Hire the Best MRI Software Specialists

I build real-time AI systems that detect threats, track objects, and turn video into actionable insights ready for real-world deployment. If you need a reliable Computer Vision solution (not just a demo), I can design, train, and deploy it end-to-end. 🎯 What I Do I specialize in building production-ready Computer Vision systems that work in real environments not just controlled demos. From surveillance and safety to traffic analytics and automation, I help businesses turn video data into practical, usable intelligence. 🔧 Solutions I Build ✔ Real-Time Object Detection (YOLOv8 / YOLO11) ✔ Multi-Object Tracking (ByteTrack, DeepSORT) ✔ Surveillance & Threat Detection Systems ✔ Fire & Smoke Detection ✔ PPE / Safety Compliance Monitoring ✔ Traffic Monitoring & Speed Estimation ✔ OCR & Document AI ✔ End-to-End AI Systems (Data → Training → Deployment) 🧠 Why Clients Choose Me Most freelancers stop at training a model. I go further — I build complete systems that actually work in production. ✔ Optimized for real-time performance (high FPS, low latency) ✔ Designed for real-world conditions (low light, fog, motion blur) ✔ Strong focus on data quality & annotation (where most projects fail) ✔ Clean, scalable, deployment-ready code 💡 Most Computer Vision models fail outside the lab — I make sure yours doesn’t. 📊 Selected Projects 🔫 Real-Time Weapon Detection & Tracking AI-powered surveillance system for detecting and tracking firearms in live video streams → Helps improve security response time and monitoring 🔥 Real-Time Fire & Smoke Detection (107 FPS) Early hazard detection system designed for fast response in critical environments → Detects fire/smoke in real-time to reduce risk and damage 🦺 Construction Safety Monitoring (PPE Detection) Helmet detection system with live violation alerts → Improves worker safety and compliance on-site 🚗 Vehicle Speed Estimation System Tracking-based system for real-time speed analysis from video → Useful for traffic monitoring and smart city solutions ⚙️ Tech Stack AI / Deep Learning: PyTorch, YOLO (v8, v11), TensorFlow Computer Vision: OpenCV, real-time video processing pipelines Tracking: ByteTrack, DeepSORT Deployment: FastAPI, Flask, Docker, GPU acceleration Language: Python 🎬 What You Can Expect ✔ Demo available before starting ✔ Clean, well-documented code ✔ Fast communication & regular updates ✔ Scalable solutions ready for deployment ✔ Support with real-world challenges (lighting, motion, noise) 💡 How I Work I follow a complete pipeline: Data Collection → Annotation → Training → Optimization → Deployment You don’t just get a model you get a working system ready to use. 📩 Let’s Build Something Real Have an idea or project in mind? 👉 Send me a message I’ll break down the best approach and can even share a quick demo or plan before you commit.

Top 1% Expert-Vetted Freelancer. I have a PhD in Computer Science (AI & Machine Learning) and I am a physician. I build custom AI systems with proprietary and public data, including creation of novel datasets and state-of-the-art models. * After a 30-minute to 1-hour initial meeting, I'll craft a clearly written report outlining a unique AI strategy for your project. * For long-term engagements, I implement AI solutions end-to-end, including data collection, data cleaning, novel model design, model implementation and training in Python, and iteration on the modeling and data strategy to achieve excellent performance. * One of my specialties is creating custom models for proprietary datasets, including images, videos, audio recordings, and domain-specific data. Book a 30-minute consultation with me to get immediate answers about your AI project: - How can you leverage AI in your organization? - What kind of AI do you need to solve a particular problem? - Should you use a model like ChatGPT/Claude or do you need a custom model? - What modeling strategy should you use? - How should you collect and annotate data? - How much data will you need? - How should you measure performance? After a consultation I’ll craft a clearly-written report outlining an AI strategy for your project. Specialties: Healthcare AI: I am an expert in healthcare AI, with a unique background that blends clinical understanding from my MD, deep technical expertise from my AI PhD, and business experience. I have completed 100+ engagements across medical specialties including radiology, dermatology, cardiology, mental health, family medicine, surgery, dentistry, physical therapy, women’s health, pediatrics, nutrition, and neurology. I have created datasets and custom models for diverse forms of biomedical data including x-rays, CTs, MRIs, clinical photographs, patient videos, medical audio, medical notes, EHR data, insurance claims, omics data, and more. Computer Vision: I create custom AI models for 2D images, 3D images, and videos on proprietary datasets. I am excited about projects from multiple industries including healthcare, manufacturing, automotive, and agriculture. I have extensive experience with classification, object detection, segmentation, and keypoint detection, to identify and localize abnormalities or features of interest. I developed the first machine learning model in the world to predict