Systems Engineering & Physical Computing Research

Kernel-level systems engineering, network forensics and protocol analysis, database virtualization, and the physical computation layer: kinetic systems, industrial instrumentation, and reservoir computing.

Trajectory

Twenty years of cross-disciplinary systems work: from modifying Linux kernels for distributed computing, to network forensics for L1–7 protocols, to building embedded measurement rigs and kinetic control systems for industrial thermal processes. Deep physics foundation. Currently modeling dynamical systems and physical reservoir computing, building toward physical/algorithmic co-design.

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Engineering Experience

Independent Systems & Research Consultant · Jul 2012 – Present Berkeley, CA · Hybrid / Remote

Concurrent engineering practice spanning embedded instrumentation, kinetic systems, and industrial measurement. Overlapped with research residencies at Manylabs and Chabot Space & Science Center; mechatronic work executed with the prototyping rigor of industrial R&D.

Consulting Design Engineer, ALL Power Labs · May 2019 – Aug 2019 · Contract Berkeley, CA

Principal Platform Design Engineer, VCE · Jun 2011 – Jun 2012 · Full-time San Jose, CA · On-site

Senior Solutions Architect, Wholem IT · May 2010 – Sep 2010 · Contract Greater Seattle Area · Hybrid

Pre / Post-Sales Enterprise Network Engineer, F5 Networks · Oct 2006 – Jun 2010 Seattle, WA · On-site

Senior System Architect & Engineer, University of Washington · Jul 1997 – Jul 2005 Seattle, WA · Full-time

Cofounder & Lead Architect, Election Verification Network · Jul 2003 – Jul 2010 Seasonal / National


Selected Projects & Research

CA-as-Reservoir · github.com/ceremona/CA-as-Reservoir Investigating cellular automata as discrete physical reservoirs for temporal computation. Characterizing memory capacity and nonlinear dynamics at the edge of chaos; exploring implications for analog and unconventional computing substrates. Implemented in Python with custom readout and statistical analysis.

Dispersion Trading Simulation · Spring 2025 Built a quantitative modeling framework in Python for options dispersion analysis.

Ongoing Graduate-Level Study · 2024 – Present Independent deep-dive into neuromorphic architectures, generative model internals, and first-principles ML framework mechanics.


Tools & Methods

Languages: Python, C, C++, Shell
Systems & HW: Linux kernel internals, Microcontrollers, Embedded systems, Networking (L1–7), Virtualization, Kubernetes, Signal conditioning
Modeling & ML: NumPy, SciPy, LTSpice; early FastAI exposure
Design & Analysis: CAD (Fusion 360), EDA (KiCad), LabVIEW
Domains: Reservoir computing, Kinetic systems, Network forensics, Instrumentation, Performance analysis, Industrial thermal systems, Data acquisition, Institutional systems migration


Education