Cedrique Rovile Njieutcheu Tassi
Papers
1
Total Citations
1,134
H-Index
1
About
Cedrique Rovile Njieutcheu Tassi is a leading researcher in the fields of uncertainty quantification in deep learning and trustworthy artificial intelligence. His most influential work, the comprehensive survey “A survey of uncertainty in deep neural networks” (2023), has garnered over 1,130 citations, establishing itself as a foundational reference for researchers and practitioners seeking to understand and implement uncertainty estimation techniques. Tassi’s major contributions center on developing frameworks that enable neural networks to not only make predictions but also reliably communicate their confidence, addressing critical challenges in safety-critical applications such as autonomous driving and medical diagnostics. By systematically categorizing methods—from Bayesian approaches to ensemble and evidential techniques—he has provided a clear roadmap for advancing model robustness and interpretability. His work has significantly shaped the conversation around responsible AI deployment, making him a key voice in the push for more transparent and reliable machine learning systems. Through his research, Tassi continues to empower the next generation of AI engineers to build models that are not only powerful but also accountable.
Research Focus
Key Achievements
Top Papers
- 1A survey of uncertainty in deep neural networks1,134 citations · 2023