Using artificial intelligence to fight infectious disease.

David C. Danko, Ph.D.

Chief Technology Officer, Biotia.
Brooklyn, New York.

I lead projects to forecast global disease outbreaks and diagnose infection using next-generation sequencing and artificial intelligence. My skill set combines leadership and business acumen with deep technological expertise in biotech, machine learning, and software. I operate at every level of an organization, from strategic alignment and management, to business development and marketing, to software architecture and implementation review. I lead teams to produce outsized impact on rapid timelines and lean budgets.

Selected impact

  • Drove the technical portion of the submission for the first New York State LDT-approved microbial assay to incorporate machine learning.
  • Built BIOTIA-ID, a pathogen-agnostic clinical NGS diagnostic platform used in both clinical and research settings.
  • Won multi-million-dollar non-dilutive funding from the Rockefeller Foundation to develop GeoSeeq, and secured a gapfill price determination for the BIOTIA-ID urine assay.
  • Led the team to the best antimicrobial resistance phenotype prediction accuracy at CAMDA in both 2025 and 2026, using machine learning over sequencing data.
  • Helped close diagnostic development partnerships with Mayo Clinic and Hospital for Special Surgery.
  • Built GeoSeeq, a platform integrating metagenomic, climate, and epidemiological data for disease modeling, along with the multimodal AI forecasting models that run on it.
  • Lead author on the Cell global urban metagenome atlas, with further work in Nature Communications, Genome Research, and JBJS; 2,100+ citations and an h-index of 17.

Selected work

2021–now

BIOTIA-ID, clinical NGS diagnostic platform

Biotia. Led as Director, then as CTO. Owned the software and bioinformatics directly, and worked across the lab, clinical, and commercial sides; grew to a 14-person cross-functional team.

A pathogen-agnostic diagnostic built on next-generation sequencing, architected as a hybrid bioinformatics and machine learning pipeline for species detection and antimicrobial resistance profiling. Achieved over 95% accuracy for species detection and the best AMR phenotype prediction accuracy at CAMDA in both 2025 and 2026. Drove the urine assay through New York State LDT approval, analytically validated and published in Microbiology Spectrum, then expanded the platform to tuberculosis, prosthetic joint infection, and bacterial isolates. Scope ran from research through regulatory submission and clinical deployment, including go-to-market support for the commercial offering.

Sequencing, metagenomics, machine learning, NYS LDT, FDA

2022–now

GeoSeeq, genomic and epidemiological data platform

Biotia. Built and ran the engineering organization behind it.

Originated GeoSeeq and built it into a platform integrating metagenomic, climate, and epidemiological data for infectious disease modeling, then defined its product strategy and roadmap with leadership and commercial teams. Delivered a pilot contract in under a month and converted it into a multi-year, multi-million-dollar agreement, later renegotiated on terms giving Biotia co-ownership of everything produced in the term. Raised non-dilutive funding from the Rockefeller Foundation on the strength of the platform. Built ML/AI dengue forecasting models that demonstrated its capability, and integrated clinical-grade metagenomic pipelines into a SaaS product for external researchers. Ran private alpha and public beta releases, and supported fundraising through technical storytelling and investor communication.

Platform strategy, SaaS, multimodal forecasting, data infrastructure, grant writing

2016–2021

MetaSUB, global urban metagenome atlas

Doctoral research, Mason Lab, Weill Cornell Medicine.

Led a global metagenomics study across more than 60 cities, producing the first systematic worldwide catalog of the urban microbial ecosystem and identifying thousands of novel microbial species and core urban signatures. Lead author on the resulting Cell paper, which meant navigating both the science and a consortium of hundreds of co-authors. Developed the machine learning methods linking microbiome signatures to environmental and geographic features.

Metagenomics, machine learning, algorithm development, large-consortium science

2016–2021

Other doctoral work

Mason Lab, Weill Cornell Medicine. Alongside MetaSUB.

Algorithms for computational biology; immune profiling from T-cell receptor structure; research on the International Space Station identifying persistent environmental strain transfer between the station and an astronaut; and metagenomics frameworks for NASA planetary protection, characterizing the organisms that survive spacecraft assembly.

