StateZero Platform
Virtual CRISPR engine for in-silico target prioritization — simulating perturbation outcomes across multi-omic latent spaces before wet-lab validation.
AI/ML Architect and Founder specializing in multi-omic latent alignment, automated DBTL screening cycles, and Nextflow pipeline orchestration.
My Vision
Drug discovery pipelines now rely on experimental work and manual target identification, success rate <10%.
My goal is to completely automate the biological Design-Build-Test-Learn cycle.
This requires a foundational data layer. My current work focuses on mapping multi-omic (transcriptomic and epigenomic) shifts into a unified computational latent space. By converting it to math coordinates, we can systematically model and execute virtual CRISPR perturbations.
This allows us to mathematically engineer cell states and accelerate the efficiency of therapeutic target identification.
News & Traction
About
AI/ML Architect and Founder specializing in multi-omic latent alignment, automated DBTL screening cycles, and Nextflow pipeline orchestration.
I build Bio-MLOps infrastructure at StateZero Labs — systems that connect single-cell transcriptomics, virtual CRISPR screening, and chemical latent-space alignment into reproducible pipelines for target identification and cellular regeneration.
Core Infrastructure Stack
Current Deployment
Currently at the Max Delbrück Center (Bunina Lab), working on transcriptomics, GRNs and perturbation algorithms.
Experience
Visiting Computational Researcher
Bunina Lab, Max Delbrück Center · Berlin, DE
Undergraduate Research Fellow
Laboratory for Regenerative Medicine, Gdańsk University of Technology
Research
Abramau, P.
Preprint · July 2026 · With apart research, CIC Secret Loyalties Hackathon
Abramau, P.
2026 — currently in work
Sachadyn P., Abramau P., et al.
Preprint · 2026 — coming soon
Education
Internship — Computational Biology
Max Delbrück Center for Molecular Medicine
B.Sc. Erasmus+ Biotechnology & AI
University of Barcelona
B.Sc. Biotechnology
Gdańsk University of Technology
Projects / Pre-prints
Virtual CRISPR engine for in-silico target prioritization — simulating perturbation outcomes across multi-omic latent spaces before wet-lab validation.
LLM-driven pipeline integration for Reactome and GSEA — extracting structured pathway hypotheses from unstructured literature and experimental readouts.
Contact
Currently securing a remote Autumn infrastructure sprint.