About Me, My Research, and Teaching Philosophy

About Me

I am a PhD candidate in Computer Engineering at Texas A&M University, specializing in the development of custom, efficient, and lightweight neural network architectures for analyzing biomedical images and signals. My work bridges the gap between cutting-edge AI technologies and the pressing need for accessible diagnostic tools in resource-constrained settings. For more details about my academic and professional journey, you can view my CV here.

Research

My research focuses on creating efficient and effective computational systems that make state-of-the-art diagnostic tools accessible to underdeveloped and developing regions. I aim to combat the disparity in access to advanced AI-driven technologies, which are often limited to financially robust institutions in developed countries. By designing objective tools for analyzing biomedical images, signals, and other data, I hope to enable better diagnostics, prognosis, early detection, and treatment planning for diseases like cancer and cardiovascular conditions.

My work is particularly geared toward empowering war-torn regions, refugee camps, and non-profit organizations, where financial and infrastructural constraints limit access to such advanced systems. You can read more about my research in my research statement.

Teaching

My teaching philosophy is centered on empowering students to become independent learners. To achieve this, I emphasize a systematic approach that integrates three key aspects: understanding the intuition behind concepts, mastering their implementation, and evaluating their real-world impact. By reinforcing this cycle throughout the learning process, I aim to cultivate critical thinking and problem-solving skills in my students. If you’d like to learn more about my teaching philosophy, please visit my teaching statement.