Daniel Aliaga

Daniel G. Aliaga is an Associate Professor of Computer Science at Purdue University and co-founder of the Computer Graphics and Visualization Laboratory (CGVLAB). His research lies at the intersection of visual computing, computer vision, urban modeling, and generative AI, with a focus on building computational frameworks that synthesize, reconstruct, analyze, and simulate complex urban environments. Aliaga develops semi-automatic and fully automatic methods for converting unstructured, incomplete real-world data into scalable, editable procedural models that can represent buildings, facades, vegetation, road networks, and entire cities. These techniques support a wide range of applications, including digital twins, environmental and weather simulations, urban sustainability, cultural heritage reconstruction, and immersive visualization for education and entertainment. His group has contributed advances in 3D reconstruction, spatial augmented reality, optimization for geometric modeling, camera calibration, and multimodal data fusion. Aliaga’s scholarly presence spans more than 170 peer-reviewed publications, numerous invited talks, extensive international collaborations, and advisory roles with startups. He has secured over 40 million dollars in external funding across 29 competitive grants, and has served as Associate Editor for IEEE TVCG and the Visual Computing Journal. His academic trajectory includes degrees from Brown University and UNC Chapel Hill, and research appointments at Bell Labs and Princeton University, underscoring a distinguished career bridging theory, systems, and real-world impact. Representative publications include "ObjectStitch: Object Compositing with Diffusion Model", and "Deep learning-based urban morphology for city-scale environmental modeling", which reflect their ongoing engagement with the field.