
50+ years of satellite data - Landsat (1972-present), Sentinel-1/2, MODIS, VIIRS,
GRACE-FO gravimetry, ERA5, MERRA-2, and 150+ years of Uzhydromet records from 87
synoptic stations - all analyzed with methods from 2015 or earlier. Random Forest,
SVM, Markov-Cellular Automata, traditional indices. No foundation models. No vision
transformers. No deep learning. One DL paper exists (2023, Korean group, radiometric
normalization only).

Fine-tuning Prithvi-EO-2.0 (NASA/IBM's 600M param geospatial foundation model) and
Chronos-2 for six application modules: dust storm early warning, irrigation optimization
(deep RL on AquaCrop), water allocation optimization, crop yield prediction, salinity
risk mapping, crop disease detection.

Targeting 200+ dust alert users and 10+ farmer pilots in Karakalpakstan by mid-2026.
2-3 papers planned.

**Status:** In development (data pipeline and model fine-tuning phase)

**Stack:** Google Earth Engine, Prithvi-EO-2.0, Chronos-2, PyTorch, AquaCrop-OSPy,
Gymnasium, SAC, FastAPI, Telegram Bot API

**Data:** Sentinel-1/2, Landsat, MODIS, VIIRS, GRACE-FO, ERA5, MERRA-2, CA-discharge,
Uzhydromet archives

Needs GPU compute for foundation model fine-tuning. Looking for sponsorship.


First modern ML pipeline for the Aral Sea disaster zone.

Aral Basin Environmental Platform


Building a development environment designed for deep, locked-in engineering sessions.
Arch Linux + Hyprland (tiling Wayland compositor) as the base. Cursor, Claude Code,
Obsidian, vLLM, and custom automation tools wired into the desktop workflow. No Mac -
need proper support for engineering tools (SolidWorks, ANSYS) and full control over
the system.

The goal is an environment where AI assistance is ambient, not app-switched. Terminal,
editor, browser, notes - all connected through custom integrations and scripts.

The intelligence layer absorbs what was originally a separate JARVIS project - features
that only make sense as part of the OS, not standalone:

- **Meeting listener**: live transcription, speaker diarization, automatic action items
  via LiveKit + VAD
- **Voice pipeline**: Silero VAD + Faster-Whisper + Kokoro TTS - speak to the system,
  get spoken responses
- **Temporal knowledge graph**: Qdrant + Graphiti - everything ingested into a graph
  where facts expire and update automatically, no stale context
- **Unified messaging bus**: Telegram, WhatsApp, Discord, email all routed through a
  single interface
- **Document intelligence**: NuMarkdown-8B for complex OCR and document understanding
- **Autonomous execution**: microsandbox, Windmill for multi-step workflows

**Status:** Designed, ready to deploy. Blocked on hardware (need a proper laptop).

**Stack:** Arch Linux, Hyprland, Cursor, Claude Code, Obsidian, vLLM, LangGraph,
Qdrant, Graphiti, LiveKit, Faster-Whisper, Kokoro TTS, Windmill, Runpod

If you want to fund a laptop for this, I'll open-source the entire config.


Custom AI-integrated Linux workstation. Arch + Hyprland with AI tools wired into the desktop workflow.

Agentic OS


Workflow automation for a Canadian consulting firm. CRM pipeline configuration, intake
automation, AI-assisted document drafting with human review gates, task and reminder
systems tied to deadlines.

**Stack:** Pipedrive, Google Workspace, Zapier/Make, DocuSign API, Google Apps Script


Workflow automation for a Canadian consulting firm. CRM pipelines, intake automation, AI-assisted document drafting.

AI Automation Systems


Peltier modules are the standard for VR thermal feedback. They're also heavy (50g),
slow (70ms response), and power-hungry (5-10W continuous). ThermoTouch replaces active
heat pumping with phase-change materials and resistive heating.

Microencapsulated n-alkane paraffin (melting point 32-34C) in a silicone matrix absorbs
heat from the skin through endothermic phase transition - no electricity needed for cooling.
Resistive heating handles the warm side. 5x5mm actuators match human thermal receptor
density (~1 per cm2) with air-gap isolation between cells to prevent crosstalk.

