Research

We are committed to developing novel and efficient deep/machine learning models that enhance performance, interpretability and trustworthiness, and improve impact and accessibility. Our solutions are designed to contribute to the academic advancements, strategic growth and platform investments of Canadian industries. AiX Lab is dedicated to cultivating highly skilled scholars with cutting-edge AI and equipping them to contribute to various industry sectors.

Projects

Automotive Research

Autonomous Navigation in self-driving cars

VLA and RL play fundamental roles in modern autonomous driving systems. This research introduces light-weight and efficient VLA and DRL models for end-to-end autonomous navigation in complex scenarios using camera as the only sensor.

Visual SLAM (simultaneous localization & mapping)

This research focuses on the development and deployment of high-definition simultaneous localization and adaptive mapping in real time for enhanced autonomous navigation in GPS-degraded and visually ambiguous environments. We incorporate semantic understanding into SLAM models for accurate mapping of highly dynamic environments.

Scene Perception models in self driving cars

This project aims to enhance real-time multi-object detection capabilities for self-driving cars, specifically operating effectively in various adverse weather conditions (e.g., rain, fog, and snow), which can significantly impair the accuracy and reliability of object detection systems in autonomous vehicles.

Driver Attention

This research focuses on driver's visual and manual distraction. Capturing driver’s video through camera, attention level is analyzed. Key objective is to design reliable and compact models to facilitate gaze estimation and action recognition in DMS.

Driver Impairment

This research focuses on addressing driver fatigue and the influence of alcohol and drugs on driving behavior. We aim to uncover key biomarkers and behavioral indicators for real-time impairment monitoring and contributing to a safer driving.

Driver Vigilance

This project is dedicated to understanding the dynamic assessment of driver's emotion, well-being and cognitive workload and enhance the overall driving experience. The objective is to shape the future of transportation with a focus on holistic driver well-being and mental resilience.

Adaptive Handover System

An adaptive handover system is a novel idea aimed at optimizing the control transfer between driver and automated system. This requires dynamically adjusting a TOR based on a real-time analysis of both driver's state and road/traffic conditions to perform the most appropriate action. This could mean altering a request's modality, timing, or location. Such an adaptive approach promises to enhance safety by mitigating the risks associated with a handover system.

Agriculture Research

Vision based growth monitoring

Leveraging AI and computer vision technologies, we are dedicated to transforming the assessment of the growth dynamics of crops and plants. Our vision-based approach enables precise and real-time monitoring of key indicators, contributing to enhanced crop management practices. From tracking plant development to identifying potential stress factors, our research aims to empower farmers with actionable insights for optimized yields and sustainable farming.

Clinical Research

AI-Based Musical Intervention to Improve Emotion

Mental disorders have a significant influence on the daily activities of Canadians. Musical intervention can provide a non-invasive treatment through changing emotional state and creating positive mood. The main objective of this project is a long-term solution for musical intervention through an optimized machine learning framework for a real-time emotion recognition and musical intervention system integrated in an empathetic speaker. During music play, the emotional influence will be detected from EEG and the music database will be customized.

Agentic AI for Executable Plans and Workflows

LLM-based systems that convert unstructured project descriptions into structured executable plans and workflows. Instead of simply generating text, these systems can reason through tasks, identify dependencies, allocate resources, and generate actionable execution plans.

AI-based Analysis of children’s speech-sound development

Speech and language sampling are key components of a Speech-Language Pathologist’s assessment of the presence/absence of a disorder. The main objective of this project is to develop a novel LLM-based framework to process and analyze children's speech data and recognize common developmental patterns and errors.

AI for monitoring developmental outcomes in children with hearing loss

Developing an AI-based digital application for early hearing detection and intervention that aims to address current PedAMP administration limitations by streamlining data collection, improving accuracy, and enhancing data accessibility for decision-making and program evaluation.

ALLEars - Forecasting Future Ear Growth for Earmold Design

Developing AI models that predict future ear growth in children to support personalized earmold manufacturing. This innovative research moves from reactive manufacturing to predictive manufacturing. Rather than waiting for a product to no longer fit, we use forecasting models to anticipate future needs and optimize production decisions.

