Hao Tang is a PhD candidate whose research explores the use of computational tools to simulate physical systems and provide microscopic insights into the underlying physics and materials design strategy. As a MathWorks Fellow, Hao will advance his highly promising research along four primary lines of inquiry. First, he will study quantum algorithms for machine learning problems where the training dataset is distributed in remote devices. Second, he will conduct quantum transport simulation and phase-field simulation to explore the underlying physics of recently developed denoising technology. Third, he will continue to work in spin defects simulation, which has yielded a novel computational method to calculate the temperature dependence of transition energies in solid spin qubits. Finally, he aims to develop a reinforcement learning-based algorithm for long-timescale atomistic simulation. MATLAB plays a significant role in all aspects of Hao’s work. Through his research, Hao is contributing to the development of advanced tools to simulate physical systems, with the potential for a major impact in the field of computational materials science.