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I am currently teaching in Spelman College as assistant professor of Mathematics. Before this position, I was an instructor of record for four years in the Texas Tech University (TTU) Mathematics and Statistics Department . I have taught 16 courses ranging in size from 10 students to 110 students so far. My overall instructor effectiveness score was 4.7/5 over these courses. You can access my teaching statement here. Student evaluations with selected student comments from all classes I taught so far can be found here. I will add new ones if I am not super lazy...
I believe that offering scientific research in the form of a regular class is a great way to engage and inspire students. As a researcher, if you are passionate about sharing knowledge with students, then offering a special topics class can be a rewarding experience. Not only will you be able to impart your research to a new audience, but you will also have the opportunity to deepen your own understanding of the subject matter as you work to communicate its complexities to students with varying levels of prior knowledge.
Have a look at the following examples;
Introduction to Deep Learning with PyTorch
Although deep learning has achieved several breakthroughs in recent years, it is extremely rare to see it in an undergraduate curriculum due to several reasons such as lack of awareness, resources, expertise etc. To challenge this, I developed a brand new deep learning class in Spelman College, Introduction to Deep Learning with PyTorch in Spring 2023. You can access the materials on my GitHub. We also completed pretty cool projects. Look below for snapshots.
I would like to also acknowledge Microsoft and AUC Data Initiative for their support in the creation of this class.
real-time-prediction.avi
mama-tried.mp4Mathematical Oncology
I offered in Mathematical Models class in Spelman College. The class focuses on fundamental concepts in mathematical oncology, with a specific emphasis on mathematical modelling in tumor immunology, including the latest developments in CART-cell therapies. This is also my research focus.
We covered fundamental topics such as modelling with ODEs, optimization and parameter fitting, analysis of model behavior etc. Once we have a general idea about how this process works, we replicated all of the results in CARTmath by Barros et.al . Then, students grabbed a research paper about CART-cell modelling and replicated some of the results using Matlab. You can access their amazing presentations in our Github. See the figure on the right to appreciate what they did(:
Courses Taught
Courses Taught
Physics Informed Neural Networks (upcoming in Spring 2025)
Linear Algebra
Differential Equations
General Statistics
Numerical Analysis
Calculus-1
Calculus-2
Calculus-3
Business Calculus
Undergraduate Research
Below is a compilation of undergraduate students I have previously advised, as well as those I am currently mentoring.
Undergraduate Research
Below is a compilation of undergraduate students I have previously advised, as well as those I am currently mentoring.
Spelman College, Atlanta, GA
Haley Brown, present, Operator learning for ODEs
Brianna Freburg, present, Operator learning for ODEs
Logan Joy Lemond, 2023-2024, Multifidelity operator learning
Taylor Tylee Jones, 2023-2024, Multifidelity operator learning
Brianna Severe, 2023-2024, Mathematical modeling in cancer immunology
Kristen Mosley, 2023, Neural ODEs
Jean Luois Jasmin, 2023, Spiking neural networks
Charlotte Ingram, 2023, Preconditioning in CAR-T cell therapies
Laila N. Hayes, 2022, Skin disease recognition with neural networks
Erin N. Ojeda, 2022, Predicting fashion trends using neural networks
Princess Sampson, 2022, Machine learning for Afro-American hairstyles
University of Washington, Seattle, WA
George Zhang, 2022, Object tracking in multi-particle systems
Maximiliam Kim, 2023, Numerical PDEs for porous media flow
Texas A&M San Antonio, San Antonio, TX
Edgar Faris, 2023, automated music genre recognition with neural networks
Omar Bravo, 2023, automated music genre recognition with neural networks
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