2021 — present
I came to quantitative finance as a hobby. The hobby became
Lumin, an innovative approach at the intersection of crypto
trading and frontier AI, built and rebuilt at every layer over five
years.
My day job and this hobby are the same craft. Reading the physics behind
a metrology measurement uses the same logic as reading the emotion behind
a market price: identify the generator, model its influence, engineer for
it. The reading reflexes, modelling tools and post-mortem discipline I
built at Sylvac have
been directly reusable in Lumin. Since 2021 the two trajectories have
advanced in parallel, each breakthrough on one side feeding the other.
The first iteration was python trend detection: arithmetic that
identified when a chart was moving versus oscillating, producing the
structural primitives of price action — bricks, ratios, range
regimes, momentum. The primitives combined into deterministic
strategies; the strategies refined into signatures,
statistical multi-bar configurations validated out-of-sample across four
timeframes on Binance perpetual futures. The current and final iteration
is a multi-agent reasoning model: LLM specialists that read the market
bar by bar and integrate components in context. The intelligence is not
in the model but in a wiki — a structured body of
externalised knowledge that absorbs every prior phase, in a format an LLM
reads natively.
The deliverable is a practitioner's mind downloaded into a wiki. The
approach generalises anywhere skilled experts produce value. The white
paper has the details.
LUMIN> open terminal
LUMIN> white paper
2015 — present
I joined SYLVAC SA in Crissier, Switzerland, as an applied optics
engineer building vision metrology systems — optics, illumination,
cameras, image processing, motion control. In 2018 I moved to project
management, owning end-to-end delivery of optical measurement machines:
specs, industrialisation, production handoff. Since 2021 I lead the R&D
machines team. My role is to work with the team to push the limits of
our domain, making machines more intelligent and more autonomous —
embedded agents, adaptive workflows, UX for robots. Machines should and
will interact with humans better and more naturally.
Watch the testimonial →
Machine vision · optical metrology · quality control ·
robotic guidance · automation
2012 — 2015
Before SYLVAC, I was an applied optics engineer at Hexagon
Manufacturing Intelligence. I worked on metrological machines, optical
sensor development, and image processing for precision measurement systems.
2022 — 2024
Applied Data Science: Machine Learning program at the EPFL
Extension School. This is where the quantitative finance rabbit hole
started.
2010 — 2012
MSc in Microengineering — Photonics and Applied Optics at
École Polytechnique Fédérale de Lausanne (EPFL).
Photonics (light guides and lasers), advanced image processing, optical
system design.
2007 — 2010
BSc in Microengineering at
École Polytechnique Fédérale de Lausanne (EPFL).
Optics, vision systems, mechanics, electronics, software engineering.
I work at the intersection of precision engineering, machine vision, AI,
and quantitative systems. My strength is not staying inside one domain
but connecting several until they become one solution. I'm an engineer
first: every problem reduces to understanding the physics, building the
math, writing the code, and shipping it personally — and staying on
it longer than most people think is reasonable. I think outside the box,
inside the path. Multilingual, fluent in C++ and Python.
