I’m a Senior AI Research and data Scientist with 9+ years of hands-on experience at the intersection of software engineering, healthcare, and artificial intelligence. I hold a Ph.D. in Informatics from the University of Iowa, where my work focused on leveraging NLP, LLMs, and deep learning to transform clinical decision support.

I work at the California Medical Innovation Institute, developing AI-powered tools and LLM pipelines for research data management and biomedical informatics. I specialize in designing end-to-end ML and LLMOps systems, from data ingestion and model development to scalable deployment using cloud-native and containerized solutions.

• AI for Healthcare & Clinical Informatics – Applying machine learning, deep learning, and NLP to improve symptom detection, patient outcomes, and decision support in clinical settings.
• Natural Language Processing (NLP) – Advanced expertise in NER, sentiment analysis, tokenization, clinical text mining, and embedding-based semantic search.
• Large Language Models (LLMs) – Hands-on experience with GPT, LLaMA, BERT, RoBERTa, Groq; techniques including fine-tuning, RAG, prompt engineering, few-shot & zero-shot learning, and agentic AI development.
• LLMOps & MLOps Engineering: Full lifecycle workflows using MLflow, DVC, DagsHub, LangChain, LangGraph, and experiment tracking with GitHub Actions and Astro.
• Cloud & DevOps – Deploying scalable models on AWS SageMaker and Google Cloud; integrating services like Lambda, Bedrock, S3; CI/CD automation and containerization with Docker.
• AI App Development – Development of AI-powered tools, mobile apps (e.g., OASIS), and web-based systems using FastAPI, Android, and REST APIs.
• Generative AI Applications – Building GPT-based agents for tasks like synthetic clinical note generation, data management plan (DMP) creation, and biomedical literature search.
• Data Engineering & Statistical Modeling – Expertise in Pandas, NumPy, Keras, Scikit-learn, TensorFlow, PyTorch, R, SPSS, SAS, and STATA.
• Visualization & Reporting – Creating insights through dashboards, Power BI reports, and clustering/topic modeling for health data analysis.
• Database Systems – Experience with SQL Server, PostgreSQL, MongoDB, FAISS, ChromaDB, Cassandra, and ASTAR DB.
• Networking & Systems – Proficient with TCP/IP, VLAN, VMware, Linux, Windows, Active Directory, and server clustering.

• Ph.D., Informatics, University of Iowa (2023–2025)
• M.Sc., Informatics, University of Iowa (2021–2023)
• M.Sc., Medical Informatics, Tarbiat Modares University, Tehran, Iran (2013–2016)
• B.Sc., Computer Software Engineering, Azad Najafabad, Isfahan, Iran (2005-2010)

• Sr. AI Research Scientist, California Medical Innovation Institute
• Research Assistant, University of Iowa (ML, DL, NLP, AI in healthcare)
• Intern, NIH/NCATS, RARe-SOURCE™ AI pipeline for rare disease literature
• Teaching Assistant, University of Iowa (Python, Informatics)
• Software Engineer, Khorshid Hospital & Parisian Institute (EHR, mobile apps)

• NIH-Compliant DMP Generator
  Designed an automated pipeline using GPT-4 and LLaMA to generate NIH-compliant Data Management Plans (DMPs). Integrated prompt engineering and multi-metric evaluation (automated + human review) to ensure quality and compliance.

• AI-Ready Envision Portal
  Built a FAIR-compliant ophthalmic imaging pipeline using Croissant metadata. Enabled AI-readiness and standardized data sharing for disease analysis and machine learning workflows.

• Symptom Detection in Cancer Patients
  Developed and fine-tuned multiple LLMs (BERT, GPT, LLaMA) on Electronic Health Records (EHRs) to extract 13 cancer symptoms and palliative care indicators using prompt tuning, embedding techniques, and named entity recognition (NER).

• Synthetic Clinical Notes Generation
  Utilized GPT-4 to generate synthetic, realistic clinical notes for benchmarking models in low-resource domains, improving robustness and external validation in healthcare AI pipelines.

• OASIS Mobile App for Symptom Prediction
  Co-developed an AI-driven mobile app to predict 14 cancer-related symptoms in 18,000+ patients. Contributed deep learning models for personalized recommendations and participated in A/B testing with real patients.

• AI Chatbot for Rare Disease Literature Mining
  Created an LLM-powered chatbot with NIH/NCATS for the RARe-SOURCE™ project to extract gene-disease associations (e.g., Farber disease) from biomedical literature using LangChain, Ollama, and RAG.

• Web-based NLP Applications
  Built sentiment classifiers and conducted topic modeling and clustering to identify trends and themes from web-based health content using NLTK, Scikit-learn, and custom NLP pipelines.

