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ESE Spring Seminar – “Solving Inverse Problems with Generative Priors: From Low-rank to Diffusion Models”

: Generative priors are effective countermeasures to combat the curse of dimensionality, and enable efficient learning and inversion that otherwise are ill-posed, in data science. This talk begins with the […]

BE Seminar: “Using Computers to Derive Protein Structure from Sparse Data – A Case Study for Mass Spectrometry” (Steffen Lindert, Ohio State)

Mass spectrometry-based methods such as covalent labeling, surface induced dissociation (SID) or ion mobility (IM) are increasingly used to obtain information about protein structure. However, in contrast to other high-resolution […]

ESE & CIS Spring Seminar – “Towards Transparent Representation Learning”

Machine learning models trained on vast amounts of data have achieved remarkable success across various applications. However, they also pose new challenges and risks for deployment in real-world high-stakes domains. […]

ESE & BE Spring Seminar – “Ultra-high-throughput computational imaging: towards a trillion voxels per second”

Traditional biomedical imaging techniques face throughput bottlenecks that limit our ability to study complex dynamic samples like cells, organoids, tissues, and organisms. In particular, hardware-only systems have inherent physical limitations […]

ESE Spring Seminar – “White-Box Computational Imaging: Measurements to Images to Insights”

Computation and machine learning hold tremendous potential to improve the quality and capabilities of imaging methods used across science, medicine, engineering, and art. Despite their impressive performance on benchmark datasets, […]

ESE & CIS Spring Seminar – “Beyond the black box: characterizing and improving how neural networks learn”

The predominant paradigm in deep learning practice treats neural networks as “black boxes”. This leads to economic and environmental costs as brute-force scaling remains the performance driver, and to safety […]

ESE Spring Seminar – “Physics-inspired Machine Learning”

Combining physics with machine learning is a rapidly growing field of research. Thereby, most work focuses on leveraging machine learning methods to solve problems in physics. Here, however, we focus […]

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