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Computer Vision Engineer specializing in real-time detection systems across surveillance AI, manufacturing inspection, medical imaging, and smart city infrastructure — deployed to production. I build end-to-end: camera input, model training, TensorRT optimization, edge or cloud deployment, and backend integration. Not prototypes. Working systems. WHAT I HAVE SHIPPED: Multi-tenant AI surveillance platform with 8 real-time detection agents — intrusion, loitering, occupancy, vehicle counting, LPR, fire/smoke detection, entry after hours, and late checkout. YOLO11s + PaddleOCR on RunPod GPU, RTSP stream management via MediaMTX, FastAPI backend on DigitalOcean, CI/CD via GitHub Actions with a self-hosted runner. Supabase for auth and Postgres. Full production deployment. Spine X-ray segmentation system — 25-class vertebrae labeling (C1 through L5) with automated clinical metric extraction: disc height, segment angle, neutral listhesis, interspinous gap. Dual architecture: ResNet34 U-Net baseline and a custom ConvNeXt-Tiny encoder with CBAM attention trained on real X-rays and synthetic DRR data from CT volumes. 0.92 Dice on full-spine segmentation. Real-time defect detection on NVIDIA Jetson Xavier for a food and beverage production line. TensorRT FP16 inference, zero cloud dependency, PLC integration for automated pass/fail. Defect catch rate improved significantly over manual baseline. LiDAR-camera sensor fusion pipelines for smart city traffic monitoring — ROS, DeepStream, NVIDIA Jetson, production-deployed infrastructure. CORE CAPABILITIES: → Detection, segmentation, tracking: YOLO variants, SAM2, custom PyTorch → TensorRT optimization: FP16/INT8 quantization, ONNX export, engine serialization → Edge deployment: Jetson Nano, NX, Orin, Xavier — hardware bring-up to production → Medical imaging: U-Net architectures, multi-class segmentation, DICOM, clinical metrics → Surveillance AI: multi-agent detection, RTSP streams, LPR, event-based alerting → Industrial integration: FastAPI, MQTT, EMQX, Azure IoT Hub, AWS S3, Docker, Supabase INDUSTRIES: Manufacturing quality control — food and beverage, general production lines Surveillance and security — multi-camera platforms, license plate recognition, event detection Medical imaging — vertebral segmentation, clinical measurement pipelines Smart city and traffic — LiDAR-camera fusion, intrusion detection, infrastructure monitoring I communicate clearly, surface blockers early, and deliver systems that run reliably in your environment.

Biomedical Engineer | Signal Processing · Medical Imaging AI · Computer Vision · Research Writing & Visualization 4+ years · 20+ projects · Active PeerJ submission · ECG · EEG · PPG · DICOM · Echocardiography · Draw io · GitHub → Colab You don't need to explain what ECG noise looks like or why DICOM is tricky; I already know, because this is all I do! I work with researchers, clinicians, and engineers who need a technical partner for biomedical data projects ; from raw signal acquisition through to a polished, publishable result. My work spans physiological signal processing, medical image analysis, deep learning model development, and full academic writeup. ✅ SERVICES 📡 Biosignal Processing — ECG, EEG, PPG, GSR Preprocessing · artifact removal · segmentation · HRV · peak detection · time-domain & frequency-domain feature extraction 🫀 Medical Imaging & DICOM Pipelines Echocardiography · DICOM loading & preprocessing · image enhancement · DL classification · segmentation · object detection · multi-format support (DICOM, PNG, JPEG) 🧠 Computer Vision Methods on Clinical Modalities Translating CV techniques : optical flow, attention mechanisms, Grad-CAM explainability, feature pyramid networks, contour & morphology analysis : into medical imaging pipelines for echo, ECG plots, ultrasound, and MRI ⚙️ ML/DL Model Development End-to-end pipeline design · transfer learning · hybrid feature fusion (hand-crafted + deep learning) · model fine-tuning · ablation studies · PyTorch · TensorFlow · Keras 🔁 GitHub → Google Colab Reproducible Pipelines Versioned, documented experiments on GitHub · Colab-ready notebooks clients can run and extend · clean modular code with clear READMEs 📊 Statistical Analysis Hypothesis testing · regression · performance benchmarking · ROC/AUC · confusion matrices · Python · R · RStudio 🗂️ Research Diagrams & Methodology Figures Draw io block diagrams · pipeline architecture visuals · system flow figures · publication-ready methodology illustrations ✍️ Academic & Technical Writing Full paper drafts · methodology, results & discussion sections · IEEE/APA · LaTeX · MS Word · journal editing & proofreading ✅ RECENT RESEARCH — Paper under review at PeerJ: hybrid deep learning + hand-crafted feature fusion pipeline for MI classification from echocardiography — FYP research: multimodal biosignal analysis (ECG + PPG + GSR) for ASD detection in children aged 6–12 ✅ STACK Python · MATLAB · PyTorch · TensorFlow · GitHub · Google Colab · Draw io · R · RStudio · LaTeX · MS Word Feel free to message me with your data, your goal, and your deadline, I'll tell you honestly what's achievable and how I'd approach it.

