Two threads, running in parallel for thirty-five years. The physics of how information travels through physical media. The mathematics of how machines come to know things. Both threads converge in Science Counter Inc.
Most people in technology do one of these things well. A handful do two. He has done all four: original scientific research at publication level in first-rate journals β 18 papers, mostly first author, during his postgraduate years β company building at the $66M investment scale, five years of independent foundational theory development producing a framework equivalent to the foundations of modern AI, and seventeen years of patent prosecution resulting in 20+ issued AI patents across five families.
The career follows a scientific logic β each phase building on the previous, converging toward something that only becomes visible in retrospect. Two threads run through it simultaneously:
The connection between the two threads is not metaphorical. Both are about the same underlying reality: what one physical or informational event tells you about another. That is information β at every scale, in every domain. From quantum mechanical interaction to high-level semantic composition. The participation matrix is at once an information-theoretic structure and the primary data structure of a causal AI system. It was not designed to be both. It turned out that way because the underlying physics, information theory, and epistemology are not as separate as field boundaries suggest.
Master of Engineering (with thesis) in one year. PhD in under eighteen months β 18 papers published, mostly first author, in IEEE, Optics Letters, IEE, and Optics Communications. The research was broad: photonic device physics, coupled mode theory, numerical methods for Maxwell and nonlinear SchrΓΆdinger equations, optical switching, optical logic gates, novel waveguides structures, soliton dynamics and propgations, and optical fibre communication systems β with undergraduae and graduate coursework extending to optolectronics, advanced signal processing, data networks, information theory, and microwave and antenna theory. Four external examiners praised the PhD thesis.
A soliton is a wave-packet that is self-correcting β self-focousing - as a temporal pulse the nonlinearity of the glass precisely compensates the dispersion, and the pulse maintains its shape indefinitely as it travels along the nonlinear medium of propagation. The physics is exact. The mathematics predicts, and the propagating medium (e.g., glass) attests.
Running in parallel throughout β never stopped β was the deeper question: how do we come to know something? Why does mathematical formulation predict physical reality so precisely? These were not separate programmes. They were the same programme, approached from two directions.
The sensing architecture in the ISS Platform draws directly on that doctoral physics β optical principles studied in Sydney in the early 1990s, applied three decades later to a new class of LiDAR sensing modality with zero commercial competitors in 2026.
After completing his doctorate, He arrived in Ottawa in May 1996 and immediately founded OpNet Communication Technologies (later renamed to Tellamon and then to Peleton) β incorporating in July 1996 and filing Canadian patent CA 2186817, "Optical Communication System Utilizing Photonic Patterns." The photonic pattern concept in that filing β unique participation signatures identifying each communication channel β is the earliest documented precursor to the participation matrix framework that became the foundation of SCI's AI technology twelve years later.
Two further companies followed β both hardware builders with real fabrication, real foundries, real products. Peleton Photonics ($22M) and Zenastra Photonics ($44M) β $66M total. Canadian Business Magazine featured him in a cover article titled "At the Speed of Light." Zenastra was wound down in early 2002 in the wake of the 2001β2002 telecom capital collapse β not the dot-com crash; its corporate shell was later repurposed into an Alberta oil company in 2005. Peleton operated until late 2007. These were not internet ventures. They were optical hardware companies operating at the frontier of what physics allowed. The technology was not the problem.
These companies predate the SCI IP framework. But they established something critical: institutional confidence β $66M worth β in his ability to take deep optical physics and build operating companies around it. And the earliest of them, OptNet, produced the first documented instance of the participation matrix concept β twelve years before it was formalised in the AI framework.
Science Counter Inc was founded in 2008. From the first year, He began filing patents on the epistemological framework β the mathematical structures for causal association, ontological mapping, and knowledge processing that the second parallel thread had been developing for years. These were not defensive patents filed after a product was built. They were the theory, filed as prior art before the market existed.
The significance of this timeline is not just the volume β 20+ issued patents over 17 years. It is the mathematical continuity. The participation matrix that appears in the 2012 semantic ranking patent is the same data structure that appears in the State Navigation pending family and the ISS Platform pending family. The framework was not invented for Physical AI. Physical AI was invented for the framework.
