I like to think of this as a space where I explore science, math, machine learning, AI, and materials science, all while sharing my ever-evolving interests over time. As for the name, you may think the blog is about all things Paul Dirac, but that's not the case. The name is a tribute to the awesome capabilities of Paul Dirac, and the blog is my attempt to ascend to his level through blogging about intellectual adventures.
As Dirac's self-appointed student, I use this blog as pretty much my personal diary – a place where I jot down my thoughts and ideas as I grow older. While I love science communication, I'm not very good at it. That's another reason I named this blog "Dirac's Student" – Paul Dirac himself wasn't known for being the best teacher or communicator. But, through my blogging, I hope to create a collection of posts that captures my train of thought and the evolution of my interests over the years. I'm doing this despite the fact that the golden era of blogging has come to an end and I was 10 years too late. I also don't know why I'm stubborn and stick with blogger despite knowing how to setup/configure many great static site generators.
About me
I'm a materials scientist with a never-ending hunger for learning new things. I've spent over a decade focusing on computational materials science, a subfield that's all about using models and simulations to understand materials and how they behave. I'm also pretty experienced in materials characterization, both from a hands-on and data analysis standpoint.
Like many others, I've taken the plunge into the incredible world of machine learning, AI, and quantum computing in recent years. My journey into ML/AI started five years ago when I realized that materials informatics was becoming a big deal; thanks to all the computational materials data being generated. It's a challenging field, requiring knowledge in neural networks, Bayesian methods, materials science, software engineering, and high-performance computing, but it's also super exciting!
I got into quantum computing because I was curious about how NISQ devices could potentially solve quantum physics problems more quickly. Sadly, so far, there's no clear evidence that NISQ devices are any faster at these problems than classical devices. Once we move into the era of error-correcting quantum devices, the performance gains are much more established, given enough qubits. The issue is: can we get there?
So, thanks for stopping by! Feel free to leave comments on blog posts and start a dialogue with me.
