Marwa's Portfolio

about.me — Marwa

Hi I'm Marwa

// CS Student · AI Systems & Strategy · Dubai, UAE

Just a CS student building things, breaking things, and figuring out how they work along the way. If you're working on something interesting, feel free to reach out — I'm always up for collaborating on cool things.

marwakhot007@gmail.com

+971 58 588 5323

LinkedIn GitHub

Current Role

AI Dev Intern @ StudentVerse

Education

CS (AI & Big Data) @ UOW Dubai · WAM 82

Location

Dubai, UAE

Focus

AI · ML · Big Data

projects — click to explore

01 — AI SYSTEMS

BrainBrew AI

github.com/Marwakhot/BrainBrew

PROBLEM

Passive PDF learning not engaging students in critical thinking.

SOLUTION

AI-powered platform using NLP to generate adaptive questions and difficulty levels that promote deeper understanding.

ReactTypeScriptAI/MLNLPSupabasePostgreSQLThree.js

// ARCHITECTURE

User uploads PDF

↓

React + Three.js Frontend

↓

NLP Engine

• Text extraction & chunking

• Adaptive question generation

• Difficulty level scoring

↓

Supabase + PostgreSQL

↓

Interactive Q&A Session

02 — AI SYSTEMS

ORION Talent AI

github.com/Marwakhot/Orion

PROBLEM

Manual hiring processes are slow, biased, and lack structured onboarding for new employees.

SOLUTION

End-to-end HR platform with AI CV matching, behavioral assessments, conversational chatbot using LangChain & RAG, and automated onboarding workflows.

ReactTypeScriptNode.jsLangChainRAGAI/MLPostgreSQL

// ARCHITECTURE

Recruiter

Candidate

↓

React + TypeScript Frontend

↓

Node.js REST API

↓

AI Layer

• CV parsing → skill matching

• LangChain + RAG chatbot

• Behavioral voice/text assessment

• Composite scoring engine

↓

PostgreSQL + pgvector

↓

Shortlist → Onboarding → Day One Plan

02 — MACHINE LEARNING / NLP

AutoBug — Bug Triage System

github.com/Marwakhot/bug-triage-nlp-api

PROBLEM

Slow bug assignment causing delays in development cycles and inefficient resource allocation.

SOLUTION

NLP + ML system to automatically predict the responsible team and priority for each bug report, with bulk predictions via a Streamlit dashboard.

PythonNLP (TF-IDF)LightGBMFlask APIStreamlitDockerInternal ToolingWorkflow Automation

// ARCHITECTURE

Bug Report Input

↓

NLP Pipeline

• TF-IDF vectorization

• Text preprocessing

↓

LightGBM Classifier

• Team prediction

• Priority prediction

↓

Flask API

Streamlit Dashboard

↓

Docker Container

01 — MACHINE LEARNING / NLP

NeuroNav — Robot Navigation AI

github.com/Marwakhot/Gazebo_robot_navigation

PROBLEM

Autonomous navigation challenges in complex environments with dynamic obstacles.

SOLUTION

Implemented Fuzzy Logic, Behavior Trees, and Q-Learning for obstacle avoidance in a 10m x 10m simulated arena.

PythonFuzzy LogicBehavior TreesReinforcement Learning

// ARCHITECTURE

Gazebo Simulation Environment

↓

Sensor Input

• Obstacle detection

• Position mapping

↓

Decision Engine

• Fuzzy Logic controller

• Behavior Trees

• Q-Learning (RL)

↓

Robot Actions → 10m x 10m Arena

04 — MACHINE LEARNING / NLP

LoanGuard — AI Risk Management Audit

github.com/Marwakhot/-AI-Risk-Management-Audit

PROBLEM

AI loan-approval systems contain hidden biases and security vulnerabilities — violating fairness standards and exposing institutions to legal and ethical risk.

SOLUTION

A comprehensive fairness and security audit using the NIST AI RMF. Quantified gender & age bias via the 80% Rule, uncovered a 20% manipulation success rate on the age field, and produced a prioritized risk roadmap for stakeholder review.

PythonScikit-learnFairlearnPandasMatplotlibNIST AI RMFJupyter

// ARCHITECTURE

Loan Application Data

↓

NIST AI RMF Framework

• GOVERN: scope & objectives

• MAP: risk identification

• MEASURE: quantify bias

• MANAGE: recommendations

↓

Fairlearn + Scikit-learn

↓

Audit Report + Visualizations

05 — MACHINE LEARNING / NLP

QuantRisk — VaR Validation & Capital Allocation

github.com/Marwakhot/QuantRisk-VaR-Validation

PROBLEM

Investment portfolios lack statistically validated risk quantification — making it impossible to set accurate capital reserves or meet Basel III regulatory requirements.

SOLUTION

Built a three-model VaR framework (Historical Simulation, Variance-Covariance, Monte Carlo) with Kupiec's POF backtesting. All models achieved 100% validation, recommending a $21,578 daily capital reserve at 99% confidence.

