About Me

Hello I am Aria.
I completed concurrent Ph.D.s in Electrical and Computer Engineering (ECE) and Mathematics at Northeastern University.
In my ECE thesis (Making Deep Neural Networks Transparent) I developed theoretical and practical methods to enhance the interpretability and reliability of machine learning models. My second Ph.D. thesis in Mathematics (Poisson Geometry of Flag Varieties and Representation Theory of Their Quantum Deformations) explored questions in representation theory and received the Best Ph.D. Thesis Award.
This unique path has given me a strong foundation at the intersection of rigorous theory and applied research.
My research interests lie in trustworthy AI and high-dimensional statistics, where I aim to develop methods that make AI systems both more practical and theoretically sound for long-term, real-world deployment. Along the way, I’ve had the opportunity to pursue these questions through both rigorous theoretical work and hands-on experimentation and to share the results at venues like NeurIPS, ICLR, and AISTATS.