Experienced Data Analyst & Environmental Researcher with keen interests in regenerative forestry and agriculture, natural resource conservation, and sustainable water management.

I earned my BS in Environmental Science and Data Analytics from Dickinson College. I’m passionate about community-based science, applied research, and science outreach to engage diverse stakeholders and promote social justice, while addressing environmental challenges. My work revolves around leveraging technical tools and crunching numbers to communicate immportant findings to key stakeholders and the public. I'm eager to help make science more accessible and to make a difference in the communities we serve.

In collaboration with Dr. Maggie Douglas of Dickinson College, I looked at insecticide application patterns in public PA forests and possible hazards on pollinators. With application data from the PA DCNR, we cleaned, visualized, mapped, and generated summary statistics on the types of insecticides applied, total area treated, intensity in which they were treated, and toxic load on honeybees. These findings were compared to agricultural statistics (see Douglas et al. 2022) to gain a better understanding of pest management in public forests and hazards on pollinator populations.

Update: Our findings from PA forests will be combined with an analysis of WI forests and developed into a manuscript for publication.

As part of this program, I completed a Machine Learning Foundation course and had the incredible opportunity to collaborate with Nestlé on a project titled:

FlavorGenius: Unleashing AI's Mastery in Flavor Discovery

With the rise of wellness and clean eating trends, there is an increasing demand for healthy and tasty snacks that align with consumer preferences for both nutrition and flavor. As part of my collaboration with Nestlé in the Cornell Tech's Break Through Tech AI Program, our project aimed to leverage Natural Language Processing (NLP) techniques to generate novel flavor combinations for the rapidly growing healthy snack market.

Throughout my studies and projects, I have gained proficiency in a variety of programming languages and technologies, including:

• Python: Extensive experience with Python for data analysis, machine learning, and automation (using libraries like Pandas, NumPy, Scikit-learn, and TensorFlow).
• R: Used for statistical analysis and data visualization, especially in environmental science research.
• SQL: Applied for database management and querying large datasets.

• Pandas, NumPy, Matplotlib, Seaborn: For data cleaning, analysis, and visualization.
• Scikit-learn, TensorFlow, Keras: For machine learning and deep learning models.

Feel free to reach out if you're interested in collaborating or discussing anything related to environmental science, data analytics, or AI! You can connect with me through LinkedIn or open an issue on any of my repositories if you'd like to chat.

Thanks for visiting my profile! 😊