UCLA. Double major: molecular biology and political science. First in family to go to college. Didn't go to the best high school. Everything so new. Brain desperately trying to fit the epistemology of Western civilization and science into a coherent puzzle.
South Campus: STEM people, pre-med, the "real" scientists. Stoichiometry was the killing field for budding scientists. If you didn't pass, you weren't STEM.
North Campus: Philosophy, political science — where the "exiled" went.
My Life: Running sprints between North and South Campus. Living a dual life. Always avoiding the question of disloyalty to my motherland.
Reading Plato and Aristotle. These dudes with one name — Plato, Aristotle. Trying to figure out how philosophy books from North Campus fit with equations from South Campus.
Tied for Worst Class: Thermodynamics. Expected total focus on T in PV=nRT from stoichiometry class.
The Problem: Professor just started writing equations. Watched wondering: Why do I have to learn this? I don't even know what we're talking about. Can someone explain how this is supposed to fit in my head? Since that process is painful, I need to make a space for it, or I might as well read one of these philosophy books.
The Breaking Point: End of first class. Incredibly frustrated. All questions were math questions.
I stood out like a sore thumb. Asked the professor: "What is entropy in layman's terms?"
A Frozen Quiet Stopped the Room.
The professor looked uncomfortable. His math convolutions did not want to give up driving the steering wheel to the weakling on the right side who had to ask. Distant neurons — could they give some symbolic language of anything it could grab and use?
The Professor Said: "Mmmmmm..." Looked up. Almost pausing into group discomfort. "Randomness... yes, randomness."
Not Told:
Little Did I Know: I was to use a theory I had come to believe just by keeping up with science in general: Everywhere we look, we run into observer/information issues.
The Clues Building:
All These Clues: Would lead me to a fateful entanglement with my earlier self.
The Breakthrough Understanding: Entropy makes sense because it is consistent with our world experience. BUT examined in terms of ontology, information theory's deductions just felt like they explained thermodynamics. After some quick research, a beautiful model unfolded.
Concepts in Immunology: That confusing mix of innate immunity and the wild monoclonal theory where antibodies are supposedly created for every possible invader. Somehow that was done all the time and worked. And that was supposed to explain everything.
Dropped the class first time. Had to retake it as my final class at UCLA.
The Question Forming: How does this fit together? What's the ontology?
Cornell University, studying Sovietology and geopolitics. Career goal: State Department or CIA. CIA application literally in hand.
The Twist: Two months later, the Berlin Wall falls. Everything changes.
Required to take Philosophy of Science. Initial reaction: "waste of time, I know it all."
But Now — With UCLA Background: Having struggled through thermodynamics, having run between North and South Campus, having tried to fit Plato and Aristotle with PV=nRT, this class suddenly has different resonance.
Learns About:
The Pieces Start Connecting: Information theory might explain thermodynamics. Ontology matters for understanding. Paradigms structure how we think. That uncomfortable thermodynamics professor couldn't explain entropy in plain language because he didn't have ontological clarity.
Still Dismissive: Didn't fully understand why this mattered. Yet.
Now understanding ontology matters.
The Audacious Bid: Professor Jim Stigler, UCLA psychology professor, wins $5M grant to analyze the nation's obsession: The Math Gap. Why is US education falling behind, particularly compared to Asian countries — Taiwan, Hong Kong, Singapore?
Old way: VCR tapes, hoards of grad students watching hours of video, looking at pre-created code cards, using clunky tools to track SMPTE time codes
New way: Adopt absolutely brand new tech — digital video MPEG-1
The Vision: Create software from the ground up using QuickTime:
The Challenge: Asked to create the system. In 1996, it needed to be instant — essentially the performance of a similar system now in 2025.
The Bleeding Edge: MPEG was so new that encoders had to be purchased from custom jobs — literally home-based startups in Silicon Valley. Went to personally pick up encoders literally from a garage full of wires and soldering irons.
Breaking the problem of retrieval of complex queries into first principles logic: set theory — Union, Intersection, Difference.
The Connection: This is exactly what information theory teaches. Breaking things down to atomic units. Just like thermodynamics can be explained through information theory.
Since set operations were lightning fast even in 1996, the system flew.
