Architected an offline, cross-platform workspace distributed via F-Droid and winget, powered by a Rust-native AI engine and local-first design.

• Architected an offline, cross-platform workspace (distributed via F-Droid and winget) driven by a Rust-native AI engine. Engineered multi-model LLM inference with Vulkan/Metal GPU acceleration and integrated a sandboxed Model Context Protocol (MCP) for secure, autonomous file operations.
• Built a Rust-backed audio intelligence pipeline featuring Whisper STT for real-time word-level transcription, Kokoro neural TTS, and voice activity detection; integrated alongside a local vector database for semantic search and a high-performance math engine for multivariable calculus and hypothesis testing.
• Implemented Git-like version control utilizing SHA-256 content-addressable blob storage with automatic snapshots. Designed a Lua 5.3 scriptable plugin architecture with a programmatic API, an interactive force-directed knowledge graph, and parallel state-driven orchestration for chained routines.

Engineered a multi-language cryptanalysis suite with adaptive probabilistic solvers and rigorous empirical validation for classical cipher families.

• Engineered a multi-language cryptanalysis suite supporting 7 classical cipher families, implementing adaptive MCMC (with parallel tempering), Baum-Welch HMM/EM, and KPA-constrained solvers optimized over Kneser-Ney smoothed 5-gram language models.
• Formulated a statistical cipher-type detector (leveraging Index of Coincidence, Kasiski examination, and entropy) alongside a phase-transition analyzer that empirically maps ciphertext length against decryption success rates to validate theoretical unicity distances.
• Executed a rigorous 130-run empirical benchmarking framework, achieving a 100% success rate and 0.0 Symbol Error Rate (SER) on substitution and transposition ciphers by accelerating the evaluation hot loop with Numba JIT-compiled tensor operations.

Architected a dual-module hospital and blood bank platform with secure API design, asynchronous workflows, and production observability.

• Architected a dual-module hospital and blood bank platform deployed via Docker Compose with a non-root Gunicorn/tini runtime. Secured the API with dual Session/JWT authentication, Argon2 hashing, and strict validation (Pydantic v2, Flask-Limiter, Talisman CSP).
• Engineered an SQL-heavy allocation engine with trigger-backed forensic auditing and compatibility matching. Implemented a Redis/Celery async pipeline for scheduled workflows and PDF/Pandas CSV exports, supported by SQLite WAL tuning to handle concurrent booking contention.
• Integrated production-grade observability featuring structured JSON logging (structlog) with cross-request X-Request-ID tracing, RFC 7807 error contracts, dependency health probes, and custom Prometheus business metrics visualized in Grafana.

Developed an adversarial optimization framework to cloak identity signals against biometric and semantic recognition models while preserving visual fidelity.

• Engineered an adversarial optimization engine using Projected Gradient Descent (PGD) to mathematically cloak biometric and semantic identities against FaceNet and CLIP architectures.
• Enforced strict L-infinity norm constraints within the joint loss function to guarantee human-imperceptible perturbations, achieving a structural similarity index (SSIM) of > 0.98 across all cloaked outputs.
• Validated attack efficacy via an automated offline benchmarking pipeline, proving high black-box transferability by successfully blinding industry-standard oracle models (ArcFace, CLIP ViT-L/14) and optimizing Mean Residual Similarity (MRS) through comprehensive ablation studies.

Built a high-throughput geometric reconstruction system that approximates photographic images with optimized primitive-based composition under strict compute budgets.

• Architected a GPU-accelerated sequential hill-climbing engine to iteratively reconstruct high-fidelity photographic images using geometric primitives, employing a coarse-to-fine multi-stage schedule to capture both foundational structures and micro-details.
• Formulated an analytic closed-form color solver and integrated Sobel-based gradient maps for structure-aware shape routing, completely eliminating stochastic color search loops and maximizing algorithmic convergence speed.
• Executed rigorous empirical ablation studies across 11 architectural variants, achieving a peak Structural Similarity (SSIM) of 0.6059 and PSNR of 17.96 dB within a strict 1-minute compute budget, significantly outperforming standard evolutionary baselines.

Implemented a lightweight cross-platform local AI assistant with desktop and Android runtimes focused on low-latency contextual help.

• Built a cross-platform AI assistant that idles at under 15 MB RAM using a multi-process architecture - a Rust watcher handling OS-level hotkeys, UIAutomation context extraction, and IPC via Named Pipes, with a Python inference engine loading GGUF models on-demand via llama-cpp and a Tauri/React floating overlay as the UI.
• Implemented an Android counterpart in Flutter and Kotlin using an Accessibility Service for active-window context traversal and a native llama.cpp bridge for on-device inference, distributed as F-Droid-compatible APKs alongside a Windows WinGet package.
• Designed a style distillation system that automatically extracts personalised writing rulebooks from outgoing message history, applied at inference time to mirror the user's tone without fine-tuning.

