Artificial Intelligence

Exploring the Intelligence Shaping Our World

Research, commentary, and practical insights on AI across healthcare, cybersecurity, education, and enterprise systems — from a practitioner with 700+ citations.

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700+
Research Citations
21+
Published Papers
h-14
h-index
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Where AI Is Changing Everything

Applied research and commentary on AI's impact across critical industries.

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AI in Healthcare
Diagnostic models, clinical decision support, and the ethical challenges of deploying AI in patient care environments.
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AI × Cybersecurity
How machine learning reshapes threat detection, anomaly identification, and the emerging arms race between attackers and defenders.
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AI in Education
Personalized learning systems, adaptive curriculum, and what large language models mean for how knowledge is taught and acquired.
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Enterprise AI
Operationalizing AI at scale — from IT outsourcing transformation to agentic workflows and the future of knowledge work.
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Generative AI
Foundation models, prompt engineering, retrieval-augmented generation, and the practical reality of building with LLMs in 2025–2026.
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AI Ethics & Policy
Governance frameworks, bias mitigation, transparency requirements, and the regulatory landscape shaping responsible AI deployment.
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AI Perspectives

Practical takes on artificial intelligence — where the technology stands, where it's going, and what it means in practice.

Healthcare
How AI Is Changing Health Care
A look at how machine learning is reshaping diagnostics, clinical workflows, and patient outcomes — and the challenges that come with it.
2020 · 5 min read
 Education
The Roles of Artificial Intelligence in Education
From adaptive learning platforms to AI tutors — how intelligent systems are transforming how we teach and how students learn.
2022 · 6 min read
 Enterprise
How AI Is Changing the Game for IT Outsourcing
Automation is restructuring global IT outsourcing economics. What it means for service providers, buyers, and the workforce in between.
2020 · 4 min read
 Generative AI
What Is the Best AI Content Creation Software in 2022?
A practical survey of the early LLM-powered content tools — many of which were the precursors to today's foundation model ecosystem.
2022 · 5 min read
 Research
AI on the IoT Frontier: Security Implications
Examining how artificial intelligence becomes both a tool and a target in Internet of Things environments — key findings from published research.
Research Paper · IEEE
 2026
From GPT-4 to Agent Swarms: What Changed in Three Years
A practitioner's view of how AI capability has evolved from single-model completions to multi-agent orchestration — and what's next.
2026 · 7 min read
// about

Researcher. Practitioner. Builder.

I'm Mayur Rele — Senior Director of IT & Information Security at Parachute Health, with 15+ years across DevOps, cloud infrastructure, and cybersecurity. My AI research focuses on real-world applications: how intelligent systems behave in healthcare, enterprise, and security environments.

With 700+ citations and an h-index of 14, my published work spans IoT security, DevSecOps, machine learning applications, and the intersection of AI with critical infrastructure.

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LinkedIn
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GitHub
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Google Scholar — Research Publications
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ResearchGate — Full Paper List
Senior Director, IT & Information SecurityParachute Health · Current role
700+ Research Citations · h-index 14Published across IEEE, ISACA, and peer-reviewed journals
Scientist of the Year 2024Recognized for contributions to applied AI research
TITAN Influencer Award 2024Technology leadership recognition
Award JudgeGlobee Cybersecurity, Stevie Awards, DevOps Excellence Awards
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Essential AI Books

The books that shaped how I think about artificial intelligence — from foundational theory to frontier research to societal impact. Click any card to find it on Amazon.

