Projects

I wanted a model of a sales organization that had reps in it.

Not a conversion funnel — those exist — but something with agents who make decisions, develop opinions about their accounts, get burned out, and respond to organizational structure. The interesting question isn’t what the average conversion rate is; it’s what conditions produce a particular distribution of outcomes, and what levers actually move it.

sf_majick is a fully parameterized agent-based simulator for B2B sales orgs. Four rep archetypes — Closer, Nurturer, Grinder, Scattered — each with distinct personality traits governing action affinity, distraction rates, and burnout thresholds. Nine micro-actions (email, call, meeting, proposal, internal prep, and four others) carry attention cost and sentiment effects; macro transitions (lead conversion, stage advancement, close/lost) fire stochastically with probabilities modulated by sentiment, momentum, and rep behavior. Reps accumulate workload stress via an end-of-day burnout model; stress above archetype threshold craters available attention and increments days burned out. Every entity carries a sentiment state that decays under neglect and updates with each interaction.

The structural variable of most interest is the requirement gate: whether a micro-action must be completed before a stage can advance (AND logic), or whether completing any one of several qualifies (OR logic). AND-gate configurations produce low stage-advance rates and concentrated drop-off; OR-gate configurations produce higher throughput with a different distribution of deal stall. The figure below shows the funnel shapes each produces.

The calibration layer (OrgCalibrator) back-fits simulation parameters to a Salesforce SOQL export: win rate, cycle time, and stage distribution determine the probability fields; revenue distributions are fitted from deal-size data. The result is an OrgConfig whose simulated org behaviorally matches the real one. Experiments run against this baseline using ExperimentRunner — N Monte Carlo iterations per scenario, with OLS-adjusted action effect coefficients that control for stage, rep, and outcome before attributing sentiment deltas to specific micro-actions. This separates actions that move deals from actions that merely correlate with deals that were going to move anyway.

The web interface runs on a Raspberry Pi: a five-tab Flask app for org configuration, calibration diagnostics, and a Salesforce CSV drop that auto-fits and saves a calibration file. Simulation runs execute locally; the Pi’s ARM CPU is 10–30× too slow for the Python engine.

The code is on GitHub.

I became a full-time caregiver and started spending my days with a toddler.

After enough time you start to read them. You notice patterns. I decided to do something useful with that rather than just observe it.

The library book recommender came first. The problem was concrete: we were burning library holds on books Heiki would inspect for four seconds and set face-down. The scraper pulls the Sno-Isle catalog nightly via the BiblioCommons API — roughly 165,000 titles across genres, audiences, and formats. A Flask web UI runs on a Raspberry Pi in the house; three users (Heiki, Simon, Madeleine) share the catalog and maintain independent taste profiles. The engine is TF-IDF plus cosine similarity: it builds a preference fingerprint from books you’ve rated or engaged with, weighted across four signals — star ratings, rereads, reads, and false starts — and ranks unread books against that fingerprint. Holds go through the BiblioCommons API directly from the interface.

The week planner came next. It’s a CLI tool for building and managing a weekly schedule: activities in append-only per-week JSON files, Rich terminal display, interactive editing via questionary. The Google Calendar integration supports push (plan to GCal, idempotent re-push) and pull (external events into the local file). Activities carry an optional location field that renders as a Maps link in the terminal and populates GCal’s native location on push. Multi-account pull normalizes timezones across calendars.

Both are in daily use. The code is on GitHub.

I was bored in grad school. I started a D&D group with a bunch of people who had never played before, which meant I was constantly building characters from scratch. At some point I decided to automate it — and once you have an automated character funnel, you have a simulation, and once you have a simulation you have data.

So I ran 10,000 characters through the 2014 Player’s Handbook rules and wrote up what came out.

The simulator rolls attributes using the 4d6-drop-lowest method with two constraints: at least one score must be 16 or higher, and the average must be at least 12. Species, class, background, and alignment are then selected by weighted matching against the rolled array. Chi-squared residuals and correlation matrices reveal where the mechanics produce non-obvious outcomes.