An automated framework for sequential LoRA fine-tuning on creative writing datasets. Implements a memory-efficient pipeline for training LLMs on long-form literary works via incremental learning.

• LoRA / PEFT: Injects trainable rank-decomposition matrices into self-attention layers with minimal parameter overhead.
• Contextual SFT: JSON-based instruction-response pairs carry previous chapter context to preserve narrative continuity.
• Auto Hardware Scaling: Detects available compute and scales from 1B-parameter models (CPU) to 3B (GPU).
• Fault-Tolerant Pipeline: Progress-tracking system enabling interruption recovery and sequential file processing.

Hybrid classification pipeline using VGG16 as a frozen feature extractor feeding an SVM classifier - high accuracy under varying computational constraints.

• Transfer Learning: VGG16 as feature extractor producing 512-dim vectors.
• Dimensionality Reduction: PCA compresses to 256 dims retaining 95% variance.
• SVM Classifier: RBF kernel fine-tuned via GridSearchCV.
• Custom Serialisation: Unified persistence for the full pipeline.

Determines signature authenticity by comparing a query signature against a known reference - entirely offline via metric learning.

• Siamese Architecture: Dual-branch CNN sharing weights to map signatures into a common embedding space.
• Metric Learning: Contrastive / Triplet Loss to minimise distance between genuine pairs and maximise it for forgeries.
• Preprocessing Pipeline: Normalisation, binarisation, and noise reduction for robust feature extraction.

Developed a fully local face-clustering pipeline that groups photos by person using ArcFace embeddings and DBSCAN, optimized for CPU-only systems.

• Built a three-stage inference pipeline with RetinaFace detection, ArcFace 512-dimensional embeddings, and DBSCAN clustering to automatically discover identities without predefined class counts.
• Optimized runtime with ONNX Runtime for CPU inference, delivering practical throughput while preserving high clustering quality via the buffalo_l model stack.
• Implemented automatic photo organization, configurable CLI tuning (`eps`, `min_samples`), dataset and custom-folder workflows, and an API layer for programmatic clustering and statistics retrieval.

Built a modern JavaFX desktop platform for university scheduling, room-conflict detection, and automated conflict resolution with native Windows integration.

• Implemented schedule import/export, conflict detection, and auto-resolution using an IntervalTree-based conflict engine and a backtracking resolver that reassigns events while ensuring no new overlaps are introduced.
• Engineered a custom undecorated JavaFX interface with native-feel window controls, weekly drag-and-drop calendar, multi-tab dashboard, analytics charts, and animated toast notifications.
• Integrated SQLite persistence (WAL mode, foreign keys, indexed overlap queries), GitHub Releases auto-update checks, background-threaded JavaFX tasks for non-blocking I/O, and comprehensive automated test coverage across model, DB, algorithms, and I/O layers.

Production-grade full-stack parking ecosystem using Vue.js components, Flask Blueprints, and a service-layer pattern, deployed via Docker Compose multi-container orchestration.

• Auth & RBAC: JWT authentication with Redis caching to minimise database latency.
• Async Processing: Celery workers for CSV exports and PDF generation with ReportLab.
• Automated Reporting: Celery Beat for scheduled monthly reports.
• High Performance: Gunicorn + Uvicorn ASGI workers for production throughput.

A temporal sequence classification system recognising dynamic hand gestures in real-time by modelling time-dependent 3D landmark data.

• Sequential Modelling: Deep RNN using LSTM units to capture temporal dependencies.
• 3D Spatial Features: MediaPipe integration for 21 high-fidelity 3D hand landmarks per frame.
• Data Augmentation: Noise injection, scaling, and translation for robustness.
• Production Readiness: Modular inference engine managed with uv.

Identifies distinct customer personas and visualises the internal logic of clustering algorithms through an interactive dashboard.

• K-Means Clustering: Elbow Method & Silhouette Analysis for optimal k selection.
• Animation Engine: Shows centroid initialisation and convergence steps live.
• Statistical Validation: ANOVA tests and cluster stability analysis.
• Full-Stack: FastAPI backend + Plotly for 3D visualisation.

Predicts real estate values using an automated model selection engine that benchmarks ensemble methods against a linear baseline via cross-validation.

• Ensemble Learning: XGBoost and Random Forest compared against Linear Regression.
• Automated Model Selection: Dynamic evaluation via 5-fold Cross-Validation.
• Statistical Confidence: Prediction engine providing confidence intervals.
• System Integration: FastAPI microservice with interactive dashboard.