This piece is part of a series by participants in the Winter 2026 Open Knowledge Fellowship, coordinated by the Mina Rees Library. Fellows will share insight into the process of converting a syllabus to openly-licensed and/or zero-cost resources, as well as their experiences teaching undergraduate courses at CUNY.

Md Abu Hanif is a dedicated and driven PhD student in Computer Science at the CUNY Graduate Center, where he is deeply engaged in cutting-edge research at the intersection of machine learning and data science. His focus is on developing and applying advanced algorithms for mmWave radar sensing, an area with immense potential for future technological advances, particularly in domains such as autonomous systems, healthcare monitoring, and smart environments.

My academic journey has been complemented by practical experience in pedagogy. I have had the privilege of serving as a teaching assistant for several core courses in Artificial Intelligence and machine learning. This role has not only solidified my understanding of complex concepts but also strengthened my commitment to effective, accessible technical education. I strongly believe that knowledge, especially in rapidly evolving fields like AI, should be open and readily available to a broad audience. This commitment drives me to contribute to educational initiatives that demystify complex technical subjects and foster the next generation of innovators. My work and passion are geared toward advancing the state of the art in radar-based sensing technologies and ensuring that the foundational principles of machine learning are within reach for all aspiring students and researchers.

I joined the Open Knowledge Fellowship because of a deep personal and professional commitment to equity and access in technical education. As a PhD student in Computer Science at the CUNY Graduate Center, working in machine learning and data science, I am increasingly aware that the classroom is not just a place where technical skills are taught, but also a space where inequalities can either be reinforced or actively challenged. I wanted to be part of a program that would help me rethink how machine learning is taught and make it more accessible to students from diverse backgrounds.

One of the most important takeaways from the fellowship was realizing how deeply textbook-centered teaching shapes a course’s structure. In machine learning, commercial textbooks are often expensive, quickly outdated, and disconnected from how the field is actually practiced. Moving away from a traditional textbook forced me to rethink not only the materials I assign but also how students engage with ideas in the classroom. Designing a Zero Textbook Course pushed me to focus on process, experimentation, and understanding rather than memorization.

Machine learning is uniquely well-suited to open education. Much of the field already exists in the open: research papers, open-source libraries, interactive notebooks, and publicly available datasets. Through the fellowship, I curated a course built entirely around open resources, including An Introduction to Statistical Learning, Dive Into Deep Learning, Python-based machine learning libraries, and open datasets from platforms such as open datasets from platforms such as the UCI Machine Learning Repository, OpenML, and NYC Open Data. Using these materials allows students to work directly with real tools and data, rather than simplified or proprietary examples.

My own research focuses on machine learning and data-driven sensing systems, particularly mmWave radar–based technologies. Teaching alongside this research made me especially attentive to questions of transparency, bias, and interpretation. As part of the course design, students complete hands-on programming labs using Jupyter notebooks, where they are encouraged to experiment, document their work clearly, and reflect on why models behave the way they do. Reproducibility is treated not as an advanced topic, but as a basic habit of responsible technical practice.

The final project asks students to apply machine learning techniques to an open dataset of their choice and to reflect on the ethical and social implications of their work. This combination of technical analysis and critical reflection was directly inspired by conversations and readings from the Fellowship.

Overall, my experience in the Open Knowledge Fellowship was transformative. It helped me rethink my approach to teaching, discover high-quality open resources, and imagine more inclusive ways of introducing complex technical ideas. By sharing this course publicly on the CUNY Academic Commons under an open license, I hope to contribute to a broader effort to make machine learning education more transparent, accessible, and equitable for CUNY students.

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