CASC’s Machine Intelligence Group was founded in 2020 to create a home base for technical staff and postdocs conducting fundamental and applied research in machine learning (ML) in support of the Laboratory’s national security missions. Our research portfolio includes a broad set of topics stemming from the growing field of scientific ML, including representation learning, adversarial methods, uncertainty quantification, graph learning, generalizability, interpretability, and model calibration.
Group Lead
Peer-Timo Bremer: large-scale data analysis, topological techniques, scientific ML, interpretability, data management
Research Staff
Hongjun Choi: deep learning, computer vision, graph-based modeling, implicit neural representations, representation learning, scientific machine learning, retrieval augmented generation (RAG)
James Diffenderfer: numerical optimization (nonlinear programming, constrained optimization), machine learning, numerical analysis
Mark Heimann: graph-based methods, unsupervised learning, dimension reduction, kernel methods
Bhavya Kailkhura: robust ML, adversarial deep learning, non-convex optimization and decentralized control, robust statistics
Shusen Liu: explainable AI, latent representation interpretation, high-dimensional data visualization, information visualization, visual analytics
Vivek Narayanaswamy: supervised learning, unsupervised learning, computer vision, scientific ML, uncertainty quantification, robustness, generalization, interpretability
Sam Sakla: deep learning, computer vision, self-supervised learning, fine-grained classification, object detection, manifold learning, multi-resolution image/signal processing
Gautam Singh: generative models, large language models, agent learning, model-based RL, data-efficient learning, data-centric AI, AI4code
Luning Sun: scientific machine learning, multiscale/multi-fidelity data modeling, AI for science, uncertainty quantification
Jayram Thathachar: memory-augmented neural networks, multimodal reasoning, optimization, big data algorithms, theoretical computer science
Kowshik Thopalli: deep learning, multimodal models, generative AI, interpretable ML, AI safety, computer vision, domain and test-time adaptation, scientific ML and uncertainty verification