Immune repertoire, spaceflight microbiology, planetary protection

2021–now

Also built at Biotia

Internal platforms behind the diagnostic and research work.

  • A patient-facing clinical portal for ordering tests, accessing results, and communicating with clinicians.
  • A lab automation system that streamlines operations and feeds workflows into the LIMS.
  • A bioinformatics job orchestration system for bursty on-demand workloads, running jobs of several thousand CPU hours with observability and recovery built in.
  • On-demand research compute, with sandboxing for AI agents.
  • Observability, alerting, networking, and security tooling across the platform.

Experience

Aug 2022–now

Chief Technology Officer

Biotia.

Built and manage the bioinformatics and platform engineering teams across the US and Hungary, hiring two managers and standing up an offshore team. Set technology direction across clinical diagnostics and platform products, spanning engineering, bioinformatics, regulatory submission, partnerships, fundraising, and go-to-market, from strategic alignment down to implementation review.

The role runs past its usual bounds. Secured a gapfill price determination for the BIOTIA-ID urine assay, authored federal grant submissions at the $17.5M / five-year scale, and contributed to New York State biosurveillance programs. Also handle forward-looking financial modeling for the company, product conception for both internal and external tools, build-versus-buy decisions, and market landscape analysis — and brought AI-assisted development into the teams' workflows.

2021–2022

Director of Software Engineering and Bioinformatics

Biotia.

Originated GeoSeeq as a concept and won its first non-dilutive funding from the Rockefeller Foundation on the strength of it, the result that led to promotion to CTO. Built and ran the bioinformatics group behind BIOTIA-ID, developing the novel tools for pathogen detection and classification the platform was built on. Inherited and managed a COVID-19 diagnostic platform under FDA Emergency Use Authorization, and led a successful secondary EUA for strain identification. Presented strategy, products, and results to customers and prospective investors.

2016–2021

Doctoral Researcher, Computational Biology and Medicine

Weill Cornell Medicine. Advised by Chris Mason and Iman Hajirasouliha.

Thesis: Characterizing the Urban Microbiome and Human Response to the Ecosystem of the Built Environment. Identified a cross-cultural urban microbiome distinct from that of the people moving through it, built the MetaSUB core analysis pipeline for metagenomics at consortium scale, developed linked-read deconvolution algorithms (Minerva, Ariadne) for assembling organisms out of mixed samples, and studied how people exchange microbes with the environments they inhabit, from subway systems to the ISS.

2015–2016

Research Assistant, Powrie Lab

Kennedy Institute of Rheumatology, University of Oxford.

Studied the role of the gut microbiome in inflammatory bowel disease and built bioinformatics analyses designed to be usable by bench biologists.

2013–2015

Undergraduate Researcher, Berger Lab

MIT CSAIL.

Co-developed MICA, a tool for very fast sequence search over large protein databases, and independently built AMMOLITE for searching large molecule databases. Both are entropy-scaling approaches to datasets growing faster than the hardware under them.

