Rubab Shah

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Rubab Shah

Graduate of 2026
BS Computer Science

Aspiration Statement

I’m particularly excited about interdisciplinary fields such as Computational Biology. I aim to pursue my higher studies in a similar field and work as a Researcher.

Core Skills

  • C++
  • Data Science
  • Machine Learning
  • Python

Core Competencies

  • Adaptability
  • Takes Initiative

Preferred Career Paths

First priority: Research

Second priority: Artificial Intelligence

Third priority: Data Science

Academic Awards / Achievements

  • Dean's List 2024, 2025

Experience

Leadership / Meta-curricular

  • Vice President, Computer Science and Engineering Club
  • Design, Computer Science and Engineering Club

Internship / Volunteer Work

  • Teaching Assistant - Data Science, Habib University (September – December 2025)
  • Research Assistant, Lums (May – November 2025)
  • Teaching Assistant - Database Systems, Habib University (August – December 2024)
  • Undergraduate Researcher, Habib University (June – August 2024)

Publications / Creative Projects

  • Publication – "ML-Driven Distribution Network Aggregation Considering Load and Inverter-Based Resources", 2024 26th International Multi-Topic Conference (INMIC), Karachi, Pakistan, 2024, pp. 1-6, doi: 10.1109/INMIC64792.2024.11004393.

Final Year Project

Project Title

Alkhidmat Public Chat Portal

Description

The AI-powered multilingual chat portal for Alkhidmat Foundation addresses manual query-handling delays by providing a centralized, inclusive solution. Using a Self-RAG (Retrieval-Augmented Generation) and Agentic AI pipeline, it automates responses for donor, healthcare, and general domains in English, Urdu, and Roman Urdu. The system utilizes multilingual-e5-base embeddings and pgvector for semantic search, with OpenAI and Alif for generation. To ensure accuracy, it employs a domain classification and confidence scoring engine that fuses retrieval quality with token probability. Key benefits include 24/7 accessibility for underserved communities, reduced staff workload, and automatic human-agent escalation for complex queries. Moreover, there is a dedicated admin dashboard to view LLM analytics and update the RAG Knowledge Base.

Project Pictures