Model Card: Patrick Long

Leadership Style

Task Oriented People Oriented Introvert Extrovert

Analyzer Introverted, Task-oriented

Director Extroverted, Task-oriented

Collaborator Introverted, People-oriented

Promoter Extroverted, People-oriented

PhD	Neuroscience, University of Vermont
Postdoc	Harvard Medical School, University of Michigan Medical School
Domain	Brain development, myelination, glioblastoma
Methods	Molecular biology, translational research

Current	Associate Director, AI Engineering @ IQVIA
Team	Global team (US, UK, India)
Domain	Real-world data (claims, EHR)
Therapeutics	Rare disease, oncology, metabolic, CNS
Prior	Tech transfer, biotech consulting

Scientific Thinking

0.30

First-principles reasoning, mechanistic understanding

Applied Healthcare ML

0.25

Predictive models on real-world clinical data

RWD Intuition

0.20

Understanding the data generating process

Leadership

0.15

Team building, cross-functional influence

Neuroscience Domain

0.10

Expertise in life science and translational research

🔮 Transformer/LLM

Attends to context across multiple levels: individual, team, business, culture; draws on wide experience (bench → tech transfer → applied AI)

Diplomatic tendencies may soften directness; can be verbose

🌲 XGBoost

Iterative self-improvement; learns from mistakes; sees value in marginal gains

Builds intuition through practice rather than upfront; needs to see things play out

🕸️ GNN (Graph Neural Network)

Relational understanding; leverages connections between people, teams, stakeholders for better outcomes

Sensitive to network effects; consensus-building can slow decision-making

📈 Linear Regression

Reduces complexity for explainability; prioritizes actionable insights

Effectiveness varies by context (heteroscedastic); simplicity may mask overconfidence

L1 (Sparsity): Scientific training grounds me in mechanistic thinking, focusing on what's clinically meaningful vs. noise
L2 (Smoothing): Working across clinical domains prevents over-specialization
Dropout: Rotating across therapeutic areas, methods, and the pre-clinical to clinical spectrum

Benefits from experienced peers to escape local minima
May require explicit negative feedback; implicit signals sometimes dropped
Converges iteratively; flexibility can come at the cost of long-term directional certainty
Context window limitations on administrative tasks

Building ML teams Translating technical ↔ business Clinical domain + ML integration Patient identification models