Selected publications

  1. Couto-Rodriguez M, Danko DC, Wells HL, Rey S, Jirau Serrano X, et al. Analytical validation of a highly accurate and reliable next-generation sequencing-based urine assay. Microbiology Spectrum 14(6), 2026. doi:10.1128/spectrum.02026-25
  2. Fidler G, Wells H, Combs F, Papciak J, Couto-Rodriguez M, Rey S, et al. BIOTIA-DX RESISTANCE achieved the best antimicrobial resistance phenotype prediction accuracy at CAMDA 2026. bioRxiv, 2026. doi:10.64898/2026.05.13.724982
  3. Wangprapa P, Nagy-Szakal D, Wells HL, Fidler G, Sangtian M, et al. Analytical validation of a metagenomic next-generation diagnostic platform for urinary tract infection in a Thai tertiary hospital setting. Frontiers in Cellular and Infection Microbiology 16, 2026. doi:10.3389/fcimb.2026.1751074
  4. Mak L, Tierney B, Wei W, Ronkowski C, Toscan RB, Turhan B, et al. CAMP: a modular metagenomics analysis system for integrated multistep data exploration. NAR Genomics and Bioinformatics 8(1), 2026. doi:10.1093/nargab/lqaf172
  5. Al-Ghalith GA, Ryon KA, Henriksen JR, Danko DC, Farthing B, et al. XTree enables memory-efficient, accurate short and long sequence alignment to millions of genomes across the tree of life. bioRxiv, 2025. doi:10.64898/2025.12.22.696015
  6. Lei H, Du S, Tong X, Chan WL, Leung MHY, Bøifot KO, Bezdan D, et al. Global biogeography of airborne viruses in public transit systems and their host interactions. Microbiome 13(1), 2025. doi:10.1186/s40168-025-02173-z
  7. Danko DC, Papciak JC, Golden J, Vörösmarty C, Cak A, Corsi F, et al. The challenges and opportunities in creating a One Health warning system for pandemics. Cell Reports Sustainability, 2025. doi:10.1016/j.crsus.2025.100485
  8. Sierra MA, Ryon K, Arikatla MR, Elshafey R, Bhaskar H, Proszynski J, et al. The Microbe Directory: a centralized database for biological interpretation of microbiome data. Database 2025, 2025. doi:10.1093/database/baaf060
  9. Meleshko D, Yang R, Maharjan S, Danko DC, Korobeynikov A, et al. Blackbird: structural variant detection using synthetic and low-coverage long-reads. Bioinformatics Advances 5(1), 2025. doi:10.1093/bioadv/vbaf151
  10. Wu J, Hu Y, Perlin MH, Danko D, Lu J, Oliveira M, Werner J, et al. Landscape of global urban environmental resistome and its association with local socioeconomic and medical status. Science China Life Sciences 67(6), 2024. doi:10.1007/s11427-023-2504-1
  11. Flores VS, Amgarten DE, Iha BKV, Ryon KA, Danko D, Tierney BT, et al. Discovery and description of novel phage genomes from urban microbiomes sampled by the MetaSUB consortium. Scientific Reports 14(1), 2024. doi:10.1038/s41598-024-58226-0
  12. Danko DC, Vörösmarty C, Cak A, Corsi F, Patel S, Papciak J, Wells H, et al. Forecasting dengue fever in Brazil using multimodal data, including climate data. bioRxiv, 2024. doi:10.1101/2024.03.12.584724
  13. Zhang Y, Ruff SE, Oskolkov N, Tierney BT, Ryon K, Danko D, Mason CE, et al. The microbial biodiversity at the archeological site of Tel Megiddo (Israel). Frontiers in Microbiology 14, 2023. doi:10.3389/fmicb.2023.1253371
  14. Mak L, Meleshko D, Danko DC, Barakzai WN, Maharjan S, Belchikov N, et al. Ariadne: synthetic long read deconvolution using assembly graphs. Genome Biology 24(1), 2023. doi:10.1186/s13059-023-03033-5
  15. Ma Y, Sannino D, Linden JR, Haigh S, Zhao B, Grigg JB, Zumbo P, et al. Epsilon toxin-producing Clostridium perfringens colonize the multiple sclerosis gut microbiome overcoming CNS immune privilege. The Journal of Clinical Investigation 133(9), 2023. doi:10.1172/jci163239
  16. Guevarra RB, Hwang J, Lee H, Kim HJ, Lee Y, Danko D, Ryon KA, et al. Metagenomic characterization of bacterial community and antibiotic resistance genes found in the mass transit system in Seoul, South Korea. Ecotoxicology and Environmental Safety 246, 2022. doi:10.1016/j.ecoenv.2022.114176
  17. Wu J, Danko D, Afshinnekoo E, Bezdan D, Bhattacharyya M, et al. Annotating unknown species of urban microorganisms on a global scale unveils novel functional diversity and local environment association. Environmental Research 207, 2022. doi:10.1016/j.envres.2021.112183
  18. Park J, Foox J, Hether T, Danko DC, Warren S, Kim Y, Reeves J, Butler DJ, et al. System-wide transcriptome damage and tissue identity loss in COVID-19 patients. Cell Reports Medicine 3(2), 2022. doi:10.1016/j.xcrm.2022.100522
  19. Echeverria AP, Cohn IS, Danko DC, Shanaj S, Blair L, Hollemon D, et al. Sequencing of circulating microbial cell-free DNA can identify pathogens in periprosthetic joint infections. The Journal of Bone & Joint Surgery 103(18), 2021. doi:10.2106/jbjs.20.02229 Co-first author.
