Hire the Best Stanford CoreNLP Specialists

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.

I design and build production-grade security systems for companies that need to secure cloud infrastructure, pass audits, and operate in regulated environments. Most teams don’t have a security tool problem. They have an architecture, integration, and execution problem. That’s where I come in. What I Do I help startups and enterprise teams move from: ❌ fragmented tools and partial controls ❌ audit delays and failing security reviews ❌ reactive fixes and security debt → to ✅ engineered, scalable security architecture ✅ audit-ready, continuously compliant environments ✅ automated, integrated security operations Core Expertise Cloud Security & Cryptography • Multi-cloud security architecture (AWS, Azure, GCP) • TLS PKI systems with automated certificate lifecycle (IaC + CI/CD) • Encryption architecture (CMEK, KMS, data masking, data protection) • Cryptographic hardening aligned with FIPS and modern standards • DNS security, network isolation, and zero-trust patterns Identity & Access Management • SSO (SAML, OIDC), enterprise identity federation • RBAC and least-privilege system design at scale • SCIM provisioning and identity lifecycle automation • Integration with enterprise IdPs (PingFederate, Azure AD, Okta) • Cross-account and multi-environment access control Compliance & Audit Readiness • SOC 2, ISO 27001, PCI DSS, HIPAA, FedRAMP-aligned environments • End-to-end delivery: gap assessment → implementation → audit support • Control design, remediation, and evidence automation (Vanta, Drata, custom pipelines) • Continuous compliance monitoring vs point-in-time audits • Closing audit findings fast (not just identifying them) Security Engineering & Incident Response • CI/CD security (SAST, DAST, IaC scanning, secret management) • Vulnerability management with automated remediation workflows • Cloud misconfiguration detection (CIS benchmarks, runtime analysis) • Secure system design across infrastructure and application layers • Incident response, forensics support, and system hardening AI-Driven Security I don’t just integrate tools — I design security platforms. I have built and architected multi-agent AI-driven cybersecurity systems combining: • Cloud security analysis (AWS integrations, IAM analysis, misconfiguration detection) • Offensive security (recon, exploitation, privilege escalation simulation) • Vulnerability management (SAST, DAST, fuzzing, CI/CD integration) • SOC automation (SIEM/SOAR integrations, alert enrichment, playbooks) • Forensics and incident investigation workflows • Compliance reporting mapped to frameworks (SOC 2, ISO 27001, PCI, HIPAA) Key capabilities: • Multi-agent orchestration and communication • Automated remediation workflows • MITRE ATT&CK mapping and executive reporting • API-driven integrations across security tooling ecosystem • Role-based access for security, DevOps, and compliance teams This enables: → Continuous security instead of periodic assessments → 80% reduction in manual security effort → Faster audit readiness and real-time visibility How I Work • Engineering-first — I build and implement, not just advise • Work directly in production systems (cloud, identity, pipelines) • Design for real audit constraints, not theoretical compliance • Fast execution, clear communication, and ownership Typical Clients • SaaS companies preparing for SOC 2 / ISO 27001 / HIPAA • Cloud-native platforms handling sensitive or regulated data • Startups entering enterprise sales with security blockers • Organizations with fragmented security tools that don’t work together Important I’m not a fit for checklist-based security or surface-level audits. If you need: • real security architecture • working implementations • systems that pass audits and hold up in production - we’ll work well together.

I am a Full Stack & Staff Applied AI Software Engineer with over 8 years of experience specializing in machine learning and deep learning applications. My expertise spans multiple sectors, including health-tech and research. I have co-founded and contributed to various startups, where I developed innovative AI solutions and worked on agentic systems to enhance user experience and drive operational efficiency. My background includes a strong foundation in biomedical engineering, which informs my approach to solving complex problems in healthcare. If you are seeking an engineer who can transform your vision into reality with cutting-edge AI technologies, let's connect. I am ready to take your project to the next level.

