Presenter: Reza Ramezan, Associate Professor, Teaching Stream (Research Active), University of Waterloo Description: Neural spike trains are electrochemical signals that are localized in time and serve as the primary means […]
This dissertation addresses two questions in survey methodology: How can we produce granular, reliable estimates? And how can we validate them without ground truth? We develop three methods: (1) Gaussian data thinning enables out-of-sample model validation using only direct survey estimates and their sampling variances. We characterize its bias–variance tradeoff to guide model comparison. — […]
Presented by: Adam Wang, Assistant Professor of Radiology @ Stanford University Description: “Computed tomography (CT) is a mainstay of medical imaging, but its capabilities are constrained by radiation dose and the information captured by conventional x-ray detectors. Photon-counting CT (PCCT) measures individual x-ray photons and their energies, creating new opportunities for high-resolution, low-dose, and quantitative imaging. […]
Welcome to the second Computational Media seminar of the fall quarter. Open to all, class enrollment is not required. Food will be provided. Please join us in-person at E2-280 or via Zoom to learn more about the works of Professor Jim Whitehead: Title: Sides Taken: Generative Artists Made by Generative AI Presenter: Dr. Jim Whitehead, Professor of […]
Presented by: Quan Xiao, PhD Candidate @ Cornell University Description: “Agentic AI systems combine large language models with system-level design choices such as post-training data selection, retrieval, tool use, and memory management in augmented inference. Training these systems involves multiple coupled objectives beyond task success, including safety, retrieval quality, and adaptation efficiency. Sequential tuning can cause […]
Presented by: Francesco Trotti, Marie Skłodowska-Curie Postdoctoral Fellow @ University of Bologna, Italy Description: “Developing autonomous robotic systems capable of operating reliably in uncertain and dynamic environments requires the integration of planning, learning, and control while addressing fundamental challenges related to safety, robustness, and computational efficiency. This talk will present a research journey from stochastic […]
We motivate, formulate, and solve several structured optimal transportation (OT) problems for learning and control. The field of OT had its inception in 1781, when Gaspard Monge sought the optimal map to transport mass from a given initial distribution to a desired terminal distribution. Both OT and its entropy-regularized variant, the Schrödinger bridge problem (SBP), […]