About

Ziqi Han is a highly skilled and motivated software engineer and research scientist with a passion for machine learning algorithms and software development. Currently, Ziqi is a Research Software Development Engineer in the Machine Learning Group at Microsoft Research Lab, where he is working on designing and developing a carbon neutralization algorithm model in collaboration with environmental scientists. He is also responsible for designing and building a supervised learning model to predict the motion of molecules in force fields in collaboration with quantum physicists using GNN (Graph Neural Network). Ziqi has a Master's degree in Information System from the University of Maryland, College Park, and has previously worked as an intern in the Machine Learning Group at Microsoft Research Asia. He also has experience working as a Software Algorithm Engineer at Bilibili Group, where he developed an algorithm based on reinforcement learning to automatically adjust the video resolution of the player according to the real-time network state. Ziqi also used MapReduce to realize the parallel computing of machine learning algorithm (gradient descent), which improved the computing speed. As a Research Assistant at ShanghaiTech University, Ziqi worked with researchers from the Institute of Automation of the Chinese Academy of Sciences and Professor Jun Wang of University College London to complete research projects. He used reinforcement learning algorithms to solve multi-objective optimization problems and applied them in the field of communication. Ziqi was responsible for the implementation of reinforcement learning algorithms and the collection and analysis of experimental data. With 1.93 years of relevant experience, Ziqi has a strong technical background in software engineering, reinforcement learning, ML, supervised learning, neural networks, and MapReduce. He is a quick learner and has excellent problem-solving skills. Ziqi is excited to join Microsoft as an SWE intern and is looking forward to contributing his skills and knowledge to the team.