I help businesses, hospitals, startups, and research teams convert messy operational data into intelligent decision-support systems using Machine Learning, Mathematical Optimization, and Predictive Analytics. My core work includes forecasting, scheduling, resource allocation, clinical/engineering data analysis, AI model development, and optimization dashboards that help clients reduce delays, improve utilization, and make faster data-driven decisions. I specialize in Python-based analytical systems, that do not only analyze data but also support real operational decisions. I combine Operations Research, Machine Learning, Forecasting, Simulation, and Python-based analytics to solve problems related to scheduling, resource allocation, prediction, classification, capacity planning, business intelligence, and system optimization. What I Can Help You With Computational Optimization & Mathematical Modeling I formulate and solve real-world decision problems using linear programming, mixed-integer linear programming, scheduling models, resource allocation models, routing models, and constraint-based optimization. I work with Python, Pyomo, CPLEX logic, CBC solver, MATLAB, GAMS, and Excel-based optimization tools. Healthcare Optimization & Decision Support I have strong experience in healthcare operations research, including operating room scheduling, patient-flow optimization, hospital resource planning, PACU/ICU capacity analysis, staff allocation, and AI-driven healthcare decision-support frameworks. My focus is on improving utilization, reducing delays, and supporting evidence-based operational decisions. Machine Learning & Predictive Analytics I develop supervised machine learning models for regression, classification, forecasting, risk prediction, and performance analysis. My experience includes Random Forest, XGBoost, CatBoost, SVM, Decision Trees, feature engineering, model validation, and performance evaluation using MAE, RMSE, R², accuracy, AUC, F1-score, and other KPIs. Computer Vision & Image-Based AI I work on image preprocessing, feature extraction, computer vision regression/classification, medical image analytics, segmentation-based analysis, and AI-assisted visual decision systems. I can support projects involving OpenCV, Python, image datasets, clinical imaging features, and machine learning-based image interpretation. Forecasting & Business Analytics I help businesses and researchers build forecasting models, demand prediction systems, KPI dashboards, market analytics, operational performance reports, and decision-ready business insights. I can analyze structured data and translate results into clear recommendations for management, research, or client-facing reports. Research, Reports & Technical Documentation Along with modeling and analytics, I prepare professional technical reports, research manuscripts, white papers, methodology sections, literature reviews, results interpretation, and publication-style documentation. I can turn raw datasets, models, and technical concepts into clear, polished, and client-ready deliverables. Tools & Technologies Python | Pyomo | Scikit-learn | Pandas | NumPy | Matplotlib | OpenCV | MATLAB | Simulink | CPLEX | CBC Solver | GAMS | Excel Solver | Machine Learning | Optimization | Forecasting | Predictive Modeling | Computer Vision | Business Analytics | Decision Support Systems Industries I Work With Healthcare Systems Business Operations Supply Chain and Logistics Manufacturing and Production Systems Research and Academic Projects AI/ML-Based Decision Systems Engineering and Technical Consulting My goal is simple: to help you solve complex problems with clear models, reliable data analysis, and practical decision-support outputs. Whether you need an optimization model, a machine learning pipeline, a forecasting system, a computer vision workflow, or a complete analytical report, I can help you build a solution that is technically sound, well-documented, and ready for real-world use. Let’s connect and turn your operational chaos into confident, data-driven decisions using the power of analytics, optimization, and intelligent AI systems.

Holds a PhD in Computer Science (Ghent University) focusing on optimization of wireless systems and AI/ML. Experienced in design and optimization of wireless systems (4G/5G (3GPP), Wi-Fi(IEEE 802.11), software-defined radio systems, digital signal processing, algorithm development, AI model training, data analysis, and project management. Skilled in Python, C/C++, and TensorFlow, with hands-on expertise in automation, technical tool development, and machine learning applications. Proven ability to contribute to interdisciplinary research projects, develop scalable software solutions, and work with diverse teams across academia and industry. Experienced in creating technical documentation, performance analysis tools, and training content for AI platforms. Strong background in system integration, scripting, and experiment-driven problem solving. Proficient in Machine Learning/AI, Python, and C/C++. # What I do: • Wireless systems & DSP: link- and system-level modeling, PHY/MAC algorithm design, KPI analysis (RSRP/RSRQ/SINR, latency, throughput), coverage/heatmap tooling, performance evaluation. • SDR prototyping: end-to-end builds with USRP/FPGA; experiments on srsRAN, OpenAirInterface. • AI/ML for comms: feature engineering (FFT/IQ pipelines), classification models, federated/reinforcement learning, model training/evaluation in TensorFlow & PyTorch. • Automation & data tooling: Python pipelines for data labeling, parsing, and visualization (e.g., Grafana), experiment orchestration, and reproducible research assets. • Technical content: research-grade writing, specs, API docs, tutorials, grant proposals, MCQs/exams, and training content for AI platforms. # Selected outcomes: • ML-based preprocessing for signal-type identification that improved classification accuracy (FFT-driven pipeline in Python/TensorFlow). • Federated learning–based spectrum sensing that dramatically reduced data transfer needs during training while maintaining model performance (PyTorch). • Dynamic spectrum-sharing research across 4G/5G that increased spectrum-utilization efficiency, validated via simulation and analysis. • Robust post-processing/visualization tools for drive-test data, enabling faster KPI reporting and decision-making. # Technical content & documentation: • Experienced technical content writer with a strong background in implementing solutions and producing detailed reports for large-scale research initiatives. • Skilled in authoring original research articles, technical documentation, grant proposals, and in disseminating scientific findings. • Comfortable translating complex DSP/communications and AI concepts into clear, accurate materials for engineers, researchers, and non-technical stakeholders alike. # Toolbox: • Python, C/C++, MATLAB • TensorFlow, PyTorch • Linux, shell scripting, Git • SDR (USRP), FPGA integration • srsRAN, OpenAirInterface, openwifi • Grafana • Data labeling/QA workflows # Collaboration: I’m available for consulting, scoped research/prototyping, algorithm development, KPI analysis/reporting, automation tooling, and technical writing.

I refactor your brittle, high-latency data workflows and volatile schema mutations to engineer clean, secure, production-grade GenAI platforms that eliminate operational lag and guarantee absolute output accuracy. - Slashed LLM hallucinations by 40% and accelerated processing speeds 10x by overhauling legacy monolithic parsing with a layout-aware multi-agent architecture and semantic chunking. - Permanently eliminated a cluster of 75+ daily data defects and reduced pipeline lag by 12 hours across 500,000+ daily records by deploying serverless, SOLID-principled PySpark ETL workflows. - Accelerated executive decision cycles by two weeks and cut underlying API infrastructure costs by 25% by building real-time, metadata-grounded Streamlit and centralized BI ecosystems. Ready to transition your AI infrastructure from an unpredictable, costly prototype into a high-fidelity, secure production engine? Let's connect to map out your architecture.