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Deep interpretable generative learning for science and engineering Talk, Rising Stars at the Conference on Parsimony and Learning (CPAL), 2024 -
Deep interpretable generative learning for science and engineering Talk, Rising Stars in Data Science at the University of Chicago, 2023 -
Generative models for learning interpretable representations of neural data Talk, MetaConscious Group at MIT, 2023 -
Design deep neural architectures based on dictionary learning Talk, Decision Theory - APMTH 231 at Harvard University, 2023 -
Deep learning for inverse problems in engineering and science Talk, Department of Management Sciences at the University of Waterloo, 2023 -
Interpretable deep learning for deconvolution of multiplexed neural signals Talk, Computational and Systems Neuroscience (COSYNE), 2023 -
Deep learning for inverse problems in engineering and science Talk, Healthy ML Group at MIT, 2023 -
Deep learning for inverse problems in engineering and science Talk, ECE Department at the University of Waterloo, 2023 -
Deep learning for inverse problems in engineering and science Talk, My PhD Defense at Harvard University, 2023 -
Interpretable multimodal deconvolutional representation learning Talk, MURI annual meetings at ARL, 2023 -
Deep representation learning for computational neuroscience Talk, DiCarlo Lab at MIT, 2022 -
Interpretable unrolled dictionary learning networks Talk, Conference on the Mathematical Theory of Deep Neural Networks (DeepMath), 2022 -
Design interpretable and efficient neural architectures for science and engineering Talk, Anima AI + Science Lab at Caltech, 2022 -
Deconvolution of multiplexed neural signals using interpretable deep learning Talk, Uchida Lab at Harvard University, 2022 -
Deep unrolled learning using bilevel optimizations Talk, Vector Institute, 2022 -
Deep unrolling for inverse problems Talk, Poggio Lab at MIT, 2021 -
Unfolded neural networks for implicit acceleration of dictionary learning Talk, Amirkabir Artificial Intelligence Student Summit (AAISS) at Amirkabir University of Technology, 2021 -
Perceptual stereo speech enhancement Presentation, Applied Sciences Group at Microsoft, 2021 -
Model-based deep learning Tutorial, IEEE ICASSP Conference, 2021 -
Introduction to deep learning for computational neuroscience Workshop, Neurosur, 2021 [github] -
Dictionary learning based autoencoders for inverse problems Talk, Decision Theory - APMTH 231 at Harvard University, 2021 -
On the relationship between dictionary learning and sparse autoencoders Talk, Computational and Applied Math Seminar at Tufts University, 2020 -
On the relationship between dictionary learning and sparse autoencoders Talk, Pierre E. Jacob's Group at Harvard University, 2020 -
Multichannel end-to-end neural architectures for speech enhancement Presentation, Amazon AI - AWS, 2019 -
Autoencoders for unsupervised source separation Talk, Decision Theory - APMTH 231 at Harvard University, 2019 -
State-space models and deep deconvolutional networks Talk, CRISP Group at Harvard University, 2018