Mohammad Afshari
Papers
1
Total Citations
131
H-Index
1
About
Mohammad Afshari is a leading researcher at the intersection of artificial intelligence and human-centered machine learning, with a primary focus on Human-in-the-Loop (HITL) reinforcement learning (RL). His seminal survey, "Human-in-the-Loop Reinforcement Learning: A Survey and Position on Requirements, Challenges, and Opportunities" (2024, 131 citations), has become a foundational reference in the field, redefining RL as an inherently collaborative paradigm between humans and autonomous agents. Afshari’s major contribution lies in systematically mapping the requirements and challenges of integrating human feedback into RL systems, bridging the gap between theoretical autonomy and practical, safe deployment. His work emphasizes that even superhuman-performing agents must be designed with human oversight, shaping how researchers approach interactive AI training. Beyond this, Afshari’s research explores the broader implications of HITL frameworks for trustworthy AI, influencing both academic discourse and industry practices. With his work cited widely in subsequent studies on human-AI interaction, Afshari is recognized for advancing a more responsible, human-aware approach to reinforcement learning, making him a pivotal voice in the evolution of autonomous systems.
Research Focus
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Top Papers
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