Bio
I’m SVP of Product at Citeline, a Norstella company, where I lead product across our clinical and regulatory intelligence portfolio. With over 15 years in product spanning ed tech and life sciences, I joined the team that became Citeline when it was a ~30-person startup and grew with it through Informa and into Norstella. My early career in sales taught me how often customers get sold a solution that isn’t quite the one they need. This influenced exactly why I build the way I do: close to clients, grounded in our decades of curated trial, site, and drug data, and focused on AI-powered tools that help life sciences teams plan confidently, recruit strategically, and get therapies to patients faster.
What makes Norstella’s AI different?
Most AI you encounter today is general-purpose: powerful, but trained on the open web and dependent on how well you prompt it. Norstella’s AI is the opposite. It’s purpose-built for life sciences and embedded directly in the workflows where drug development decisions actually get made, from protocol design and patient feasibility to site and investigator selection.
What makes it work is what sits underneath it. Our AI is grounded in proprietary, expert-curated data spanning 40+ years of global drug R&D and 445,000+ clinical trials. This is depth no general model can acquire through training alone. Crucially, we connect that trial intelligence to real-world patient data through NorstellaLinQ, mapping real patient populations to the trials, sites, and investigators that serve them. That linkage turns separate datasets into a single, connected view across the Norstella ecosystem, so insight isn’t trapped in any one tool. And it’s all built for trust in every output. Whether a forecast, a recommended protocol criterion, or a ranked site, it is traceable to the specific data and evidence behind it. In a field where a single decision can shape a multi-year, multi-million-dollar program, that combination of proprietary trial data, real-world evidence, and explainability is what separates AI you can act on from AI you have to second-guess.
What can pharma teams do now that was not possible two years ago?
Precision. Recruitment has become precise and proactive. We can now match individual patients to a trial’s criteria in real time by running AI models against real-world data, then alert their treating physicians and investigators. The right patient can be offered a trial option at the right moment, before they’re placed on standard therapy. Two years ago, recruitment meant waiting for patients to find a trial; now we bring the right trial to the right patient.
Foresight. The data needed to plan a successful study spans trial, site, investigator, and real-world patient data, including how sites and investigators have actually performed. It has always lived in disparate, disconnected sources. We now combine and harmonize those sources into a single view and run AI models across them to simulate scenarios, forecast outcomes, and anchor teams to the decisions most likely to succeed. What used to be weeks of fragmented manual analysis is now predictive, evidence-backed foresight.
How can AI accelerate the journey from pipeline to patient?
AI shortens the journey at both ends. On the planning side, it compresses the slowest, riskiest steps (protocol design, feasibility, site selection, and recruitment) from months of manual analysis into rapid, data-grounded decisions. On the patient side, it removes a long-standing barrier: trial eligibility has been locked inside dense criteria, medical ontologies, and clinical language no patient should have to decode. Powered by a standardized AI interface, patients and their physicians can now search for relevant trials in plain language, with the AI handling the mapping to medical codes behind the scenes.
But speed only matters if it’s trustworthy, and that depends on keeping people in the loop. AI does the heavy lifting while our subject-matter experts curate the data underneath it and continuously review and train the models. The better the intelligence we feed the AI, and the stronger the layer of experts refining its outputs, the faster and more safely we get patients to the treatments that can help them.