Samuel Thorpe
Experienced Data Science & ML Leader in BioMed/Health tech
PhD Neuroscientist turned Engineering Manager
ABOUT

Samuel Thorpe
Born and raised in California, I received my PhD in Mathematical Behavioral Science from the University of California at Irvine. My research there centered around analyzing EEG data from healthy adults as they performed cognitive tasks, particularly attention tasks. I then completed a post doc at the University of Maryland Child Development Lab where I worked with everything from infant and monkey EEG to structural and functional MRI, studying the development of the brain. After realizing I wanted the geographic freedom to return to California, I decided to move from academia to the private sector and completed The Data Incubator Program, which helped me launch my career in data science.
​
My first role at NovaSignal allowed me to grow from Data Scientist to Principal Data Scientist as I led ML modeling projects for ischemic stroke and cerebrovascular event prediction using Transcranial Doppler (TCD). I partnered with clinical teams to integrate models in real-time diagnostic platforms and designed signal processing tools for modeling emboli detection and blood flow dynamics.
​
I've been lucky enough to continue growing as a manager and project lead at Koneksa,
where I led design of AWS-based micro service infrastructure for FDA-regulated clinical trials, built scalable ETL pipelines integrating biosensor and PRO data across 30+ studies. At Koneksa I discovered a passion for managing a team and mentoring other engineers and scientists.
There is nothing I love more than working with a room full of brilliant people, but I am also just enough of an introvert to want to close my office door and obsessively develop a project from end to end. Mainly, I am indefatigably curious. I find the world to be a profoundly interesting place to be.
SKILLS
Programming & Platforms
Mastery of Python (expert) and
Matlab (>10 yrs) for knowledge
discovery and visualization,
together with Git, Docker, and
Linux/Bash tools for dev and
deployment.
Data Infrastructure
AWS — S3, EC2, Lambda, Athena,
CI/CD, data lakes & observability
— CloudWatch, New Relic
Extensive Applied ML
Classification, cross-validation,
GLMs, clustering, bootstrapping;
Scikit-learn, TensorFlow, PyTorch
Excellent Signal Processing
Fourier analysis, wavelets, SVD,
ICA, EMD, digital filtering
Engaging Communicator
Strong communicator across
teams and audiences; experienced
in grant writing, technical
documentation, and scientific
publishing
Dynamic Systems
Experience
Numerical ODE, nonlinear systems,
manifold learning, graph theory