Omer Tariq

Research Scientist — Spatial Intelligence & Embodied AI
Neubility, Seoul, South Korea

I am a Research Scientist at Neubility, working on spatial intelligence — 3D/4D geometry, multimodal grounding, and vision-language alignment — for real-world embodied and on-device systems. I hold a Ph.D. in Computer Science from KAIST (2025), advised by Prof. Dongsoo Han.

My research focuses on transformer-based and BEV architectures for multi-camera 3D object detection, VLM/VLA model deployment on NVIDIA Jetson Orin via TensorRT INT8/FP16 quantization, and sim-to-real transfer for autonomous robot perception. At Neubility I deliver production perception pipelines running sub-30 ms end-to-end on a single SoC — achieving 8× inference speedup at <1% mAP degradation.

I am interested in research roles at the intersection of spatial intelligence, embodied AI, and efficient edge deployment.

17+
Publications
69
Citations
5
h-index
1
ICML poster
KAISTPh.D. CS, KAIST (2025) · UETB.S. EE, UET Taxila (2014)

Recent News

Jan 2026 Paper accepted to IJCNN 2026 (Maastricht, Netherlands): "Uncertainty Aware and Decoder Aligned Learning for Video Summarization"
Aug 2025 Joined Neubility (Seoul) as a Research Scientist, working on multi-camera 3D perception and Embodied AI for autonomous delivery robots
Jul 2025 Thrilled to announce my latest publication in the IEEE Internet of Things Journal (Impact Factor: 8.9)! 🚀 🔬 NanoMST: A Hardware-Aware Multiscale Transformer Network for TinyML-Based Real-Time Inertial Motion Tracking
Jun 2025 Successfully defended my Ph.D. dissertation at KAIST — Thesis: Robust Domain-Invariant Inertial Localization in Real-Time Federated Edge Computing
Mar 2025 Started as a Machine Learning Engineer at Monitra, UK
Mar 2025 Started as a Teaching Assistant for Special Topics in Computer Science (CS492) at KAIST

Selected Publications

Full list on Google Scholar  ·  All publications

SAD-LoRA: Spectral Alignment for Low-Rank Knowledge Distillation
Omer Tariq, Syed Muhammad Raza, Jeongbae Son
ICML 2026 ColorAI Workshop (Poster), 2026
SAD-LoRA aligns the LoRA adapter subspace with the teacher's data-weighted spectral subspace during knowledge distillation, improving rank efficiency with zero inference overhead.
ConvXformer: Differentially Private Hybrid ConvNeXt-Transformer for Inertial Navigation
Omer Tariq, M. Bilal, M. U. Hassan, Dongsoo Han, Jon Crowcroft
IEEE Trans. Systems, Man, and Cybernetics: Systems (Early Access), 2026
A hybrid ConvNeXt-Transformer architecture with formal epsilon-DP privacy guarantees for distributed multimodal sensor fusion, maintaining localization accuracy while protecting user data.
NanoMST: A Hardware-Aware Multiscale Transformer Network for TinyML-Based Real-Time Inertial Motion Tracking
Omer Tariq, Dongsoo Han
IEEE Internet of Things Journal, 2025
NanoMST is an efficient multi-scale transformer for inertial motion tracking that achieves high-precision trajectory estimation with minimal computational cost and real-time performance on edge devices, outperforming larger models across benchmarks while requiring only 298K parameters and supporting 8-bit quantization.
DeepILS: Towards Accurate Domain Invariant AIoT-enabled Inertial Localization System
IEEE Internet of Things Journal, 2025
This paper presents DeepILS, a domain-invariant AIoT-enabled inertial localization system that achieves high accuracy across diverse environments without requiring environment-specific retraining.

View all publications →

Honors & Awards

  • KAIST MS/Ph.D. Full Scholarship (20,000 USD/year) 2021
  • Distinguished Service Award, SUPARCO 2018
  • Distinguished Performance Award — PakTes-1A satellite launch 2018

Academic Service

Reviewer

  • IEEE Internet of Things Journal
  • IEEE Internet of Things Letters
  • Engineering Applications of AI
  • Nuclear Science & Techniques