The Insight: Explicit ontologic understanding is powerful. So powerful that social sciences — which lack the ability to create experiments and controls — can control one thing: absolute clarity of thinking.
TIMSS project software (underlying tech) ended up being used by:
The Big Win: Co-founded Digital Lava (DGV) — spinoff of the technology. Took it to NASDAQ in the year 2000.
The Lesson Learned: Ontological clarity works. Information theory as organizing principle works. Breaking problems into atomic units with clear set operations creates magic. Had proof it worked at scale. NASDAQ-level proof.
Working in biotech, bioinformatics/computational biology. Team tasked with identifying targets for stroke drug.
Armed with Proof: Had just proven that ontological clarity creates breakthrough results at the highest level:
Asked the scientists: "What is your operant model of stroke?" Thought if we could map the intersection of their conceptions, could target in silico work effectively. Had literally just done this successfully — from educational research all the way to a NASDAQ IPO.
Meeting after meeting: Silence. Sensed disregard for such an "irrelevant question."
Suggested Danger Theory (created by Polly Matzinger — the misfit stripper-turned-scientist who famously added her dog as co-author, pissing off the scientific establishment).
The Response: "Never heard of it."
That's when my science heart broke and I mentally checked out.
The Bitter Irony: Had co-founded a NASDAQ company based on ontological clarity. Still couldn't get biomedical scientists to take the approach seriously. Just like that thermodynamics professor — they could do the math but couldn't explain the ontology.
Couldn't conceive of spending 60-hour weeks not being ontologically grounded and clear. Not after proving it worked at the highest levels.
Frustrated with science, returned to childhood hobby: photography. Became professional photographer for 15 years. A "sabbatical" from the ontological chaos.
The Specialty: San Francisco City Hall weddings. Small, intimate elopement-style ceremonies.
The Clientele: A very particular profile:
The Ironic Position: Had co-founded a NASDAQ company. Now photographing the next generation of people trying to do the same thing.
During shoots, talked with clients and picked their brains. One hobby: asking biomedical-related people about Danger Theory.
The Pulse Check: Got a real-time pulse on the industry over 15 years. Asked perhaps 100+ medical professionals and academics since 2008.
The Depressing Pattern: Not one person could articulate anything beyond "Inflammation," "Reaction to danger," "Reaction to damage." That's about it.
Some of the smartest leaders at all levels in biomedicine could not articulate their ontology. The foundation of their field. The framework they operated within. Just... vague concepts.
Déjà Vu: Just like that thermodynamics professor saying "randomness" without context. Just like the biotech scientists who couldn't articulate their stroke model.
The Irony: AI started taking over photography. Of all things.
Photographed a wedding for Aravind Srinivas, the founder of Perplexity. During the shoot, he told me about Perplexity and gave me the app.
The Realization: Had to leave photography. AI wasn't just coming — it was here, and the person revolutionizing search was standing in front of my camera. Jumped into AI headfirst. "Take no prisoners mode" — had a lot to catch up on.
Working in startup on computational immunology. First efforts using AI.
The Grind: Coding almost 24/7 for a year.
AI had nasty habits:
The Realization: "The only way to use AI is to create a full model that is mathematically sound and represents the best research of the particular paradigm at hand."
The Discovery: Didn't realize how messy the ontology of different fields actually are. Even messier than expected. Just like that thermodynamics professor — people using concepts they can't explain.
Partner develops crippling joint pain and autoimmune-like symptoms. Walking on an incline hurts.
Doctor's Response: "Walk on flat surfaces and take Celebrex."
Thoroughly pissed off: Invoked "kraken ADHD mode."
Small town in central Oregon where nurse practitioners are essentially primary providers. Found one who was just as stubbornly focused on figuring this out.
Fighting the AIs Again: Brought in the big guns — panel of experts and judge models. Started pulling papers, parsing research.
"I need to model and store all her labs and symptoms for dual use": 1) Reports for medical providers and specialists, 2) Symptoms translated to clinical terms of art
The database was created to present context to AIs, BUT equally (if not more) importantly — had to create ontologically clear model(s) to:
The Memory Returns: Back to that thermodynamics professor who couldn't explain entropy. Back to that philosophy class. Back to TIMSS project. Back to co-founding Digital Lava. Back to that biotech job. All of it suddenly makes sense.
