Robert G. Schaut, PhD

Problem. A smartphone-read multiplex lateral-flow platform sitting inside a university, with real companion-animal diagnostic potential and no company around it.

Approach. Founded the company and led technology licensing negotiations with The Ohio State University for the foundational IP. Recruited the founding scientific team, structured founder and employee equity, and built the capital strategy — investor materials, financial model, and a non-dilutive pipeline spanning NSF SBIR, NIH STTR, and state translational programs. Established data intake standards and cloud architecture, and direct AI/ML model development and analytical validation.

Problem. A bacterial-vectored prostate cancer vaccine with strong preclinical immunology, a founding team, and no financing or clinical path.

Approach. Translated the preclinical immunology into an investment case for venture audiences and raised the round with the founding team. Authored the Phase I protocols with the scientific advisory board, shaped the planned Phase IIa/b design, and aligned the investigator brochure with the preclinical and published evidence. Manage CRO and CMO relationships covering trial planning, drug lot validation, and investigational product release.

Problem. Institute leadership wanted to build a One Health research center bridging human, animal, and environmental health, and needed the program structure, research portfolio, and translational strategy designed before committing to it.

Approach. Advised on center design and portfolio planning, shaped the AI/ML-driven research initiatives, and guided external partnership development with industry and government sponsors.

Problem. A healthcare, life science, and animal health portfolio whose most valuable data sat in fragmented government and NGO systems — much of it still on paper — and was therefore unusable for modeling.

Approach. Owned the portfolio: partner negotiation, roadmap, timelines, and budget, with two direct and four indirect reports. Directed integration of fragmented sources into cloud AI/ML analytics environments, including data IP terms and controlled-access handling, applying FAIR principles with HL7 FHIR and OMOP. Converted paper diagnostic and field records into training-ready datasets with the partners who held them.

Problem. Oracle Health had two scientists and an ambition to build external research partnerships, with no program structure, no contracting template, and no delivery team.

Approach. Built the program to a cross-functional team of twelve PhD-level AI/ML, data, and life scientists and project managers. Negotiated umbrella research and IP agreements with legal for multi-group projects, and directed AI/ML programs across regenerative medicine analytics, bioreactor process optimization, and in silico vaccine target screening.

Problem. The early discovery portfolio needed target validation and vaccine efficacy testing capacity, and too many programs were failing late — after the expensive work had already been done.

Approach. Led the target enablement laboratory and built ex vivo and in vitro de-risking platforms that raised probability of technical success and killed weak programs earlier. Scouted emerging intervention areas — antibiotic alternatives, immunomodulatory compounds, bacterial and mRNA vaccines — and presented candidate strategies to oversight and steering committees. Created a grant-directed program that used USDA-NIFA funding to underwrite mechanistic work on high-risk projects.

Problem. E. coli O157:H7 behaves as a commensal in cattle, which makes it a hard vaccine target and had defeated earlier approaches.

Approach. Led the immunology program, comparing mRNA, whole-cell (modified-live and chemically inactivated), and subunit strategies head to head. Engineered the recombinant strains the program depended on by Lambda Red recombineering and CRISPR-Cas9, and built the ELISA, flow cytometry, mass spectrometry epitope mapping, histology, IgA repertoire, and microbiome readouts.