Denis Zenios
I'm a computational engineer. I work at the intersection of physics, engineering, and software, on the problems that don't have an off-the-shelf answer.
The hardest problems live where several fields overlap. Take robotics: a robot that loses its target isn't a vision problem or a control problem or a software problem on its own. It's all of them at once.
I work across all three, producing systems whose behaviour you can trust. Only edge cases imposed by nature are out of reach. The case studies below show what that looks like.
Case Studies
A closer look at how a hard problem became a working system.
Real-Time Robot Tracking, Re-architected in Rust
A vision-guided robot kept losing the person it was meant to follow whenever they moved too quickly. The problem was diagnosed as a concurrency-model failure, and the system was rearchitected from Python to Rust, after which the robot held its target where it had previously lost it, with end-to-end latency roughly halved.
Read moreDetecting and Tracking Flying Objects
A startup needed to see drones in the sky reliably, tell them apart from the birds and planes that fool most detectors, and know where each one was heading. I built a real-time system that does all three, and beats the published state of the art at drone detection on every object size, on the same public benchmark. On one brutally hard clip the best published model missed the drone in all 300 frames; mine found it in 262.
Read moreBuilding an AI Agent You Can Trust to Act on Your Behalf
Getting an AI to generate things is easy. Getting one you can trust to act on your behalf, unsupervised, is the hard part, and it is an engineering problem more than a prompting one. I built such a system for personalized cold outreach, where a single mistake lands in a stranger's inbox with my name on it. By hand I could write maybe 50 personalized emails a day; the system now runs until it hits the provider's send limit, around 400 to 500 a day, at the same personalization and the same reply rate, with me out of the loop.
Read moreWork
Selected engineering work across automation, robotics, and research systems.
Lets an operator inspect a hazardous industrial site remotely. The whole system is driven by plain language: the operator tells it what he is looking for, and the system handles the rest, built on semantic navigation and an adaptive memory of the places it has seen.
ProprietaryImproved robot responsiveness in high-stakes situations. A Rust port of the Loaden runtime targeting edge devices with hardware accelerators, trading Python's flexibility for predictable low-latency execution and compile-time safety.
GitHubCut down time spent on physical experiments by identifying laser parameter limits in simulation before any rock is touched. A finite element model of laser-induced thermal stress in rock, built with FEniCSx and Gmsh and validated against analytical solutions.
GitHubA real-time system that detects drones, tells them apart from birds, planes, and helicopters, and forecasts where each is heading. It beats the published state of the art, with the biggest gains on the small, distant drones.
ProprietaryGives operators a single screen to monitor a live robot without needing terminal access or raw log files. Built as a full-stack dashboard that streams telemetry, camera feeds, and system logs in real time via a Flask/Socket.IO backend and React frontend.
Cut the human time required to run targeted outreach campaigns to zero. A cloud-based system that accepts a high-level category instruction and operates end-to-end without intervention.
ProprietaryBuilt a system that made it systematic to detect whether political risk ratings are in structurally distinct states rather than relying on visual inspection or domain intuition. The pipeline implements statistical jump models, graph auto-encoders, and tuned classifiers on political risk time series.
GitHubBuilt a system that made it tractable to synthesise patterns across ~600 academic papers that no human team could read in full in a reasonable timeframe. The pipeline runs end-to-end NLP analysis using LDA and BERT topic modelling with semantic cross-validation and visualisations.
GitHub (LDA phase)Frees tradespeople from carrying loads on construction sites by automating the logistics entirely. Built as the core automation stack for an autonomous load-carrying robot, covering coordination logic, motion planning, and system-level control in Python and ROS.
GitHub