Welcoming the first cohort of Databricks student fellows

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Summary

Databricks launched its inaugural Student Fellows cohort, selecting a highly diverse group of students from over 5,000 applications across hundreds of universities and dozens of countries worldwide.

Fellows were chosen for their campus leadership and hands-on technical expertise, and will serve as bridges between academic theory and real-world data and AI practice by hosting workshops, hackathons, and mentorship programs at their universities.

Five standout fellows are spotlighted, representing schools like MIT, Harvard, and UC San Diego, with accomplishments ranging from Forbes 30 Under 30 recognition to published research and production-scale data engineering work.

Welcoming the Inaugural Cohort of Databricks Student Fellows Applications are now open for the Databricks Student Fellows . We have received thousands of applications from the next generation of data and AI leaders, eager to improve their real-world skills in AI and data engineering, and bring that knowledge back to their campuses and classmates. Selecting the first cohort from so many talented applicants was no easy feat, but today we are thrilled to introduce the very first Databricks Student Fellows. A Global Community of Builders This distinguished group of student fellows was selected from over 5,000 applications submitted by students from hundreds of universities and dozens of countries. From major global research institutions to specialized technology colleges spanning multiple continents, this inaugural cohort represents a highly diverse and motivated network of builders. Narrowing down the final selection from such a massive, competitive pool was incredibly difficult, but the students chosen stand out as exceptional builders ready to make a global impact on their campuses and beyond. What Defines a Databricks Student Fellow? Our selection committee looked far beyond standard academic transcripts. The student fellows were selected based on their deep involvement and contributions on campus, a proven dedication to learning and applying data and AI, and a commitment to pursuing careers in the field. These fellows are the campus catalysts who already run hackathons, lead developer clubs, and mentor fellow students. By bringing Databricks’ tech stack, specialized workshops, and industry-recognized certifications directly to their campuses, they will serve as the primary bridge connecting academic theory with the real-world scale of the Databricks platform. Fellow Spotlights: Meet the Cohort While every single fellow brings an incredible perspective to the program, we want to highlight five exceptional students from our inaugural class to showcase their unique contributions: Sarah Mashiat (Princeton University)

Sarah Mashiat is a junior at Princeton University studying Computer Science and Engineering with a passion for building and understanding large-scale AI systems. Her experience spans AI software engineering, cybersecurity, and machine learning research, including work on natural language processing, data engineering, and evaluating bias and fairness in generative AI models as a researcher in Princeton's Ramaswamy ML Interpretability Lab. Beyond her technical work, Sarah is an active campus leader and communicator, serving as a Staff News Writer for The Daily Princetonian and a Girls Who Invest Campus Ambassador. As a Databricks Student Fellow, she is excited to deepen her expertise in scalable AI infrastructure while collaborating with a global community of students and engineers advancing the future of data and AI. Michael Estrada (CIDIS ESPOL)

Michael Estrada is a Computer Engineering student and AI Research Assistant at CIDIS ESPOL, specializing in Computer Vision and Deep Learning solutions for industrial automation. With a strong background in data engineering from his experience as a Business Intelligence Intern at Vitapro, Michael excels at building automated ETL pipelines and scalable data workflows. He served as the President of the KOKOA Free Software Club (2025-2026), leading open-source and high-performance computing initiatives on his campus. An award-winning developer and co-author of research published in VISAPP 2026, Michael is passionate about leveraging advanced data analytics and AI to solve real-world industrial challenges. Harmandeep Gill (University of Toronto)

Harmandeep Gill is a Computer Science and Astrophysics student at the University of Toronto who is passionate about applying AI and data systems to scientific discovery and real-world challenges. She has built production AI applications, machine learning platforms, and large-scale research systems, while serving as Vice President of both Google Developer Student Clubs and the University of Toronto Machine Intelligence Student Team. As a Databricks Student Fellow, she's excited to help more students explore data and AI while advancing her expertise in scalable machine learning and modern data platforms. Suraj Reddy (Massachusetts Institute of Technology)

Suraj Reddy is an undergraduate student at MIT studying electrical engineering, computer science, physics, and artificial intelligence. Passionate about AI research, safety, and robotics, he has contributed to the research community through work in continual learning and generative modeling, while also serving as a Teaching Assistant for MIT's Graduate Deep Learning course and conducting research at the Improbable AI Lab. Beyond his research, Suraj is an active campus leader who enjoys helping others learn and build with emerging technologies. As a Databricks Student Fellow, he's excited to help fellow students move from notebook experiments to production-scale AI systems and share what he's learned about building reproducible machine learning workflows. Nicolas dos Santos Xavier (UNIFOR)

Nicolas dos Santos Xavier is a Computer Science student at UNIFOR in Fortaleza, Brazil, with a strong passion for Data Engineering and Artificial Intelligence. Dedicated to building tools that solve real-world problems, he focuses his studies and practical work on data modeling, ETL processes, and Generative AI. Nicolas is particularly interested in developing local RAG (Retrieval-Augmented Generation) architectures and exploring how data-driven solutions can be used to optimize complex systems. He is deeply driven by the potential of AI and data to...