Visual Analytics for Macromolecular Science
This dissertation presents a suite of scientific visual analytics systems that tightly couple large-scale computational modeling - including both AI-powered inference and traditional molecular simulations - with interactive, scalable visualization.
Overview
This dissertation presents a suite of scientific visual analytics systems that tightly couple large-scale computational modeling - including both AI-powered inference and traditional molecular simulations - with interactive, scalable visualization. By enabling experts to directly explore, interpret, and refine complex macromolecular data generated from intensive computation, these systems bridge the persistent “non-optimizable gap” that automation alone cannot overcome. This integrative approach empowers more effective human–machine collaboration, accelerates scientific discovery, and addresses challenges that demand nuanced human insight in molecular science.
Presenters
Brief Biography
Deng Luo is a Ph.D. Candidate in the Computer Science program under the supervision of Professor Ivan Viola at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. He received his Bachelor's degree in Bioinformatics from Southern University of Science and Technology in 2016. He joined KAUST and received his Master's degree in Bioscience in 2019. He has a multidisciplinary background, including bioinformatics, molecular biology, computer science, and entrepreneurship.
