Anh-Dung Le
Translating technical complexity into clearer decisions, models, and products.
This site is a working notebook with projects, notes, and observations from building and evaluating technical systems across AI, biotechnology, IP, regulatory science, applied engineering, and deep-tech commercialization.
A place for my work in progress
This site is a personal archive for projects, notes, and problems I'm working through, especially where technical complexity meets practical use.
I'm interested in how ideas move from research, code, IP, or workflow pain points into useful tools, products, agreements, and decisions.
Engineering fundamentals like constraints, models, optimization, failure modes, integration, and process design are the connective tissue across these domains, and a thread I'm actively sharpening and writing about here.
Projects & explorations
A selected set of tools, systems, and technical areas I've worked on or continue to explore.
Project
In Silico Biolabs
Founder-led testing and development of a computational biology platform built around licensed Sandia pathway-discovery technologies (RetSynth, Met2Saf) for drug discovery, specialty chemicals, and industrial biotechnology.
Computational biologyRetSynthMet2SafPathway synthesisOpen project →Project
PBPK modeling
Scientific modeling work in physiologically-based pharmacokinetics (PBPK) for intravenously delivered nanoparticle drugs: model structure design, distribution equations, parameter estimation, sensitivity analysis, and validation.
PBPKNanoparticlesPharmacokineticsBiodistributionOpen project →Professional work
Professional AI platform work
Architecture and engineering work on an internal AI-enabled platform involving public data ingestion, semantic matching, retrieval-augmented generation, drafting support, and workflow orchestration. Discussed here only at a general, conceptual level: no product-specific or proprietary details.
AI systemsRAGContext managementSemantic matchingOpen project →Project
Applied engineering systems
An ongoing area of notes and exploration around engineering fundamentals, modeling, optimization, process flows, constraints, and system-level technical reasoning. Current focus: building a cross-disciplinary view of optimization, from pharmacometrics to machine learning to quantum.
engineering fundamentalsmodelingoptimizationmachine learningOpen project →
Notes
Short reflections and practical observations from projects, technical work, and commercialization problems.
Note
Enabling the black box: what does a biotech software patent need to teach?
An educational exploration of reproducibility, claim scope, and technical documentation in computational biotechnology.
Open note →Note
How I think through IP strategy
A practical note on matching patents, licensing, trade secrets, FTO, and ownership questions to the way a technology may reach use.
Open note →Note
Building useful AI tools without turning everything into a chatbot
Notes on workflow design, data structure, retrieval, and where language models actually help.
Open note →Note
LLM wikis and knowledge graphs: what should persist?
Why retrieval is only part of the picture, and how LLM wikis and knowledge graphs hold the understanding behind the documents.
Open note →Note
Agent memory: when live state matters
A companion note on agent memory: why robotics, logistics, and other state-driven workflows need more than documents and a knowledge graph.
Open note →Note
n8n vs. Claude Code (and other AI agents) for automation
When AI coding agents are the right tool, when a workflow orchestration platform is, and why the strongest pattern is usually hybrid.
Open note →Note
Technical diligence in deep tech
Observations from evaluating technologies across maturity, IP, market fit, and adoption risk.
Open note →Note
Optimization is not just maximizing a number
Reflections on tradeoffs, objective functions, constraints, sensitivity analysis, and why optimization depends on defining the right problem.
Open note →
Background
My background spans technical and commercialization roles across FedTech, In Silico Biolabs, the FDA Office of Clinical Pharmacology, Sandia National Laboratories, and academic research in nanoscience. The common thread is translating technical complexity into clearer systems, decisions, agreements, models, and products.