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Learn more about Akshat

• Can you tell us about your first experience using a computer?

  My first computer was a shared family desktop in India, the kind of machine the whole household treated as slightly sacred. Like most kids, I started with games, but what hooked me was breaking things. I'd change a setting, something would stop working, and I'd have to quietly figure out how to fix it before anyone noticed. Looking back, that was my first real lesson in computing: the machine isn't magic, it's a system, and systems can be understood. That instinct, poke at it, break it, understand it, fix it, is basically still my job description today.

• What is the technological evolution that has marked you most and what will be, in your eyes, a turning point in this field?

  The deep learning revolution, without question. I grew up learning that computers do exactly what you tell them, then suddenly we had systems that learn patterns we never explicitly programmed. I felt this personally when I trained a chest X-ray classifier during my research work at UCL: watching a model learn to spot disease patterns from thousands of images changes how you think about what software is.
  
  The next turning point, in my view, won't be raw capability; it will be trust. AI is moving from impressive demos into hospitals, courts, and infrastructure, and the hard problem now is evaluation: how do we rigorously verify that these systems are reliable enough for high-stakes use?. Whoever solves that unlocks the real deployment era of AI.

• Explain your expertise, your interest in it and, more broadly, its importance in the computer world.

  I'm a full-stack ML/AI engineer, which means I work across the whole lifecycle: training models in PyTorch, building the applications around them in Python and TypeScript, and deploying them on real infrastructure with tools like FastAPI, LangGraph and Kubernetes.
  
  What drew me in was impact. I've worked on medical imaging ML at UCL, built a clinical platform used daily by around 25 hospital staff, and helped develop a water-quality scanner deployed at over 150 field sites in India to detect arsenic contamination. Those projects taught me the thing I care most about professionally: there's a huge gap between a model that works in a notebook and a system people can actually rely on. Bridging that gap, making AI dependable in the real world, is, I'd argue, where most of computing's value now lives.

• Do so-called 'visionaries', such as Bill Gates, Steve Jobs and Mark Zuckerberg inspire you or do you have other role models?

  Honest answer: not really, or at least not primarily. They built extraordinary companies, but founder mythology tends to erase the thousands of engineers who did the actual building. The people who inspire me are different. Teacher-engineers like Andrej Karpathy, who take genuinely hard ideas, neural networks from scratch, and make them legible to anyone with curiosity and a laptop. Tim Berners-Lee, who invented the web and gave it away. And the largely anonymous world of open-source maintainers and public-interest technologists, whose free work quietly underpins almost everything we use. Technology as a public good inspires me far more than technology as an empire.

• Provide a valuable anecdote related to your subject or your days at school.

  During my work with CSIR on the arsenic water-quality scanner, everything worked beautifully in the lab. Then we took it to the field. Rural deployment sites are nothing like a lab bench: harsh and inconsistent lighting, unreliable power, and users encountering the interface for the first time with no one standing behind them to help. Things that had never failed suddenly failed constantly.
  
  That project rewired me as an engineer. We had to redesign around reality rather than expecting reality to accommodate our design, and the scanner eventually ran across 150+ sites. It's the lesson I now pass on to every student: code that works and a system that works are two very different achievements, and the second one is where engineering actually begins.

• What would you say to an uncomfortable person in front of a screen who says that "IT is too complicated"?

  I'd say: you're right that it looks complicated because you're seeing every layer at once. But nobody ever needs every layer at once. You drive a car without understanding the engine; you can use, and even build with, computers the same way, learning one layer at a time as your curiosity pulls you deeper.
  
  As a tutor I've watched students go from "I just can't do this" to building their own projects, and the difference was never ability; it was sequencing. Complexity is a curriculum problem, not an intelligence problem. And honestly, there's never been a friendlier time to start: today's tools meet beginners more than halfway.

• What are your other passions, related or non-related to computers?

  Outside of code, my biggest passion is political theory and how democracies actually work. I'm currently working through Ian Shapiro's Yale lecture series on my commutes, and some of my best evenings are spent in long, spirited political debates with close friends. I'm also drawn to philosophy, especially epistemology: how we know what we know turns out to be surprisingly relevant to both AI and teaching.
  
  Closer to the machine, I'm an inveterate Linux tinkerer; my gaming laptop dual-boots a setup I've broken and rebuilt more times than I'd like to admit. And I'm currently designing a pet first-aid app with friends, which combines two things I love: building useful software and animals.

• What makes you a Superprof in IT?

  Three things, I think. First, I teach from real practice: I'm a working ML/AI engineer, so when a GCSE or A-Level student asks "but when would anyone actually use this?", I can answer with systems I've genuinely built for hospitals, research labs and field deployments. Second, I know the exam terrain properly: I tutor GCSE Computer Science and OCR A-Level (H446), so lessons connect first principles to the mark scheme rather than choosing between them. Third, and most importantly, patience. I treat every "silly question" as the most important question in the room, because it usually is: it's the exact spot where understanding is about to happen. My goal is never just a grade. It's a student who understands why, because that's the understanding that survives the exam hall.