexico to approximately 13 generations ago (1620 CE). This points to a genetic legacy from the seventeenth century Manila galleon trade between the colonial Spanish Philippines and the Pacific port of Acapulco. Indeed, within Mexico we observe the highest level of this trans-Pacific ancestry in Acapulco, located in the state of Guerrero. This colonial Spanish trade route from East Asia to Europe was centred on Mexico and appears in historical records, but its legacy has been largely ignored. Identities and stories were suppressed due to slavery, assimilation of the immigrants as 'Indios' and incomplete historical records. Here we characterize this understudied Mexican ancestry. This article is part of the theme issue 'Celebrating 50 years since Lewontin's apportionment of human diversity'.

    View details for DOI 10.1098/rstb.2020.0419

    View details for PubMedID 35430879

  • Opportunities and challenges for the use of common controls in sequencing studies. Nature reviews. Genetics Wojcik, G. L., Murphy, J., Edelson, J. L., Gignoux, C. R., Ioannidis, A. G., Manning, A., Rivas, M. A., Buyske, S., Hendricks, A. E. 2022


    Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.

    View details for DOI 10.1038/s41576-022-00487-4

    View details for PubMedID 35581355

  • Bayesian model comparison for rare-variant association studies. American journal of human genetics Venkataraman, G. R., DeBoever, C., Tanigawa, Y., Aguirre, M., Ioannidis, A. G., Mostafavi, H., Spencer, C. C., Poterba, T., Bustamante, C. D., Daly, M. J., Pirinen, M., Rivas, M. A. 2021


    Whole-genome sequencing studies applied to large populations or biobanks with extensive phenotyping raise new analytic challenges. The need to consider many variants at a locus or group of genes simultaneously and the potential to study many correlated phenotypes with shared genetic architecture provide opportunities for discovery not addressed by the traditional one variant, one phenotype association study. Here, we introduce a Bayesian model comparison approach called MRP (multiple rare variants and phenotypes) for rare-variant association studies that considers correlation, scale, and direction of genetic effects across a group of genetic variants, phenotypes, and studies, requiring only summary statistic data. We apply our method to exome sequencing data (n = 184,698) across 2,019 traits from the UK Biobank, aggregating signals in genes. MRP demonstrates an ability to recover signals such as associations between PCSK9 and LDL cholesterol levels. We additionally find MRP effective in conducting meta-analyses in exome data. Non-biomarker findings include associations between MC1R and red hair color and skin color, IL17RA and monocyte count, and IQGAP2 and mean platelet volume. Finally, we apply MRP in a multi-phenotype setting; after clustering the 35 biomarker phenotypes based on genetic correlation estimates, we find that joint analysis of these phenotypes results in substantial power gains for gene-trait associations, such as in TNFRSF13B in one of the clusters containing diabetes- and lipid-related traits. Overall, we show that the MRP model comparison approach improves upon useful features from widely used meta-analysis approaches for rare-variant association analyses and prioritizes protective modifiers of disease risk.

    View details for DOI 10.1016/j.ajhg.2021.11.005

    View details for PubMedID 34822764

  • Mapping the human genetic architecture of COVID-19. Nature COVID-19 Host Genetics Initiative 2021


    The genetic makeup of an individual contributes to susceptibility and response to viral infection. While environmental, clinical and social factors play a role in exposure to SARS-CoV-2 and COVID-19 disease severity1,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. We describe the results of three genome-wide association meta-analyses comprised of up to 49,562 COVID-19 patients from 46 studies across 19 countries. We reported 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3-7. They also represent potentially actionable mechanisms in response to infection. Mendelian Randomization analyses support a causal role for smoking and body mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19, with unprecedented speed, was made possible by the community of human genetic researchers coming together to prioritize sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.

    View details for DOI 10.1038/s41586-021-03767-x

    View details for PubMedID 34237774

  • Neural ADMIXTURE: rapid population clustering with autoencoders bioRxiv Dominguez Mantes, A., Mas Montserrat, D., Bustamante, C., Giró-i-Nieto, X., Ioannidis, A. G. 2021
  • Discovering prescription patterns in pediatric acute-onset neuropsychiatric syndrome patients. Journal of biomedical informatics Lopez Pineda, A., Pourshafeie, A., Ioannidis, A., McCloskey Leibold, C., Chan, A. L., Bustamante, C. D., Frankovich, J., Wojcik, G. L. 2020: 103664


    OBJECTIVE: Pediatric acute-onset neuropsychiatric syndrome (PANS) is a complex neuropsychiatric syndrome characterized by an abrupt onset of obsessive-compulsive symptoms and/or severe eating restrictions, along with at least two concomitant debilitating cognitive, behavioral, or neurological symptoms. A wide range of pharmacological interventions along with behavioral and environmental modifications, and psychotherapies have been adopted to treat symptoms and underlying etiologies. Our goal was to develop a data-driven approach to identify treatment patterns in this cohort.MATERIALS AND METHODS: In this cohort study, we extracted medical prescription histories from electronic health records. We developed a modified dynamic programming approach to perform global alignment of those medication histories. Our approach is unique since it considers time gaps in prescription patterns as part of the similarity strategy.RESULTS: This study included 43 consecutive new-onset pre-pubertal patients who had at least 3 clinic visits. Our algorithm identified six clusters with distinct medication usage history which may represent clinician's practice of treating PANS of different severities and etiologies i.e., two most severe groups requiring high dose intravenous steroids; two arthritic or inflammatory groups requiring prolonged nonsteroidal anti-inflammatory drug (NSAID); and two mild relapsing/remitting group treated with a short course of NSAID. The psychometric scores as outcomes in each cluster generally improved within the first two years.DISCUSSION: and conclusion Our algorithm shows potential to improve our knowledge of treatment patterns in the PANS cohort, while helping clinicians understand how patients respond to a combination of drugs.

    View details for DOI 10.1016/j.jbi.2020.103664

    View details for PubMedID 33359113

  • LAI-NET: LOCAL-ANCESTRY INFERENCE WITH NEURAL NETWORKS Montserrat, D., Bustamante, C., Ioannidis, A., IEEE IEEE. 2020: 1314–18
  • Class-Conditional VAE-GAN for Local-Ancestry Simulation MLCB Proceedings Mas Montserrat, D., Bustamante, C., Ioannidis, A. G. 2019
  • Reconstructing admixture and migration dynamics of post-contact Mexico Esteban Rodriguez-Rodriguez, J., Blanco-Portillo, J., Ioannidis, A., Moreno-Estrada, A. WILEY. 2018: 228
  • Integrated Power Divider for Superconducting Digital Circuits IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY Oberg, O. T., Herr, Q. P., Ioannidis, A. G., Herr, A. Y. 2011; 21 (3): 571–74
  • Digital circuits using self-shunted Nb/NbxSi1-x/Nb Josephson junctions APPLIED PHYSICS LETTERS Olaya, D., Dresselhaus, P. D., Benz, S. P., Herr, A., Herr, Q. P., Ioannidis, A. G., Miller, D. L., Kleinsasser, A. W. 2010; 96 (21)

    View details for DOI 10.1063/1.3432065

    View details for Web of Science ID 000278183200086