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ESE Spring Seminar – “Physics-inspired Machine Learning”

Glandt Forum, Singh Center for Nanotechnology 3205 Walnut Street, Philadelphia, PA, United States

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 on the converse, i.e., physics-inspired machine learning, which can be described as incorporating structure from physical systems into machine learning methods to obtain models with […]

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

Raisler Lounge (Room 225), Towne Building 220 South 33rd Street, Philadelphia, PA, United States

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 issues as robust reasoning and alignment remain challenging. My research opens up the neural network black box with mathematical and statistical analyses of how networks […]

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

Raisler Lounge (Room 225), Towne Building 220 South 33rd Street, Philadelphia, PA, United States

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, however, deep learning methods are known to behave unpredictably on some real-world data, which limits their trusted adoption in safety-critical domains. Accordingly, in this talk […]

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

Raisler Lounge (Room 225), Towne Building 220 South 33rd Street, Philadelphia, PA, United States

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 preventing the simultaneous improvement of resolution, field of view, and frame rate. In this seminar, I propose that large-scale, machine learning-accelerated computational imaging will be […]

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

Raisler Lounge (Room 225), Towne Building 220 South 33rd Street, Philadelphia, PA, United States

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. Decisions made by deep learning models are often difficult to interpret, and the underlying mechanisms remain poorly understood. Given that deep learning models operate as […]