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David Huang, M.D., Ph.D.
PRECISE Seminar: Optical Coherence Tomography - From Conception to Current Frontiers
April 25, 2024

Optical coherence tomography (OCT) is a technology invented in 1991 to image small critical tissue structures throughout the body with micrometer resolution. It is widely used in the management of eye and coronary heart diseases. In 2023, OCT received broad attention when its inventors received the prestigious Lasker-DeBakey Clinic Medical Research Award and the National Medal of Technology and Innovation from President Biden. For me, it was the culmination of 3 decades of work as an engineer, clinician, and translational researcher, as well as an even longer…

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Amitha Domalpally, M.D., Ph.D.
PRECISE Seminar: Image Curation for AI in Ophthalmology
February 26, 2024

This presentation will explore the critical process of curating medical imaging data for AI algorithm development in ophthalmology, highlighting the challenges and current limitations in data curation. It will discuss benchmark datasets, reference standards for FDA validation, and innovative strategies to enhance data availability. Attendees will gain insights into best practices and future directions in image curation for advancing AI applications in eye care.

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Aniruddh Puranic
Postdoc Candidate Talk: Sample-Efficient and Robust Neuro-Symbolic Learning from Demonstrations
February 26, 2024

Safety, robustness, and interpretability are some of the major challenges in developing systems for human-robot interactions. Learning-from-demonstrations (LfD) is a popular paradigm to obtain effective robot control policies for complex tasks via reinforcement learning without the need to explicitly design reward functions. However, this paradigm typically requires large datasets of user demonstrations. It is also susceptible to imperfections in demonstrations and raises concerns of safety and interpretability in the learned control policies. To address…

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Michele Caprio, Ph.D.
PRECISE Postdoc Series: Imprecise Probabilities Machine Learning
February 8, 2024

In this talk, I will present Imprecise Probabilities (IPs), from their historic philosophical motivations to their applications to frequentist and Bayesian statistics. In turn, I will explore how the statistical approaches to IPs allowed to start the field of Imprecise Probabilistic Machine Learning, and why such a field is of paramount importance in modern ML. I will provide plenty of references, together with some new results on the comparison between Conformal Prediction and Imprecise Probabilistic Machine Learning, and how the latter can be used to derive regions that are narrower than…

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Yatin Manerkar, Ph.D.
PRECISE Seminar: Formal Methods for Computer Architecture - Reducing the Barriers to Entry
December 8, 2023

Formal methods can provide strong correctness guarantees for today's computing systems, but their usage is often restricted to formal methods experts. Formal verification is then bottlenecked on these experts, limiting its effectiveness. This problem is acute in computer architecture, since many architects do not have formal methods expertise.

In this talk, I will present recent work with my students and collaborators that reduces the barriers to entry for computer architects to use formal methods. First, I will discuss our work on automatically generating formal…