AI InternFlask REST services, thermodynamic feature engineering, and pytest validation for a compressor CBM platform.
00 / Origin
Shaunak
Rane
Machine-learning systems engineer
I build validated ML systems that turn complex physical and operational data into reliable decisions across industrial diagnostics, agent orchestration, graph learning, and distributed energy.
01 / Agent systems
Multi-agent swarmGemini 2.5 GatewaySupabase pgvector
Aegis
threat intelligence
Autonomous investigation system combining SHA-256 claim deduplication, quantitative market anomaly detection (Z-scores), and 15-minute RSS ingestion with an asynchronous two-stage Gemini verification swarm.
Agent Swarm2-stage decoupled
Market MLZ-score anomalies
RSS Ingestion15 min loop
Production engineering: serverless edge key cycling, structured JSON Schema contracts, and pgvector semantic recall.
02 / Energy systems
Continuous RLDePIN Architecturezk-SNARKs
Gridium
protocol
A distributed microgrid economy pairing a 15-node Ohm's law physics simulator with a continuous-control PyTorch DDPG agent, 500ms Socket.io gateway, constant-product EVM AMM, and Groth16 zk-SNARK settlement.
500msphysics tick rate
7D → 1DDDPG continuous fee
15Ohm's law nodes
Closed-loop stability: DDPG steers AMM swap fees dynamically to avert localized microgrid brownouts.
03 / Industrial intelligence
AI internshipIndustrial MLFlask
Compressor
evidence system
A condition-monitoring platform that separates what the thermodynamics support from what a polished forecast merely suggests.
3plant-specific contracts
3,879DFI rows reproduced
17/17validation gates
Forecasting remains an experimental visualization and never drives maintenance alerts.
04 / Graph intelligence
Independent studyGraph MLPhysics benchmark
TopoFlow
pore network
A permeability study where the topology is learned, the classical baseline stays visible, and pore heterogeneity decides which model deserves trust.
1,231pore-network graphs
5rock families tested
46.2%best MSE reduction
Benchmark result: learned topology helps in heterogeneous media; it is not a universal replacement for physics.
05 / Trajectory
What I bring
to a team
I move comfortably between investigation and implementation: data forensics, ML evaluation, APIs, Docker, responsive interfaces, and the documentation that keeps claims honest.
Software InternFastAPI backend, BOM pipeline, and Random Forest attendance prediction for food operations.
Independent workTopoFlow GNN, Aegis, and Gridium across backend, ML, and distributed-system design.
