Speaker Profile
Ph.D., CEO, Sarpeda
Biography
Ivana Mikic focuses on developing AI-based methods that expand the information content of fluorescence microscopy without requiring changes to hardware or experimental workflows. Her work centers on extracting quantitative insight from complex biological systems and improving imaging data fidelity and experimental reproducibility, with applications across research, drug discovery, toxicology, and diagnostics.Over the past two decades, she has applied computer vision and multimodal AI to problems across life sciences, spanning digital pathology, fluorescence microscopy, spatial multi-omics, and organ-on-chip platforms. She previously co-founded Image Informatics and led it through a successful acquisition by Dassault Systèmes in 2016, and later built and led the AI team at Reveal Biosciences, acquired by CellCarta in 2021.
Talk
Pushing Fluorescence Microscopy Beyond Optical Limits with AI
Fluorescence microscopy underpins modern life sciences, yet hardware constraints limit the amount and quality of data captured. This talk introduces Sarpedas AI-based, software-only approach to doubling the information density of fluorescence imaging while reducing noise and background, enabling richer biological insight across drug discovery, toxicology, and diagnostics without changing microscopes or workflows.
AI and Data Sciences Showcase:
Sarpeda
Sarpeda develops AI-based, software-only technology that doubles the effective information capacity of fluorescence microscopy. It enables high-plex imaging with standard antibodies and fluorophores while removing autofluorescence and other structured background, without hardware changes.
Session Abstract – PMWC 2026 Silicon Valley
The PMWC 2026 AI Company Showcase will provide a 15-30 minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.
