CS @ ASU · Graduating May 2026
I'm Advait Darbare — a senior at Arizona State University majoring in Computer Science. I'm passionate about building intelligent, autonomous systems that merge AI reasoning with real-world impact.
Exploring the intersection of agentic AI, multi-agent orchestration, and scalable backend systems. I love designing systems that can reason, adapt, and automate complex workflows.
From developing ML components at Prophecy to researching data observability at Acceldata, I've grown from technical marketing into full-stack AI system design — combining creativity, engineering, and AI orchestration.
Performed in-depth analysis of research papers on data quality and machine learning model performance, deriving critical insights to support strategic planning.
Created internal documentation (reports, presentations, flowcharts, wireframes) supporting marketing efforts for Acceldata's data observability platform.
Compiled and analyzed data quality metrics to demonstrate the value proposition of data observability in enhancing ML model efficacy.
Engineered a PySpark ML pipeline for linear regression in an end-to-end ETL workflow, integrating vector assembly, train-test splitting, model fitting, and performance evaluation.
Developed a Word2Vec component using PySpark for the Prophecy Platform, enhancing NLP capabilities with tokenization and embeddings.
Built a data ingestion component for sourcing datasets from Hugging Face, streamlining ETL processes for ML pipelines.
Designed and executed 22 TPC-H benchmark ETL pipelines, showcasing efficient data extraction, transformation, and loading at scale.
AI-powered stock market assistant with intelligent query routing, real-time market data, and investment advisory. Multi-agent system using Claude AI, LangGraph, and FastAPI.
Automated tool that collects daily check-ins, summarizes them via GPT-4, supports semantic search, and posts updates to Slack for efficient team coordination.
Exploring the key design patterns behind agentic AI systems
Read on Medium →A comprehensive comparison of AI tool integration approaches, exploring the differences between OpenAI's function calling, LangChain tools, and the Model Context Protocol.
Read on Medium →From RAG to Agentic RAG — where retrieval meets reasoning.
Read on Medium →