About Parseus AI

I grew up in a city of poets and wine, Shiraz, in the land of Cyrus the Great. As a kid, I was the sort of person who had to know how things were made. I wove Persian rugs, following a pattern knot by knot, where a single misread row pushes the whole motif out of line and there is no cheap way back, so you check every row as it forms rather than at the end. I embroidered by hand, where the thread and the daylight were finite and a mistake meant unpicking an afternoon of work, so I learned to plan the design around the cloth I actually had. I baked pastries, where the sequence and the timing are fixed and unforgiving. And I played chess competitively, which taught me to plan several moves ahead and commit to a line knowing I would have to live inside the constraints it created.

None of it looked like engineering at the time. Looking back, the instinct was already there, the one that still drives my work: take a process apart until you understand it well enough to build something you can rely on.

I loved all of it. But the thing that truly caught me was the computer. I was thirteen when I got my hands on a Commodore 64, and while everyone else wanted to play the games, I stayed up after the house went quiet, writing my own in BASIC. The crafts had taught me to follow a process someone else had set. Here was the opposite: I could write the process myself, line by line, and the machine would do exactly what I told it, no more and no less. If it broke, I had only my own logic to fix. That was the moment the work turned from making things to making the rules things run by.

That pull led me to study computer engineering in Shiraz, through both a bachelor’s and a master’s degree.

My career began at home. Having ranked as the top student across several years of both my bachelor’s and master’s, I was offered a faculty position in Shiraz University’s school of computer science and engineering.

I was the first woman appointed there in the years after the Iranian Islamic Revolution, when a woman’s place in academic life was being newly questioned and constrained. I was twenty-five, standing in front of students who had never had a woman teach them at that level. I learned only later that this became an inspiration for many young women to pursue their own ambitions regardless of the constraints around them.

From there I went on to a PhD at the University of Waterloo and a postdoctoral appointment at Simon Fraser University. Along the way I built the scientific and engineering foundation that real work depends on, learned to invent new technology and push it past its limits, and came away with the habit I value most: never being done learning.

The bridge into industry was a postdoctoral grant, one meant to push academic research into the real world. It set the pattern for everything since: take something that works in principle, and make it work in practice.

So I had lived on every side of the divide between the lab and the world, and between startups and large corporations. Each taught me something the others couldn’t.

The work itself was rarely tidy. I built computer vision systems for the mining and welding industries that had to see clearly through dust, welding arc light, constant vibration and harsh weather. In mining, a single missed detection could jam a crusher, halting the operation and putting a life at risk to clear it. On the welding line, the system guided operators to lay a flawless weld straight through the blinding glare of the arc, addressing a problem the industry couldn’t escape: skilled welders are scarce, and their careers are cut short as they lose their eyesight by their early fifties. The impact was direct: lives saved, eyesight protected, and productivity raised sharply.

At Amazon, two products stand out. One was the chaos of trucks waiting to load and unload: drivers stuck in hour-long lines that I helped turn into a few minutes, across hundreds of sites. It sounds like logistics, but it’s really about a promise, getting people’s orders to them on time. The other mattered to me more personally. I built the systems that catch unsafe and inappropriate images before they reach the public website. A miss could put harmful content in front of anyone browsing, including children, so the machine did the heavy work of catching what it could at scale, but on the calls that carried real consequences, a person made the final decision, never the model alone.

Later I took on an older world entirely: the factories that build the circuit boards inside nearly every device we use. I built that sprawling physical operation something like a nervous system, connecting and centralizing the data and knowledge scattered across its sites. The plant could anticipate problems instead of reacting to them, plan and schedule production on evidence instead of guesswork, and run with visibility at every level.

A system can perform brilliantly in testing and still fail the moment it goes live. What made these systems work was never how advanced they were. It was the discipline around them: an architecture designed for the environment it would actually run in, not the one in the demo; operations that kept it reliable on the worst day, not just the average one; performance measured against real outcomes rather than benchmarks; and leadership that kept engineers, operators and decision-makers working from the same picture.

And trust, which runs through all of it: reasoning made visible, limits clearly drawn, and a person kept in charge of the decisions that carry weight, someone who can see what the machine sees and still overrule it. The machine does the work, and people keep the judgment.