Expert in ✅ Telecommunication ✅Data Science ✅ Machine Learning ✅ Deep Learning ✅ Strong Mathematical and Statistical understanding and experience in developing and designing Quality Products. Detailed expertise includes: ✅ Product Design ✅ Machine Learning mathematical concepts and algorithms ✅ Python (NumPy, Pandas, Scikit-learn, TensorFlow, Keras, Fast.ai, PyTorch, Flask, Matplotlib, Seaborn) ✅ Signal Processing ✅ Vehicle to Infrastructure Communication Models ✅ Vehicle to Vehicle Communication Models ✅ Orthogonal Frequency Division Multiplexing (OFDM) ✅ Non-Orthogonal Multiplex access (NOMA) ✅ Reconfigurable intelligent surfaces (RIS) ✅ Working with Big Datasets ✅ MATLAB ✅PCBWeb Designer ✅ Proteus ✅ Technical Writing If you are looking for an expert telecommunication Engineer with machine learning and data science expertise you are at the right place. I would be thrilled to work with you. Let's connect and explore how we can create an impactful solutions together! Regards,

I help PhD students, research labs, and industry teams take wireless-networking + AI research from idea to a submission-ready paper in 𝗜𝗘𝗘𝗘, 𝗘𝗹𝘀𝗲𝘃𝗶𝗲𝗿, and 𝗠𝗗𝗣𝗜 venues. My specialty is building simulations that are actually reproducible, publishable, and defensible in peer review. If you're working on 𝟲𝗚 𝗡𝗼𝗻-𝗧𝗲𝗿𝗿𝗲𝘀𝘁𝗿𝗶𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀, 𝗢-𝗥𝗔𝗡, 𝗩𝟮𝗫/𝗩𝗔𝗡𝗘𝗧, 𝗜𝗼𝗧/𝗜𝗼𝗠𝗧 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗼𝗿 𝗗𝗥𝗟/𝗠𝗔𝗥𝗟 𝗳𝗼𝗿 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀, I can build it, debug it, write it up, and ship it. 𝗔𝗰𝘁𝗶𝘃𝗲 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Northeastern University (Prof. Josep Jornet), University of Luxembourg SIGCOM (Prof. Symeon Chatzinotas, Dr. Eva Lagunas). 🔹 𝗥𝗘𝗖𝗘𝗡𝗧 𝗪𝗢𝗥𝗞 (𝟮𝟬𝟮𝟱 – 𝟮𝟬𝟮𝟲) 𝗢𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝘁𝗼𝗼𝗹𝗸𝗶𝘁𝘀 (GitHub: Muhammaduazir69) ▸ 𝗡𝗦𝟯-𝗡𝗧𝗡-𝗧𝗼𝗼𝗹𝗸𝗶𝘁 — end-to-end 6G Non-Terrestrial Network stack for NS-3 ▸ 𝗡𝗧𝗡-𝗖𝗛𝗢-𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 — 3GPP Rel-17/18 Conditional Handover with TTE-aware mechanisms ▸ 𝗢-𝗥𝗔𝗡 𝗡𝗧𝗡 𝗺𝗼𝗱𝘂𝗹𝗲 — O-RAN architecture extended to satellite/aerial segments 𝗔𝗰𝘁𝗶𝘃𝗲 𝗷𝗼𝘂𝗿𝗻𝗮𝗹 𝗽𝗮𝗽𝗲𝗿𝘀 ▸ Teleoperation survey for semi-autonomous vehicles → IEEE Communications Surveys & Tutorials (3-layer framework: Communication / Control / Autonomy-Human Integration) ▸ 𝗛-𝗠𝗔𝗗𝗥𝗟 𝗳𝗼𝗿 𝗢-𝗥𝗔𝗡 𝟯𝗗 𝘃𝗲𝗵𝗶𝗰𝘂𝗹𝗮𝗿 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀 — hierarchical MAPPO / MASAC / Q-learning with AA-DCRA algorithm; OMNeT++ 6.0.3 + Simu5G + Veins/SUMO + PyTorch/LibTorch → MDPI Sensors ▸ 𝗣𝗜-𝗗𝗥𝗟 𝗳𝗼𝗿 𝟱𝗚 𝗻𝗲𝘁𝘄𝗼𝗿𝗸-𝘀𝗹𝗶𝗰𝗲 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 for