Hi, Nice to meet you!
My name is Padam Thapa. I am from Nepal. Currently, I am a Ph.D. Student in the Department of Computer Science at the University of New Orleans (UNO) with a research concentration in Machine Learning and Deep Learning for Computer Vision. My research mainly focuses on understanding defects in the levee system by identifying and localizing them using sensor data, videos, and real images collected from Unmanned Aerial vehicles (UAVs). I have also utilized synthetic images generated from GANs, Diffusion Models, and Unity3D to make the models robust for their localization and detection in the levee faults. Also, as a proactive, solution-oriented professional, I am excited to apply my skills in AI and advanced algorithms to tackle real-world challenges, aiming to leave a positive impact on society and technology.
Python, R
Tensorflow-Keras, Pytorch
Sklearn, Pandas, NumPy
Matplotlib, Seaborn, plotly
SQLite, MySql, PostgreSQL
MS Excel, VBA Excel
DynamoDB, MongoDB
Elasticsearch
Django, Flask
PowerBI
Microsoft Azure, AWS
Others: Git, Pytest, NLTK, Streamlit, MLOps
PROJECTS
Microsoft Malware Prediction
The ML model where the malware is detected automatically when the user happens to interact with the Trojans or Worms. Out of the 3 algorithms used, XGBoost outperformed the best.
Stock Price Prediction
An AI model trained with Nepal Stock Exchange dataset to optimize stock trading strategy and outcome with maximum investment return using Deep Reinforcement Learning and LSTMs.
Credit Card Fraud Detection
Used highly imbalanced transactions dataset with 3 classification models trained with both imbalanced and synthetically balanced datasets.
Detecting COVID-19 with Chest X-Ray
Used a ResNet-18 model and trained it on a COVID-19 Radiography dataset to create an Image Classification model that can predict Chest X-Ray scans with reasonably high accuracy
Traffic Sign Classification
A multi-class Classification project using Deep Convolutional Neural Network Model which can classify the images of 43 distinct types of Traffic Signals.
Explainable AI: Scene Classification using ResNet-18
A Deep CNN and ResNet Model trained for classification of scenary from Satellite Images using GradCam visualization technique which helps to explain how AI models think.
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