Profiles

I hold a Bachelor’s degree in Mechatronic Engineering from Universidad Autónoma de Bucaramanga, a Master’s in Artificial Intelligence and Robotics from the International University of Applied Sciences in Germany, and am pursuing an M.Eng. in AI at the University of Cincinnati. My early career combined machine learning engineering and software development, leading projects in computer vision, NLP, and robotics. I have contributed to AI solutions in retail, UAV systems, and edge devices, and authored professional courses on neural networks and computer vision.

My research interests include computer vision, deep learning, and AI for robotics. I focus on developing perception and reasoning systems for real-world applications, such as retail analytics, UAV navigation, and edge computing. I am also interested in reinforcement learning and multimodal AI systems that combine visual, thermal, and language data.

Andrea Rocha is a research specialist in statistics at KAUST, working with the Stochastic Processes and Mathematical Statistics group. Before joining KAUST, she was an associate professor in the Department of Scientific Computing at the Federal University of Paraíba (UFPB), Brazil, where she also held various academic and coordination roles over nearly 15 years.

Her academic background includes a Ph.D. in Computational Mathematics, an M.Sc. in Statistics, and a B.Sc. in Statistics, all from the Federal University of Pernambuco (UFPE). Her doctoral and master’s work was supervised by Professor Andrei Toom.

Andrea’s research bridges theory and application in computational statistics, with a focus on probabilistic modeling, regression analysis, and stochastic processes. She has authored several publications in international journals and co-authored academic books on probability and random processes.

Andrea’s research lies at the intersection of computational mathematics, statistical inference, and applied probability. Her main areas of interest include:

• Statistical modeling of complex data using Bayesian methods
• Machine learning techniques for regression and classification
• Probabilistic simulation and stochastic processes
• Diagnostic and influence analysis in regression models
• Dispersion models and beta regression

She has contributed to the theory and application of statistical methods through both individual research and collaborations.

Her research interests include computer vision and deep learning, with a focus on video understanding and efficient models.