ManimML is a project focused on providing animations and visualizations of common machine learning concepts with the Manim Community Library.

Deep Learning the T Cell Receptor Binding Specificity of Neoantigen

Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy

Matplotlib styles for scientific plotting

A small python library to adjust and annotate axis ticklabels in plots.

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its go…

Deep Learning Methods for Parsing T-Cell Receptor Sequencing (TCRSeq) Data

Reference-free cell-type deconvolution of multi-cellular spatially resolved transcriptomics data

Introduction to Parallel Programming class code

Graph Algorithms

A game theoretic approach to explain the output of any machine learning model.

Implementation of Graph Auto-Encoders in TensorFlow

Representation learning on large graphs using stochastic graph convolutions.

Official Implementation for "StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery" (ICCV 2021 Oral)

Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.

Python package designed for general financial and security returns analysis.

Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires

Collection of Tumor-Infiltrating Lymphocyte Single-Cell Experiments with TCR

Analysis of SARS-CoV-2 specific T-cell receptors in ImmuneCode reveals cross-reactivity to immunodominant Influenza M1 epitope

A Graphical User Interface for streamlining analysis of high-dimensional cytometry data

Deep learning for distinguishing morphological features of Acute Promyelocytic Leukemia

Structural TCR Repertoire Analytics Package

Graph Neural Networks with Keras and Tensorflow 2.

Precision HLA typing from next-generation sequencing data

Archiving Parler.

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)