ADEEB NOOR
About / Doctoral Foundations

D3 Pharmacovigilance: where the systems view began.

My PhD in Computer Science at the University of Colorado Boulder focused on characterizing drug–drug interactions through statistical inference, big-data mining and Semantic Web technologies. The deeper lesson carried forward: consequential decisions depend on how evidence is represented, connected and translated into action.

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2015PhD in Computer Science, University of Colorado Boulder
D3Pharmacovigilance system for characterizing drug–drug interactions
Big DataStatistical inference across heterogeneous biomedical evidence
Semantic WebKnowledge representation as part of decision support

The dissertation

Characterization of Drug-Drug Interactions through Statistical Inference and Semantic Web Technologies developed a computational approach to pharmacovigilance that combined inference from large-scale data with structured biomedical knowledge.

The work was not only about detecting possible interactions. It was about the harder systems problem: how incomplete, heterogeneous and differently represented evidence changes what a computational system can safely conclude.

The enduring research question became: what does a system hide when its summary appears correct?
Dissertation →
Research lineage

Mentorship that shaped the work.

The doctoral research sat at the intersection of natural-language and computational methods, biomedical informatics, linked data and pharmacovigilance.

ADVISOR · 01

James H. Martin

PhD Advisor · University of Colorado Boulder. His research group page documents the doctoral lineage and lists the 2015 dissertation on drug–drug interaction characterization.

Contribution: computational rigor, language and data-driven inference
Context: Computer Science, University of Colorado Boulder
Research group →
CO-SUPERVISION · 02

Michel Dumontier

Principal Co-Supervisor · Domain Expert. Biomedical informatics and Semantic Web expertise shaped the knowledge-representation and translational dimensions of the doctoral work.

Contribution: biomedical knowledge graphs and semantic technologies
Continuity: drug discovery, personalized medicine and FAIR data
Maastricht profile →
What carried forward

The PhD became a research operating system.

Later projects changed domains, but the intellectual structure stayed recognizable: examine what the representation omits, test whether aggregate results hide meaningful differences, and connect technical evaluation to the decision that follows.

01 · KNOWLEDGE

Completeness matters

Missing or uneven knowledge changes the apparent reliability of a system.

02 · REPRESENTATION

Structure matters

How evidence is encoded changes what can be inferred and connected.

03 · EVALUATION

Summaries can hide structure

Aggregate performance may conceal differences that matter at the decision level.

04 · ACTION

Decisions are the endpoint

The system is not finished when it produces a score; the consequential question is what happens next.

From D3 to today

A continuous research arc.

The same foundations now appear in work on heterogeneous DDI knowledge, Anti-DDI, personalized medicine, RIDI and institutional decision intelligence.

Anti-DDIWhat negative or missing interaction evidence means for biomedical knowledge.
GenomeFitGenomic evidence translated toward personalized screening and drug response.
RIDIWhat aggregate AI evaluation hides about allocation identity.
Institutional SystemsHow evidence becomes policy, operating models and consequential decisions.

Interested in this research lineage or a related collaboration?

Current work spans decision intelligence, biomedical knowledge, digital health and evaluation of consequential systems.

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