Selected Publications

Book Chapter

S. Nikan, M. Ahmadi, “Recognition of human faces under different degradation conditions,” in Trends in Digital Signal Processing, 1st ed. Singapore, Singapore: Pan Stanford Publishing Pte. Ltd., vol. 1, ch. 11, pp. 333-356, 2015.

Journal

R. Yahyaabadi*, S. Nikan, 2026, “Driver gaze zone estimation using multi-head attention graph neural networks,” IEEE Transactions on Intelligent Transportation Systems, DOI: 10.1109/TITS.2026.3713815.
P. Mirmohammadsadeghi*, S. Nikan, A. Shami, 2026, “On the real-world deployment of visual SLAM: challenges, solutions, and cost analysis,” IEEE Transactions on Intelligent Transportation Systems, vol. 27, no. 4, pp. 3918-3943. DOI: 10.1109/TITS.2026.3659036
E. Hosseini*, S. Nikan, 2026, “Feature expansion with semi-dynamic feature sets (SDFS) and LLM-powered explainability: a novel approach to discovering missing features,” IEEE Transactions on Artificial Intelligence, DOI: 10.1109/TAI.2026.3681539.
W. Yao*, R. Yahyaabadi*, H. Hassani*, S. Nikan, 2026, “A survey on audio–visual large language models,” Knowledge-Based Systems, vol. 350, 116569.
M. Ruiz*, H. Hassani*, S. Nikan, 2026, “A survey on deep learning for face anti-spoofing in automotive applications,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 8, no. 4, pp. 497-517.
R. Yahyaabadi*, S. Nikan, 2025, “Efficient 2D/3D gaze estimation using TGGNet: A transformer graph approach,” IEEE Transactions on Cognitive and Developmental Systems, DOI: 10.1109/TCDS.2025.3600102, pp. 1-12.
J. A. Miller*, S. Nikan, M. H. Zaki, 2025, “Modelling driver state and takeover behaviour in conditional automation using physiological signals,” IEEE Open Journal on Intelligent Transportation Systems, vol. 7, pp. 477-491.
H. Hassani*, S. Nikan, A. Shami, 2025, “Attention prioritized experience replay with application to self-driving cars,” IEEE SMC Magazine, vol. 11, no. 4, pp. 24–32.
J. A. Miller*, M. H. Zaki, S. Nikan, 2025, “At the heart of intersections: analyzing their influence on driver heart behaviour,” Data Science for Transportation, vol. 7, no. 11, pp. 1-16.
H. Hassani*, S. Nikan, A. Shami, 2025, “Improved exploration–exploitation trade-off through adaptive prioritized experience replay,” Neurocomputing, vol. 614, 128836, pp. 1-11.
F. Dehrouyeh, I. Shaer, S. Nikan, F. B. Ajaei, A. Shami, 2025, “TinyML-enabled resource-efficient framework for real-time network security in electric vehicle charging networks,” IEEE Transactions on Network Science and Engineering, vol. 13, pp. 5092-5109.
H. Hassani, S. Nikan, and A. Shami, 2025, “TinyDrive: multiscale visual question answering with selective token routing for autonomous driving,” arXiv preprint arXiv:2505.15564.
K. Aghamohammadesmaeilketabforoosh, J. Parfitt, S. Nikan, J. M. Pearce, 2025, “From blender to farm: transforming controlled environment agriculture with synthetic data and SwinUNet for precision crop monitoring,” PLoS One, vol. 24, no. 4, pp. e0322189.
