Maksim Imakaev is a Staff Data Scientist in Boston with 14 years bridging quantitative biophysics, computational genomics, and applied wastewater epidemiology. Trained at MIT (Ph.D. and postdoc with Leonid Mirny), he was an early contributor to Hi-C analytics and polymer chromosome modeling and now builds sequence-to-feature AI models that predict genome folding from sequence alone. At Biobot Analytics he translates deep computational methods into public-health scale pathogen surveillance, and he enjoys doing "in silico" Hi-C experiments by virtually mutating sequences to probe chromatin effects. Combining full-stack data science skills with domain-first biological insight, he is seeking roles that push AI-driven genomics from research prototypes to impactful academic or industry applications.
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