multiple abnormalities simultaneously from a CT scan. For a dermatology practice, I built the first CV model to predict Fitzpatrick skin type, pigmentation, redness, and wrinkle severity (mean accuracy 85%)—now a core model at Kesty AI. Artificial Intelligence R&D: I have led multiple AI research projects across industry and academia. I have developed novel AI methods, including HiResCAM, a convolutional neural network explanation method with mathematical guarantees. I have published original research across multiple areas of AI, including computer vision, natural language processing, explainable AI, expert systems, and applied AI. My research papers have been cited over 1,000 times. My healthcare AI blog Glass Box has over 700,000 readers from 185 countries. Natural Language Processing (NLP): I have a deep understanding of large language models (LLMs) like Claude, Gemini, ChatGPT, and Llama. I have leveraged Transformers and other NLP techniques for numerous applications, including customized chatbots, medical note generation, and structured information extraction. Advising Entrepreneurs: Before focusing on AI consulting, I spent seven years as the founder of a health AI startup. I led my previous company from concept to deployed B2B SaaS product serving medical practices. Our AI history-taking assistant and AI scribe saved clinicians 2+ hours daily. I managed engineering teams of 5-10 (60+ contributors), secured two U.S. patents, and raised competitive grant funding. I enjoy working with entrepreneurs and discussing pitch decks, fundraising, customer discovery, designing an MVP, and evaluating the ROI of an AI product. If you’d like to talk with me about your AI project, please feel free to send me a message or book a consultation using the link on my profile.

Most Machine Vision projects fail between the prototype and production. I've shipped 54+ that didn't. ⚙️YOLO Detection | Pose Estimation | Object Tracking | AI Agents | LLM Integration Sports & Fitness AI | CCTV & Surveillance AI | Retail AI | Healthcare AI You have a working concept... or a clear problem involving cameras, video, or image data. The challenge is making it fast, accurate, and stable under real-world conditions. Wrong framework choices. Inference too slow for live video. Models that break the moment lighting, angle, or environment changes. And systems that detect things but can't reason about them or act on them autonomously. That's exactly where most builds stall. I design and build real-time computer vision pipelines that go all the way... from model training to live deployment... and increasingly, from visual perception to autonomous AI agents that understand, decide, and narrate. LLM APIs (OpenAI, GPT-4o, Gemini, Claude) | AWS (EC2, S3, Lambda) | Azure Cloud Services | MLOps & API Integration | Model Deployment & Scaling While most CV engineers stop at training the model, I go further: → High-speed inference optimization using TensorRT, ONNX, OpenVINO, FP16/INT8 (up to 5× faster) → LLM agents integrated with vision pipelines for alerts, reasoning, and automation → Mobile AI deployment using Core ML (iOS) and TFLite (Android) with 10+ shipped apps → Edge AI deployment on Jetson, OpenVINO, CUDA, and embedded systems → End-to-end pipelines: data → training → optimization → real-time deployment Key Accomplishments: ⭐ $5M+ revenue from AI solutions ⭐ 100+ computer vision systems delivered ⭐ Built and launched 2 SaaS products ⭐ Real-time sports AI (7+ sports, 15+ teams) ⭐ 10+ mobile AI apps (iOS Core ML, Android TFLite) ⭐ Production AI for surveillance, industrial & safety use cases ⭐ Medical imaging AI deployed in 5+ hospitals ⭐ Up to 5× faster inference (ONNX, TensorRT, FP16/INT8) ⭐ Large-scale tracking & re-ID (1M+ labeled data) ⭐ Agentic AI systems for autonomous decision-making If you have read this far, please note that I appreciate you taking the time to learn about me. Personally, it’s been an amazing journey and knowledge exercise to get to this level of competence in AI and software development. Domain Expertise: ✅ athlete tracking | shot detection | scoring | drill analysis | pose estimation ✅ defect inspection | PPE compliance | staff monitoring | meter reading | quality control ✅ ANPR | crowd monitoring | people counting | intrusion detection | perimeter security ✅ tumor detection | ultrasound | X-ray/CT analysis | lesion segmentation | medical imaging ✅ aerial monitoring | traffic flow | license plate recognition | vehicle & accident detection ✅ customer analytics | receipt extraction | shelf monitoring | inventory tracking Tech Stack: YOLOv5–YOLOv8–YOLOv11, Detectron2, MMDetection, DeepSORT, StrongSORT, MediaPipe, OpenPose, Pose Estimation, Action Recognition, Segmentation (semantic & instance), OCR, anomaly detection, object tracking, PyTorch, TensorFlow, TFLite, Core ML, OpenCV, FastAPI, Flask, ONNX, TensorRT, OpenVINO, CUDA, AWS, Azure, GCP, edge AI, mobile AI, real-time inference, video analytics, AI automation, LLM integration (GPT-4o, Claude, Gemini, Groq), LangChain, LangGraph, CrewAI, RAG systems. 💬 If your project involves cameras, video, or images... and you need it fast, accurate, fully deployed, and intelligent enough to reason and act autonomously... I am the engineer you are looking for.

