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divyanshudubey0

Divyanshu Dubey

@divyanshudubey0

Elite AI ML Specialist and Full Stack Engineer , IIT Madras Scholar

Índia
Inglês, Hindi, Gujarati
Algumas informações são exibidas no idioma inglês.
Sobre mim
🚀 Elite Full-Stack Engineer & AI Specialist 🎓 Data Science at IIT Madras. With a track record of 50+ production-grade deployments, I handle everything from micro-fixes to massive digital ecosystems. I specialize in cutting-edge AI/ML (Agentic workflows, RAG, custom models) and high-performance Web/Mobile development. From optimizing small scripts to architecting full-scale startup platforms that secure long-term contracts, I deliver world-class, revenue-driving solutions. Big or small—your project gets elite engineering execution. Let's scale! 🌟... Saiba mais

Habilidades

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divyanshudubey0
Divyanshu Dubey
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Tempo médio de resposta: 1 hora

Conheça meus serviços

Software e Sites de IA
I will develop custom ai agents, rag pipelines and machine learning models

Portfólio

Experiência profissional

MicroAcquire

AI/ML(Machine Learning) Researcher

MicroAcquire • Meio período

Jan 2024 - Nov 20251 yr 10 mos

Project Overview: Collaborated with the Micro-Nano R&D Centre to apply cutting-edge machine learning and data science methodologies to high-dimensional scientific datasets for multiple CSIR-backed and academic research projects. The Challenges: Scientific datasets are notoriously complex, non-linear, and high-dimensional. Traditional modeling falls short, requiring advanced, physics-informed AI architectures, hyper-precise validation frameworks, and deep model interpretability to make the predictions viable for academic publication. The Solution & Execution: I took charge of the core ML infrastructure and predictive modeling:Advanced Architecture: Built and deployed deep learning and ensemble pipelines using GBRNN and LSTM models. Hyper-Optimization: Integrated Bayesian hyperparameter tuning and enforced strict 5-fold and 10-fold cross-validation to ensure zero overfitting. Explainable AI (XAI): Applied SHAP (SHapley Additive exPlanations) to interpret complex model decisions, translating black-box AI into actionable scientific insights. The Results: Successfully bridged the gap between computational physics and artificial intelligence. The work resulted in an independent research publication where our ensemble models achieved a remarkable predictive accuracy of R^2 == 0.97+ for material informatics.