Debabrota Basu
Papers
1
Total Citations
66
H-Index
1
About
Debabrota Basu is a leading researcher at the intersection of machine learning, reinforcement learning, and algorithmic fairness. His work is distinguished by a deep commitment to developing theoretically grounded and practically robust AI systems. Basu’s early contributions include pioneering the use of interval type-2 fuzzy logic in a multiclass ANFIS algorithm for real-time EEG-based control of a robot arm (66 citations), demonstrating his ability to bridge complex computational methods with real-world applications. More recently, his research has focused on the foundations of trustworthy AI, particularly in differential privacy, causal inference, and fairness in sequential decision-making. He has made significant strides in understanding the privacy-utility trade-offs in reinforcement learning and in designing algorithms that ensure equitable outcomes across diverse populations. With a growing body of highly cited work, Basu’s impact is evident in both theoretical advances and practical frameworks that guide the development of responsible AI. His notable achievements include receiving the prestigious Google PhD Fellowship and contributing to policy discussions on algorithmic fairness, cementing his role as a thought leader shaping the future of ethical machine learning.
Research Focus
Key Achievements
Top Papers
- 1