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Computational Systems Engineer
Sir-Lord Wiafe
SIR
LORD
WIAFE

I build frameworks that decode how complex systems evolve over time, from brain networks to any domain where history shapes the present. Dynamic systems. Operator theory. Signal processing. State estimation.

30+
Publications
95+
Citations
5+
Frameworks Built
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PhD Candidate · TReNDS Center · Georgia State University · Atlanta, GA
DYNAMIC SYSTEMS SIGNAL PROCESSING CONTROL THEORY STATE ESTIMATION OPERATOR THEORY

Real-Time System Dynamics

My work is dynamic systems modeling and signal processing. I build the math that describes how complex systems evolve in time.

Dynamic Time Warping  ·  Warp Quantification Inertial State-Space Model  ·  Functional Inertia DyCoM  ·  Operator Framework Complex-Valued Phase Synchrony  ·  Directional Coupling Kalman Filtering  ·  State Estimation Communication Theory  ·  Signal Processing Dynamic Time Warping  ·  Warp Quantification Inertial State-Space Model  ·  Functional Inertia DyCoM  ·  Operator Framework Complex-Valued Phase Synchrony  ·  Directional Coupling Kalman Filtering  ·  State Estimation Communication Theory  ·  Signal Processing
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Operator Frameworks
Composing signal processing operations into unified, domain-agnostic architectures
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Dynamic Analysis
Time-varying systems, state-space modeling, and temporal alignment algorithms
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State Estimation
Kalman filtering, inertial constraints, and history-dependent state evolution
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Phase & Amplitude
Complex-valued synchrony, directional coupling detection, wavelet analysis
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Systems Engineering
Backend development, algorithm design, ML pipelines, and software architecture
Beyond The Brain
My Methods,
Any Domain

I build every framework to be representation-agnostic. The same math works whether the signals come from brains, markets, or machines.

From: Functional Inertia (ISSM)
State-Space Modeling
→ APPLIES TO:
Robotics Trajectory Financial Markets Autonomous Vehicles IoT Sensor Networks Climate Systems
From: DyCoM Framework
Operator Decomposition
→ APPLIES TO:
Audio Processing Radar / Sonar Network Traffic ECG / EEG Analysis Multi-agent Systems
From: Warp Elasticity / DTW
Temporal Alignment
→ APPLIES TO:
Speech Recognition Gesture Detection Time-Series Prediction Manufacturing QA Video Understanding
From: Complex-Valued Phase Synchrony
Directional Coupling
→ APPLIES TO:
Power Grid Stability Oscillator Networks Communications Coupled Oscillators
From: Timescale-Aligned Amplitude Balance / DTW
Signal Balance Mapping
→ APPLIES TO:
Battery Management Thermal Systems Resource Allocation Smart Grids
From: Communication Theory + FC
Signal Complementarity
→ APPLIES TO:
Sensor Fusion Multi-modal Systems Wireless Networks Fault Detection
Latest Work · 2026
Functional Inertia Reveals History-Dependent Organization

The brain does not process the present in isolation. I formalized this as functional inertia, a measurable constraint that captures how accumulated history resists ongoing reorganization in any dynamical system. The framework works at three scales at once: dynamical regimes, system-level magnitude, and circuit-level distribution.

See All Papers →
KEY CONTRIBUTIONS
1
Inertial State-Space Model (ISSM), which treats accumulated history as a latent state variable
2
Three recurrent dynamical regimes: Locked, Stabilizing, Shifting
3
The same constraint produces opposite clinical signatures depending on system context
NIH R01MH123610 · NSF #2112455 2026
Technical Toolkit
Built to
Engineer.

I started in electrical engineering, picked up real software experience along the way, and I'm finishing a computer science PhD. That mix covers the full stack of a systems engineer.

Full Skill Profile →
Dynamic Systems Python · MATLAB Signal Processing State-Space Models Kalman Filtering Machine Learning Temporal Alignment Control Theory Operator Theory Backend (Node/SQL) Neuroimaging (fMRI)