A few things stood out. Strength and Dexterity are moderately negatively correlated — not because the game intends this, but because sequential generation and species bonus mechanics create it as an artifact. Intelligence is systematically underutilized as a primary class attribute. Gnomes and Tieflings are structurally disadvantaged: their species bonuses don’t align well with any physical class, leaving them clustered in a narrow band of options. Bard is the most mechanically unique class in the PHB; Fighter is the least. The adventuring population skews heavily Human and Elf, which turns out to be a direct consequence of their attribute flexibility rather than player preference for those species.

The image below shows chi-squared standardized residuals for species-class combinations — red means the pairing occurs more than the marginal distributions would predict, blue means less.

The code is on GitHub.

I found myself at a wine tasting at Turnbull Winery in Napa. The server was describing how the position of a vine within the vineyard — its row, its elevation, its exposure — produces measurable variance in the grape and ultimately in the wine. I was fascinated. The question underneath it was familiar: how much does location explain outcome, and can you measure it precisely?

So I went home and started drawing polygons on Google Earth.

GrapeExpectations is a precision viticulture pipeline built around that question. The study area is a Washington State wine production region — 3,598 equal-area hexagonal cells at 1,000 m² each, covering nine years of Sentinel-2 satellite imagery, PRISM climate records, USGS topography, and gSSURGO soil data. The pipeline assembles those layers, engineers features from the DEM (slope, curvature, aspect, local relief), pulls spectral indices via Google Earth Engine, and stacks them into a multi-output ensemble: Random Forest, ExtraTrees, Gradient Boosting, XGBoost, and KNN feeding into an ElasticNet meta-learner. The target is weekly NDVI across the growing season — a proxy for vine canopy density and vigor. The stacked model hits R² = 0.967 on the held-out tune set. A companion analysis produces a frost risk raster by combining topographic position, elevation deviation, and NDVI history. I also reached out to Washington State University about the approach, and we explored deployment of Distributed Temperature Sensing fiber optic cables for direct canopy temperature measurement — the same photonic sensing technology I worked with at the UW Photonic Sensing Lab.

The vineyard work raised a version of the question I hadn’t expected: does this generalize? A vine in a particular microclimate is one data point in a larger pattern. What about coffee?

JavaScript (named for the Kona district, not the language) maps current and projected coffee suitability across Hawaiʻi’s Big Island — Kona and Kaʻu — at 500m resolution. The pipeline follows the same logic: hexagonal grid, terrain features from the DEM, ERA5-Land climate variables, Sentinel-2 NDVI composites, and SSURGO soil data, assembled into a labeled feature matrix from 674 coffee-farm polygons. A Random Forest classifier distinguishes coffee-farm cells from background; PCA extracts the environmental fingerprint of each district; k-means identifies microclimate zones within each region. The two districts turn out to be quite different — different thermal optima, different feature importance profiles, different climate variance explained. Forward projections using NEX-GDDP-CMIP6 (SSP2-4.5 and SSP5-8.5) extend the analysis to 2035 and 2045. Under projected warming, Kona’s thermal window contracts upslope. Kaʻu’s expands.

That result pointed somewhere bigger. If altitude and climate shape the chemistry of where coffee grows, do they shape the chemistry of the coffee itself? And if they do for coffee, do they do it for tea, for apples, for dairy?

TerraMetabolica is the attempt to answer that systematically. The framework is built around Region Sample Units — 65 RSUs spanning boreal Finland to the Himalayan plateau, each defined by climate, geology, altitude, and 4–6 native staple foods. Metabolite values are assembled from USDA FoodData Central and primary literature: organic acids, polyphenols, fatty acids, terpenes, umami compounds. The patterns that emerge are not subtle. Elevation shows consistent directional associations with protective metabolite concentrations across unrelated biological systems on multiple continents — chlorogenic acids in Arabica coffee, catechins in green tea, malic acid in apples, conjugated linoleic acid in grass-fed dairy. These are different crops, different synthesis pathways, different continents, pointing the same direction. Coconut lauric acid, genetically constrained, shows no altitude relationship across five RSUs — an intentional null that confirms the framework is discriminating rather than just inflating everything. The North American Prairie RSU has zero measured bioactive compounds across its staple foods. That absence is itself a finding.

The pipeline also runs in the other direction. Rather than asking what a location does to a crop’s chemistry, you can ask where a crop belongs — and whether you can answer that question without any labeled data at all.