Expert-Vetted Top 1% on Upwork | Top 10 Machine Learning Agency on Upwork | 7+ Years in Production AI | Sports, Industrial, Satellite, Healthcare I'm a Computer Vision Engineer and Machine Learning Engineer with 7+ years delivering production-grade AI systems. Upwork has Expert-Vetted me as a Top 1% specialist in this niche, and our team is ranked among the Top 10 Machine Learning agencies on Upwork. I work with product teams and startups across sports analytics, industrial inspection, satellite and aerial imagery, access control, healthcare, and generative AI — any domain where visual data needs to become reliable, actionable output running in production. As a Computer Vision Engineer, my core work covers object detection, multi-object tracking, pose estimation, image segmentation, image processing, and real-time video analysis. I build end-to-end pipelines in Python using OpenCV, PyTorch, TensorFlow, and Keras, from dataset preparation and model training through TensorRT optimization and Docker deployment on cloud or NVIDIA Jetson edge hardware. I use C++ for performance-critical components where Python latency is a bottleneck. The domains where computer vision engineer experience creates the most value: sports analytics (player tracking, performance metrics, automated statistics from broadcast video), industrial inspection (defect detection and quality control on production lines), satellite and aerial imagery (object detection and segmentation for infrastructure analysis), access control and security (vehicle identification, multi-camera real-time monitoring), and healthcare and biomechanics (pose analysis, body measurement, and biomedical signal processing connected to AI coaching backends). As a Machine Learning Engineer and Data Scientist, I also build systems for structured and time-series data: demand forecasting, anomaly detection, biomedical signal analysis, and structural health monitoring. My data scientist workflow covers scikit-learn, pandas, NumPy, and SciPy alongside deep learning frameworks, with experiment tracking and evaluation metrics to ensure models perform consistently in production. When projects require generative AI or LLM components, I deliver RAG pipelines with LangChain and vector databases, synthetic dataset generation tools, and document processing systems using the Gemini API. Regardless of domain, the computer vision engineer approach stays the same: combine OpenCV-based preprocessing with deep learning inference into a scalable, testable pipeline that holds up under real-world conditions — variable lighting, occlusion, low resolution, multi-camera setups, and edge hardware constraints. I work with YOLO-family models, ByteTrack and DeepSORT for tracking, MediaPipe and MMPose for pose estimation, TensorRT and ONNX for inference optimization, and FastAPI with Docker for production deployment. I work with a specialized team that includes a computer vision PhD, deep learning researchers, and mathematical optimization specialists. This lets me scope complex systems, split parallel workstreams, and deliver a full Computer Vision Engineer engagement faster than a solo contributor could. Clients typically work with me when they need: - a Computer Vision Engineer to build detection, tracking, or segmentation systems from scratch - a Machine Learning Engineer to productionize a research model and meet latency requirements - a Data Scientist who can go from raw data to a deployable model end-to-end - an AI engineer to integrate LLM or generative components into an existing backend - real-time or edge inference optimized for NVIDIA Jetson or mobile deployment - a Python developer who understands both the AI pipeline and the surrounding system architecture If you need a Computer Vision Engineer with the full stack from dataset to deployed API, let's talk. Main stack: Python, OpenCV, PyTorch, TensorFlow, Keras, YOLO, ByteTrack, MediaPipe, MMPose, CoreML, TFLite, TensorRT, ONNX, scikit-learn, FastAPI, Docker, PostgreSQL, C++, NumPy, pandas, SciPy.

