<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Abdulmalik Ajisegiri</title><description>Abdulmalik Ajisegiri is a systems engineering and model risk professional specializing in model validation, AI/ML governance, advanced analytics, and MBSE.</description><link>https://abdulmalikajisegiri.com/</link><item><title>Validating Models Like a Skeptic: The Outcomes-Analysis Playbook</title><link>https://abdulmalikajisegiri.com/research/validating-models-like-a-skeptic/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/validating-models-like-a-skeptic/</guid><description>The SR 11-7 validation playbook: conceptual soundness, outcomes analysis with challenger models, ongoing monitoring — the attempt to kill your model.</description><pubDate>Sun, 20 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Building an LLM Evaluation Harness: BLEU, ROUGE, SBERT, and Risk Tagging</title><link>https://abdulmalikajisegiri.com/research/llm-evaluation-harness-bleu-rouge-sbert/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/llm-evaluation-harness-bleu-rouge-sbert/</guid><description>A repeatable LLM evaluation harness: what BLEU, ROUGE, and SBERT actually measure, where each misleads, and the architecture turning scores to governance.</description><pubDate>Wed, 16 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Stacking Ensembles That Actually Generalize: Lessons from Clinical Risk Modeling</title><link>https://abdulmalikajisegiri.com/research/stacking-ensembles-clinical-risk-modeling/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/stacking-ensembles-clinical-risk-modeling/</guid><description>Out-of-fold stacking discipline and validation skepticism that keep ensemble gains honest — R² 0.78 base model vs 0.81 stacked, on a clinical risk project.</description><pubDate>Fri, 11 Sep 2026 00:00:00 GMT</pubDate></item><item><title>When R² Lies: Evaluating Regression for High-Stakes Decisions</title><link>https://abdulmalikajisegiri.com/research/when-r2-lies-regression-evaluation/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/when-r2-lies-regression-evaluation/</guid><description>Why R² can’t tell you a clinical model is safe: calibration, cost-aware thresholds, subgroup slices, and honest uncertainty beyond the leaderboard number.</description><pubDate>Mon, 07 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Survivorship Bias and Lookahead: Two Backtest Killers, with Code</title><link>https://abdulmalikajisegiri.com/research/survivorship-bias-lookahead-backtest-killers/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/survivorship-bias-lookahead-backtest-killers/</guid><description>Two classic backtest bugs in runnable numpy: survivorship bias tests only assets that exist today; lookahead bias trades on unknowable information.</description><pubDate>Wed, 02 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Monte Carlo for Decisions Under Uncertainty</title><link>https://abdulmalikajisegiri.com/research/monte-carlo-decisions-under-uncertainty/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/monte-carlo-decisions-under-uncertainty/</guid><description>When simulation beats closed-form math: the 1/√N error tax, antithetic variates and common random numbers for variance reduction, and honest reporting.</description><pubDate>Sat, 29 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Discrete-Event Simulation Experiments: Replications and Honest Statistics</title><link>https://abdulmalikajisegiri.com/research/discrete-event-simulation-experiment-design/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/discrete-event-simulation-experiment-design/</guid><description>An Arena poker-chip simulation as an experiment-design case study: independent replications, paired comparisons with common random numbers, intervals.</description><pubDate>Mon, 24 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Angle of Arrival on a Budget: Localizing Sound with Four Microphones and a Cortex-M4</title><link>https://abdulmalikajisegiri.com/research/angle-of-arrival-embedded-audio-localization/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/angle-of-arrival-embedded-audio-localization/</guid><description>Localizing sound with four microphones and a Cortex-M4: time-difference-of-arrival, GCC-PHAT cross-correlation, DMA double-buffering, and honest limits.</description><pubDate>Thu, 20 Aug 2026 00:00:00 GMT</pubDate></item><item><title>DMA Double-Buffering for Continuous Sensor Capture</title><link>https://abdulmalikajisegiri.com/research/dma-double-buffering-sensor-capture/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/dma-double-buffering-sensor-capture/</guid><description>Why per-sample interrupts collapse at high rates, and how DMA ping-pong keeps sensor streams gapless: buffer sizing, overrun math, cache coherency.</description><pubDate>Sat, 15 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Low-Power TM4C123 Design: Hibernation, EEPROM, and a Validating UART CLI</title><link>https://abdulmalikajisegiri.com/research/low-power-embedded-design-tm4c123/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/low-power-embedded-design-tm4c123/</guid><description>An unattended weekend pet feeder on the TM4C123: RTC-wake hibernation, EEPROM schedules with wear budgets, a validating UART CLI, comparator water sensing.</description><pubDate>Tue, 11 Aug 2026 00:00:00 GMT</pubDate></item><item><title>How System Calls Actually Work: A Tour of a Tiny C Kernel</title><link>https://abdulmalikajisegiri.com/research/how-system-calls-work-tiny-c-kernel/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/how-system-calls-work-tiny-c-kernel/</guid><description>Tracing a userland call like cat through the trap, dispatch table, and syscall implementations of a tiny C kernel — the user/kernel boundary demystified.