About Me
HEY, I’M ARAVINDH 👋
I’m a Machine Learning Engineer in the making, building intelligent systems with Python and a strong focus on how problems can be modeled—whether as classification or continuous prediction tasks.
I enjoy blending algorithms, curiosity, and experimentation to solve real-world problems. My work spans applied machine learning, computer vision, explainable AI, and reinforcement learning. If my GitHub feels like a lab, that’s intentional—constant iteration is how real learning happens.
I build models that don’t just run, but learn, adapt, and improve through thoughtful design and evaluation. I care deeply about why a model works, not just whether it works.
Outside the code, I enjoy explaining machine learning concepts in Tamil when needed, making complex ideas more accessible.
If it’s not learning, it’s not fun.
Profile
- Current Role: ML Research Analyst
- Focus: Applied AI, Research, XAI
- Location: Coimbatore, India
- Education: B.Tech AI & ML (CGPA: 8.21)
- Languages: Python, C, SQL, Java(Basic)
Technical Arsenal
Deep Learning & AI
- CNN Architectures
- Deep Q-Networks (DQN)
- Transfer Learning
- Federated Learning
- BERT & LLM Finetuning
- Prompt Engineering
Advanced ML
- Bayesian Optimization
- Explainable AI (SHAP, LIME)
- Feature Engineering
- Ablation Studies
- Ensemble Methods
- Model Robustness
Computer Vision & NLP
- Medical Image Analysis
- Dermoscopic Imaging
- OpenCV
- Text Classification
- Transformers
- Object Detection
Deployment & MLOps
- REST APIs (FastAPI/Flask)
- Docker (Basics)
- MLflow
- Git & GitHub
- Linux Environments
Core Libraries
- PyTorch, TensorFlow
- Scikit-learn, Pandas
- Hugging Face
- Streamlit
- Matplotlib / Seaborn
Experience
Machine Learning Research Analyst
Data Conquest Research Hub, Coimbatore
- Designed and implemented end-to-end ML pipelines for healthcare imaging and cybersecurity datasets.
- Applied explainable AI techniques (SHAP, LIME) for model interpretability and failure analysis to ensure transparency in high-stakes decisions.
- Conducted rigorous feature engineering, ablation studies, and Bayesian hyperparameter optimization to maximize model performance.
- Evaluated models using precision, recall, F1-score, ROC-AUC, and threshold tuning, handling class imbalance effectively.
- Maintained reproducible research workflows and produced publication-ready documentation.
Key Projects
Skin Cancer Prediction using Explainable AI
Oct 2024 – Dec 2024
Malware Detection via Dynamic Algorithmic Configuration
Jan 2025 – Apr 2025
IPL Match Outcome & Performance Forecasting
May 2025
Reinforcement Learning: Tic Tac Toe Agent (DQN)
Jun 2025
CAPTCHA Recognition System
Jul 2025
House Price Prediction API
Aug 2025
Spotify Analytics Dashboard
Sep 2025Achievements & Engagements
Paper Published via IEEE IDCIoT 2025
Malware Detection via Algorithmic Configuration
Paper Published in National Conference
Skin Cancer Prediction using Explainable AI
Speaker at Google DevFest 2023
Delivered a talk on Machine Learning at Madurai.
Competitive Programming
Solved 90+ LeetCode problems & 728 Skillrack challenges.
Co-organiser: ARTIFEST 2023
AI Symposium at MKCE.
Education & Certifications
B.Tech - Artificial Intelligence & Machine Learning
M. Kumarasamy College of Engineering | 2021 – 2025
CGPA: 8.21
Certifications
- Google Cloud Associate Cloud Engineer
- AWS Cloud Fundamentals
- Oracle Cloud Infrastructure AI Foundations Associate
- Data Analytics with Python (NPTEL)
- Machine Learning (ICT Learnathon)
Get In Touch
Interested in collaborating on research, AI development, or discussing widespread ML adoption? Feel free to reach out.