Your data. Explained.
katalytics started from a simple observation: most data work stops at a chart or a number, and leaves you to work out what to actually do with it.
So every project we take on ends with a clear answer, explained in plain language — and an honest read on how far you can trust it.
Victor Flores, PhD
Principal Scientist & ConsultantPhD in Civil Engineering with a focus on data-driven Bayesian analysis of tabular data as well as dynamical structures and damage assessment. Victor specializes in applying data-driven solutions to engineering and business challenges.
Utban Ahmed, PhD
Mathematical Modelling SpecialistSpecializes in the mathematical modelling of wave phenomena in fluids, with a focus on computational acoustics, perturbation theory, and physics-informed, data-driven models.
Xinyu Jia, PhD
Bayesian Inference, Structural DynamicsDr. Jia specializes in Bayesian learning of physics-based models, uncertainty quantification in structural dynamics, and structural reliability. His research applies advanced modeling techniques to improve risk assessment and system reliability in Mechanical and Civil Engineering systems.
Prof. Costas Papadimitriou
Structural Health Monitoring, Uncertainty QuantificationProf. Papadimitriou specializes in Bayesian uncertainty quantification, structural health monitoring, and probabilistic structural dynamics. His expertise includes Finite Element Model updating and structural reliability, with applications in Civil and Mechanical Engineering.
Prof. Lambros Katafygiotis
Structural Dynamics, Bayesian StatisticsProf. Katafygiotis specializes in structural dynamics, with expertise in earthquake engineering, stochastic dynamics, model updating and structural health monitoring. His work focuses on advancing structural reliability and improving system identification methods in Civil Engineering.
Explainability
We default to explaining the answer, not just handing it over. Whatever gets us there — a simple model or a complex one — if we can't tell you plainly what it means, we don't ship it.
Transparency
You see the data we used and the choices we made, not just the final chart. If a number surprises you, we can walk it back to the assumption that produced it.
Precision
Every analysis is checked against a second method before it goes out the door. Precision isn't just about being exact — it's about stating exactly how exact.
Collaboration
We'd rather ask five extra questions upfront than deliver a polished answer to the wrong problem. That takes real back-and-forth, not a kickoff call and a deck at the end.
Our Story
katalytics started from a simple observation: most organizations have more data than they have certainty about what it means.
What began as two engineers doing data science on the side has grown into a consultancy that solves the specific problems our clients actually have.
The team draws on Civil and Mechanical Engineering, Machine Learning, and Bayesian statistics — disciplines that don't usually share a room, but should.
Why Choose Us?
We're not generalists who happen to touch data — we're engineers and statisticians first, which means we ask different questions before we start modeling anything.
Transparency and explainability aren't values we mention in a pitch — they're constraints we design under.
That means answers you can check yourself, not just take on faith — whether you're optimizing operations, forecasting demand, or trying to work out if last quarter's dip was signal or noise.