Changed My Ontology Engine: After some quick research from UCLA days, a beautiful model unfolded.
Created Framework: Structure and function-based immune system:
The Breakthrough — Information theory is PRIMARY. Thermodynamics is DERIVATIVE.
The Ontologically Flushed Out Testable Hypotheses Were A Sight To Behold:
Built using first principles:
Built using information theory principles — provided argumentation backed by science to get:
Rheumatology referral (notoriously difficult to get in rural Oregon) and much-improved medical support.
In Silico Success: Have a diagnosis. Have more processed, detailed justifications for immediate use of biologicals.
The Results: Medical system finally mobilized. Partner getting proper care.
The Vindication: Danger Theory is now accepted as a critical component of the immune system. It's mainstream. It's taught. It's fundamental.
The Disrespect: After asking perhaps 100+ medical professionals and academics since 2008 — from 2008 to 2025 — not ONE remembered that name or could articulate the formal understanding of the ontology.
The Realization: "I was right all along."
The Pattern Holds: They use the ideas (inflammation, danger signals, damage response, entropy, randomness) but can't name the framework. Can't articulate the paradigm. Don't understand their own ontological foundations.
If the people making life-and-death decisions can't articulate the theoretical framework they're operating within... what does that mean for science? For medicine? For AI?
Running AI startup, working on business automation tools.
The Fear: Anything being built could become an agent employed by larger entities at lower cost. Commodification looming.
Little by little, built an ontology engine from components already created:
Started remembering the philosophy class in layers as encountered paradigms, theories, schools of thought.
Magic Happens: Once ontology engine was employed — Could fluidly change schools of thought. Delve into different papers. Pull out what worked and what didn't. Navigate competing frameworks with clarity. Model immune system with information theory as organizing principle. Save my partner's health and quality of life.
The Core Insight From UCLA Thermodynamics: Information theory is primary. Thermodynamics is derivative.
Without ontological clarity:
With ontological clarity (Information Theory Primary):
From UCLA asking "What is entropy?" to discovering information theory might explain thermodynamics, to Cornell philosophy class, to TIMSS breakthrough, to co-founding a NASDAQ company, to biotech heartbreak, to 15-year photography sabbatical, to rural Oregon healthcare crisis, the lesson remains the same: You can't solve problems you can't clearly define. You can't build on foundations you haven't examined. And you definitely can't use AI effectively without ontological clarity.
That "waste of time" philosophy class? That uncomfortable moment asking "What is entropy?" Those sprints between North and South Campus at UCLA? Turned out to be the most practical things I ever learned.
The irony: That thermodynamics professor in the 1980s couldn't explain entropy beyond "randomness." Didn't connect it to information theory. Didn't have ontological clarity. I proved information theory works in 1996 — helping social scientists create breakthrough tools through absolute clarity of thinking. Proved it so well that we took a spinoff company to NASDAQ in 2000.
Then spent decades watching biomedical scientists (who have experimental control AND vastly more funding) fumble without it — just like that thermodynamics professor. Even with a NASDAQ IPO on my resume based on these exact principles, couldn't get the biotech establishment to take ontological clarity seriously.
Spent 15 years photographing Silicon Valley's elite while they built the AI revolution, asking them about Danger Theory, watching them fail to articulate their ontologies.
The person who finally woke me up? Aravind Srinivas, standing in front of my camera at San Francisco City Hall, telling me about a search engine built on understanding how information actually works. The founder of Perplexity, showing me that AI was already here — and those who understood information theory at a fundamental level were the ones building the future.
Now I'm using the same principles from UCLA thermodynamics epiphany, TIMSS, and Digital Lava — information theory as primary organizing principle, atomic units, set operations, explicit ontological frameworks — in ontologically clear AI models to do what the medical system couldn't: save the person I love.
The method works. It's always worked. Information theory explains thermodynamics. Information theory explains immune systems. Information theory went to NASDAQ. The question is: why does it keep getting dismissed?
Why couldn't that thermodynamics professor just say:
"Entropy is a measure of information disorder. The universe tends toward maximum entropy because it tends toward maximum information uncertainty. That's why your dorm gets messy — there are more high-entropy (disordered) states than low-entropy (ordered) states. That's the connection between information and energy."
That one moment of ontological clarity could have changed everything.