I am a Machine Learning Engineer & Deep Learning Researcher with a proven track record of designing, developing, and deploying intelligent systems that bridge the gap between cutting-edge research and real-world applications.
Over the past years, I’ve worked extensively across time-series analysis, EEG brain signal processing, computer vision, and large-scale data science projects. My journey has been defined by curiosity, precision, and impact — from building reproducible pipelines for complex datasets to experimenting with advanced deep learning architectures.
I thrive on solving hard, interdisciplinary problems, and I’m passionate about transforming raw data into actionable insights that drive innovation.
Beyond engineering, I’m also an AI educator and content creator. Through my Instagram page @ExplainsAI, I’ve been consistently producing high-quality educational content on Machine Learning and Deep Learning for nearly two years, building a community of 30,000+ learners worldwide. This experience has sharpened my ability to communicate complex ideas clearly and inspire others to explore the world of AI.
- Programming & Data: Python (pandas, NumPy, SciPy, scikit-learn), SQL
- Machine Learning: Feature Engineering, Model Development, Anomaly Detection, Optimization, Reproducible ML Pipelines
- Deep Learning: CNNs, RNNs, Transfer Learning, Attention Mechanisms, Computer Vision pipelines
- Signal Processing: EEG analysis, Time-series modeling, FFT, Brainwave band analysis
- Computer Vision: Image preprocessing, object detection, feature extraction, deep learning for vision
- Visualization: Matplotlib, Seaborn, Plotly
- Engineering Practices: Git, Jupyter/Colab, Kaggle, Docker (basic), Workflow Automation
- Designed and executed end-to-end ML workflows on diverse real-world datasets (EEG, stock prices, climate data, image datasets).
- Conducted advanced EDA and feature engineering that significantly improved downstream model performance.
- Built robust pipelines for time-series and multimodal data, ensuring reproducibility and scalability.
- Applied deep learning architectures (CNNs, RNNs, hybrid models) to solve challenging computer vision and signal processing tasks.
- Created a thriving AI community on Instagram with 30,000+ followers, consistently delivering accessible, high-quality ML/AI content.
- Recognized for the ability to translate research into practice, combining academic rigor with engineering pragmatism.
- Applied Machine Learning & Deep Learning Research
- Computer Vision & Image Understanding
- Brain Signal Processing (EEG) & Cognitive AI
- Building scalable, production-ready ML systems
- Interdisciplinary applications of AI in healthcare, finance, and anomaly detection
- Founder of @ExplainsAI
- 28,000+ followers in under 2 years
- Mission: make Machine Learning & Deep Learning concepts accessible, engaging, and practical for everyone
Thank You!