National Action Council for Minorities in Engineering

Non-corporate scholarships:

Sidney and Katherine Friend Scholarship
Joint endowment established between NACME and New York University, Tandon School of Engineering. One high school senior is selected for funding beginning their freshman year and supported for a maximum of five years. The recipient is awarded $2,500 each year. The minimum grade point average required is 2.8 on a 4.0 scale.

William Randolph Hearst Endowment Scholarship
The William Randolph Hearst Foundation supported the creation of this endowed scholarship. Two students are selected in either their freshman or sophomore year of an ungraduated program in engineering. The recipients are awarded $2,500 each for up to four years. The minimum grade point average required is 2.8 on a 4.0 scale.

Phillip D. Reed Undergraduate Endowment Fellowship
Philip D. Reed endowment fellowship is made possible through an endowment from the Philip D. Reed Foundation. One student is selected in their sophomore year. The award provides $5,000 for up to three years.

NACME Google Applied Machine Learning Intensive (AMLI) Summer 2021 Bootcamp

The goal of the NACME-Google AMLI Summer Bootcamp is to provide students with an introduction to computer science content to be qualified for positions as entry-level Machine Learning Specialists. Specifically, to prepare talent to understand how to apply statistics and machine learning to solve real-world data science problems. The bootcamp aims to expose URM students to advanced concepts in artificial intelligence/machine learning using Google Education’s open-sourced curriculum. The AMLI curriculum will enable NACME partner institutions to give students the foundation they need to work with large datasets and solve real-world problems.

As machine learning (ML) becomes a powerful tool across industries — from healthcare and retail to investment banking and insurance — there is a growing need for a workforce that understands how to apply ML strategically and use ML models to collaborate with data scientists and engineers for maximum business impact. The 8-week machine learning curriculum gives students the strong computer science foundation they need to work with large datasets and solve real-world problems. Classroom time is dedicated to hands-on learning, with faculty-supported, collaborative project work. Students learn actively outside of class, with instruction and new concepts delivered remotely.