GitHub · Untitled-7 · Resume
DANIEL J. MUELLER // TECHNICAL PROFILE
https://danieljosephmueller.com
RESEARCH MODE
Cross-domain engineering focused on systems that can be built, instrumented,
measured, stress-tested, and reduced to first-principles behavior.
PRIMARY SURFACES
Artificial intelligence · scientific computing · embedded hardware · neural systems
infrastructure analysis · data engineering · simulation · experimental instrumentation
COMPUTE
ORION workstation
3 × NVIDIA A100 40 GB
1 × NVIDIA RTX A6000
256 GB system memory
Ubuntu / CUDA / local multi-GPU inference and scientific workloads
WORKING RANGE
microcontroller-scale instrumentation
↓
embedded sensing + signal acquisition
↓
GPU-accelerated model systems
↓
large-scale data pipelines + visualization
↓
national infrastructure and dependency analysis
Most work is organized around systemic constraints which dictate allowable paradigms: the system must survive measurement.
Research therefore converges on a closed engineering loop, to ensure privacy even in the event of compromise:
flowchart LR
A[Model] --> B[Instrument]
B --> C[Acquire]
C --> D[Measure]
D --> E[Invalidate]
E --> F[Refine]
F --> A
D --> G[Deploy]
G --> H[Scale]
H --> A
The implementation layer spans Python, Linux, CUDA, embedded systems, local model serving, numerical analysis, simulation, browser visualization, data pipelines, and experimental control. Work is biased toward systems where software, hardware, physical behavior, and data all interact.
|
Local multi-GPU AI infrastructure built around A100-class accelerators for inference, experimentation, model serving, agent systems, and generative workflows. The emphasis is not API composition. It is owning the environment, and familiarity with the structure: runtime behavior, memory pressure, accelerator allocation, model topology, orchestration, and failure modes. |
Embedded systems are treated as accessories, tools, and outlets rather than a means to an end. Typical work crosses firmware, electronics, sensing, timing, isolation, calibration, and host-side analysis. |
|
Big datasets are processed to maximize value, and increase differentiability between like-points. Design priorities: reproducibility, explicit schemas, inspectable transformations, and keeping source data close to analytical outputs. |
Research focuses on interdependence, failure propagation, resilience, and cross-domain coupling in large systems. The relevant unit of analysis is usually not an isolated asset. It is the graph of dependencies around it. |
A systems-level research environment for national infrastructure, public-impact risk, environmental outputs, and cyber-physical resilience.
The repository combines sector analysis with data tooling across electric power, generation, energy supply chains, communications, water, transportation, healthcare, finance, government systems, agriculture, and defensive cybersecurity.
DATA public + derived datasets
ANALYSIS dependency / resilience / emissions / system risk
VISUALIZATION local browser-based geospatial interfaces
CYBER defensive version categorization and hardening workflows
PIPELINES category-scoped ETL and structured outputs
RESEARCH UNIT systems and dependencies, not isolated components
A national municipal-data pipeline built to normalize, partition, and visualize U.S. town-hall records at operational scale.
RECORDS 473,210
SOURCE FORMAT CSV
PROCESSING Python
PARTITIONING national / state / chunked
VISUALIZATION browser-based geographic interface
PIPELINE merge / clean / normalize / export
This is representative of a recurring pattern: take unstructured or ambient source data, make the transformation explicit. Produce something directly useful for machines.
ARTIFICIAL INTELLIGENCE
local inference
agent architectures
multi-GPU systems
model evaluation
generative systems
AI infrastructure
NEURAL / BIOLOGICAL SYSTEMS
brain research
signal acquisition
neural interfaces
experimental instrumentation
biological system modeling
SCIENTIFIC / TECHNICAL COMPUTING
simulation
numerical analysis
signal processing
automated experiments
measurement pipelines
reproducible analysis
HARDWARE
embedded systems
electronics
sensing
instrumentation
compute architecture
physical-system integration
INFRASTRUCTURE
electric power
communications
logistics
transportation
resilience
cyber-physical dependencies
cascading failure analysis
SOFTWARE / DATA
Python
Linux
CUDA
automation
visualization
ETL
local services
research tooling
graph TD
R[Research] --> AI[AI Systems]
R --> HW[Hardware]
R --> NS[Neural Systems]
R --> INF[Infrastructure]
R --> SC[Scientific Computing]
AI --> GPU[Multi-GPU Compute]
AI --> AG[Agents]
AI --> GEN[Generative Systems]
HW --> EMB[Embedded Systems]
HW --> INS[Instrumentation]
HW --> SIG[Signal Acquisition]
INF --> EN[Energy]
INF --> COM[Communications]
INF --> LOG[Logistics]
INF --> CPR[Cyber-Physical Resilience]
SC --> SIM[Simulation]
SC --> NUM[Numerical Analysis]
SC --> DATA[Data Pipelines]
SC --> VIS[Visualization]
| RESOURCE | ACCESS |
|---|---|
| Resume — Daniel J. Mueller — 2026 | |
| Societal Progression — Part I | |
| National Infrastructure Research | Repository |
| U.S. Town Halls Dataset | Repository |
| Untitled-7 | Console |