AI systems that get evaluated, not just demoed.

I build applied AI: retrieval systems, LLM pipelines, and agents — with the eval harnesses and failure-mode work that make them trustworthy in production, not just in a notebook.

AI Engineer
Selected work
01

GraphRAG Hybrid RAG

A retrieval system that knows which kind of question it's answering.

Routes queries between vector search and graph traversal depending on question type, backed by a full evaluation harness that compares retrieval strategies head to head rather than assuming one wins.

Python RAG Graph traversal Eval harness
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02

Portfolio Health Reporting Agent

An agent that reads messy spreadsheets and tells you what actually needs attention.

A data-resilient AI agent for project health reporting: multi-schema Excel parsing, a five-signal weighted RAG framework with manual risk overrides, and automated monthly executive summaries.

Python Agents RAG Excel parsing
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03

GTM-Flow-AI

Turning a company website into a structured fintech-alignment score, automatically.

An AI data pipeline that scrapes corporate websites, extracts fintech-alignment metrics using Llama 3.1 via Groq, and structures the results into a local SQLite analytics database for downstream use.

Python LLM pipelines Llama 3.1 SQLite
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04

Physio-Topological Dynamics

Fusing brain, heart, and skin signals into one shared shape.

An end-to-end framework for combining disparate bio-signals into unified topological manifolds, with automated task-detection built on top of the fused signal space.

Python Signal processing Sensor fusion Topology
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05

Uncertainty-Aware Task-Conditioned Point Cloud Upsampling

Ongoing research — unpublished.

A pipeline that lifts 2D affordance and uncertainty heatmaps into 3D, then densifies sparse point clouds conditioned on both the task ("pick up" vs. "pour from") and per-point uncertainty, with a boundary-rejection module to stop points leaking across object edges. Early full-training results show Uncertainty Agreement improving from 0.23 to 0.54 and Chamfer Distance dropping from 0.0138 to 0.0107 over the initial baseline — promising, but still early and unpublished.

Point clouds Uncertainty estimation 3D vision In progress
06

Topological Analysis of EEG Phase Synchronization Under Cognitive Load

Ongoing research — unpublished.

Applied persistent homology to EEG phase-synchronization networks across 48 subjects under rest vs. cognitive-load conditions. Cognitive load drove roughly 7.4× more reorganization in global network structure (H0) than in cyclic subnetwork structure (H1), an effect present in all 48 subjects (Wilcoxon p = 7.1×10⁻¹⁵, Cohen's d = 3.48) and robust to a 10,000-iteration permutation test. A two-feature classifier built purely from these topological distances separated rest from task with 95.8% leave-one-out accuracy — evidence that topology captures reorganization patterns that connectivity-density metrics alone can miss.

EEG Topological data analysis Signal processing In progress
About

Fourth-year AI student at Manipal Institute of Technology, with a foundation in computer vision and real-time perception pipelines — wildfire detection with YOLO and DFAN, gesture-driven interfaces with MediaPipe, and surveillance-to-report pipelines with BLIP. I've since moved into LLM and agent work, building systems that get evaluated rather than just demoed. Previously an AI intern at Flutura and a Python intern at Main Flow.