AGV teleoperation → Elsevier Vehicular Communications ▸ 𝗗𝗣-𝗜𝗗𝗦-𝗩𝗡𝗗𝗡 — dual-plane IDS for Vehicular NDN → IEEE TVT / TNSM ▸ 𝗚𝗔𝗧-𝗠𝗔𝗣𝗣𝗢 for Industrial IoT-Edge dynamic scheduling → Computers & Industrial Engineering 𝗟𝗶𝘃𝗲 𝘀𝗶𝗱𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 ▸ aeroscope.live — public ADS-B flight tracker (React / Vite) 🔹 𝗦𝗜𝗠𝗨𝗟𝗔𝗧𝗜𝗢𝗡 & 𝗗𝗘𝗩𝗘𝗟𝗢𝗣𝗠𝗘𝗡𝗧 𝗦𝗧𝗔𝗖𝗞 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗼𝗿𝘀 ▸ NS-3 + 5G-LENA (NR), NS3-AI, NS3-gym, NS3-ORAN, NetSimulyzer ▸ OMNeT++ 6.0.3 + INET, Simu5G, Veins, SUMO, FLoRa, libPTP ▸ Mininet / Mininet-WiFi with RYU OpenFlow controller ▸ Contiki-NG / Cooja for IoT/IoMT (RPL OF0 & MRHOF, CoAP, OSCORE, EDHOC, MQTT) 𝗔𝗜 / 𝗠𝗟 ▸ PyTorch, LibTorch, TensorFlow ▸ MARL: MAPPO, MASAC, QMIX, VDN ▸ Single-agent DRL: DQN, DDQN, PPO, DDPG, TD3 ▸ Libraries: EPyMARL, PantheonRL, custom MARL forks 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀, 𝗨𝗔𝗩 & 𝘀𝗲𝗻𝘀𝗶𝗻𝗴 ▸ NVIDIA Isaac Sim, Omniverse, Gazebo, ROS2, MoveIt ▸ Jetson Nano, DeepStream ▸ PX4, ArduPilot, MAVLink, QGroundControl (flight-control + mission-planning modifications) ▸ mmWave radar, SDR, LiDAR/UART integration 𝗔𝗹𝘀𝗼: CMake, Docker, C++, Python, MATLAB 🔹 𝗦𝗘𝗥𝗩𝗜𝗖𝗘𝗦 1. 𝗘𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝘄𝗶𝗿𝗲𝗹𝗲𝘀𝘀 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 — scenario design → results → publication plots 2. 𝗗𝗥𝗟 / 𝗠𝗔𝗥𝗟 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 into NS-3 or OMNeT++ using NS3-AI, NS3-gym, or custom Python ↔ C++ bridges 3. 𝗣𝗮𝗽𝗲𝗿 𝗿𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 & 𝗲𝘅𝘁𝗲𝗻𝘀𝗶𝗼𝗻 — rebuild a baseline and extend it for your contribution 4. 𝗖𝘂𝘀𝘁𝗼𝗺 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 — MAC, routing, handover, slicing, security 5. 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 — methodology, results, and discussion sections for Q1 targets 6. 𝗣𝗵𝗗 𝘁𝗵𝗲𝘀𝗶𝘀 / 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗺𝗲𝗻𝘁𝗼𝗿𝗶𝗻𝗴 — debugging, validation, defense prep 🔹 𝗥𝗘𝗦𝗘𝗔𝗥𝗖𝗛 𝗔𝗥𝗘𝗔𝗦 𝗜 𝗣𝗨𝗕𝗟𝗜𝗦𝗛 𝗜𝗡 ▸ 𝟲𝗚 & 𝗯𝗲𝘆𝗼𝗻𝗱: NTN (LEO / HAPS), O-RAN, mmWave / THz, MIMO, beamforming, network slicing, TDD/FDD ▸ 𝗩𝟮𝗫 / 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝘃𝗲𝗵𝗶𝗰𝗹𝗲𝘀: Simu5G VANETs, 5G-LENA NR V2V with obstacle detection, VNDN security, AGV teleoperation, digital twins ▸ 𝗜𝗼𝗧 / 𝗜𝗼𝗠𝗧 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆: lightweight crypto in Contiki-NG, HL7-compliant multi-level encryption, sinkhole / DoS / DDoS / ARP spoofing / SYN flood detection with hybrid IDS ▸ 𝗗𝗿𝗼𝗻𝗲𝘀 & 𝗮𝗲𝗿𝗼𝘀𝗽𝗮𝗰𝗲: PX4 / ArduPilot, MAVLink, Gazebo UAV scenarios, network-sliced UAV resource allocation ▸ 𝗔𝗜 𝗳𝗼𝗿 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴: federated learning (Flower + NS-3), digital-twin-enabled MADRL, GAT-based MARL for Industrial IoT 🔹 𝗖𝗥𝗘𝗗𝗘𝗡𝗧𝗜𝗔𝗟𝗦 ▸ 𝗢𝗥𝗖𝗜𝗗: 0009-0002-4104-2680 ▸ 𝗚𝗶𝘁𝗛𝘂𝗯: Muhammaduazir69 ▸ 𝗣𝘂𝗯𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: IEEE Access, MDPI Sensors, Elsevier Vehicular Communications ▸ 𝗚𝗹𝗼𝗯𝗮𝗹 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗼𝗿𝘀: Northeastern University, University of Luxembourg (SIGCOM), and PhD students across 15+ countries 🔹 𝗟𝗘𝗧'𝗦 𝗧𝗔𝗟𝗞 Send me your paper draft, topic idea, or GitHub repo — I'll reply with a concrete plan: what's feasible, what the right baseline is, and a realistic timeline to a submittable result. — Muhammad Uzair

• Recipient of OpenAI: High Performance Milestones on Project Lion - Pushing ChatGPT beyond knowledge boundaries • Recipient of Pak-China Project on "Quantum Dots Sensitised Solar Cells" • Recipient of Pak-US Project on "New Approaches for Lower Cost, Longer Ability and Higher Efficiency of Dye-Sensitized Solar Cells (DSSCs)" • Completed several tasks related to physics and astronomy on the training of LLMs • Created several simple and complex prompts Professor (01-07-2009 to 23-08-2023), Faculty of Engineering Sciences (FES), GIK Institute Topi, Pakistan. Teaching and research supervision in physics and photonics to undergraduate, MS and PhD students. Associate Professor (01/09/2002 to 31-06-2009), Faculty of Engineering Sciences (FES), GIK Institute Topi, Pakistan. Teaching and research supervision in physics and photonics to undergraduate, MS and PhD students. Lecturer (02/11/1996 to 31/08/2002), Department of Physics, Government College University, Lahore. Teaching college and university physics and research supervision MS students. Lecturer (22/12/1994 to 31/10/96), Department of Physics, Government T. I. College, Rabwah, Pakistan. Teaching analog and digital electronics to M.Sc. physics students. Postgraduate Research Student (22/10/1990 to 09/12/1994), School of Physical Sciences, Dublin City University, Ireland. Demonstrated to Applied Physics Undergraduates in electronics and physics laboratories. Lecturer (30/10/1986 to 31/08/1990), Department of Physics, Government College, Lahore. Teaching analog and digital electronics to M.Sc. physics students. Established microprocessor Lab and supervised M.Sc. students in electronics projects based on microprocessors. Lecturer (14/09/1985 to 29/10/1986), Government College, Gujranwala, Pakistan. Teaching college and university physics. Lecturer part-time (25/11/84 to 09/09/1985), University of the Punjab, Lahore. Demonstrated to M.Sc. Physics students in the Advanced Electronics Laboratory.