Anqi Wu

Anqi Wu is an Assistant Professor at the School of Computational Science and Engineering (CSE) within the College of Computing at Georgia Institute of Technology. She previously held a postdoctoral fellowship at Columbia University’s Zuckerman Mind Brain Behavior Institute and earned her Ph.D. in Computational and Quantitative Neuroscience from Princeton University, with a graduate certificate in Statistics and Machine Learning. Her research focuses on developing probabilistic modeling approaches and scalable inference algorithms for neural and behavioral analysis, with applications in robotics and real-world problems.

Education:

• Ph.D., Computational and Quantitative Neuroscience, Princeton University (2014–2019)
• Graduate Certificate in Statistics and Machine Learning, Princeton University
• Postdoctoral Fellow, Center for Theoretical Neuroscience, Columbia University (2019–2022)

Research Interests:

• Probabilistic Modeling for Neural Latent Discovery: Developing disentangled generative models to extract interpretable neural representations.
• Behavior Analysis: Extracting multi-layered information from animal/human behavior via hierarchical models and reinforcement learning.
• Efficient Inference: Advancing Bayesian neural networks, Gaussian processes, and scalable algorithms.

Her work bridges machine learning, neuroscience, and robotics, with applications in brain decoding, motor control, and reward learning.

Publications Trends: Recent work emphasizes reinforcement learning (e.g., EmoBipedNav for emotion-aware robotics), generative models (diffusion models for neural dynamics), and Bayesian methods (Gaussian processes for multi-region brain analysis). Applications span robotics, neuroscience, and spatio-temporal data modeling.

Affiliations:

• Director, BRAINML Lab (BRAIN Intelligence and Machine Learning)
• Affiliated with Machine Learning Center, Neuro @ GT, and Bioengineering at Georgia Tech