Computational neuroscience is a burgeoning field embracing exciting scientific questions, a deluge of data, an imperative demand for quantitative models, and a close affinity with artificial intelligence. These opportunities promote the advancement of data-driven machine learning methods to help neuroscientists deeply understand our brains. In particular, my work lies in such an interdisciplinary field and spans the development of scientifically-motivated probabilistic modeling approaches for neural and behavior analyses. In this talk, I will first present my work on developing Bayesian methods to identify latent manifold structures with applications to neural recordings in multiple cortical areas. The models are able to reveal the underlying signals of neural populations as well as uncover interesting topography of neurons where there is a lack of knowledge and understanding about the brain. Discovering such low-dimensional signals or structures can help shed light on how information is encoded at the population level, and provide significant scientific insight into the brain. Next, I will talk about probabilistic priors that encourage region-sparse activation for brain decoding. The proposed model provides spatial decoding weights for brain imaging data that are both more interpretable and achieve higher decoding performance. Finally, I will introduce a series of works on semi-supervised learning for animal behavior analysis and understanding. I will show that when we have a very limited amount of human-labeled data, the semi-supervised learning frameworks can well resolve the scarce data issue by leveraging both labeled and unlabeled data in the context of pose tracking, video understanding, and behavioral segmentation. By actively working on both neural and behavioral studies, I hope to develop interpretable machine learning and Bayesian statistical approaches to understanding neural systems integrating extensive and complex behaviors, thus providing a systematic understanding of neural mechanisms and biological functions.
Anqi Wu is an Assistant Professor at School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology. Her research interest is to develop cutting edge machine learning models for in-depth scientific discovery in neuroscience and build up neuro-inspired artificial intelligence systems. Wu received her Ph.D. degree in Computational and Quantitative Neuroscience with Prof. Jonathan Pillow and a graduate certificate in Statistics and Machine Learning from Princeton University in 2019. She holds a Master's degree in Computer Science from The University of Southern California, and a Bachelor's degree in Electrical Engineering from Harbin Institute of Technology, China. She received the USC Chevron Fellowship and was selected for the 2018 MIT Rising Star in EECS. She published a series of proceedings, and some of them were selected for oral presentations at top-tier machine learning and computational neuroscience conferences. She co-organized workshops at Cosyne and served as a Lecturer at Neuromatch Academy summer school.