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Artificial Intelligence: A Modern Approach
Stuart Russell & Peter Norvig · 4th Ed. 2020
Foundational
The definitive AI textbook — 1,100+ pages covering search, logic, probabilistic reasoning, learning, perception, and language. AIMA bridges theory and application across every major subfield. More textbook than casual read, but essential as a reference. The 4th edition adds substantial coverage of deep learning and AI ethics.
Why read it: Every serious AI practitioner should own a copy. It's the lingua franca of the field — shared vocabulary, shared algorithms, shared depth.
Find on Amazon
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The Alignment Problem
Brian Christian · 2020
AI Safety
Christian examines the gap between what we ask AI systems to do and what they actually optimize for. Through interviews with leading researchers, he traces how reward hacking, distributional shift, and value misspecification create systems that technically succeed while failing catastrophically. Accessible writing on RLHF, interpretability, and the hard problem of encoding human values.
Why read it: The best single-volume introduction to why building AI that does what we want is harder than it looks — and what researchers are doing about it.
Find on Amazon
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Human Compatible
Stuart Russell · 2019
AI Safety
Russell, co-author of AIMA, argues that the standard model of AI — build systems that optimize fixed objectives — is fundamentally broken. He proposes a new paradigm: machines that are uncertain about human preferences and defer appropriately. The book lays out why superintelligence is a real concern and what a provably beneficial AI architecture might look like.
Why read it: A leading researcher making the case that getting AI alignment right isn't optional — and offering a concrete path forward. Rigorous but readable.
Find on Amazon
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Co-Intelligence
Ethan Mollick · 2024
Practical AI
Wharton professor Mollick offers the most grounded and immediately practical take on working alongside AI. Rather than hype or doom, Co-Intelligence focuses on how professionals can use LLMs as thought partners, how organizations need to rethink workflows, and what "centaur" human-AI collaboration looks like across knowledge work. Deeply research-informed and genuinely useful.
Why read it: The 2024 book that actually helps you decide how to integrate AI into your work right now, based on real experiments rather than speculation.
Find on Amazon
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The Coming Wave
Mustafa Suleyman · 2023
AI Futures
DeepMind co-founder Suleyman argues that AI and synthetic biology represent an unprecedented "wave" of technology — one that will be impossible to contain and will fundamentally reshape the relationship between governments, corporations, and individuals. He introduces the concept of "containment" as the central policy challenge of our era, and why current institutions are not ready for it.
Why read it: The most credible insider perspective on where AI leads — written by someone who built some of it and understands both the promise and the structural risks.
Find on Amazon
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Atlas of AI
Kate Crawford · 2021
Critical Perspective
Crawford traces the physical and political geography of AI — the lithium mines, the data warehouses, the crowdworkers, the surveillance infrastructure. AI is not a cloud-based abstraction; it is built on extraction of resources, labor, and data. A necessary corrective to the disembodied narrative of "intelligent machines," grounding the technology in material reality and power structures.
Why read it: Essential counterbalance to techno-optimism. Understanding what AI costs — in energy, in labor, in rights — makes you a more thoughtful practitioner.
Find on Amazon
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AI Engineering
Chip Huyen · 2025
Production AI
Huyen covers the practical engineering challenges of building AI-powered applications: model selection, evaluation, prompt engineering, RAG, fine-tuning, deployment, and observability. The book distinguishes between ML engineering (training models) and AI engineering (integrating foundation models into products) — a distinction the industry is still learning to make. Covers LLM evaluation rigorously, an underappreciated problem.
Why read it: The most current and practical guide to shipping LLM-powered applications in production — grounded in what actually fails and why.
View on Amazon
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Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow
Aurélien Géron · 3rd Ed. 2022
Technical
The standard practical ML textbook — covers the full supervised and unsupervised learning landscape through Part I, then deep learning through Part II. Géron writes with unusual clarity, moving from theory to working code without losing rigor. The third edition covers transformers, diffusion models, and modern deployment patterns. Best learned by running every notebook.
Why read it: If you can only read one technical ML book, make it this one. Exceptionally well-organized, code-first, and continuously updated to reflect the field.
Find on Amazon
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Power and Prediction
Ajay Agrawal, Joshua Gans, Avi Goldfarb · 2022
AI Economics
The sequel to Prediction Machines, this book argues that AI doesn't just make predictions cheaper — it shifts power within organizations and industries. When AI enables point solutions (replacing a single decision), incumbents win. When AI enables system solutions (restructuring entire workflows), the disruption runs deeper. The framework helps predict who gets disrupted and who benefits in any given AI adoption scenario.
Why read it: The clearest economic framework for thinking about where AI creates value and where it destroys it — invaluable for strategy and investment decisions.
Find on Amazon
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Let's Talk AI

Speaking engagements, research collaboration, or just a conversation about where AI is heading — reach out on LinkedIn.

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