L., Chen, Lihong, H. Hassani, S. Nikan, 2025, “TS-VLM: text-guided softsort pooling for vision-language models in multi-view driving reasoning,” arXiv preprint arXiv:2505.12670.
R. Yahyaabadi, G. Farhani, T. Rahman, S. Nikan, A. Jirjees, F. Araji, 2025, “Deep Learning-Based Analysis of Power Consumption in Gasoline, Electric, and Hybrid Vehicles,” arXiv preprint arXiv:2508.08034.
M. Nabipour*, S. Nikan, 2024, “Action unit analysis for monitoring drivers’ emotional state,” IEEE Sensors Journal, vol. 24, no. 15, pp. 24758–24769.
J. A. Miller*, S. Nikan, M. H. Zaki, 2024, “Navigating the handover: reviewing takeover requests in level 3 autonomous vehicles,” IEEE Open Journal of Vehicular Technology, vol. 5, pp. 1073–1087.
H. Hassani*, S. Nikan, A. Shami, 2024, “Traffic navigation via reinforcement learning with episodic-guided prioritized experience replay,” Engineering Applications of Artificial Intelligence, vol. 137, 109147, pp. 1-11.
I. Shaer, S. Nikan, A. Shami, 2024, “Efficient transformer-based hyper-parameter optimization for resource-constrained IoT environments,” IEEE Internet of Things Magazine, vol. 7, no. 6, pp. 102–108.
K. Aghamohammadesmaeilketabforoosh, S. Nikan, G. Antonini, J. M. Pearce, 2024, “Optimizing strawberry disease and quality detection with vision transformers and attention-based convolutional neural networks,” Foods, vol. 13, no. 12, pp. 1869.
R. Yahyaabadi*, S. Nikan, 2023, “An explainable attention zone estimation for Level 3 autonomous driving,” IEEE Access, vol. 11, pp. 93098–93110.
Y. Ma, V. Sanchez, S. Nikan, D. Upadhyay, B. Atote, T. Guha, 2022, “Real-time driver monitoring systems through modality and view analysis,” arXiv preprint arXiv:2210.09441.
S. Nikan, K. Van Osch, M. Bartling, D. G. Allen, S. A. Rohani, B. Connors, S. K. Agrawal, H. M. Ladak, 2020, “PWD-3DNet: A deep learning-based fully automated segmentation of multiple structures on temporal bone CT scans,” IEEE Transactions on Image Processing, vol. 30, pp. 739–753.
S. Nikan, M. Ahmadi, 2018, “A modified technique for face recognition under degraded conditions,” Journal of Visual Communication and Image Representation, vol. 55, pp. 742–755.
F. Gwadry-Sridhar, S. Nikan, A. Hamou, S. J. Seung, T. Petrella, A. M. Joshua, S. Ernst, N. Mittmann, 2017, “Resource utilization and costs of managing patients with advanced melanoma: a Canadian population-based study,” Current Oncology, vol. 24, no. 3, pp. 168–175.
S. Nikan, M. Ahmadi, 2015, “Recognition of human faces in the presence of incomplete information,” International Journal on Advances in Software, vol. 8, no. 3–4, pp. 450–456.
S. Nikan, M. Ahmadi, 2015, “Performance evaluation of different feature extractors and classifiers for recognition of human faces with low-resolution images,” International Journal of Intelligent Systems and Applications in Engineering, vol. 3, no. 2, pp. 72–77.
S. Nikan, M. Ahmadi, 2015, “Local gradient-based illumination invariant face recognition using LPQ and multi-resolution LBP fusion,” IET Image Processing, vol. 9, no. 1, pp. 12–21.