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Zod at the Boundary: Validating Every Byte That Enters Your API</title><link>https://abdulmalikajisegiri.com/research/zod-at-the-boundary-api-validation/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/zod-at-the-boundary-api-validation/</guid><description>TypeScript types evaporate at runtime: a Zod-first Express design for the trust boundary — validate-once middleware, clean 400s, coercion, fuzz testing.</description><pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Codifying Engineering Judgment: What an Automated Code-Review Pipeline Should Actually Check</title><link>https://abdulmalikajisegiri.com/research/automated-code-review-pipeline/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/automated-code-review-pipeline/</guid><description>The three layers of automated code review, how to codify engineering judgment as checkable rules, and the line automation must not cross.</description><pubDate>Mon, 27 Jul 2026 00:00:00 GMT</pubDate></item><item><title>Verification and Validation Across the Systems Lifecycle</title><link>https://abdulmalikajisegiri.com/research/verification-validation-systems-lifecycle/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/verification-validation-systems-lifecycle/</guid><description>V&amp;V isn’t a phase, it’s a thread: building right vs. building the right thing, the four verification methods, and calibrating rigor to consequence.</description><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate></item><item><title>From Stakeholder Needs to Verification: Requirements Engineering for Complex Systems</title><link>https://abdulmalikajisegiri.com/research/requirements-engineering-complex-systems/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/requirements-engineering-complex-systems/</guid><description>From stakeholder needs to verification via a toy parcel-sorting facility: elicitation, verifiable requirements, decomposition, change, end-to-end tracing.</description><pubDate>Sat, 18 Jul 2026 00:00:00 GMT</pubDate></item><item><title>Using Optimization and Metaheuristics for Engineering Decision Problems</title><link>https://abdulmalikajisegiri.com/research/optimization-metaheuristics-decisions/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/optimization-metaheuristics-decisions/</guid><description>When gradients stall on messy trade spaces: simulated annealing, genetic algorithms, particle swarm on synthetic toys, compared under an honest protocol.</description><pubDate>Mon, 13 Jul 2026 00:00:00 GMT</pubDate></item><item><title>Model-Based Systems Engineering and Requirements Traceability</title><link>https://abdulmalikajisegiri.com/research/mbse-requirements-traceability/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/mbse-requirements-traceability/</guid><description>Traceability is what makes MBSE work: the needs-to-verification chain, typed links, trace matrices, change-impact analysis, kept alive in real projects.</description><pubDate>Thu, 09 Jul 2026 00:00:00 GMT</pubDate></item><item><title>Backtesting Quantitative Strategies Without Fooling Yourself</title><link>https://abdulmalikajisegiri.com/research/backtesting-without-fooling-yourself/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/backtesting-without-fooling-yourself/</guid><description>The ways backtests lie — lookahead bias, survivorship bias, costs, overfitting — shown in runnable Python, plus a walk-forward workflow that catches each.</description><pubDate>Sat, 04 Jul 2026 00:00:00 GMT</pubDate></item><item><title>Detecting Model Decay: Ongoing Monitoring in Production</title><link>https://abdulmalikajisegiri.com/research/detecting-model-decay-monitoring/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/detecting-model-decay-monitoring/</guid><description>Monitoring that catches decay before stakeholders do: PSI and drift checks, signal-vs-noise tracking, retraining triggers, alerts without pager fatigue.</description><pubDate>Tue, 30 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Stress Testing and Scenario Analysis for Quantitative Models</title><link>https://abdulmalikajisegiri.com/research/stress-testing-scenario-analysis/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/research/stress-testing-scenario-analysis/</guid><description>Designing historical, hypothetical, and reverse stress scenarios that break your model on purpose before reality does: severity ladders and governance.</description><pubDate>Thu, 25 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Agentic AI Model Risk Management Platform</title><link>https://abdulmalikajisegiri.com/projects/agentic-mrm-platform/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/projects/agentic-mrm-platform/</guid><description>A personal project: an agentic platform that automates the model-risk identification lifecycle — SR 11-7 classification, validation artifact generation, and pipeline tracking with SLA enforcement and audit trails.</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate></item><item><title>Clinical Risk Predictive Model — Cardiac Surgery Outcomes</title><link>https://abdulmalikajisegiri.com/projects/cardiac-surgery-risk-model/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/projects/cardiac-surgery-risk-model/</guid><description>A stacked-ensemble ML pipeline predicting operative mortality in cardiac surgery from preoperative variables, built against STS-aligned outcomes.</description><pubDate>Sun, 01 Jun 2025 00:00:00 GMT</pubDate></item><item><title>QuantEdge Labs</title><link>https://abdulmalikajisegiri.com/projects/quantedge-labs/</link><guid isPermaLink="true">https://abdulmalikajisegiri.com/projects/quantedge-labs/</guid><description>An end-to-end trading research platform — ML-calibrated opportunity scoring, options analytics, and simulation-based verification, shipped as a full-stack web terminal.</description><pubDate>Wed, 15 Jan 2025 00:00:00 GMT</pubDate></item></channel></rss>