  20. Danko D, Malli Mohan GB, Sierra MA, Rucker M, Singh NK, Regberg AB, et al. Characterization of spacesuit associated microbial communities and their implications for NASA missions. Frontiers in Microbiology 12, 2021. doi:10.3389/fmicb.2021.608478
  21. Danko D, Bezdan D, Afshin EE, Ahsanuddin S, Bhattacharya C, Butler DJ, et al. A global metagenomic map of urban microbiomes and antimicrobial resistance. Cell 184(13):3376–3393, 2021. doi:10.1016/j.cell.2021.05.002
  22. Leung MHY, Tong X, Bøifot KO, Bezdan D, Butler DJ, Danko DC, Gohli J, et al. Characterization of the public transit air microbiome and resistome reveals geographical specificity. Microbiome 9(1), 2021. doi:10.1186/s40168-021-01044-7
  23. Danko DC, Sierra MA, Benardini JN, Guan L, Wood JM, Singh N, et al. A comprehensive metagenomics framework to characterize organisms relevant for planetary protection. Microbiome 9(1), 2021. doi:10.1186/s40168-021-01020-1
  24. Butler D, Mozsary C, Meydan C, Foox J, Rosiene J, Shaiber A, Danko D, et al. Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions. Nature Communications 12:1660, 2021. doi:10.1038/s41467-021-21361-7 Co-first author.
  25. Zhelyazkova M, Yordanova R, Mihaylov I, Kirov S, Tsonev S, Danko D, et al. Origin sample prediction and spatial modeling of antimicrobial resistance in metagenomic sequencing data. Frontiers in Genetics 12, 2021. doi:10.3389/fgene.2021.642991
  26. Cho A, Eidelberg A, Butler DJ, Danko D, Afshinnekoo E, Mason CE, Chughtai B. Efficacy of daily intake of dried cranberry 500 mg in women with overactive bladder: a randomized, double-blind, placebo-controlled study. The Journal of Urology 205(2), 2021. doi:10.1097/JU.0000000000001384
  27. Danko DC. Characterizing the urban microbiome and human response to the ecosystem of the built environment. Doctoral thesis, Weill Cornell Medicine, 2021.
  28. Danko DC, Singh N, Butler DJ, Mozsary C, Jiang P, Keshavarzian A, et al. Genetic and immunological evidence for microbial transfer between the International Space Station and an astronaut. bioRxiv, 2020. doi:10.1101/2020.11.10.376954
  29. Danko DC, Mason C. The MetaSUB microbiome core analysis pipeline enables large scale metagenomic analysis. arXiv, 2020. doi:10.48550/arXiv.2009.12338
  30. Walker JM, Garcet S, Aleman JO, Mason CE, Danko D, Butler D, Zuffa S, et al. Obesity and ethnicity alter gene expression in skin. Scientific Reports 10, 2020. doi:10.1038/s41598-020-70244-2
  31. Anava S, Neuhof M, Gingold H, Sagy O, Munters A, Svensson EM, et al. Illuminating genetic mysteries of the Dead Sea Scrolls. Cell 181(6), 2020. doi:10.1016/j.cell.2020.04.046
  32. Madin JS, Nielsen DA, Brbic M, Corkrey R, Danko D, Edwards K, et al. A synthesis of bacterial and archaeal phenotypic trait data. Scientific Data 7, 2020. doi:10.1038/s41597-020-0497-4
  33. Sierra MA, Danko DC, Sandoval TA, Pishchany G, Moncada B, Kolter R, et al. The microbiomes of seven lichen genera reveal host specificity, a reduced core community and potential as source of antimicrobials. Frontiers in Microbiology 11, 2020. doi:10.3389/fmicb.2020.00398
  34. Sierra MA, Bhattacharya C, Ryon K, Meierovich S, Shaaban H, Westfall D, et al. The Microbe Directory v2.0: an expanded database of ecological and phenotypical features of microbes. bioRxiv, 2019. doi:10.1101/2019.12.20.860569
  35. Danko DC, Meleshko D, Bezdan D, Mason C, Hajirasouliha I. Minerva: an alignment- and reference-free approach to deconvolve linked-reads for metagenomics. Genome Research 29(1), 2019. doi:10.1101/gr.235499.118
  36. Danko DC, Meleshko D, Bezdan D, Mason C, Hajirasouliha I. Novel algorithms for the taxonomic classification of metagenomic linked-reads. bioRxiv, 2019. doi:10.1101/549667
  37. Shaaban H, Westfall DA, Mohammad R, Danko D, Bezdan D, Afshinnekoo E, et al. The Microbe Directory: an annotated, searchable inventory of microbes' characteristics. Gates Open Research 2, 2018. doi:10.12688/gatesopenres.12772.1
  38. Westover C, Rahmatulloev S, Danko D, O'Hara NB, Ounit R, Bezdan D, et al. Ozone treatment for elimination of bacteria in medical environments. bioRxiv, 2018. doi:10.1101/420737
  39. McIntyre ABR, Ounit R, Afshinnekoo E, Prill RJ, Hénaff E, Alexander N, Danko D, et al. Comprehensive benchmarking and ensemble approaches for metagenomic classifiers. Genome Biology 18(1), 2017. doi:10.1186/s13059-017-1299-7
  40. Yu YW, Daniels NM, Danko DC, Berger B. Entropy-scaling search of massive biological data. Cell Systems 1(2):130–140, 2015. doi:10.1016/j.cels.2015.08.004

