Zhen is a Ph.D. candidate specializing in machine learning and molecular science. His research is centered on applying machine learning in molecular science and broader domains, such as computer vision.
He excels in the complete pipeline of ML development, encompassing dataset preparation, model implementation, training, evaluation, and visualization. His portfolio spans a wide array of projects, such as low-energy 3D molecular structure generation utilizing neural network potential, molecular property optimization through reinforcement learning, prediction of reaction yields, design of molecular descriptors, and image deblurring employing latent diffusion models, among others.
His research has led to 7 peer-reviewed publications/conference papers. He has developed a strong foundation in deep learning knowledge and practical skills, and is committed to pushing the boundaries of ML capabilities and making meaningful contributions to the scientific community.