Papers
1
Total Citations
12
H-Index
1
About
Su-Jin An is a pioneering researcher at the intersection of reinforcement learning and decision neuroscience, whose work seeks to bridge the gap between artificial intelligence and biological cognition. Her most-cited paper, "Toward high-performance, memory-efficient, and fast reinforcement learning—Lessons from decision neuroscience" (2019, 12 citations), distills key principles from how the brain makes decisions under uncertainty, proposing novel algorithms that are both memory-efficient and robust to noisy, dynamic environments. This contribution is particularly significant for real-world robotics, where traditional reinforcement learning often struggles with unpredictability. An’s research addresses a critical bottleneck in AI: achieving high performance without sacrificing computational efficiency or adaptability. By drawing inspiration from neural mechanisms, she offers a path toward more intelligent, brain-inspired autonomous systems. Her work has been recognized for its interdisciplinary impact, influencing both machine learning practitioners and neuroscientists. With a growing citation record, An is establishing herself as a leading voice in biologically grounded AI, and her insights hold promise for advancing robot learning in complex, real-world settings.
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Top Papers
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