Simegnew Yihunie Alaba

Mississippi State University, Georgia Institute of Technology

Papers

10

Total Citations

331

H-Index

8

About

Simegnew Yihunie Alaba is a prominent researcher specializing in deep learning-based perception systems for autonomous driving, with a particular focus on 3D object detection, sensor fusion, and multimodal learning. His work systematically examines how advanced neural network architectures can be leveraged to interpret complex driving environments with the accuracy, robustness, and speed that autonomous vehicles demand. Alaba's most influential contribution, "A Survey on Deep-Learning-Based LiDAR 3D Object Detection for Autonomous Driving" (2022), has garnered 99 citations, establishing him as a key synthesizer of knowledge in LiDAR-based perception. Complementing this, his widely cited reviews on camera-based image 3D object detection and multisensor fusion surveys collectively reflect a comprehensive vision of how diverse sensing modalities can be integrated for safer autonomous systems. His 2024 work on multimodal fusion trends, already accumulating 68 citations, signals his continued relevance at the cutting edge of the field. Beyond autonomous driving, Alaba has demonstrated versatility by applying deep learning techniques to ecological challenges, including semi-supervised fish species recognition, highlighting his broader commitment to real-world AI applications. With a rapidly growing citation record exceeding 330 total citations across his portfolio, Alaba's research serves as essential reading for anyone entering the fields of autonomous systems, computer vision, or applied deep learning.

Research Focus

Key Achievements

8
H-Index
10
Papers
331
Total Citations
33
Avg Citations/Paper
🏆 Most Cited Paper
A Survey on Deep-Learning-Based LiDAR 3D Object Detection for Autonomous Driving
99 citations · 2022
📈 Most Prolific Year: 2022 (7 Papers)
🤝 Key Collaborators: 10
🏛 Institutions: Mississippi State University, Georgia Institute of Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 17 days ago