Talks & Workshops
    Please email me for invited talks.
  • From regularization to generation: AI for inverse problems
    CIFAR Deep Learning + Reinforcement Learning Summer School, Amii, 2025
  • Neural operators and biologically-informed latent embeddings for foundation models in neuroAI
    Workshop on Multi-modal Foundation Models and Large Language Models for Life Sciences, ICML, 2025
  • Interpretable deep learning for deconvolutional analysis of neural signals
    Computational Machinery of Cognition Lab, Technische Universität Dresden, 2025
  • 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