Fatima Farooq
Graduate Research Assistant, LIDS Lab â University of Texas at Arlington.
Interests: Graph Representation Learning ¡ Heterogeneous Knowledge Graphs ¡ Scalable GNNs
About
I am a Graduate Research Assistant in the LIDS Lab at the University of Texas at Arlington, working on graph representation learning and the scalability of GNNs for large, heterogeneous knowledge graphs.
Before UTA, I spent two years as a Senior AI researcher/developer on a nationalâscale airâdefense project at NASTP, building anomalyâdetection systems and realâtime aircraftâtrajectory tracking pipelines. I also bring two years of universityâlevel teaching experience and 6+ years of freelance experience across ML, CV, and NLP. I care about impactful, rigorous, and reproducible research that moves ideas from paper to practice.
Experience
Research Experience
Graduate Research Assistant
- Lead research on scalability and optimization of Graph Neural Network (GNN) architectures applied to large-scale, heterogeneous knowledge graphs for link prediction and node classification.
- Investigate scalability challenges of heterogeneous GNNs (e.g., RGCNs) and propose optimization strategies.
- Develop and benchmark graph sampling and partitioning strategies to enhance training efficiency and scalability.
Graduate Student Researcher
- Implemented wav2vec2/Transformer architectures to detect Urdu speech dialects; built datasets for Urdu audio and transcription achieving <16% WER and <8% CER.
- Researched intelligent solutions for language and emotion understanding; focused on computational linguistics for Pakistan and developing Asia.
- Proposed a robust Transformer-based ASR pipeline with fine-tuned wav2vec2.0, achieving state-of-the-art results in internal evaluations.
Undergraduate Research Assistant
- Built an autonomous driving simulator system with camera calibration for road lane detection and tracking.
- Implemented and evaluated fineâtuned MobileNetâSSD for object detection.
Professional Experience
Senior AI Developer
- Led aircraft trajectory classification on airâdefense radar time series; realâtime inference.
- Built anomalyâdetection pipelines for radar streams; PyQGIS geospatial preprocessing; graph DB (Memgraph/Cypher) integrations.
- Developed Qtâbased crossâplatform UIs; implemented IPC and performance testing.
Lab Instructor
- Led labs in Data Analysis/Visualization and Database Systems; OOP and Digital Logic Design.
Software Engineer â Deep Learning
- Built a drivingâsimulator data pipeline for autonomousâvehicle research using OpenCV and DL.
- Implemented object and lane detection; organized outreach (APESâ18 VR seminar).
Education
MS, Data Science (CGPA 3.74/4.00, Silver Medalist)
- Thesis: Transformerâbased Automatic Speech Recognition for LowâResource Languages (Urdu).
- Focus: Deep Learning, Computer Vision, NLP.
BS, Software Engineering (CGPA 3.48/4.00)
- Thesis: Autonomous Driving Simulator using OpenCV and Deep Learning â 1st prize, Open House & Job Fair 2019.
- Focus: Deep Learning, Computer Vision, Autonomous Vehicles.
Key Skills
Python, R ¡ PyTorch, TensorFlow, Keras ¡ Hugging Face ¡ OpenCV ¡ PyQGIS ¡ Qt ¡ Git ¡ Apache Hadoop / Spark / Pig / Hive ¡ Memgraph (Cypher) ¡ VS Code, PyCharm
Machine Learning, AI, Computer Vision, NLP, Data Analysis, Graph Databases, Anomaly Detection, Geospatial Analytics
Selected Projects
- Decision Support Tools for Air Defense â ML framework to enhance decisionâmaking in airâdefense systems.
- ASR (Urdu) for LowâResource Language â Transformerâbased model achieving ~16% WER.
- Driving Simulator for Autonomous Vehicles â Data collection and modeling using Deep Learning and OpenCV.
- Big Data Insights â Text mining on Pokec; association rules, frequent patterns, LSH.
- Movie Recommendation System â Collaborative filtering on 25M ratings; contentâbased hybrid.
- DBLP Future Prediction â Venue prediction & visualization (tâSNE); ~93% accuracy with NN baselines.
- COVIDâ19 Analytics â Global dashboards and D3 visualizations for spread/impact monitoring.
- Heart Disease Prediction â Classical ML (NB, LR, DT, RF) with feature engineering.
- 200+ client projects (Fiverr/Upwork) â Deep Learning, Data Science, Visualization deliveries.
Achievements & Distinctions
- Grand Prix Award â The 6th Cloud Programming World Cup, Tokyo, Japan (2018). Link
- Silver Medal â FASTâNUCES MS Data Science (2022).
- 1st Prize â University of Engineering & Technology, Taxila Open House & Job Fair (2019).
Teaching & Mentoring
- Lab Instructor, FASTâNUCES: Data Analysis, Visualization, Database Systems; OOP; Digital Logic Design.
- Workshops/Seminars on Virtual Reality (APESâ18, UET Lahore; 100+ participants).