Francis Ogoke

Francis Ogoke is an Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University, where he directs the FORGE Lab. His research focuses on developing artificial intelligence and deep learning methods to enhance, understand, and control engineering processes. He is interested in creating physics-informed deep learning methods to accelerate simulation-based insights, designing probabilistic frameworks for uncertainty analysis, and developing generalizable representation learning frameworks. These efforts aim to build foundational models for engineering problems. His work applies to areas including advanced manufacturing, sensing, cyber-physical systems, and digital twins. Ogoke received a Ph.D. in mechanical engineering from Carnegie Mellon University in 2024 and a B.S.E. in Chemical and Biological Engineering from Princeton University in 2019.

Francis Ogoke is an Assistant Professor at Carnegie Mellon University, specializing in Machine Learning, Deep Learning, and Additive Manufacturing. His research focuses on leveraging machine learning and deep learning techniques to address challenges in additive manufacturing, particularly in melt pool characterization, thermal control, and porosity prediction. He has developed models for high-resolution melt pool thermal imaging, deep reinforcement learning for thermal control, and generative deep diffusion models for porosity distribution. His work also includes the application of graph neural networks and vision transformers for analyzing unstructured flow field data and improving the accuracy of multiphysics simulations in additive manufacturing.