Baihan Lin researches computational neuroscience and systems biology at the Center for Theoretical Neuroscience and Zuckerman Mind Brain Behavior Institute in Columbia University. His current research interest is modeling multiscale systems and complex networks with machine learning, geometric topology and dynamical systems. His research helps understand the neural systems and cognitive processes of biological brains in their healthy and abnormal states, and construct a mechanistic theory of deep neural networks.

 

Other than fundamental research, he maintains close collaborations with IBM, Google, Microsoft and Amazon on important industrial and engineering problems by developing neuroscience-inspired algorithms for computer vision, speech recognition, natural language processing, system security, personalized medicine and computational genomics domains.

 

According to the Google Scholar, he has authored 25+ publications with an H-index of 10+ and served on program committees for IJCAI, AAMAS, INTERSPEECH, AISTATS, NeurIPS, CVPR, ICCV, KDD, ICLR, AAAI, and MICCAI, etc., as well as journals including Nature Scientific Reports, PLOS ONE, JACS, JInf, Entropy, Adv Complex Syst, IEEE Trans Knowl Data Eng, Comput Commun and Front Robot AI.