Dr. Lobaton received the B.S. degree in mathematics and the B.S. degree in electrical engineering from Seattle University in 2004. He completed his Ph.D. in electrical engineering and computer sciences from the University of California, Berkeley in 2009.
He is currently an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. Dr. Lobaton joined the department in 2011. His research focuses on the development of pattern recognition, estimation theory, and statistical and topological-data-analysis tools applied to wearable health monitoring, robotics and computer vision. He was awarded the NSF CAREER Award in 2016. Prior to joining NCSU, he was awarded the 2009 Computer Innovation Fellows post-doctoral fellowship award and conducted research in the Department of Computer Science at the University of North Carolina at Chapel Hill. He was also engaged in research at Alcatel-Lucent Bell Labs in 2005 and 2009.
2009 - Ph.D. in Electrical Engineering and Computer Sciences, University of California, Berkeley, CA
2004 - B.S. in Mathematics, Seattle University, WA
2004 - B.S. in Electrical Engineering, Seattle University, WA
- NSF CAREER Award (2016)
Awards & Honors
- 2009-2011 - Computer Innovation Fellows Postdoctoral Award
- 2004-2008 - Bell Labs Graduate Research Fellowship
- Drought Stress Detection Using Low-Cost Computer Vision Systems and Machine Learning Techniques (2020)
- Localization of Biobotic Insects Using Low-Cost Inertial Measurement Units (2020)
- Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses (2019)
- Automated species-level identification of planktic foraminifera using convolutional neural networks, with comparison to human performance (2019)
- LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites (2019)
- Vision-based integrated mobile robotic system for real-time applications in construction (2018)
- A Framework for mapping with biobotic insect networks: from local to global maps (2017)
- A comparative study of image classification algorithms for foraminifera identification (2017)
- Activity-Aware Physiological Response Prediction Using Wearable Sensors (2017)
- Coarse-to-fine Foraminifera image segmentation through 3d and deep features (2017)
Posted on June 10, 2020 | Filed Under: Research
Being able to identify crop problems early can make the difference between saving a crop and losing it, but high-tech solutions can be costly. An interdisciplinary team is leveraging existing technology for a solution.
Posted on February 15, 2020 | Filed Under: Research
Two interdisciplinary research teams with significant ECE involvement have entered the next phase of the Game-Changing Research Incentive Program to tackle plant science challenges
Posted on November 26, 2019 | Filed Under: Research
USDA $10-million grant to improve the sustainability of agriculture through the use of cover crops features cutting-edge tech, such as autonomous data collection and machine learning from ECE.