PythonPandasNumPySciPyMonte CarloyfinanceBasel IIIJupyter

// ARCHITECTURE

yfinance → 3yr Historical Prices

• AAPL, MSFT, JPM, GLD, SPY

↓

01 — Data Setup

• Normality tests (Jarque-Bera, Shapiro-Wilk)

• Correlation mapping

↓

02 — VaR Calculation (3 Models)

• Historical Simulation

• Variance-Covariance (Parametric)

• Monte Carlo (10,000 scenarios)

↓

03 — Backtesting

• 70/30 train-test split

• Kupiec's POF statistical test

↓

Capital Reserve = $21,578 @ 99% confidence

hackathon — click to explore

Space42 Bounty Challenge — 3rd Place

Google Developers Group · Univ. of Birmingham Dubai · Nov 2024

ORION — AI-Powered Talent & Onboarding Partner

Won 3rd place building ORION in a single weekend — an end-to-end HR platform with AI-powered CV matching, behavioral assessments, and automated onboarding workflows.

CV upload with instant eligibility matching against job skills
Behavioral AI assessment through voice or text
Combined scoring system for smart candidate shortlisting
Conversational AI chatbot powered by LangChain and RAG
Employee zone unlock with structured Day One plan

ReactTypeScriptNode.jsLangChainRAGAI/ML

experience & achievements — click to explore

// CURRENT

AI Dev Intern

StudentVerse

Research Trainee

UOWD Tech Club

CS Student

UOW Dubai

// EXPERIENCE

Data Science Intern

Alamar

AI Rep · Gitex 25

Dreamloop.ai

Head Girl

NIMS Dubai

Director General

NIMSMUN

MUN Chairperson

Multiple Conferences

High School Grad

NIMS Dubai

AI Developer Intern

StudentVerse · May 2026 – Present

Building the backend of SV Orbit — a multi-mode AI chatbot platform (chat, planner) now live on the App Store, integrating LLM APIs and hardcoded logic across shipped and in-development product modes.

Built the backend of Semantic Serach in SV Orbit, now live on the App Store, powering 2 shipped modes with a 3rd currently in development
Integrated LLM APIs and hardcoded logic to deliver each mode end-to-end from backend to production

Research Trainee

UOWD Tech Club · May 2026 – Present

Researching Spiking Neural Networks (SNNs) and neuromorphic AI; reviewing academic literature and evaluating how these next-generation architectures can be integrated into existing AI systems.

Researching Spiking Neural Networks (SNNs), reviewing academic literature on neuromorphic AI architectures and biologically-inspired computing approaches
Evaluating neuromorphic AI methods and assessing their potential integration into conventional AI pipelines
Contributing findings and structured literature reviews within the Tech Club's Research Department

Computer Science Student

UOW Dubai · Major: AI & Big Data · WAM 82

Pursuing a Computer Science degree with a specialisation in Artificial Intelligence and Big Data — building skills across programming, data engineering, ML systems, and applied AI.

Maintaining a WAM of 82 across rigorous AI, Big Data, and systems coursework
Building applied skills through coursework and independent projects in ML, NLP, and data engineering
Continuously learning new tools, frameworks, and research directions in AI

Data Science Intern

Alamar Foods · Dunkin' · Dubai · Oct 2025 – Apr 2026

Engineered data pipelines on AWS Redshift and built automated dashboards for Dunkin' operations — turning 2M+ rows of raw operational data into structured, decision-ready insights for cross-functional leadership teams.

Engineered data pipelines on AWS Redshift processing 2M+ rows of operational data, writing complex SQL queries to produce clean, analysis-ready datasets for stakeholder reporting
Designed KPI frameworks and built automated dashboards tracking sales pipeline performance, customer acquisition, and revenue trends
Surfaced metrics to cross-functional teams at daily, weekly, and monthly cadences — translating raw data into decision-support presentations for leadership

AI Product & Solutions Ambassador

Dreamloop.ai · GITEX Global 2025 / Expand North Star

Represented Dreamloop.ai at GITEX 2025, engaging 60+ enterprise clients and translating complex AI capabilities into clear business value for non-technical decision-makers.

Engaged 60+ enterprise clients including CEOs and founders, communicating AI-driven product value with a focus on sales-oriented storytelling for non-technical audiences
Delivered live demos of AI capabilities — text-to-game and text-to-app generation — to a broad range of decision-makers
Represented the product vision and brand at one of the world's largest tech events

Head Girl

NIMS Dubai

Led the student council and served as the main representative between students and school administration.

Organized 15–20 in-school and inter-school events end-to-end, coordinating with external sponsors, vendors, and community partners to secure funding
Represented a 5,000+ student body, communicating student needs to administration and external parties
Coordinated a 35-member council to deliver events drawing 200–300 attendees each

Director General — Model United Nations

NIMSMUN · NIMS Dubai

Organized and led a full 3-day MUN conference with 150+ delegates from across the UAE.

Led external outreach to 20+ schools across the region, coordinating with faculty advisors and student delegations to recruit 150+ participants
Secured sponsorships and served as the central point of contact across a 50-member organizing team, guest speakers, and participating schools
Delivered the full conference end-to-end across committees, sessions, and logistics

MUN Chairperson

Multiple MUN Conferences

Chaired committees at multiple Model United Nations conferences, guiding delegates through structured diplomatic discussions.

Moderated debates and maintained professional procedure
Ensured fair participation across all delegates
Helped delegates develop diplomacy, research, and public speaking skills

High School Graduate — Science Stream

NIMS Dubai

Completed high school with a focus on science and analytical subjects — building the foundation that led to pursuing Computer Science.

Built a strong foundation in logical thinking and problem solving
Developed early interest in technology and data
Active in extracurriculars and academic competitions