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auc-data-challenge-23

Rank #1 AUC Morgan-Stanley Data Challenge 23

auc_python_workshop_2022

This repository contains all the materials for the 4-Day Beginner Python Academy that I run for AUC faculty members during the summer of 2022. During this course, faculty members are introduced to the fundamentals of Python programming and its applications in data analysis and visualization using Numpy, Pandas, Matplotlib, and Seaborn.

brief-introduction-to-deep-neural-networks-mathematical-foundations

bystander-effect-car-t-modeling

cancer_immunology

This repo contains material from the Mathematical Modelling class I am teaching in Spelman College, Math Department. The class focuses on fundemantal mathematical oncology concepts. In particlar, we study **mathematical modelling in tumor immunology**, including newly emerging CART-cell therapies

diffusion-tensor-imaging-dti-with-3d-slicer

Displaying DTI data scalar measurements and tumor segmentation with 3D Slicer

hamiltonian-nn

Code for our paper "Hamiltonian Neural Networks"

intel-image-classification-with-pytorch

intro2-deep-learning-with-pytorch

This repository contains of resources from my Introduction to Deep Learning with Python class I offer in Spelman College. Inside, you'll find a variety of materials, including lectures, tutorials, and code examples

myfemus

py4scicomp

Python for Scientific Computing (FEniCS, PyTorch, VTK)

pytorch_image_classification_with_optuna

pytorch_image_segmentation_drone_images

rise-of-transfer-learning

you will find brief code implementations of some of the latest developments in AI, including Stable Diffusion, Whisper, YOLO and HuggigFace Transformers

spelman_college_machine_learning_group

This repo contains the notebooks regarding our deep learning based image recognition projects with my students in Spelman College

structurepreservingnn

Code for the paper "Structure-preserving neural networks" published in Journal of Computational Physics (JCP).

trackingwalkers-yolov8

This repository includes all the datasets, codes, and other resources discussed in our paper regarding walking droplets and granular flow experiments