π Visionary AI/ML Engineer & Research-Focused Data Scientist | Architecting Next-Gen Healthcare AI, Advanced GenAI, LLM Orchestration & Principled MLOps π
I am an AI/ML Specialist and Data Scientist, leveraging over 5.5 years of dedicated experience in architecting, developing, and deploying transformative AI solutions. My core passion lies at the confluence of Artificial Intelligence, Computational Neuroscience (e.g., Twin Brain concepts), and Healthcare. I focus on leveraging Advanced Predictive Analytics, Multimodal NLP, Generative AI (GenAI), and Explainable AI (XAI) to pioneer innovations that enhance patient outcomes, optimize clinical workflows, drive biomedical discovery, and promote health equity.
- π Holds a Master of Science in Business Analytics from Lewis University, specializing in advanced AI applications.
- π‘ Expertise in the full AI/ML lifecycle: from complex data acquisition and advanced feature engineering (including representation learning and dimensionality reduction) to sophisticated model development (Python, Scikit-learn, TensorFlow, PyTorch, JAX) and resilient, scalable deployment using MLOps best practices on Microsoft Azure, AWS, and GCP.
- π Deeply immersed in the cutting-edge of LLMs (e.g., Gemini, Claude 3 series, Grok, GPT-4/5, DeepSeek, Mixtral, Llama series), Retrieval Augmented Generation (RAG) & Advanced RAG techniques, Fine-tuning strategies (PEFT, LoRA, QLoRA), LangChain & LlamaIndex for complex agentic workflows, and building sophisticated Multi-Agent Systems (CrewAI, AutoGen, custom frameworks).
- π§ Exploring and contributing to theoretical AI concepts, including Neuro-Symbolic AI, Causal Inference in ML, and the development of Digital Twins in healthcare.
- π― Proven ability to translate highly complex technical concepts into strategic, actionable insights for clinical, research, and business stakeholders.
- π± Committed to pioneering Responsible AI by rigorously implementing AI Ethics, fairness, transparency (SHAP, LIME), privacy-preserving ML (Federated Learning, Differential Privacy), and robust model governance.
Centene Corporation Β· Contract Β· St Louis, Missouri, United States (Remote) Jan 2024 - Apr 2025 (1 yr 4 mos)
- Leveraged AI/ML for predictive modeling of patient outcomes, focusing on maternal/infant health and reducing readmissions. (Tools: Python, XGBoost)
- Deployed AI to address social determinants of health (NEST Program), improving health equity through NLP & geospatial risk mapping. (Tools: Python, SpaCy, BERT, SQL)
- Developed AI models for fraud detection, decreasing fraudulent activities. (Tools: Python, Azure Security Center)
- Contributed to NLP automation of claims processing. (Tech: Transformer models like BERT, GPT)
- Implemented MLOps practices on Azure for CI/CD of machine learning models. (Tools: Azure ML, Docker, Kubernetes)
- Collaborated with cross-functional teams to translate AI-driven insights into actionable clinical and operational improvements.
Wipro Β· Contract Β· Hyderabad, Telangana, India (Hybrid) Jun 2019 - Jun 2023 (4 yrs 1 mo)
- Led the development and implementation of AI-powered claims management solutions for key healthcare insurance clients.
- Conducted comprehensive exploratory data analysis (EDA) and advanced feature engineering on large-scale healthcare datasets.
- Built and validated machine learning models using Python, Scikit-learn, and TensorFlow to predict patient risk, optimize resource allocation, and improve claims processing efficiency.
- Developed and deployed NLP models for automated healthcare document processing, information extraction, and claims validation.
- Designed and orchestrated end-to-end data pipelines using SQL, Azure Data Factory, and Databricks for robust healthcare data integration, transformation, and analysis.
- Collaborated closely with engineering and product teams to integrate AI models into existing healthcare platforms and new applications.
- Championed and ensured AI solutions complied with Responsible AI principles, focusing on patient data privacy, security, and ethical considerations.
My comprehensive toolkit for pioneering AI solutions:
Core Programming, Data Science & Scientific Computing:
Machine Learning, Deep Learning & Reinforcement Learning:
Natural Language Processing (NLP) & Speech Technologies:
Generative AI, LLMs & Agentic Frameworks:
Cloud Platforms & Services:
MLOps, Deployment & CI/CD:
Databases, Vector Stores & Data Pipelines:
Web Frameworks, Visualization & Other Tools:
Here are a few selected projects and areas of active research. (Please check my pinned repositories for more examples!)
Developing sophisticated AI systems for real-time monitoring and prognosis of chronic diseases (e.g., diabetes, cardiovascular conditions) using multimodal data (EHR, IoT sensors, imaging). Employs deep learning for time-series forecasting, survival analysis, and early detection of adverse events.
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Architected an autonomous AI agent using LLMs and RAG to perform complex data analysis on clinical datasets. The agent interprets natural language queries, performs statistical analysis, generates hypotheses, visualizes data, and synthesizes insights for research and clinical decision support.
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Leading the conceptualization and development of AI for PantryPal AI: a hyper-local, seasonal meal planning app focusing on advanced food waste reduction. Leverages LLMs for personalized meal plans based on dietary needs, health goals, pantry inventory, and local store savings. Includes predictive spoilage models.
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LumiNIC-Q is an AI-driven decision support system designed to enhance the quality of care in Neonatal Intensive Care Units (NICUs). It analyzes real-time physiological data, clinical notes, and lab results to predict potential complications (e.g., sepsis, IVH), optimize treatment protocols, and provide actionable insights to clinicians for improved neonatal outcomes.
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This section is dedicated to showcasing visual outputs, architectural diagrams, and conceptual representations of AI projects and research.
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AI Model Architectures (e.g., Custom CNNs, Transformer Visualizations) ![]() View Details |
Data Visualizations & Insights (e.g., Complex Data Dashboards, t-SNE plots) ![]() View Details |
GenAI Creations & Outputs (e.g., AI-generated images, text summaries) ![]() View Details |
Interactive Demo Screenshot (Description) ![]() View Details |
Research Poster Snippet (Description) ![]() View Details |
I'm passionate about pushing the boundaries of AI, especially in healthcare and "Twin Brain" research. Always open to discussing novel ideas, collaborating on impactful projects, or exploring pioneering opportunities.
- π LinkedIn: linkedin.com/in/harinath-reddy-yelamplalle-209596126
- πΌ Portfolio (More Visuals & Case Studies): harinathreddyai.com * π« Email: harinathreddyyb@gmail.com
- π‘ Research Interests: Twin Brain Concepts, Neuro-Symbolic AI, Causal ML, Generative Models in Drug Discovery.
β‘οΈ My AI models occasionally try to convince me that the optimal solution to a complex problem is simply "more data... or a good cup of coffee." Often, they're not wrong about the coffee! π