ED
About
As an AI Engineer, I specialize at converting ideas into deployable models. I am a Computer Science student specializing in Artificial Intelligence, with a strong academic background. I am particularly interested in computer vision, natural language processing (NLP), and generative AI. I enjoy working on efficient deep learning models and have developed innovative AI solutions throughout my academic career.
Work Experience
SchlumbergerOnsite
2023 June - 2023 August
Data Scientist Intern
Worked on creating product embeddings using multi-task learning based on RFM attributes.
Education
Amrita Vishwa Vidyapeetham
2024 - 2024
Bachelor's Degree in Computer Science with Artificial Intelligence
Skills
Deep Learning: PyTorch, Lightning AI, Transformers, PEFT, Bitsandbytes, Hugging Face Ecosystem
LLMs: Langchain, Llamaindex, Langgraph
Databases: MongoDB, Supabase, Pinecone, Qdrant, ChromaDB
Languages: JavaScript, Python, SQL
Frameworks: React, Streamlit, FastAPI, Gradio
Projects
ModelHub
modelhub.vercel.app
ModelHub is a platform for sharing, discovering, and running machine learning models
Full Stack Developer
JavaScript
React
Node.js
Python
Podcastify
narrateit.streamlit.app
Convert a convert articles from URLs into listenable audio Podcasts.
Python
LLMs
Streamlit
Python
Chromadb
LLMs
Streamlit
Research Papers
This paper presents a study on the detection of Parkinson's Disease (PD) from T1-weighted MRI scans using Convolutional Neural Networks (CNNs). The study investigates the potential for bias propagation in CNN models due to data leakage and evaluates the generalizability of the models to external datasets.
This paper presents a approach to ASR for Dravidian languages like Tamil and Telugu. It builds upon the strengths of the powerful Whisper model, known for its multilingual capabilities, and fine-tunes it specifically for these under-resourced languages. This approach achieves significant improvements in WER compared to existing models.
This paper presents an approach to forecast rainfall in Kerala using LSTM models. The study compares the performance of LSTM models with traditional time series forecasting methods and demonstrates the superior accuracy of LSTM models in predicting rainfall in the region.