Name: Rachneet Kaur
Type: User
Company: University of Illinois at Urbana Champaign
Bio: PhD candidate at UIUC, Has a masters in mathematics from IIT Delhi. Past AI research internships with Visa Research, 3M & Quantlab Financial LLC
Twitter: kaurrachneet6
Location: Urbana Champaign
Blog: https://kaurrachneet6.github.io/
Rachneet Kaur's Projects
Alzheimer and Dementia Study on ADNI Dataset
CS598 HDA code
Practice for coding interviews
This repository is for CS 543 Computer Vision, UIUC Machine Problems
CS 412 - Data Mining, UIUC, Machine Problems
CS450-Numerical Analysis, UIUC machine problems
CS510 Advanced Information Retrieval, UIUC Machine Problems
CS 598 Deep Learning, UIUC Machine Problems
Deep Learning for Multiple Sclerosis Differentiation Using Multi Stride Dynamics in Gait
Distributed k-Means Clustering on General Topologies
IE 411 Optimization for large scale systems Machine Probelem
EEG analysis during anxiety-inducing height control experiments in virtual reality
EPI Judge - Preview Release
This repository contains codes for finite element and finite difference schemes in MATLAB created during my intern at TIFR CAM
刷算法全靠套路,认准 labuladong 就够了!English version supported! Crack LeetCode, not only how, but also why.
Website for the Women in AI and Finance Workshop at ACM ICAIF 2023 - NY, Nov 2023
IE498 Compting for ISE, UIUC machine problems
IE529 Statistics of Big Data, UIUC Machine Problems
IE531 Algorithms for Data Analytics, UIUC Machine Problems
Code for my public webpage
IE498 Computing for ISE Project - Maxit Game
A Modern C++ Data Sciences Toolkit
Predicting Alzheimer’s disease progression trajectory and clinical subtypes using machine learning
The official code for "One Fits All: Power General Time Series Analysis by Pretrained LM (NeurIPS 2023 Spotlight)"
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
This Repository contains MATH 410 (Linear Algebra and Financial Applications) course project coded in Python
This repository is for our work on Predicting Multiple Sclerosis from Gait Dynamics Using an Instrumented Treadmill - A Machine Learning Approach