Author

Ivan Specht

Stanford UniversityORCID 0000-0003-2834-8191Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:Bayesian inference· 1 Assay:DNA methylation· 1 Research Focus:drug response / drug discovery· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Research Focus:phylogenetics / evolution· 1 Research Focus:protein folding & dynamics· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Julia A. PalaciosiD· 2 shared Brian HieiD· 1 shared Claudia L. DriscolliD· 1 shared Garyk BrixiiD· 1 shared Seyone ChithranandaiD· 1 shared Soyoon ParkiD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

6 top collaborators · 15 mutual co-author links

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2 shared papersEvolutionary Biology

Julia A. PalaciosORCID

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2 papers

Recent·Most cited

Evolutionary BiologybioRxivPreprintSep 5, 2026

Forecasting viral evolution from phylogenetic trees

Ivan Specht, Soyoon Park, Seyone Chithrananda…·Brian Hie

This paper introduces antiGen, a machine learning model that forecasts viral mutations from phylogenetic trees, achieving state-of-the-art results for SARS-CoV-2 and other viruses.

DOI 10.64898/2026.08.27.741102

machine learning (classical)protein folding & dynamics

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Evolutionary BiologyGeneticsPeer-reviewedApr 17, 2026

Efficient Bayesian Phylogenetics under the Infinite Sites Model

Ivan Specht, Julia A. Palacios

The paper introduces inPhynite, an efficient Bayesian phylogenetics algorithm under the infinite sites model, achieving 225x speedup without accuracy loss on large datasets.

DOI 10.1093/genetics/iyag103

humanDNA methylation

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