One of my favorite questions people ask me is: “So, are you a mechanical engineer or an electrical engineer?”

It's my favorite because this exact question haunted me at the beginning of my PhD. I often wondered whether I would be good enough in electromagnetics as a mechanical engineer.

Fortunately, my advisor was a living example of someone who had freed himself from the shackles of so-called 'fields of expertise'. He never looked at a problem and thought, “Oh, this is a biomedical problem,” or “This is a computer science problem,” or “This is a theoretical physics problem.” Instead, he simply asked: “Is this interesting?” and if the answer was yes, dove right in.

While I'm not quite as fearless as he is, over the years I’ve started to realize something important: many problems become much clearer when you learn how to look at them from a more fundamental perspective.

One of the greatest gifts we have as humans is the ability to shift our perspective on the same object. When we look at a problem and keep zooming out, further and further, eventually it becomes nothing more than a set of symbols and relationships. But when we try to actually solve it, that's when we start zooming back in, watching out for changes in every nook and corner, every constraint, every boundary condition.

Take topology optimization for example. At its most specific level, the problem might look like this: we want to minimize the compliance of a structure subject to the stress PDE, volume constraint, and material properties.

But if we zoom out, the statement becomes much simpler. We are trying to minimize (or maximize) an objective function that is governed by a PDE and subject to constraints.

If we replace structural compliance with thermal compliance, and swap the stress PDE with the heat equation, we suddenly have a thermal optimization problem. If we want to minimize pressure drop in a pipe and search for the optimal flow path, we replace the PDE with the Navier–Stokes equations and now we have a fluid optimization problem.

Still mechanical engineering, right?

But what if we replace the objective function with transmission efficiency in a waveguide, governed by Maxwell’s equations?

At that level of abstraction, the structure of the problem hasn’t really changed.

When you zoom out far enough, all you see are the fundamental building blocks. But when you start zooming in on the specific details, that’s when the structure begins to reveal itself. You start seeing the atoms, the nuclei, the number of protons and electrons, the electron orbitals, and eventually even the more fundamental particles like quarks and leptons.

These optimization problems are much the same. From far away they look almost identical: they all have an objective function, a set of equations, and a handful of constraints. It’s only when you zoom in you see the intricate details and the problem suddenly takes on a label: mechanical engineering, electromagnetics, fluid mechanics, or something else entirely.

In the end, it’s all about perspective.

My research is on optimization. And every time I run simulations, I notice the same pattern. Getting from 0 to 70 - 80% performance is quick and breezy. But pushing it past 90% is where the real effort begins. And trying to get it past 99.99% is almost impossible. It becomes an asymptotic problem at that point.

And we don’t run simulations forever chasing that last 0.001%. We set stopping criteria. We define convergence thresholds. We find the pareto optimal. We decide what’s “good enough”. And then we complete it. We move on.

So why don’t we do the same in life?

Why do we keep chasing that last 0.001% as if it defines our worth? Why do we stall, waiting for tomorrow’s version of ourselves to be more polished, more knowledgeable, more ready to take that step forward? Why do we believe that someday we’ll reach some utopian, perfectly optimized version and only then will we allow ourselves to feel adequate? Why do we act as if anything less than our own version of perfect is failure?

Why is it that when we're playing a musical instrument our bar is Mozart or Rachmaninoff? Why is it that when we're playing a friendly tennis match our bar is Nadal or Federer? Why is it that when we're doing research and trying to make a contribution our bar is Feynman or Maxwell? Why is it that when we're writing our own thoughts our bar is Pessoa or Rilke? 

Why are we trying to reach >98% efficiency in a multi-objective optimization problem where the best we can theoretically get is to the Pareto front and then feeling like we're not good enough?

It’s funny when I think about my dad’s obsession with space. As a teenager, he had his ears glued to the radio during the moon landing. Yet later in life he decided to become a surgeon. But this story isn’t really about my dad. Or my mom, who for as long as I can remember showed my sister and me the constellations, the planets lining up in the sky, supermoons, and shooting stars visible from the northern hemisphere. Or even my sister, who devoured books by Jules Verne, Carl Sagan and Isaac Asimov as if she’d been starved of stories for a week.

What I’m trying to say is that when I look back and try to figure out why I picked up a book on special relativity in seventh grade, or struggled through A Brief History of Time in tenth grade, I can’t really identify a starting point. I was trying to understand equations and ideas far beyond my ability at the time (and failing miserably I must add).

What I do remember is staying up late at night with a tiny flashlight under the blankets, finishing Angels and Demons and reading about CERN. I remember wondering what it would feel like to become a particle physicist.

By the time the Higgs Boson was confirmed, I had grown up a bit and become more “realistic.” I chose engineering instead. The dreamer became a realist, looking for tangible problems to solve. I became interested in biomechanics and spent the beginning of my PhD working in that area.

But PhD life has a way of expanding your perspective. I was exposed to more diverse ideas and research directions, 90% of the credit for that goes to my advisor. Gradually, the realist started becoming a dreamer again. In some ways, the journey felt a bit like Gabriel's Horn: the more you move forward, the more there seems to be left ahead of you. A PhD is supposed to make you an expert in a niche, but instead I found myself thinking about science fiction again, about how intergalactic travel might someday be possible not only in Obi-Wan Kenobi’s universe, but perhaps in ours as well.

One idea my advisor shared during my first year still sticks with me: “Everything in nature is topologically optimized. Nature is the best optimizer.” If that’s true, then the best designs might come from learning how nature solves problems.

My goal is to develop organic design frameworks that can work across many areas of physics.

If I had to play a “kiss, marry, befriend” game with disciplines, I’d choose math to kiss, physics to marry, and engineering to befriend. Because when those three work in synergy, I can’t imagine the kinds of ideas we might create.

And the more I learn, the more I realize how much there is still left to discover.