• Zeinali, N., S. White, et al. Using Large Language Models to Detect Anxiety and Nausea/Vomiting Documentation in Clinical Notes of Patients with Cancer, to be prepared for CIN Journal (2025).
• AlBashayreh, A., Zeinali, N., S. Gilbertson-White. An Informatics Approach to Characterizing Spiritual Care Documentation in Electronic Health Records of Older Adults, ACI Journal (2025).
• AlBashayreh, A., Zeinali, N., S. Gilbertson-White. Innovating the Detection of Care Priorities in Heart Failure Using Large Language Models, Innovation in Aging (2024).
• Zeinali, N., S. Gilbertson-White, et al. Machine Learning Approaches to Predict Symptoms in People with Cancer: A Systematic Review, JMIR Cancer (2024).
• Zeinali, N., S. White, et al. Symptom-BERT: Enhancing Cancer Symptom Detection in EHR Clinical Notes, Journal of Pain and Symptom Management (2024).
• AlBashayreh, A., Bandyopadhyay, A., Zeinali, N., et al. NLP Differentiates Cancer Symptom Information in EHR Narratives, JCO Clinical Cancer Informatics (2024).
• White, S., AlBashayreh, A., Bandyopadhyay, A., Zeinali, N., et al. Concordance Between Patient-Reported and Provider-Documented Symptoms, ACI (2024).
• Bandyopadhyay, A., AlBashayreh, A., Zeinali, N., et al. Predicting Development of Cancer-Related Symptoms Using EHR Data, Open JAMIA (2024).
• Nazari, E., Zeinali, N., et al. Application of Big Data Analysis in Healthcare Based on Health System Framework, Dokkyo Journal of Medical Sciences (2021).
• Zeinali, N., Asosheh, A., et al. Interoperability in Hospital Information Systems, Journal of Health and Biomedical Informatics (2017).
• Zeinali, N., Asosheh, A., et al. The Conceptual Model to Solve Interoperability in Health Information Systems, IST (2016).
• Delaram, Z., Zeinali, N., et al. The Common Applications of Social Networks in Healthcare, Health Information Management (2016).

• Zeinali, N., Patel, B., et al. Evaluating the Effectiveness of Open-Source LLMs for NIH DMPs, International Data Week (SciDataCon 2025).
• Zeinali, N., AlBashayreh, A., et al. Comparison of BERT Implementations for Cancer Symptoms Extraction, IEEE AIMHC 2024.
• Zeinali, N., Gilbertson-White, S., et al. Advanced Detection of Nausea/Vomiting and Anxiety in Cancer Patients, AMIA 2024 Annual Symposium.
• AlBashayreh, A., Zeinali, N., et al. Leveraging Spiritual-BERT for Spiritual Care Documentation in Heart Failure, AMIA 2024.
• AlBashayreh, A., Zeinali, N., et al. Innovating Care Priorities in Heart Failure Using LLMs, GSA 2024 Poster.
• AlBashayreh, A., Zeinali, N., et al. Disparities in Advance Directive Completion in Older Adults, Hospice & Palliative Care Assembly 2024.
• Zeinali, N., Gilbertson-White, S., et al. NER for Anxiety and Nausea/Vomiting in Cancer Clinical Notes, AMIA Informatics Summit 2025.

• Excellent Award Research — Spring 2025
• Ballard and Seashore Dissertation Fellowship, University of Iowa — Spring 2025
• Student Impact Grant, University of Iowa — Summer 2024
• AMIA 10x10 Program (funded by CCOM, University of Iowa) — Spring 2024
• Research and Travel GPSG Award, University of Iowa — Spring & Fall 2024
• Research Assistant Grant, College of Nursing, University of Iowa — Spring 2024
• Publication Grant, University of Iowa — Winter 2024
• Travel GSS Award, Graduate College, University of Iowa — Spring & Fall 2024
• Travel CS Award, Computer Science Dept., University of Iowa — Spring & Fall 2024
• Recruitment Fellowship, IGPI, University of Iowa — 2021–2024
• Recruitment Fellowship, Tarbiat Modares University — 2013–2016

• 🔭 I’m building and refining this portfolio page to reflect my evolving journey.
• 🌱 Actively learning and exploring MLOps, LLMOps, Generative AI, Agentic AI, Model Context Protocol (MCP) , and Federated Learning
• 💬 Feel free to ask me about Machine Learning, Deep Learning, NLP, Generative AI, and Agentic AI.
• 📫 Let’s connect: [email protected]
• 📅 Last updated: May 22, 2025