Hi, I’m Corinne Hultman- a biologist, data enthusiast, educator, and forever-curious human
With a Master’s in Biology from Fordham University and experience ranging from Peace Corps volunteer work in Senegal to mentoring students in the Bronx, I blend science, data skills, and a commitment to making a positive impact. Whether analyzing bat echolocation in urban environments or diving into machine learning, I’m on a mission to use data and biology to help create a better world.
I earned my Bachelor’s in Biology from UMass Amherst, where I gained hands-on experience in the Warren Laboratory monitoring bird nests, mist netting, collecting samples, and managing the lab’s research database. This early work grounded me in scientific rigor and the importance of detailed data.
After graduation, I embarked on the transformative journey of joining the Peace Corps as an agriculture volunteer in Senegal. I learned to enrich desert soil, lead trainings in Wolof, write grant proposals, and embrace the unknown. This experience expanded my worldview, taught me resilience, and deepened my commitment to making a meaningful impact.
A training in the Tamabcounda Region focusing on soil health and crop rotation. The goal of these trainings included increasing female engagement in food security initiatives.
For my Master’s at Fordham University, I studied how bats adjust their echolocation in urban environments. This project sparked my love for coding and data analysis — starting with R and expanding into GIS, Python, and machine learning. This further inspired me to complete a data science certification to sharpen my skills and apply data-driven approaches to biological and environmental challenges.
After graduating from Fordham, I explored many pathes, but found my roles in education to be especially rewarding. I’ve taught middle school biology and mentored ecological research projects with the Wave Hill Organization in the Bronx. I helped students explore science firsthand. Teaching has deepened my passion for communication and education — inspiring curiosity and confidence in the next generation of scientists.
My students working at Wave Hill in the Bronx studying arthropod diversity of specimens collected from urban green roofs.
Across my work in biology, research, education, and international service, one theme has stayed constant: a drive to understand the world, solve meaningful problems, and communicate with clarity and heart. Now, I’m channeling those skills — critical thinking, data analysis, organization, and communication — into a career in data science. I’m excited to help uncover the stories data has to tell, while bringing energy, empathy, and a strong sense of purpose to the causes I care about most.
Curious About My Skills? Take a Look!
This project focused on building a predictive model for gold recovery efficiency in a mining process. It involved structured data preparation, exploratory analysis of metal concentrations and particle sizes, and identifying anomalies in total substance levels. Several models were trained and evaluated using sMAPE and cross-validation to determine the best-performing approach. The final model demonstrated strong predictive performance, offering actionable insights for process optimization.
This project aimed to identify the most profitable region for a new oil well on behalf of OilyGiant by analyzing geological and production data from three locations. A regression model was developed to predict reserve volumes in new wells, with bootstrapping used to estimate profit distributions and assess risk. By comparing predicted outcomes across regions, the analysis recommended the location with the highest expected return. These insights support data-driven investment decisions while accounting for uncertainty in resource estimation.
This project analyzes UAP (formerly UFO) sightings in the U.S. from 1910 to 2014 to uncover patterns in location, timing, duration, and craft type. Using Python and Streamlit, Corinne built an interactive web app to explore the data and make insights accessible. While not aiming to prove extraterrestrial life, the project demonstrates how data can support public awareness and future monitoring efforts.