I'm a Top Rated freelancer and Computer Science graduate who works at the intersection of three fields most people treat separately: Statistics & Machine Learning, Bioinformatics, and AI. That combination is exactly what messy, high-dimensional data needs — the statistical rigor to trust the result, the ML to find the pattern, and the biological context to know what it means. With 3+ years of experience and 24 successful projects (4.98/5 avg. rating), here's what I bring: 📊 Statistics & Machine Learning Rigorous statistical inference and predictive modeling — hypothesis testing, causal inference (Mendelian Randomization), feature engineering, and ensemble models (XGBoost, LightGBM, Random Forest). I don't just build models that score well; I build models you can defend. Example: a coronary artery disease prediction model reaching 0.956 AUC, with feature ablation to prove what actually drives it. 🧬 Bioinformatics & Omics End-to-end analysis of complex biological data — scRNA-seq, RNA-seq, WGS/WES — using reproducible pipelines (Nextflow, Snakemake). From raw reads to normalized matrices to differential expression and biomarker discovery. Example: cut analysis time by 40% on large genomic projects through custom automated pipelines. 🤖 AI & Deep Learning Deep learning frameworks for real diagnostic problems — computer vision, medical image analysis, NLP, and LLM-based tools. ✨ What ties it together Most freelancers do one of these. I connect them — applying AI and ML to biological and clinical data with statistical discipline, then communicating the findings in publication-ready visuals that both scientists and stakeholders can act on. 🛠️ Tech Stack Python (Pandas, Scikit-learn, TensorFlow, PyTorch) · R (Tidyverse, Bioconductor) · Bash · SQL · Linux · Git · Docker · Cloud 💡 Ready to turn your raw data into discoveries you can trust? Let's talk.

I am a Machine Learning Engineer with four years of experience working across deep learning research, large scale AI systems, and production model deployment. Over the years, I have worked extensively in medical imaging, computer vision, NLP, signal processing, and large language models, building systems that range from experimental research pipelines to deployed real world AI applications. My day to day work primarily involves Python, PyTorch, TensorFlow, Keras, HuggingFace Transformers, sentence transformers, scikit learn, OpenCV, Pandas, and NumPy. I enjoy working deeply on both the research and engineering sides of machine learning, especially problems that require understanding model behavior rather than simply applying existing architectures blindly. A large part of my background is research driven. I have authored multiple peer reviewed publications in indexed journals and IEEE conferences, including publications in Neurocomputing, Healthcare Analytics, Engineering Applications of Artificial Intelligence, Telematics and Informatics Reports, and other Elsevier and IEEE venues. My research has focused heavily on explainable AI, healthcare AI, and advanced deep learning systems. Some of my published work includes CARDxnosis, an explainable knowledge driven framework for ECG diagnosis and clinical report generation, an explainable AI system for trustworthy arrhythmia detection, a CNN RNN Attention hybrid architecture for automatic modulation classification, ensemble deep learning approaches for lung cancer detection from CT scans, and SRGAN based white blood cell image generation and classification pipelines. Alongside published work, I am currently involved in research on brain tumor segmentation, ADHD and ASD classification from brain connectome graphs, epileptic seizure prediction from EEG signals, and interpretable tabular learning using graph neural networks combined with Kolmogorov Arnold Networks. Beyond research, I have substantial hands on experience building and deploying production grade AI systems. One of my major recent projects was LaborBERT v4, a domain adaptive transformer fine tuning system processing hundreds of thousands of records through a large scale training pipeline. The project involved multiple experimental setups including contrastive learning, masked language model pretraining, temporal contrastive learning, cross attention based fusion, multi task training, and Matryoshka Representation Learning. I have also built hybrid embeddings plus LLM systems for taxonomy mapping using OpenAI embeddings alongside locally hosted LLaMA and Mistral models through Ollama. In addition, I have worked on deployed clinical AI systems and a portable on device diagnostic AI solution with embedded deep learning models for point of care inference, which gave me valuable experience in optimization, deployment constraints, inference design, and production reliability. My broader project portfolio includes vehicle detection using Mask R CNN, human activity recognition on the Kinetics 700 dataset, facial keypoint detection with MultiRes UNet, semantic segmentation pipeline redesign, Stable Diffusion based image editing workflows, toxic comment classification, RASA based conversational AI systems, and large scale scraping and automation pipelines using Playwright and Selenium. I have also worked with Flask and Django based deployment pipelines and cloud hosted ML systems. From an engineering perspective, I care strongly about clean and maintainable systems. I follow disciplined workflows involving modular code design, Git based version control, reproducible experimentation, structured evaluation, bootstrap validated metrics, and detailed documentation. I am also comfortable preparing scientific reports, research papers, and journal submissions using both LaTeX and Word. What ties all of this together is that I genuinely enjoy solving difficult technical problems, especially the kind that require balancing research depth with practical engineering constraints. I am most motivated by projects where thoughtful experimentation, careful system design, and real world usability matter equally.