Publications
Publications
17 published papers, including 4 first-author papers in IEEE high-impact journals (avg. IF > 7) and a poster at ICML 2026, plus 3 papers under review. 69 citations · h-index 5 on Google Scholar. Bold denotes my authorship position.
Published
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SAD-LoRA: Spectral Alignment for Low-Rank Knowledge DistillationO. Tariq, S. M. Raza, J. Son · ICML 2026 ColorAI Workshop, Seoul, South KoreaAligns the LoRA adapter subspace with the teacher's data-weighted spectral subspace during knowledge distillation, improving rank efficiency with zero inference overhead.Poster
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Uncertainty Aware and Decoder Aligned Learning for Video SummarizationO. Tariq, M. Raza, S. Jeongbae · 2026 Int. Joint Conf. on Neural Networks (IJCNN), Maastricht, NetherlandsUncertainty-aware framework with decoder-aligned learning for robust video summarization.Accepted
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NOS-Gate: Queue-Aware Streaming IDS for Consumer Gateways under Timing-Controlled EvasionM. Bilal, O. Tariq, H. Ahmed · IEEE Transactions on Consumer ElectronicsQueue-aware streaming intrusion detection system robust to timing-controlled evasion attacks on consumer gateways.
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ConvXformer: Differentially Private Hybrid ConvNeXt-Transformer for Inertial NavigationO. Tariq, M. Bilal, M. U. Hassan, D. Han, J. Crowcroft · IEEE Trans. Systems, Man, and Cybernetics: Systems, pp. 1–14Differentially private hybrid ConvNeXt-Transformer architecture providing formal ε-DP guarantees for distributed multimodal sensor fusion without sacrificing localization accuracy.Early Access
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ADP-QFed: Privacy-Preserving Quantized Federated Learning for Intelligent Edge Sensing in IoT SystemsO. Tariq, M. Bilal, D. Han · IEEE Internet of Things JournalCombines adaptive differential privacy with quantized federated learning for privacy-preserving collaborative edge intelligence in IoT systems.IF 8.9
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Quantum-Inspired Reinforcement Learning for Secure and Sustainable AIoT-Driven Supply Chain SystemsM. B. A. Dastagir, O. Tariq, S. Al-Kuwari, A. Farouk · IEEE Internet of Things JournalQuantum-inspired reinforcement learning for secure and sustainable AIoT-driven supply chain optimization.IF 8.9
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NanoMST: A Hardware-Aware Multiscale Transformer Network for TinyML-Based Real-Time Inertial Motion TrackingO. Tariq, D. Han · IEEE Internet of Things JournalUltra-lightweight multiscale transformer (298K params, 8-bit quantization) for TinyML inertial motion tracking — 4.7× compute reduction over LSTM baselines with real-time edge performance.IF 8.9
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DeepILS: Towards Accurate Domain Invariant AIoT-Enabled Inertial Localization SystemO. Tariq, B. Dastagir, M. Bilal, D. Han · IEEE Internet of Things JournalDomain-invariant AIoT-enabled inertial localization achieving sub-meter accuracy across diverse environments without environment-specific retraining.IF 8.9
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Meta-Swin: Lightweight Image Super-Resolution Swin Transformer for Metaverse ApplicationsO. Tariq, M. B. A. Dastagir, M. Bilal, D. Han · 2nd Int. Conf. on Intelligent Metaverse Technologies & Applications (iMETA), Dubai, UAELightweight Swin Transformer for image super-resolution designed for resource-constrained metaverse applications.
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EmoHEAL: A Fusion-Based Framework for Emotion Recognition Using Wearable SensorsO. Tariq, Y. Oh, D. Han · 2024 IEEE SENSORS, Kobe, JapanMultimodal fusion framework for emotion recognition using wearable sensor data with health monitoring applications.
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FedNav: A Federated Learning Approach for Secure AIoT-Enabled Inertial OdometryO. Tariq, M. Bilal, D. Han · IEEE AIoT Congress, Melbourne, AustraliaFederated learning framework for privacy-preserving collaborative inertial odometry training across distributed IoT devices.
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2D Particle Filter Accelerator for Mobile Robot Indoor Localization and Pose EstimationO. Tariq, D. Han · IEEE Access, vol. 12, pp. 18473–18487Real-time FPGA-based particle filter SLAM accelerator for mobile robot indoor localization — 60% latency reduction over software baseline at 30 FPS.IF 3.6
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TabCLR: Contrastive Learning Representation of Tabular Data Classification for Indoor-Outdoor DetectionM. B. A. Dastagir, O. Tariq, D. Han · IEEE AccessContrastive learning approach for tabular sensor data to improve indoor-outdoor detection classification accuracy.IF 3.6
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Compact Walsh–Hadamard Transform-Driven S-Box Design for ASIC ImplementationsO. Tariq, M. B. A. Dastagir, D. Han · Electronics, vol. 13, no. 16Area-efficient S-Box design for ASIC using Walsh-Hadamard transforms with strong cryptographic properties.IF 2.6
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DeepIOD: Context-Aware Indoor–Outdoor Detection Framework Using Smartphone SensorsO. Tariq, M. B. A. Dastagir, D. Han · Sensors, vol. 24, no. 16Context-aware indoor-outdoor detection using smartphone sensors for adaptive location-based application behavior.IF 3.5
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Smart Card-Based Privacy-Preserving NFT Authentication via Non-Interactive Zero-Knowledge ProofM. B. A. Dastagir, O. Tariq, D. Han · Proc. SmartWorld/UIC/ScalCom, Haikou, ChinaPrivacy-preserving NFT authentication using smart cards and zero-knowledge proofs.
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HILO: High-/Low-Level Co-Design for Visual-SLAM Feature Extraction on FPGAM. B. A. Dastagir, O. Tariq, D. Han · Proc. IPIN-WiP, Beijing, ChinaCo-design approach for accelerating visual SLAM feature extraction on FPGA, optimizing algorithm performance and hardware efficiency jointly.
Under Review
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TRIP: Towards Real-Time Deep Inertial Odometry for Pose-Invariant Pedestrian NavigationO. Tariq, Y. Oh, D. Han · IEEE Internet of Things JournalReal-time deep learning framework for inertial odometry that maintains accuracy regardless of device orientation for pedestrian navigation.Under Review
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Quantum Latent Generative Modeling for Intelligent Transportation SystemsM. B. A. Dastagir, O. Tariq, S. Al-Kuwari, A. Farouk · IEEE Trans. Intelligent Transportation SystemsQuantum-enhanced generative modeling for traffic pattern prediction and intelligent transportation management.Under Review
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Q-Quant: Few-Shot Quantum-Aware Ternary Quantization of YOLO for Low-Power Edge PerceptionM. B. A. Dastagir, O. Tariq, S. Al-Kuwari, A. Farouk · IEEE Trans. Intelligent Transportation SystemsFew-shot quantum-aware ternary quantization of YOLO for ultra-low-power edge perception in transportation.Under Review