Hello, my name is Hajar Zaid. I am a Master’s student in Data Science at CUNY Graduate Center, within the Computer Science department. I earned my Bachelor’s in Mathematics with a concentration in Pure Mathematics from Queens College. I am driven by a desire to explore, experience, and experiment with divergent ideas that broaden my perspective on research questions and their applications. Some of my current interests are:

  • Theory of Deep Learning (specifically in the area of High-Dimensional Learning)
  • Computational and Theoretical Neuroscience
  • Numerical Linear Algebra and Spectral Theory
  • Generalization and computational limits of AI

(note: list may be updated as I attempt to hone down relevant questions in areas that align with my interests)

I am also driven by the fact that many mathematical theories reappear across disciplines, often disguised under different names and that advancements do not always emerge within a single discipline. These theories are highly studied and well understood within mathematics, yet their applications in other fields are not always recognized (see The Unreasonable Effectiveness of Mathematics in the Natural Sciences). The goal for a mathematician is to abstract and uncover these underlying structures, revealing deep connections between seemingly unrelated areas. However, mathematics has often been studied in isolation from its applications—I believe bridging this gap is essential for meaningful progress.

Here are just a few reasons why you should care:

  1. Recognizing fundamental mathematical structures across disciplines provides a more profound comprehension of complex systems.
  2. Abstracting problems mathematically allows solutions to apply across different domains, reducing redundancy in problem-solving.
  3. Using well-established mathematical frameworks leads to more robust, interpretable, and theoretically grounded models.
  4. Theoreticaly grounded models lead to better extrapolation and reasoning beyond observed data.
  5. Stronger extrapolation and reasoning drive greater innovation and discovery.