Strategic Advisory at the Intersection of AI and Biology
Providing technical due diligence and scientific oversight for high-stakes decisions in the life sciences sector.
Mitigating Risk, Accelerating Innovation
Feller Scientific, LLC translates the frontier of computational biochemistry into actionable strategic breakthroughs.
The rapid convergence of machine learning and molecular biology presents massive opportunities but carries significant technical risk. We provide objective clarity for venture capital firms, biopharma executives, and TechBio startups navigating this complexity.
Our focus is on deep technical oversight: verifying the feasibility of AI architectures, evaluating the biological validity of predictive models, and guiding the infrastructure required to scale.
Technical Due Diligence
Rigorous assessment of proprietary AI models and computational pipelines for venture capital and private equity firms prior to investment.
Scientific Strategy
Interim scientific leadership and strategic advisory to guide predictive modeling strategies and ensure alignment with therapeutic goals.
Molecular Architecture
Design and oversight of chemical language models and generative systems for peptide therapeutics and protein engineering.
Representative Experience
Biopharma Consulting
Provided deep technical expertise in molecular AI and predictive modeling for global leaders including Novo Nordisk and Ormoni Biosciences.
TechBio Leadership
Drive data strategy as VP of Data for Nucleate and lead the TechBio Transformers Austin chapter to foster ecosystem growth.
Advanced Academic Research
Pioneering target discovery, predictive ML models, and generative systems for protein engineering at the University of Texas at Austin.
Strategic Evaluation
Critically evaluating emerging technologies by cutting through hype. Applying rigorous literature analysis and scientific intuition to identify viable, high-conviction projects.
About the Principal
Aaron Feller
Aaron is a computational biologist and AI researcher specializing in predictive protein engineering and frontier generative architectures.
As a Charles W. Smith Jr. Fellow at the University of Texas at Austin, his research bridges the gap between deep learning and molecular biology, with significant contributions published in top-tier journals such as Nature Communications.
Beyond his academic work, Aaron drives ecosystem growth as VP of Data for Nucleate and leads TechBio initiatives to accelerate the translation of computational breakthroughs into therapeutic reality.
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