People / Students / Class of 2026
Summer Sun graduated from Emory University in May 2025 with a Bachelor of Science in Data Science and a minor in Computer Science. She is currently pursuing a Master of Science in Machine Learning and Data Science at Northwestern University. Her academic and professional experiences center around machine learning, artificial intelligence, data science, and human-centered technology applications.
Summer’s interest in AI began during her work with the Emory Center for AI Learning, where she contributed to the development of an AI-powered educational chatbot designed to support medical training. She worked across both technical and product-oriented aspects of the project, including backend development, interface design, AI integration, testing, and user feedback coordination. Through this experience, she developed a strong interest in the intersection of machine learning, usability, and real-world problem solving. She also helped organize initiatives and workshops related to large language models and responsible AI practices, gaining experience communicating technical concepts to broader audiences.
In addition to her work in AI education, Summer has participated in projects involving data analytics, machine learning applications, and AI-driven product development. Through internships and collaborative projects, she has worked with large-scale datasets, built predictive and analytical models, and contributed to AI workflow design and evaluation. Her experiences include developing data-driven solutions related to environmental and public-interest topics at Science for Georgia, as well as supporting the design and testing of AI-assisted systems in industry settings at Autocore. These opportunities strengthened her interest in bridging technical development with practical user and business needs.
At Northwestern, Summer is also involved in practicum research focused on retrieval-augmented generation (RAG) systems and enterprise AI applications. As part of a collaborative project with Zebra Technologies, she has been working on evaluating and improving AI systems for technical knowledge retrieval using large language models, vector databases, and graph-based retrieval methods. This experience further deepened her interest in scalable AI systems, information retrieval, and applied machine learning.
As a member of the MLDS program, Summer hopes to continue strengthening her technical foundation while gaining a broader understanding of AI product ecosystems and applied machine learning. She aspires to contribute to the responsible development of AI technologies that are both technically effective and meaningful to users.
