Working on a hard problem at the intersection of biology, data, and AI? Let's talk.

AI is now standard across drug discovery and development.
The differentiator is no longer whether you use it, but whether it holds up in the clinic.
I designed, built, and ran an integrated AI + multi-omics platform as a founding member and CSO-level leader, across two decades of translating computational biology into clinical and commercial reality.
That means when I evaluate a platform, a pipeline, or a target company, I know the difference between genuine predictive power and a well-marketed wrapper, and I bring that judgment to founders deciding what to build, pharma teams deciding what to trust, and investors deciding where to put capital.

For Biotech Founders & Startups

Decide what to build versus license, get your biology data ML-ready, and build the scientific and regulatory story that raises the next round. I've built discovery platforms, GMP and clinical operations, and 75-person R&D orgs from the ground up, so the plan I give you is one that I have actually executed.

• AI/ML platform strategy and build-vs-buy decisions

• Multi-omics and data-readiness architecture for machine learning

• Discovery-to-IND de-risking and target validation

For Pharma & Biotech R&D Leaders

Turn AI/ML and multi-omics from pilots into predictive, translational programs. I've designed clinical protocols with built-in multi-omic biomarker strategies, run FDA End-of-Phase-1 meetings, and correlated molecular signatures to drug concentration for PK/PD de-risking, the work that makes AI outputs clinically credible.

• Integrated analytics and ML-driven biomarker discovery

• Translational and regulatory strategy (IND, Orphan Drug, EOP1)

• Independent review of internal AI/platform initiatives

For Investors & Boards

Know whether the "AI platform" in your deal is real before you commit. I provide scientific and technical due diligence that stress-tests platform claims, pipeline assumptions, and data foundations, the questions a former builder asks that a generalist analyst can't.

• AI-claim validation: real capability vs. marketing

• Scientific & commercial due diligence, risk profiling

• Asset and pipeline valuation, board-ready diligence memos

Working on a hard problem at the intersection of biology, data, and AI? Let's talk.

AI is now standard across drug discovery and development.

The differentiator is no longer whether you use it, but whether it holds up in the clinic.

I designed, built, and ran an integrated AI + multi-omics platform as a founding member and CSO-level leader, across two decades of translating computational biology into clinical and commercial reality.

That means when I evaluate a platform, a pipeline, or a target company, I know the difference between genuine predictive power and a well-marketed wrapper, and I bring that judgment to founders deciding what to build, pharma teams deciding what to trust, and investors deciding where to put capital.