For Saka, the award represents \u201cvisibility and affirmation.\u201d<\/p>\n
\u201cIt recognizes not just my presence in STEM, but the way I lead: through mentorship, community engagement and equity-driven practice,\u201d she says.<\/p>\n
Her research focuses on equity and belonging in education with the goal of helping Black students, particularly girls, see themselves as capable contributors to STEM. As a mentor, she strives to translate that research into professional learning frameworks that foster more inclusive classrooms.<\/p>\n
During the COVID-19 pandemic, Saka saw that lockdowns amplified inequities, with some children in her community unable to access the devices needed for online learning. In response, she volunteered to deliver virtual coding and mathematics classes to those with limited enrichment opportunities.<\/p>\n
Her experiences as a Black immigrant woman in academic leadership, often one few at the table, have shaped her sense of purpose and approach to her work.<\/p>\n
\u201cIt\u2019s about navigating systems that were not originally designed with everyone in mind,\u201d she says.<\/p>\n
Madhusudan aims to do just that in the future. She plans to pursue a career in computer science and artificial intelligence (AI) research, guiding future students through challenges she once encountered as an undergraduate student.<\/p>\n
\u201cIn classes where women were outnumbered, I would be a little intimidated to speak up,\u201d Madhusudan says. \u201cIt wasn\u2019t that anyone expressed anything externally \u2014 it was more internal. You look around and you don\u2019t see many similar voices,\u201d she says.<\/p>\n
![\"A](\"https:\/\/brocku.ca\/brock-news\/wp-content\/uploads\/2026\/03\/Chinyere-Saka_Headshot-1050x840.jpg\")
PhD in Educational Studies student Chinyere Saka recently received the Women in STEM Graduate Leadership Award.<\/p><\/div>\n
Encouragement from her professor sparked her interest in AI and large language models (LLMs).<\/p>\n
\u201cI need a personal connection to my research,\u201d she says. \u201cLooking at bias and how it shows up in large language models \u2014 that felt meaningful.\u201d<\/p>\n
Her research examines cultural alignment and bias in LLMs. \u00a0Her recent work, for example, explored how language models trained predominantly on older texts may reflect outdated social norms.<\/p>\n
Taking the next step in her journey as a researcher was daunting, but the support of faculty and research partners gave her the confidence to publish her first academic paper.<\/p>\n