👋 Hey there! I'm Trilok Padhi! 👋

About Me

I am a PhD research student at Georgia State University, Atlanta passionate about knowledge graphs and multimodal learning. In my research, I explore the intersection of knowledge representation and multimodal models to solves problems related to society pertaining to marketing, toxicity, extermism, and healthcare informatics. I have a strong background in machine learning, natural language processing, and computer vision.

Skills

💡 Languages: Python, C, C++
🛠️ Frameworks: Pytorch, Tensorflow, Django, React, Spring Boot
🧠 Databases: PostgreSQL, MySQL, MongoDB
🚀 Tools: Git, Docker, Jenkins

Research Projects

Here are some of my research projects that I am currently working on which are private, that are not publicly accessible because they are still under development/paper is under review. If you are interested in gaining access to these repositories, please follow these steps:

Send me an email at [email protected] with the subject line "Request for Access: GitHub Repositories".
In the email, please include your GitHub username and the reason for your request.:

Knowledge Infused MultiModal Marketing - A knowledge infused multimodal model for classification of marketing campaigns
Mental Health Analysis of LLMs - A project in which we do the analysis of LLMs for mental health pateints
Knowledge Guided Prompting - A project in which we develop knowledge guided prompts

Exploratory Projects

Here are some of the projects I've been working on, which are fueled by motivation to built everything from scratch and are currently under development. Check back for updates!

LLM from Scratch - LLM from Scratch is a repository dedicated to understanding the inner workings of machine LLMs by building them from the ground up using basic programming and mathematical concepts.
Deep Learning from Scratch - Deep Learning from Scratch is a comprehensive repository that focuses on the implementation of deep learning algorithms from the ground up. This repository aims to provide a deeper understanding of the underlying principles and mechanics of deep learning models by coding them from scratch.
Exploration with LLMs - Exploration related to finetuning/prompting/PEFT of LLMs