Ann Huang

Kempner Graduate Fellow
PhD Student in Neuroscience

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KEMPNER GLOBAL COMMUNITY I speak: English, Mandarin

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Ann Huang aims to reverse-engineer the computational mechanisms that support biological intelligence via neural network modeling and reinforcement learning, and develop neuro-inspired algorithms and architectures to endow deep learning systems with better data efficiency and generalization. Prior to coming to Harvard University, Huang obtained research experience in hippocampal representational dynamics, resource-rational decision-making, neural network modeling, neural coding, and reinforcement learning. She earned her BS in Neuroscience from McGill University in 2023. Outside of her research,  Huang enjoys traveling, exploring the city, enjoying good food, outdoor activities, hiking, reading, and  debating with friends whether neuro-inspired AI is plausible.

Research Focus

Huang’s current research focuses on computational neuroscience, reinforcement learning, and neural dynamics. Her research examines neural computations that support flexible intelligence, transfer learning, and generalization in biological systems and AI, as well as neuro-inspired deep learning and neural population geometry.