**Simulation results:**
- Cooling response: ~5-10ms (10-15x faster than Peltier)
- Heating response: ~250ms (matches human perceptual asymmetry - we're far more sensitive to cold)
- Weight: under 5g per actuator vs 50g for Peltier
- Power: 2W average vs 125W for a 10-module Peltier glove
- Battery life: 7.5 hours vs 18 minutes on the same 15Wh pack

FEA validation in Fusion 360 confirmed Peltier baseline (36-40C heating, 18-24C cooling)
matching published ThermalWear data within 4%.

**Limitations:** 250ms heating is borderline. PCM regeneration takes 8-10s after sustained
contact (>3 min). Encapsulation durability over 10,000+ flex cycles is untested.

**Next:** transient thermal simulation, flexible PCB fabrication, single-cell prototype
with IR camera validation, then 4x4 array and 20-participant user study.

**Status:** Phase 1 complete (FEA validation), Phase 2 in progress (prototype design)

**Stack:** Fusion 360, FEA thermal simulation, flexible PCB design


Passive thermal haptics for VR. Phase-change materials replace Peltier modules - 3.5x faster, 25x lighter, 7+ hour battery life.

ThermoTouch


## Overview

**Role: CTO**

Building the future of construction and development. Currently cooking some sauce under the hood.

More details coming soon.


Building the future of construction and development - currently cooking some sauce under the hood.

BuildCored MVP


Red team agents (ReconAgent, ExploitAgent, ReportAgent) attack web applications.
Blue team agents (MonitorAgent, DefenderAgent, ForensicsAgent) defend in real-time.
The thesis: speed is the difference between caught and breached. Cerebras inference
at 1000-1700 tok/s means the blue team can actually react before attacks land.

The demo runs two scenarios - Cerebras-powered blue team blocks 70-80% of attacks;
simulated slow inference (3-5s delay) catches only 10-20%.

Targets OWASP Juice Shop (100+ documented vulnerabilities). Event bus for real-time
agent communication. Security Genome for cross-session learning with embeddings.
Monochrome frontend for live attack/defense visualization.

Built for the Cerebras hackathon. 24-hour coding sprint.

**Status:** Tier 4 in development (FastAPI + WebSocket backend). Shipping this month.

**Stack:** Python 3.11, Cerebras inference, FastAPI, WebSocket, Next.js, Docker


Autonomous AI pentesting platform. Red team attacks, blue team defends, in real-time.

SENTINEL


## Overview

This website serves as a digital notebook, inspired by Leonardo da Vinci's codices. It showcases research proposals, projects, reading lists, and writing.

## Technical Stack

- Next.js 13 with TypeScript
- Chakra UI for styling
- MDX for content management
- Vercel for deployment


Personal website built with Next.js and Da Vinci aesthetics

imamatdin.com


Telegram-based knowledge sharing platform. Book cataloging, resource sharing between
communities, reading list management, integration with external book databases.

**Stack:** Python, Telegram Bot API


Telegram bot for the Agora Library knowledge sharing platform.

Agora Library Bot


Natural language to manufacturable STL files. Describe a part in plain English,
get OpenSCAD code back, automatically compiled and validated. Errors are fed back
to the LLM for correction. Includes CADBench for evaluating LLM-generated CAD
on format compliance, compilation, geometric accuracy, and parametric robustness.

**Stack:** Python, OpenSCAD, Groq/Gemini, LLM tool use


AI-powered CAD code generation with validation. Describe parts in plain English, get manufacturable STL files.

SynCAD


Trained DDPG agents for datacenter cooling across multiple climate zones. 94.4% water
savings in Seattle (+-0.5% variance across seeds), strong generalization to Phoenix,
Chicago, and other climates. Sinergym simulation environment.

Expanding into territory where no published work exists: Decision Transformers (offline
RL) and Dreamer-style world models on the EnergyBench dataset (1.2 billion observations).
Also developing carbon-aware cooling optimization and multi-agent coordination for
datacenter clusters.

**Status:** Initial research complete, expansion in progress

**Stack:** Python, Sinergym, Stable-Baselines3, DDPG/SAC/TD3, PyTorch

Related: "The Digital Dust Bowl" on Medium connects datacenter cooling to the Aral Sea crisis.


RL agents for energy-efficient HVAC control. 94.4% water savings. Expanding to world models on 1.2B observations.

Projects