Hi, I'm Navaneeth Sivakumar

Machine Learning Engineer

Computer Science student at VIT Chennai, specializing in AI/ML. Passionate about developing innovative solutions and working on cutting-edge technologies. Experienced in research and industry projects, with a strong foundation in machine learning, computer vision, and software development.

05+ Projects

01+ Years Experience

About Me

I'm a Computer Science student at VIT Chennai, specializing in Artificial Intelligence and Machine Learning. My expertise lies in building innovative and research-driven solutions using deep learning, computer vision, and large language models. I've worked on impactful projects such as smart glasses for the visually impaired, spam detection systems, intelligent alarm systems, and Neural Radiance Fields (NeRF) for real-world scale 3D reconstruction. Additionally, I've conducted research in transformers, LLMs, and federated learning, aiming to bridge advanced AI models with practical applications.

Technical Skills

Programming Languages

Python
C++
Java
SQL
HTML/CSS

Machine Learning & AI

PyTorch
TensorFlow
Scikit-Learn
Computer Vision
Natural Language Processing

Tools & Technologies

Git
Docker
Linux
Jupyter Notebook
VS Code

View My Resume

Education

B.Tech in Computer Science

Vellore Institute of Technology, Chennai

2021 - 2025

Specialization in Artificial Intelligence and Machine Learning

CGPA: 9.03/10.00

Senior Secondary Education

Devi Academy Senior Secondary School

2019 - 2021

Score: 92.2%

Secondary Education

Narayana Olympiad CBSE School

2017 - 2019

Score: 91.6%

Professional Experience

Technical Intern

Siemens Healthineers, Bangalore, India

Jan 2025 – Present

Designed and developed a PoC for an LLM-powered clinical application to improve healthcare workflow efficiency; the solution is currently in the process of patent filing.
Designed and implemented a FastAPI server architecture to unify access to OpenAI SDK and locally hosted LLMs via OpenAPI endpoints.

Intel Unnati Industrial Training Program

Intel

June 2024 – July 2024

Developed a system to analyze contract documents using the Contract Understanding Atticus Dataset (CUAD)
Incorporated text preprocessing, Named Entity Recognition (NER) for clause classification
Integrated template comparison to detect and highlight deviations, ensuring contract compliance and consistency

Part-time Research Associate

Indian Institute of Science (IISc), Bangalore

Aug 2023 – April 2024

Worked on advanced machine learning projects and research in computer vision
Collaborated with research teams on cutting-edge AI applications
Developed and implemented novel algorithms for computer vision tasks

Projects

MedLensAI - AI-powered Patient-Centric Diagnostic Assistant

2025

Developed a multi-modal clinical assistant using Google's MedGemma model
Implemented image-based diagnostic suggestions and cross-referenced report summaries
Built features for symptom-image correlation and follow-up investigation suggestions
Created a Streamlit-based interface with image upload and doctor-style prompt box

View Project

Smart Glasses for Visual Impairment

2024

Developed a smart glass prototype with facial recognition and threat detection capabilities
Implemented real-time object detection and navigation assistance
Integrated voice feedback system for enhanced user experience

View Project

AI-Powered Spam Detection System

2024

Implemented an AI-powered spam detection system for instant messaging platforms
Developed custom NLP models for message classification
Achieved 95% accuracy in spam detection

View Project

Intelligent Alarm System

2023

Created a smart alarm system using calendar and sleep data for optimal wake-up times
Implemented machine learning algorithms for sleep pattern analysis
Integrated with calendar APIs for smart scheduling

View Project

Publications

SURE: Session-Based Uninteresting Item Removal for Enhanced Recommendations

IEEE Access Volume: 13 Page(s): 43904 - 43918

2025

Sequential recommendation systems struggle with short user interaction sequences, often resulting in poor recommendation accuracy due to insufficient or uninteresting data. To address this, we propose SURE (Session-based Uninteresting Item Removal for Enhanced Recommendations), which combines association rule mining and backward prediction to enrich sequences and remove irrelevant items. Our method enhances recommendation quality by refining input data, achieving a 7.31% average gain in MRR and outperforming state-of-the-art techniques on multiple real-world datasets.

View Publication

Contact Me

Interested in collaborating on AI/ML projects or discussing opportunities? I'm always open to connecting with fellow developers and researchers!

navaneeth.sivakumar66@gmail.com

+91 9176359890

Chennai, Tamil Nadu, India