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Teaching Special Seminar: Mai Elkady, "Reflections on Teaching Computer Science: Experience, Challenges, and Insights"

Event Type
Seminar/Symposium
Sponsor
Siebel School of Computing and Data Science
Virtual
wifi event
Date
Feb 19, 2025   9:00 am  
Views
19
Originating Calendar
Siebel School Special Seminar Series

Zoom: https://illinois.zoom.us/j/85473809354?pwd=UTZbyRpnPypJiyyEFOcmpGwM62hO4R.1

Abstract: 
In this talk, Mai Elkady will share her experiences as a graduate instructor and teaching assistant for foundational and introductory computer science courses, including Foundations of Computer Science and Programming in C. She will discuss key challenges in CS education, such as maintaining student engagement, fostering effective debugging skills, and designing fair assessments that adequately measure students’ understanding. Drawing from her teaching experience, she will outline the strategies she has implemented to address these challenges, while also offering insights into additional approaches that could further enhance the learning experience. 

Bio:

Mai Elkady is a Ph.D. candidate at Purdue University specializing in machine learning with a focus on discrete generative models, graph learning, and computational biology. She earned her Master’s degree in Computer Science from Purdue University as a Fulbright Scholar.

During her Master's, Mai contributed to TeraPCA, a computationally and memory efficient tool for analyzing tera-scale genomic datasets. In her Ph.D., she expanded her work into discrete generative models and their evaluation, developing Discrete Tree Flows (DTFs) to model discrete normalizing flows and Vertical Validation (VV) to evaluate implicit graph generative models on thin support regions.

Mai also has extensive teaching experience, serving as a teaching assistant for multiple undergraduate courses and as an instructor of record for a programming course through Purdue’s Graduate Teaching Fellowship program. Her commitment to education earned her the Purdue Graduate Teaching Award in Spring 2020.

In addition to her academic work, Mai interned at Microsoft and Block Inc., contributing to both applied and research projects in Natural Language Processing (NLP), heterogeneous graph learning, and scalable Graph Neural Network architectures.

Faculty Host: Abdu Alawini

Meeting ID: 854 7380 9354; Password: csillinois

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