Google Scholar, 2,113 citations, h-index 17, i10-index 25.

Technical

Technology
AI and ML development and integration, bioinformatics, software development, data infrastructure
Biology
Metagenomics, microbiology, antimicrobial resistance, sequencing on Illumina and Nanopore platforms
Infrastructure
Cloud infrastructure, DevOps, data governance
Clinical and regulatory
Clinical diagnostics, New York State LDT, FDA, reimbursement across Medicare and Medicaid (CMS)
Leadership
Product strategy, team leadership, fundraising, partnerships, go-to-market, operational modeling, public speaking

Selected talks

  • HJF Unlocking AMR Innovation 2026, Bethesda, MD. Keynote. The business of antimicrobial resistance.
  • ISMB 2026, Washington, DC. CAMDA track. ML/AI antimicrobial resistance phenotype prediction.
  • SFAF 2026, Santa Fe, NM. Panelist. Antimicrobial resistance.
  • ISMB/ECCB 2025, Liverpool, UK. CAMDA track. ML/AI antimicrobial resistance phenotype prediction.
  • Grand Challenges Annual Meeting 2023, Dakar, Senegal. Poster. AI forecasting for dengue from global climate and genomic data.
  • ASM Microbe 2023, Houston, TX. AI forecasting for dengue from global climate and genomic data.
  • MetaSUB annual meeting 2022, Miami Beach, FL. GeoSeeq as a repository for consortium-scale sequencing data.
  • AGBT 2020, Marco Island, FL. Main session. The global urban microbiome.
  • RECOMB 2019, Washington, DC. Main session. Algorithms for linked-read deconvolution.

Side projects

2025–now

Independent AI infrastructure

Designed, built, and operated solo.

I keep a working system in production to stay current on what modern AI actually costs to run: a priority-queue broker arbitrating one GPU across competing inference workloads, a hybrid BM25 and embedding retrieval store over a personal document corpus with vision-model OCR, and an agent confined behind an egress-allowlisting proxy. Operating it is how I keep opinions about inference economics, retrieval quality, and agent isolation grounded in something other than vendor claims.

Education

2016–2021

Doctor of Philosophy, Computational Biology and Medicine

Weill Cornell Medicine.

Advised by Chris Mason and Iman Hajirasouliha, splitting the work between a genomics lab and a computational one: the MetaSUB global urban microbiome study on one side, and algorithms for pulling individual organisms out of mixed metagenomic samples on the other.

2011–2015

B.S., Computer Science

Massachusetts Institute of Technology.

Research and Innovation Scholar, Department of EECS. Academic All-American in 2014 and 2015. Rowed lightweight crew; won club nationals with Vesper Boat Club and the Dad Vail with MIT, both in 2014.

Get in touch

I am always glad to hear from people working on clinical sequencing, outbreak forecasting, or getting AI through regulatory review. cv@gimmebits.com