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Vibhu Gautam
Professor of Practice
Profile Summary
Vibhu Gautam is a seasoned data science and DevOps professional with over 15 years of combined industry and academic experience. Currently a Professor of Practice at UPES, he integrates technical expertise and practical knowledge in Data Science, MLOps, Cloud Deployment, and Containerization. With contributions at Nestlé, Micron, McKinsey, BCG, and academic research at the University of York, he bridges innovation, strategy, and education in emerging technologies.
Work Experience
Over the years, Vibhu Gautam has held strategic and technical roles across global organizations. At Nestlé, he led global HR data science projects. At the University of York, his research in Bayesian ML contributed to safe autonomous systems. His earlier stints at Micron, BCG, and McKinsey enriched his expertise in predictive analytics, process optimization, and data-driven decision-making. He now applies this experience in teaching and curriculum development at UPES.
Research Interests
His interests include scalable and safe AI for autonomous systems, MLOps and cloud-native ML deployment, Bayesian machine learning, NLP for healthcare, big data frameworks like PySpark, and responsible AI.
Teaching Philosophy
Vibhu's teaching approach emphasizes real-world, project-based learning. By integrating hands-on tools, real datasets, and platforms like GitHub and cloud services, he empowers students to become ethical, innovative, and industry-ready professionals.
Courses Taught
He teaches Object-Oriented Programming (Python, C), Operating Systems (Linux, Shell Scripting), Cloud Computing (AWS), DevOps and Software Architecture, Docker and Containerization, and Portfolio Development Projects integrating LinkedIn, GitHub, and YouTube.
Awards and Grants
- Marie-Sklodowska-Curie Early-Stage Research Fellowship (EU, 2020)
- Best Team Award at BCG (2015); Stellar Client Contribution at McKinsey & Company (2014)
Scholarly Activities
Published research on Bayesian Neural Networks and NLP in top-tier conferences (SafeAI, EAI); mentored students on ML projects; contributed to open-source ML deployment tools; conducted industry workshops on DevOps and MLOps.
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