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UCLA
Data Scientist
June 27, 2026 – Present
IU-Xray-Report-Generation
June 13, 2024 – June 13, 2024
IU-Xray-Report-Generation is a project aimed at generating medical reports from chest X-ray images. The project leverages advanced deep learning techniques to automatically interpret X-ray images and produce comprehensive and accurate reports.
View ProjectISIC2016-Classification
April 9, 2024 – April 9, 2024
ISIC2016-Classification — GitHub repository
View ProjectProtoPNet
March 11, 2024 – April 1, 2024
This repository contains code adapted from the ProtoPNet project (https://github.com/cfchen-duke/ProtoPNet) for the classification of medical images. The primary dataset used for this adaptation is the MedMNIST dataset (https://medmnist.com/), specifically focusing on the PneumoniaMNIST subset.
View ProjectClustring-With-Genetic-Algorithm
January 25, 2024 – January 25, 2024
This project implements a Genetic Algorithm for clustering the Iris dataset. The goal is to optimize the clustering process by using a genetic algorithm, which aims to enhance the accuracy and efficiency of clustering.
View ProjectCredit-Card-Fraud-Detection
December 22, 2023 – December 22, 2023
This project focuses on detecting credit card fraud using machine learning techniques. The goal is to develop a model that can accurately classify credit card transactions as either fraudulent or legitimate based on various features.
View ProjectDataScience-Bootcamp
August 13, 2023 – September 1, 2023
Welcome to my Data Science Bootcamp projects repository! This repository houses the code for a collection of projects completed during my immersive Data Science Bootcamp.
View ProjectIntroduction_to_Machine_Learning
September 1, 2022 – Present
Machine Learning Course, Sharif University of Technology
View ProjectCultural Fit Analysis
The candidate's projects show a strong focus on data science and machine learning, aligning with a Data Scientist role. However, all projects are personal, and there is only one listed work experience with a future start date, making it difficult to assess cultural fit in a professional team environment. The diversity of projects within data science is good, but there's limited exposure to collaborative or production-level environments.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's experience level is listed as 0, and there are no completed psychometric or English tests to provide insights into work attitude, stress handling, team collaboration, or communication clarity.