Lead Software Engineer
Custom software and AI/ML systems for client engagements.
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Ethan
Callanan
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I believe great design is invisible. Today, AI is hyper-present: sold as the product rather than designed in service of one. I pursue the design of systems where the intelligence is invisible. Software that silently learns and improves. To enhance the user, not replace them.
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About
ProfileBuilding systems that learn.
I’m interested in intelligence as a property of systems: how software can preserve experience, adapt over time, and coordinate work.
My background is in AI research and software engineering, spanning inference optimization, test-time compute, automated planning, and financial applications of AI.
Currently, I’m an applied researcher and core platform engineer at Evolver, where I build infrastructure for continually learning agents. My work spans memory systems, agent harnesses, and orchestration for large-scale parallel workloads.
Outside work, I tinker with custom tooling around nixos, neovim, and the terminal. I’m drawn to software that rewards deep use: tools that can be shaped and molded around oneself. I also experiment with personal AI systems, and hardware -- bringing software experiments to life.
My computer is an extension of myself.
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History
Previous WorkTest-time compute & financial applications
Graduate research in test-time compute inference scaling techniques, as well as financial applications in collaboration with the JP Morgan AI Research Lab.
MoreAutomated Planning
Automated-planning research and open-source tooling for model acquisition and propositional-logic encodings.
CloseDuring a research internship at MuLab, I worked on model acquisition in automated planning—inferring action models from observed plan traces.
That work included MACQ, a Python toolkit and foundational API for model-acquisition research, and Bauhaus, an open-source library for constructing propositional-logic encodings. I designed and authored MACQ and continue to maintain it alongside Christian Muise.
Timeseries Forecasting
Financial forecasting tools spanning more than $30B in transaction volume.
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Projects
Selected Projects
BCI Game Controller
A brain-computer-interface game controller built with the OpenBCI Ultracortex Mark IV using BrainFlow.
Signal-processing filters and transforms turn raw electrode readings into features for several machine-learning models. Trained Transformers, CNNs, and simpler ML models, with the best achieving 94.4% accuracy.
Demonstrated at CUCAI 2023 by playing Flappy Bird in front of a live audience.
MACQ
The Python model acquisition toolkit is a standard library for model-acquisition research in automated planning. I designed and authored the library during a research internship at MuLab and continue to maintain it alongside Christian Muise.
MACQ provides foundational APIs for PDDL problems, traces, and planners, plus implementations of model-acquisition algorithms that infer planning models from complete traces and translate inferred action models back to PDDL.
Entropy-Gated Branching
Entropy-Gated Branching for Efficient Test-Time Reasoning is a paper I co-authored with Xianzhi Li, Abdellah Ghassel, and Xiaodan Zhu.
It selectively expands reasoning sequences at high-uncertainty steps, avoiding compute spent on low-diversity branches where a model is already confident. The method improves the quality and diversity of candidate reasoning steps while using test-time computation more efficiently.
Published at EACL 2026.
Praxis Interface
A data-analysis tool built to help data scientists reason about data that evolves over time. It was used internally by Praxis for financial forecasts covering more than $30B in transactional volume.
Attention Detection
VAAD combines face tracking and attention detection as an activation mechanism for virtual assistants. Instead of requiring a wake word, the system explores whether a person can address an assistant more like they would address a coworker.
Neural Networks from Scratch
Implementations of neural networks and their underlying operations in NumPy and PyTorch. Rebuilding familiar architectures from their primitives is a useful exercise for keeping intuitions sharp and understanding the algorithms beneath high-level frameworks.
EVIM
A fast, deeply customized Neovim configuration featuring a custom LLM chat plugin and a broad collection of editing and navigation refinements.
Bauhaus
An open-source Python package for constructing propositional-logic encodings through a concise, composable API.
ecal.dev
The site you are viewing: a FastAPI and HTMX application whose content is authored in Markdown, with interactive Canvas motion studies.