System architecture
Architecture definition, performance modeling, and trade studies for active sensing concepts. System-of-systems reasoning that connects sensor physics to what the mission — or the platform — actually requires.
Systems architecture · Sensing · Autonomy · Space
Someone has to be accountable for whether it actually works.
CyLabs Engineering provides chief-engineer-level technical authority on programs where a wrong architecture gets discovered late and expensively. Physics, modeling, and validation — carried from concept through hardware that survives the field.
18+years in advanced sensing
21patents granted or pending
10+peer-reviewed publications
PhDremote sensing
Why a chief engineer, now
Producing the analysis is cheap. Knowing which answer is right is not.
Simulation output, trade tables, and code have never been easier to generate, and the volume of plausible-looking engineering has never been higher. What has not scaled is the judgment to know which assumption breaks, whether a result is physically possible, and what has to be true for the system to work at all.
That judgment is not a document. It comes from having been wrong before — on hardware, in the field, with consequences — and from having owned every stage of the thing rather than reviewing it from a distance.
- Concept formulation
- Physics & modeling
- Laboratory demonstration
- Prototype
- Integration & test
- Deployment
Plenty of people can critique an architecture. Fewer have carried a system through every one of these stages and watched which assumptions survived contact with hardware. That is what makes the judgment at the front end worth paying for.
Capabilities
What the role actually covers.
Chief engineer is a function rather than a title — owning the technical answer end to end and being the person who says whether it will work. In practice that reaches from architecture and first-principles physics through the processing chain to the field.
Technical authority & review
Outside chief-engineer scrutiny ahead of a design review, test readiness review, or go/no-go decision. Where the analysis is thin, where the risk actually sits, and what has to be true for the system to work — stated plainly to the people who need to hear it before the money is committed.
Physics-based modeling & validation
Closing the loop between theory, model, and measurement. First-principles analysis, error budgets, and the calibration and test campaigns that establish whether real performance matches what was predicted — rather than assuming the simulation was right.
Sensing & perception
Radar and lidar, RF and optical, coherent and direct detection, including synthetic aperture techniques. Multi-sensor fusion, navigation and PNT, and turning raw returns into a state estimate a platform can act on. Doctoral foundations in the underlying physics.
Signal processing & machine learning
Estimation and detection theory applied to real sensor data, and machine learning built on top of sensing physics rather than in place of it. Numerical modeling and analysis in Python and MATLAB.
Field integration & validation
Getting sensors onto real platforms and proving they work there — integration aboard ships and uncrewed surface vessels, calibration, field trials, and structured data-collection campaigns that produce evidence instead of anecdotes.
Domains
Two markets, one underlying problem.
A spaceborne sensor that has to close its link budget and a vehicle that has to not hit anything are the same engineering problem in different clothing: extract a reliable estimate from noisy physics, and prove it holds up outside the lab. I have shipped both.
Space, defense & intelligence
Spaceborne and airborne sensing programs. Architecture and physics-based modeling for coherent measurement concepts, government-funded investigation of detection phenomena, experimental validation strategy, and technical review readiness ahead of major milestone decisions.
Northrop Grumman · Lawrence Livermore National Laboratory
Autonomy, robotics & uncrewed systems
Sensor strategy for platforms that have to perceive and act in the real world — ground vehicles, robotic platforms, and uncrewed surface vessels. Radar and lidar selection and configuration, patented calibration methods proven at fleet scale, sensor fusion, at-sea integration, and the validation work that separates a demo from a system safe enough to deploy.
Pony.ai · Lyft Level 5 · Apple
The two are converging — uncrewed platforms, counter-UAS, and robotic autonomy all depend on the same sensing stack. Work that crosses between them is where I'm most useful.
Engagements
How the work is typically structured.
Architecture & sensor trade study
Which sensors, in what configuration, and will the result meet the requirement. Performance modeling, parameter trades, and a defensible recommendation — delivered before the architecture becomes expensive to change.
Independent technical review
A focused outside read on a program you already own. Assumptions checked against first-principles physics, gaps in the analysis identified, and risk ranked by what actually threatens the outcome — delivered as written findings and a working session with the team, early enough to still change the decision.
Algorithm development & validation
Signal processing, calibration, fusion, and machine learning for a specific sensing problem — developed against real or modeled data, and validated experimentally rather than only in simulation.
From prototype to fielded system
Moving a demonstrated concept toward deployment: integration onto the actual platform, field trials, and the data-collection campaign that proves it. Whether that's TRL advancement on a government program, a sensor suite installed on a vessel, or hardening a perception stack for production.
Engagements run from short advisory reviews through sustained embedded technical leadership. Remote, on-site, or hybrid.
Background
Cyrus F. Abari, PhD
Chief engineer with 18+ years leading multi-disciplinary R&D teams building advanced sensing systems — for defense, intelligence, and space programs, and for the autonomous vehicle and robotics platforms that depend on the same physics. Full-lifecycle ownership: concept formulation, laboratory demonstration, prototyping, integration and test, and deployment.
Selected experience
- CEO CyLabs Engineering
- Senior Staff Radar Systems Engineer, Technical Fellow Northrop Grumman
- Principal Investigator & Systems Lead Lawrence Livermore National Laboratory
- 3D Sensor Systems Technical Lead Manager Pony.ai — autonomous vehicles
- Systems Engineer — Autonomy & Sensing Lyft Level 5
- Systems Design Engineer — Opto-electronics Apple
- Wireless Systems R&D Engineer Ericsson AB
Contact
Describe the problem. I'll tell you if I'm the right person.
Best starting point is a short note about the system, the decision you're facing, and the timeline. If it isn't a fit, I'll say so directly.
cyrus.abari@cylabseng.com LinkedIn
Remote, or on-site in the Washington, DC metro area.