KushCountry applies the same hexagonal grid architecture to outdoor cannabis in California’s Emerald Triangle with one structural constraint: the California Department of Cannabis Control withholds all spatial data for licensed cultivators. No training polygons exist. Terrain, climate, soil, and satellite canopy structure (Sentinel-2 NDVI) across 8,900 hexagonal cells are stacked, scaled, and handed to k-means without labels; the question is whether the resulting clusters self-organize around documented cultivation conditions. The answer is cluster 0: mean elevation 527m, growing degree days 1,395, vapor pressure deficit 1,568 Pa — mid-elevation interior, warm-dry Mediterranean conditions, the recognized Emerald Triangle cultivation profile. It covers 22.2% of the study area. Enforcement records from the Regional Water Quality Control Board confirm the archetype independently: cluster 0 is only 2.9% of Humboldt County terrain but 9.9% of documented violation parcels, a lift of 3.41. The archetype recovers at four of four agronomic criteria across K=4 through K=7; bootstrap cluster stability averages ARI=0.974 across 50 subsamples.

The framework — Unsupervised Terrain Fingerprinting — generalizes to any specialty crop where cultivation locations are suppressed, restricted, or absent.

The whole thing lives under one umbrella now: GeoGastronomy. The code is on GitHub.

Published in Journal of Glaciology, 2026

Tidewater and lake-terminating glaciers are systematically thicker than glaciers ending on land — and that difference accounts for roughly 20% of non-ice-sheet global glacier volume.

We arrived at this by training a shallow neural network on glacier-averaged thickness measurements from GlaThiDa and surface attributes from the Randolph Glacier Inventory (216,501 glaciers globally), then comparing models trained with and without water-terminating glaciers in the training set. The gap between those models is the imprint of ice-ocean interaction on global ice volume. It is consistent with a simple mechanical explanation: water pressure at the ice front permits thicker termini than are stable in air, setting a different boundary condition for equilibrium glacier geometry.

The pipeline handles the messy reality of co-registering two independently maintained global datasets across different measurement epochs, using distance and area thresholds to filter unreliable matches. Leave-one-out cross-validation across 273 co-registered glaciers produces per-glacier uncertainty estimates that propagate through to a global volume uncertainty budget.

In November 2022, a fiber optic cable running under Red Square on the University of Washington Seattle campus picked up something nobody had asked for.

The UW Photonic Sensing Lab operates a distributed acoustic sensing array: a fiber optic cable connecting UW Seattle to UW Bothell, running the length of the city and sampling ground motion across thousands of channels at roughly 6 meter spacing. We were searching for signals from trains moving through underground tunnels to audit their schedule. While scanning repeating 10-minute windows of data, we found instead a series of high-amplitude, low-frequency events centered under Red Square between the evening of November 10 and the afternoon of November 14, 2022.

It is currently unknown what is causing these signals as they span several channels of approximately 6 meter spacing per channel and do not always have similar features. An early hypothesis suggested these could be percussive signals from a nearby marching band or similar performance, however the events occur throughout the day and are non-dispersive. A percussive signal should be detected farther along the cable and be dispersive, however these detected signals are confined to only a few channels.

These signals are also accompanied by long “ring-down” time after detecting extremely large amplitudes as if the cable were kicked or picked up. There are also periods of extended shaking of several minutes as if a jackhammer is being operated right next to the cable. A likely candidate for these signals could easily be construction or maintenance in the parking garage below Red Square.

Two steps would narrow it down: a tap test in or under Red Square to precisely locate the cable within the structure, and a review of facilities maintenance records for the parking garage during those dates.

When large amplitudes were detected, the data was passed through two bandpass filters (0.01–0.1 Hz and 0.1–1 Hz) and plotted as a space-time heatmap. Selecting a single channel then revealed the ring-down in the time series — signals persisting for over a minute after the initial event.

Monopoly has always been a fun way to kill time. First as a child playing star wars monopoly with my family, then later I made a board and dice from paper to play with fellow recruits in basic training. While on paternity leave I began writing a python based command line interface version of monopoly.

The board and properties are defined as classes, with NumPy for dice. The full mechanics are implemented: buying properties, building houses and hotels, paying rent, drawing Chance and Community Chest cards, going to jail. CPU opponents make automated buy and build decisions. The terminal output is colorized. It ends when someone goes bankrupt — unlike our current housing market, which does not.

The code is on GitHub.