Abbas S.

PhD machine learning specialist delivering end-to-end solutions in computer vision, sensor data analytics, and edge AI deployment. Expert in object detection, predictive modeling, and real-time inference systems from cloud to edge devices with production ML models deployed across healthcare, industrial, and smart city applications ​ What I can do for you Computer vision & image analysis: - Object detection, classification, and segmentation using YOLO, Faster R-CNN, and transformer-based models - Real-time video analytics for surveillance, quality inspection, and people/vehicle tracking - Pose estimation, facial recognition, and visual anomaly detection Sensor data & predictive analytics: - Time series forecasting, anomaly detection, and predictive maintenance from IoT sensor streams - Multi-sensor fusion and feature engineering for physiological, environmental, and industrial sensors - Signal processing pipelines: filtering, spectral analysis, pattern recognition, and event detection Edge AI & TinyML deployment: - Deploy optimized ML models on microcontrollers (Arduino, ESP32, STM32, nRF52) and edge devices (Raspberry Pi, Jetson) - Model quantization, pruning, and compression using TensorFlow Lite, TFLite Micro, Edge Impulse, ONNX End-to-end ML pipelines: - Data collection, cleaning, feature engineering, model training, validation, and deployment - Cloud and edge integration with MQTT, REST APIs, dashboards - Model monitoring, A/B testing, and continuous improvement ​ Relevant experience Computer vision projects: - Real-time object detection and tracking from LIDAR and camera feeds for traffic analytics and smart city applications - Agricultural crop disease detection on Jetson Nano using custom CNN/YOLO models with 91% accuracy - Quality inspection system for manufacturing defect detection using semantic segmentation Sensor data & predictive analytics: - Healthcare monitoring systems analyzing physiological sensor data for fall detection, activity recognition, and patient vital tracking - Predictive maintenance models for industrial equipment using vibration, temperature, and acoustic sensors - Environmental monitoring networks with real-time air and water quality assessment across multiple deployed nodes - Time series forecasting for energy consumption and demand prediction less than5% MAPE Edge AI deployments: - On-device activity recognition with TensorFlow Lite models on ARM Cortex-M4 microcontrollers - Real-time pose estimation on Raspberry Pi 4 at 30 FPS for fitness and rehabilitation applications - Multi-sensor fusion system combining IMU, GPS, and camera data for autonomous navigation ​ Tech stack: Computer vision & deep learning: - Frameworks: TensorFlow, Keras, PyTorch, OpenCV, scikit-learn, Hugging Face Transformers - Architectures: YOLO (v5/v8/v11), Faster R-CNN, ResNet, EfficientNet, Vision Transformers (ViT), U-Net - Tools: LabelImg, CVAT, Roboflow for dataset annotation and augmentation Machine learning & data science: - Classical ML: Random Forest, XGBoost, LightGBM, SVM, clustering algorithms - Time series: ARIMA, LSTM, Prophet, seasonal decomposition - Libraries: NumPy, Pandas, SciPy, scikit-learn, Statsmodels Signal & sensor processing: - Custom filtering, FFT, wavelet analysis, spectral analysis - Multi-sensor fusion, Kalman filtering, feature extraction - Python/MATLAB signal processing toolboxes Edge AI & embedded deployment: - Optimization: TensorFlow Lite, TFLite Micro, Edge Impulse, ONNX Runtime, TensorRT - Hardware: Arduino, ESP32, STM32, nRF52, Raspberry Pi (3/4/5), Jetson Nano/Xavier, Google Coral - Model compression: quantization (INT8/INT16), pruning, knowledge distillation Cloud & infrastructure: - Platforms: AWS (SageMaker, Lambda, IoT Core), Google Cloud (Vertex AI), Azure ML - APIs: REST, MQTT - Deployment: FastAPI, Flask Development tools: - Version control: Git, GitHub - Visualization: Matplotlib, Seaborn, Plotly, Streamlit, Gradio ​ With a PhD in Computer Science and Engineering (USA), Master in Electronic and Electrical Engineering (UK), I combine deep theoretical knowledge with practical implementation experience. Whether you need a computer vision system for production, sensor analytics for predictive insights, or ML models optimized for edge devices, I deliver scalable, production-ready solutions with comprehensive documentation and support.