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Representation learning through the lens of inverse problems in AI and neuroscience Electrical & Computer Engineering Department, University of Alberta, 2025 -
Generative AI and representation learning for inverse problems Electrical Engineering & Computer Science Department, York University, 2024 -
Sparse autoencoders for gaining mechanistic interpretability of neural signals Chen Institute Workshop on AI for Neuro: How Modern AI Technologies Enable Advances in Neuroscience, Caltech, 2024 -
Understanding the role of feedback in vision systems Department of the Navy, Office of Naval Research, 2024 -
A feedback mechanism in deep generative networks to remove degradation from visual stimuli From Neuroscience to Artificially Intelligent Systems (NAISys), Cold Spring Harbor Laboratory, 2024 -
A feedback mechanism in deep generative networks to remove degradation from visual stimuli Swartz Foundation Meeting, University of Washington, 2024 -
Neural operators for functional ultrasound imaging Andersen Lab, Caltech, 2024 -
AI-powered neuroimaging for brain-computer interfaces IST Council at Caltech, 2024 -
Deep unrolling for inverse problems in engineering and science Decision Theory - APMTH 231, Harvard University, 2024 -
Deep unrolling for inverse problems in engineering and science Department of Biomedical Data Science, Stanford University, 2024 -
Deep interpretable generative learning for science and engineering Rising Stars at the Conference on Parsimony and Learning (CPAL), 2024 -
Deep interpretable generative learning for science and engineering Rising Stars in Data Science, University of Chicago, 2023 -
Generative models for learning interpretable representations of neural data MetaConscious Group, MIT, 2023 -
Design deep neural architectures based on dictionary learning Decision Theory - APMTH 231, Harvard University, 2023 -
Deep learning for inverse problems in engineering and science Department of Management Sciences, University of Waterloo, 2023 -
Interpretable deep learning for deconvolution of multiplexed neural signals Computational and Systems Neuroscience (COSYNE), 2023 [talk] -
Deep learning for inverse problems in engineering and science Healthy ML Group, MIT, 2023 -
Deep learning for inverse problems in engineering and science Department of Electrical and Computer Engineering, University of Waterloo, 2023 -
Deep learning for inverse problems in engineering and science My PhD Defense, Harvard University, 2023 -
Interpretable multimodal deconvolutional representation learning MURI annual meetings, ARL, 2023 -
Deep representation learning for computational neuroscience DiCarlo Lab, MIT, 2022 -
Interpretable unrolled dictionary learning networks Conference on the Mathematical Theory of Deep Neural Networks (DeepMath), 2022 -
Design interpretable and efficient neural architectures for science and engineering Anima AI + Science Lab, Caltech, 2022 -
Deconvolution of multiplexed neural signals using interpretable deep learning Uchida Lab, Harvard University, 2022 -
Deep unrolled learning using bilevel optimizations Vector Institute, 2022 -
Deep unrolling for inverse problems Poggio Lab, MIT, 2021 -
Unfolded neural networks for implicit acceleration of dictionary learning Amirkabir Artificial Intelligence Student Summit (AAISS), Amirkabir University of Technology, 2021 -
Perceptual stereo speech enhancement Applied Sciences Group, Microsoft, 2021 -
Model-based deep learning IEEE ICASSP Conference Tutorial Session, 2021 -
Introduction to deep learning for computational neuroscience Neurosur Workshop, 2021 [github] -
Dictionary learning based autoencoders for inverse problems Decision Theory - APMTH 231, Harvard University, 2021 -
On the relationship between dictionary learning and sparse autoencoders Computational and Applied Math Seminar, Tufts University, 2020 -
On the relationship between dictionary learning and sparse autoencoders Pierre E. Jacob's Group, Harvard University, 2020 -
Multichannel end-to-end neural architectures for speech enhancement Amazon AI - AWS, 2019 -
Autoencoders for unsupervised source separation Decision Theory - APMTH 231, Harvard University, 2019 -
State-space models and deep deconvolutional networks CRISP Group, Harvard University, 2018