Yining Liu

She is particularly interested in helping students develop both the theoretical foundations and practical skills needed to apply computational methods to real-world problems.

At Columbia, Liu teaches courses across the data science and computer science curriculum, including Machine Learning for Data Science, Data Science Capstone and Ethics, Computing in Biology, and Discrete Mathematics. She has taught classes ranging from small, specialized courses to large-enrollment lectures. Through her teaching, she emphasizes clear conceptual understanding, problem-solving, and the responsible application of computational and data-driven methods. She also advises students in Columbia’s Data Science Undergraduate Program and mentors undergraduate and master’s students on research projects.

Liu’s teaching is informed by her research in machine learning and computational biology. Her research focuses on probabilistic and optimization methods for biomedical and single-cell studies, with applications including cell segmentation, cell type annotation, and the analysis of single-cell data. Her work draws on techniques from machine learning, optimal transport, probability, and geometry. This research background allows her to bring current computational methods and interdisciplinary perspectives into the classroom.

Before joining the Columbia faculty, Liu earned her Ph.D. in Computer Science from Columbia University, where she also received her M.Phil. and M.S. Her doctoral research focused on probabilistic and optimization methods for biomedical and single-cell studies. She received her B.A. in Mathematics with High Distinction from the University of California, Berkeley. She has also taught at UC Berkeley, including Discrete Mathematics and Probability Theory, and received the Outstanding Graduate Student Instructor Award in 202