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Laknath1996/README.md

Hello there!

I am a PhD candidate in the Department of Biomedical Engineering at Johns Hopkins University. I broadly work on {statistical, deep} learning, with an aspiration of reducing the gap between machine and natural intelligence. My doctoral research focuses on learning under non-stationary distributions and distribution shifts, with applications in large language models, computer vision, and biomedical data science.

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  1. neurodata/prolearn neurodata/prolearn Public

    Prospective Learning: Learning for a Dynamic Future (NeurIPS 2024)

    Python 4

  2. neurodata/value-of-ood-data neurodata/value-of-ood-data Public

    The value of out-of-distribution data (ICML 2023)

    Python 8

  3. gen-models gen-models Public

    Course project for EN.553.741 Machine Learning II at JHU. Implements Wasserstein GAN, variational autoencoder and denoising diffusion probabilistic models.

    Python

  4. semi-supervised-learning semi-supervised-learning Public

    Repository for the EN.553.738 High-Dimensional Approximation, Probability and Statistical Learning group project on graph-based semi-supervised learning

    MATLAB 1

  5. DeepPhaseUnwrap DeepPhaseUnwrap Public

    This repository Introduces a joint convolutional and spatial quad-directional LSTM (SQD-LSTM) network for phase unwrapping in 2D images (ICASSP 2021)

    Python 35 5

  6. sEMG-Hand-Gesture-Recognition sEMG-Hand-Gesture-Recognition Public

    The source code for the real-time hand gesture recognition algorithm based on Temporal Muscle Activation maps of multi-channel surface electromyography (sEMG) signals (ICASSP 2021)

    Python 47 10