Valentina Roquemen-Echeverri.
I am a PhD Candidate at OHSU.
About
I am a PhD candidate in Biomedical Engineering with 3+ years of combined academic and industry experience in algorithms, data science, and machine learning. My expertise lies in signal processing, statistical analysis, and the development of ML models to address complex challenges in healthcare and beyond.
I am proficient in using leading tools and frameworks, including Pandas, SciPy, Scikit-learn, TensorFlow, and PyTorch, for developing data-driven solutions and implementing cutting-edge machine learning applications.
Passionate about bridging the gap between technical concepts and real-world applications.
Here are some programming languages I've been working on:- Python
- C/C++
- R
Education
Spring 2022 - In progress
Doctor of Philosophy in Biomedical Engenieering
Oregon Health and Science University, USA
Advisors:
Dr. Clara Mosquera-Lopez
Dr. Peter Jacobs
April 2015 - December 2021
Bachelor of Science in Physics
Universidad de Antioquia, Colombia
Undergradate thesis:
An AI-Powered Tool for Automatic Heart Sound Quality Assessment and Segmentation
Research
As a graduate researcher in the AIMS Lab, I have contributed to various projects in diabetes and computational cardiology. My responsibilities have included data visualization, model development, and statistical analysis.
AI-powered diabetes simulation platform
The goal of this project is to build an open-source AI-based digital twin generator for replicating personalized glucose dynamics in people with type 1 and type 2 diabetes.
Explainable AI for prediction and prevention of nocturnal hypoglycemia in T1D
The goal of this project is to develop and evaluate (in a clinical trial) the effect of a personalized machine-learning-based recommender system that analyzes glucose profile and physical activity to predict at bedtime the likelihood of overnight hypoglycemia.
Modeling hypoglycemia risk in T1D
The goal of this project is to build mixed effects machine learning models to study the effect of different forms of exercise on hypoglycemia risk during and up to 24 hours after physical activity.
Device and algorithms for structural heart disease diagnosis (Telluscope)
The goal of this project is to build a new AI-powered cardiac auscultation device for improved structural heart murmur diagnosis.
Get in Touch
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