Exploring the intersection of AI, navigation, and hardware optimization
Developing deep learning methods for robust inertial navigation across diverse environments using domain adaptation techniques.
Creating efficient deep learning models for resource-constrained devices through quantization, architecture search, and knowledge distillation.
Exploring differential privacy and federated learning approaches to protect user data while enabling collaborative model training.
Omer Tariq, B. Dastagir, M. Bilal, D. Han
A domain-invariant inertial localization system that maintains accuracy across diverse environments without environment-specific training.
IEEE Internet of Things Journal 2025
Omer Tariq, Dastagir M.B.A., M. Bilal, D. Han
A lightweight Swin Transformer for image super-resolution designed specifically for metaverse applications with constrained computing resources.
iMETA Conference 2024
Monitra, UK (Mar 2025 - Present)
Developing ML pipelines for fault detection in PRPD images with Azure integration
ISI Lab @ KAIST (Mar 2025 - Present)
Assisting graduate students in Special Topics in Computer Science (CS492)
DAIM Lab @ KAIST (Nov 2023 - Mar 2024)
Developed industrial autonomous mobile robot with SLAM-based navigation
KAIST, South Korea (Nov 2021 - Present)
Researching inertial localization, sensor fusion, and privacy-preserving navigation
NECOP, Pakistan (Apr 2019 - Sep 2022)
Led SoC/RTL verification and digital IC design optimization
SUPARCO, Pakistan (Oct 2014 - Apr 2019)
Designed satellite payloads and high-speed PCBs for space applications
LUMS, Pakistan
Stanford University (Coursera)
Udemy
EPFL (Coursera)
University of Washington (Coursera)
Georgia Tech (Coursera)