Device-level intuition built from first principles, not inherited from coursework. The publications in IEEE, Optics Letters, and Optics Communications across multiple distinct areas mean not a narrow specialist β a researcher who moved between device physics and system architecture. That is exactly the rare combination that makes a sensing architecture this unconventional conceivable.
This is another direct technical lineage behind part of the ISS (Intelligent Surround Sensing) platform. The optical-physics phenomena at the heart of that work are understood intuitively by very few researchers; most sensing engineers have never worked with them at all. Decades of publishing in that area mean the core sensing insight was not a lucky guess β for someone with this background, it was close to inevitable.
Metropolitan optical networks, OC-48 to the home in 2000 β visionary timing. OC-48 is 2.5 Gbps. Delivering that to residential subscribers in 2000 was genuinely ahead of the market. The technical challenge involved multi-wavelength laser sources, modulators, WDM systems β all of which require exactly the systems-level thinking that the LiDAR architecture reflects. Not building components. Building end-to-end photonic systems that had to work reliably in the field.
Silica-based passive and hybrid photonic integrated circuits β this is the fabrication layer. Understanding what can be manufactured into a silica waveguide at the process level β V-grooves, grating couplers, mode converters, hybrid integration of active and passive elements β is precisely the knowledge that makes an unconventional optical sensing architecture manufacturable rather than theoretical. When the optics are this novel, the question is whether they can actually be built β and he had already run a company that fabricated silica photonic devices.
This is a sustained, productive, multi-decade foundational IP portfolio across distinct technical domains that converge exactly in the SciPhAI platform SciPhAI platform. The photonics intuition, the information communication thoery, and the knowledge-discovery mathematics are not parallel careers β they are the threads that were always heading toward the same conclusion.
I completed a Master of Engineering and PhD in Electrical Engineering at the
University of New South Wales in Sydney, Australia β 18 papers in under two years,
across a programme spanning photonic device physics, electromagnetic theory,
optical switching, soliton dynamics, ultra-high-speed fibre communications,
and information theory. The breadth was not accidental. I was building a foundation,
not specialising.
Running in parallel throughout β since undergraduate β was a different
question: how do we come to know something? How does a mind β human or machine β
acquire knowledge of a domain? Telecommunications engineering at the level I was
working β all optical terabyte backbone transmission, OC-48 multiple access systems, DWDM, information limits of physical channels β is applied
information theory. Shannon's entropy, channel capacity, mutual information. You are
working at the theoretical limits of what physics allows for information transmission.
That trains a particular way of thinking about what information actually is.
When the AI framework came, it was the same question asked in a different domain.
The COP formula defines information content of compositions analytically.
The participation matrix is an information-theoretic structure.
The connection to Shannon is not superficial β it is the same intellectual tradition.
From 2002 I focused entirely on the foundational AI question β not as an academic
exercise, but toward a genuinely ambitious goal: to accelerate the rate at which
humanity produces credible knowledge. Science and technology are the engines of
human prosperity. The bottleneck is not intelligence β there is enormous brain power
in the world that cannot reach the systems designed to evaluate and distribute
knowledge. Building a machine that could assess the merit of a contribution on
the basis of its content alone β its credibility, its novelty, its significance β
required first answering the foundational question: what is the structure of
knowledge itself?
Five years of mathematical work produced the participation matrix, the association
strength measures, the conditional occurrence probability β the complete analytical
framework. By 2011 it was running as a live public service: a conversational
knowledge discovery system that a user anywhere in the world could query, assembling
a body of knowledge in real time and returning answers across multiple modes β
novel, informative, consensus, causal. We called it Mr. SCI. The term
Retrieval-Augmented Generation was coined nine years later.
Science Counter Inc is the convergence of thirty-five years of parallel work β
physics and epistemology, engineering and mathematics, information theory and
knowledge representation β into a coherent framework for building machines that
genuinely understand the world they sense. The participation matrix concept appears
first in a 1996 Canadian patent on photonic communication patterns. It appears again
in AI patent filings from 2008. It runs through State Navigation in 2019 and into
the ISS sensing platform in 2024. One mathematical insight. Thirty-five years.
We are at the beginning of what it can do.
Investor conversations, Tier-1 partnership enquiries, patent licensing, and press requests all go directly to Hamid.
Investor enquiries info@science-counter.com