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 applying computational methods to problems in genomics, medical data science, and population genetics.
I work on novel algorithm design (particularly ancestry related) for several large-scale genomic studies that aim at understanding genetic causes of disease.
I also focus on projects at the intersection of history and population genetics, including work with native communities. As the grandson of Cappadocian refugees expelled from their native land, I try to engage with the complex sentiments of displaced indigenous peoples in these projects. Pain over the disruption of community heritage and over dispossession from traditional sites often remains raw. If engagement with descendant communities is lacking, research into our past can often feel like a continuation, even a legitimation, of dispossession. Combined alongside a dialogue with native communities, however, genetics can play a small role in helping to reclaim ancestral stories and dispersed ancestral connections. I hope our work in this area plays a constructive role in that process.
As written by the poet Rumi in the language of the Cappadocians (Rûm),
پیمی تیِ پَاثیِسْ پیمی تی خاسِس
“Tell me what happened to you, tell me what you have lost.”
[Rumi; Konya ms 67; translit. πε με τι έπαθες, πε με τι έχασες]
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)
Paths and timings of the peopling of Polynesia inferred from genomic networks.
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 2021
- High Resolution Ancestry Deconvolution for Next Generation Genomic Data bioRxiv 2021
Native American gene flow into Polynesia predating Easter Island settlement.
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 2011; 109 (10)
Mapping the human genetic architecture of COVID-19.
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
Reconstructing ancient migrations from modern genomes across Latin America and the Pacific
WILEY. 2021: 50
View details for Web of Science ID 000625180200189
- Neural ADMIXTURE: rapid population clustering with autoencoders bioRxiv 2021
Discovering prescription patterns in pediatric acute-onset neuropsychiatric syndrome patients.
Journal of biomedical informatics
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
IEEE. 2020: 1314–18
View details for Web of Science ID 000615970401111
- Class-Conditional VAE-GAN for Local-Ancestry Simulation MLCB Proceedings 2019
Reconstructing admixture and migration dynamics of post-contact Mexico
WILEY. 2018: 228
View details for Web of Science ID 000430656803170
- Integrated Power Divider for Superconducting Digital Circuits IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2011; 21 (3): 571–74
- Digital circuits using self-shunted Nb/NbxSi1-x/Nb Josephson junctions APPLIED PHYSICS LETTERS 2010; 96 (21)