Less emphasis on buzzwords. More on: problem → approach → what actually worked (and what didn't).
End-to-end LiDAR + 3D deep learning system that learns expert dormant pruning directly from before/after point clouds and outputs robot-ready pruning plans, matching expert pruning within ~2.5% volume error.
Point Transformer v3PyTorchTreeQSMOpen3D+7
- →Trained a Point Transformer v3 model that achieves 0.86 F1 (0.81 precision, 0.91 recall) while keeping mean pruned-branch volume error to just 2.5% on held-out trees.
- →Recovered 92.5% of expert pruning locations with only ~1–2 extra cuts per tree on average; many of those extras were validated by horticulture experts as genuinely useful cuts.
- →Among the first systems to learn pruning behavior directly from before/after LiDAR scans without manual branch annotations or hard-coded pruning rules.
Recently I've worked a lot with a software called TreeQSM which is used to extract structural traits from 3D Point Clouds of trees. It's great at extracting QSM traits from forest-style trees, but it breaks down on open-vase architectures (topology, branch IDs, order, and parent/child links don’t reflect reality). I built a post-processing tool that reconstructs a clean directed skeleton from TreeQSM cylinders and rewrites the cylinder topology (parent/branch/extension/position) using classic graph algorithms, so downstream trait extraction stays meaningful.
PythonTreeQSMNetworkXNumPy+2
- →Recovered consistent parent/child topology and branch grouping on open-vase trees (≈90% agreement with expected structure in internal validation).
- →Made TreeQSM cylinder outputs usable across multiple research workflows by exporting a cleaned CSV with corrected topology fields + visualizations for QA.
An end-to-end Instagram analytics platform that turns raw account + hashtag data into dashboards, sentiment trends, and AI-generated strategy recommendations. It combines owned-account performance tracking with competitive hashtag intelligence, backed by an automated scraping pipeline and a secure multi-tenant database.
Next.jsTypeScriptTailwindshadcn/ui+6
- →Unified three analytics pillars (my content performance, hashtag competitive intel, and comment sentiment) into one consistent dashboard experience.
- →Automated recurring data collection with n8n + Apify, turning manual tracking into a repeatable pipeline (snapshots, dedupe, incremental updates).
- →Designed a secure multi-tenant backend using Supabase Auth + Row-Level Security so each user only accesses their own analytics and tracked assets.
CLIP-style dual encoder that uses LiDAR-derived 3D tree architecture as privileged supervision to shape genotype embeddings. Trains SNP ↔ 3D alignment contrastively, then predicts architecture traits from SNPs only at inference—improving geometry-sensitive genomic prediction in a small-n, ultra-high-p regime.
PythonPyTorchscikit-learnXGBoost+8
- →Built a paired genotype↔3D dataset for peach architecture: ~131k SNPs across 122 trees plus LiDAR point clouds.
- →Mean branch angle prediction: GenoCLIP achieved MAE 6.8 and PCC 0.644, outperforming genotype-only DPCFormer (MAE 7.4, PCC 0.591) and approaching the strongest classical baseline RF (MAE 6.7, PCC 0.68).
- →Branch count prediction: GenoCLIP improved MAE vs DPCFormer (43.9 vs 48.0) while remaining competitive in correlation (PCC 0.4408 vs 0.4567); RF remained the best small-n baseline (MAE 40.2, PCC 0.445).
Automated tool that takes any GitHub repo URL, statically analyzes code structure and relationships, then uses OpenAI to generate ready-to-run VS Code CodeTour files for instant onboarding.
PythonFlaskReactOpenAI API+3
- →Generates structured .tour files in ~3 minutes for small-to-medium repos, eliminating manual onboarding doc creation
- →Static itemization engine tracks where symbols are defined/used, enabling LLM to follow real execution flows instead of alphabetical file lists
- →Dual interface: CLI (main.py) for quick generation and React + Flask web UI for non-technical users