CV

Mitacs Accelerate Intern

Upbeing.ai

Senior ML Engineer

Arbisoft

NLP Engineer

ISSM.ai

NLP Engineer

Eighty.ai

Research Associate

Robotics and Intelligent Computing (RICE) Lab, LUMS

NLP Engineer

Ai XPRT (AuditXPRT)

Research Assistant, Hate Speech and Generative Models

Knowledge and Data Engineering (KADE) Lab, LUMS

PhD

Dalhousie University

Computer Science

• Model Editing
• Machine Unlearning
• Interpretability
• Representation Learning

MS

Lahore University of Management Sciences (LUMS)

Computer Science

• Thesis: Hate-Speech and Offensive Language Detection in Roman Urdu

Doctoral Research Scholarship

Dalhousie University

Undergraduate Merit Scholarship

COMSATS University

Resolving Lexical Bias in Model Editing

International Conference on Machine Learning (ICML)

Current adapter methods are critically vulnerable to strong lexical biases, leading to issues such as applying edits to irrelevant prompts with overlapping words. This paper presents a principled approach to learning a disentangled representation space that facilitates precise localization of edits

Hate-Speech and Offensive Language Detection in Roman Urdu

Empirical Methods in Natural Language Processing (EMNLP)

Despite its prevalence, Roman Urdu (RU) lacks language resources, annotated datasets, and language models for this task. In this study, we: (1) Present a lexicon of hateful words in RU, (2) Develop an annotated dataset called RUHSOLD consisting of 10, 012 tweets in RU with both coarse-grained and fine-grained labels of hate-speech and offensive language, (3) Explore the feasibility of transfer learning of five existing embedding models to RU, (4) Propose a novel deep learning architecture called CNN-gram for hatespeech and offensive language detection and compare its performance with seven current baseline approaches on RUHSOLD dataset