Speaker(s):
How do we design and scale STEM courses in a way that values diversity, equity, inclusion, and accessibility? First, Dr. Manisha Sharan shares how linguistic diversity impacts STEM teaching and learning in higher education. Dr. Sharan will share strategies and scaffolding for educators, curriculum developers, and institutional leaders to help linguistically diverse students succeed in STEM. Then, Dr. Vandana Janeja discusses the growing demand for data science skills and the challenges of scaling data science courses and curriculum throughout STEM, particularly at resource-constrained institutions. In this presentation, Dr. Janeja will share approaches for meeting these challenges in a way that prioritizes diversity, equity, and inclusion.
Speaker(s)
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Manisha Sharan, Ph.D.
Professor
Baker College
Dr. Manisha Sharan has a Ph.D. in Molecular Biology with over 10 years of Higher Education experience. She is the Chair of the DEI (Diversity, Equity and Inclusion) Events Committee of Baker College and Member of the Strategic Plan Work group of the IDEA (Inclusion, Diversity, Equity and Access) Council of Macomb Community College. She has previous experience in R&D Laboratory Management. Dr. Sharan has published 16 papers/articles in Scientific Journals/Books. Dr. Sharan is very passionate about the impact of learning environment and resources on an aspiring learner/student.
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Vandana Janeja, Ph.D.
Professor and Chair, Information Systems Department
University of Maryland, Baltimore County
Vandana Janeja is Professor and Chair of the Information Systems department at the University of Maryland Baltimore County (UMBC). She is the director of iHARP, an NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions. Her research is in the area of data science with a focus on spatio-temporal mining, data heterogeneity across multiple domain datasets. She heads the Multi Data lab at UMBC which brings together important societal projects such as climate change, ethics in data science, misinformation detection and security through the lens of her research in data science. She is the UMBC lead for the IUSE NSF grant which focuses on developing undergraduate data science curriculum and establishes a data science ecosystem of a foundational data science course, ethics module, analytics certificate and peer mentoring network of data science scholars. She is also the PI on an NSF SaTC research grant which brings together sociolinguistic annotations in conjunction with AI for better discernment and detection of deepfakes. This grant also includes a training element to see the impact of linguistics training on deep fake discernment by students.
