I'm a Computer Science Ph.D. student at the University of Maryland with a strong interest in the intersection of machine learning and computer architecture. My journey began as an undergraduate, where I was captivated by the potential of machine learning to solve complex, real-world problems. That curiosity deepened during my master's studies when I took an advanced computer architecture course taught by Dr. Bahar Asgari, who is now my Ph.D. advisor. In that course, I was introduced to cutting-edge research and developed a deeper appreciation for the architectural challenges posed by input sparsity, workload variability, and resource bottlenecks. I became particularly fascinated by the promise of hardware-software co-design—an approach that combines algorithmic intelligence with architectural innovation to enhance system performance.

Today, my research focuses on how machine learning can be leveraged to make hardware architectures more efficient, adaptive, and intelligent. I'm especially interested in techniques that address the inefficiencies caused by sparsity, exploring methods to dynamically optimize computation and memory access patterns. My goal is to help design systems that are not only faster and more efficient, but also better suited to the demands of modern, data-intensive applications.