Majid Dalits is a PhD student at New York University, affiliated with Theoretical Computer Science at NYU and advised by Prof. Christopher Musco. He was selected as part of the 2025 cohort of Apple Scholars in AI/ML PhD Fellowship. Majid's research focuses on developing algorithms with provable guarantees to enhance the efficiency and scalability of machine learning systems, especially in the context of deep learning and generative AI. He designs algorithms that are adaptable across diverse machine learning architectures and tasks, ensuring their relevance as technologies advance. His work addresses the economic and environmental sustainability concerns of machine learning systems by improving their computational efficiency without increasing resources. Before his PhD, he completed a B.S. at the University of Tehran, where he had the privilege to collaborate with Prof. Goharshady and Dr. Zandieh.