BE Computer Engineering @ PESMCOE, SPPU · CGPA 9.34/10
Building AI systems across LLM infrastructure, machine learning, and autonomous robotics.
Structural code retrieval for LLM coding agents.
SkeletonGraph is a structure-aware retrieval layer for coding agents. It builds a zero-LLM repository index from source code structure and exposes targeted retrieval through the Model Context Protocol (MCP), helping agents retrieve relevant symbols and dependencies instead of repeatedly consuming entire files.
| Index | tree-sitter symbol graph + PageRank centrality |
| Retrieval | BM25 + code embeddings + structural signals via RRF |
| Interface | MCP tools + portable sg CLI |
| Evaluation | SWE-bench Verified & Pro |
Highlights
- Zero-LLM indexing — repository structure is built without API calls
- Hybrid lexical, semantic, and structural retrieval
- Cross-file dependency graph built through tree-sitter parsing
- MCP integration for use with compatible coding agents and IDE workflows
- Benchmarked against BM25, repository-map, and graph-based retrieval baselines
Constraint-preserving hierarchical memory for long-horizon LLM conversations.
HierMem is an OS-inspired hierarchical memory architecture designed to preserve constraints and relevant context across long conversations. Inspired by virtual-memory paging, it combines a lightweight curator, a priority-aware constraint store, and a multi-level memory hierarchy to manage context under a bounded token budget.
| Curator | Reads a compact L0 index to identify relevant memory |
| Retrieval | Keyword · Semantic · Hierarchical · Hybrid |
| Assembly | Attention-aware bounded context construction |
| Memory | Constraint extraction and hierarchical L0 → L3 archival |
| Evaluation | Result |
|---|---|
| Memory compression | 4.7× vs. raw context |
| Constraint survival | 93.3% over long sessions |
| LLM-as-Judge | 8.4–8.7 / 10 |
| Evaluated against | RAG · RAG + Summary · MemGPT-style · Raw context |
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Step-level diagnosis and targeted fine-tuning for LLM reasoning failures.
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Deepfake detection designed for robustness across changing data distributions.
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Weather-aware ship-route optimisation using adaptive path planning.
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End-to-end machine-learning pipeline for real-time fraud scoring.
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Multimodal AI platform for food analysis and nutrition-oriented insights.
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Multi-agent governance simulation with adaptive decision policies.
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Software & AI Lead
Developing autonomous navigation and perception systems for PESMCOE's ABU Robocon 2026 robots.
- Path planning under partial observability and dynamic obstacles
- Computer vision and real-time perception pipelines
- ROS2-based software architecture
- Simulation and validation using NVIDIA Isaac Sim and Gazebo
- Integration with embedded controllers and physical robot hardware
ROS2 NVIDIA Isaac Sim Gazebo Python C++ Computer Vision
| Languages | Python · C++ · TypeScript · SQL · MATLAB |
| Machine Learning | PyTorch · Transformers · QLoRA/PEFT · XGBoost · SigLIP · sentence-transformers |
| LLM Systems | Ollama · llama.cpp · GGUF · LiteLLM · RAG · MCP · tree-sitter |
| Robotics | ROS2 · NVIDIA Isaac Sim · Gazebo · Path Planning · Reinforcement Learning |
| Backend & Data | FastAPI · Flask · PostgreSQL · MongoDB · Firebase |
| Cloud & Tools | Google Cloud · AWS · AMD Developer Cloud · Docker · Git · pytest |
"The gap between research and production is where I work."