Conference

R. Yahyaabadi* and S. Nikan, “Saliency-guided knowledge distillation for driver-aware salient object detection,” Accepted in COMPSAC 2026.
E. Chao*, A. Fenster, S. Nikan, et al., “SR-MedT++: boundary uncertainty in kidney ablation for clinical reliability,” Accepted in IEEE EMBC 2026.
R. Yahyaabadi*, S. Nikan, “ManeuverVLM: a novel multimodal fusion of scene images and temporal signals for maneuver prediction,” Accepted in IJCAI 2025 Workshop MKLM, 2025.
T. Xu*, S. Pallithotungal*, M. Harrington*, M. Zahra*, R. Moghrabi*, N. Broeders*, S. Nikan, “Drowsy or Not Drowsy,” Accepted in IEEE International Symposium on Signals, Circuits and Systems (ISSCS'25), 2025.
M. Ruiz*, S. Nikan, “Benchmarking lightweight deep learning models for in-vehicle face anti-spoofing,” Accepted in IEEE International Symposium on Signals, Circuits and Systems (ISSCS'25), 2025.
G. Fu*, W. Fang*, J. Liu*, S. Nikan, “A lightweight mobilenetv3-fpn framework for driver drowsiness detection,” Accepted in IEEE International Symposium on Signals, Circuits and Systems (ISSCS'25), 2025.
R. Yahyaabadi*, S. Nikan, “Vision-language model for driving maneuver prediction: a new algorithm combining scene images and dynamic signals,” Accepted in CREATE TRAVERSAL & ORF-SITE-CAV Workshop & Hackathon 2025, Ottawa, Canada, June 2025.
F. Dehrouyeh*, I. Shaer*, S. Nikan, et al., “Pruning-Based tinyml optimization of machine learning models for anomaly detection in electric vehicle charging infrastructure,” Accepted in IEEE ICC'25 - CSM Symposium, Montreal, Canada, 2025.
R. Yahyaabadi*, S. Nikan, "Skeleton-based driver action recognition using ResGGCNN," in IEEE International Symposium on Signals, Circuits and Systems (ISSCS'23), Iasi, Romania, pp. 1-4, 2023.
M. Nabipour*, S. Nikan, "A Deep Learning-based remote plethysmography with the application in monitoring drivers’ wellness," in IEEE International Symposium on Signals, Circuits and Systems (ISSCS'23), Iasi, Romania, pp. 1-4, 2023.
E. Kazmierowski, W. Yao*, S. Nikan, et al., “Automatic speech recognition for preschooler’s speech: a benchmarking evaluation of whisper 2.0 zero-shot performance,” in The 2026 SAC Conference, Halifax, NS, Accepted, 2026.
J. Tindan, S. Nikan, et al., “Developing a clinically informed evaluation framework for automatic speech recognition models,” in The 2026 SAC Conference, Halifax, NS, Accepted, 2026.
M. Samadi, H. Kharrati, M.A. Badamchizadeh, H. Hassani*, S. Nikan, “Diagnosing faults in smart grids using liquid time-constant network,” in ICEET, Turkey, 2023.
M. Mohseni*, S. Nikan, A. Shami, “AI-based traffic forecasting in 5G network,” in CCECE2022, September 2022.
S. Nikan, D. Upadhyay, "Appearance-based gaze estimation for driver monitoring," in NeurIPS, pp. 127-139, 2023.
Y. Ma*, V. Sanchez, S. Nikan, et al., "Robust multiview multimodal driver monitoring system using masked multi-head self-attention," in CVPR, pp. 2616-2624, 2023.
S. Nikan, S. K. Agrawal, H. M. Ladak, 2020, “Fully automated segmentation of the temporal bone from micro-CT using deep learning,” In Medical imaging: biomedical applications in molecular, structural, and functional imaging (SPIE 2020), Houston, TX, USA, vol. 11317, pp. 461-466.
S. Nikan, S. Agrawal, H. Ladak, 2019, “Automated multi-structure deep segmentation of micro-CT images of temporal bone,” in London Health Research Day (LHRD’19), Poster Presentation.
S. Nikan, F. Gwadry-Sridhar, M. Bauer, 2017, “Pattern recognition application in ECG arrhythmia classification,” in HEALTHINF 2017, Porto, Portugal, pp. 48–56.
S. Nikan, F. Gwadry-Sridhar, M. Bauer, 2016, “Machine learning application to predict the risk of coronary artery atherosclerosis,” in CSCI’16, Las Vegas, USA, pp. 34-39.
S. Nikan, M. Ahmadi, 2015, “Partial face recognition based on template matching,” in SITIS 2015, Bangkok, Thailand, pp. 160–163.
S. Nikan, M. Ahmadi, 2015, “Partial GMP-CS-LBP face recognition using image subblocks,” in The Tenth International Conference on Systems (ICONS’15), Barcelona, Spain, pp. 36–39. (Best Paper Award)
S. Nikan, M. Ahmadi, 2015, “Classification fusion of global & local G-CS-LBP features for accurate face recognition,” in International Conference on Testing & Measurement: Techniques and Applications (TMTA’15), Phuket, Thailand, pp. 303-307.
S. Nikan, M. Ahmadi, 2014, “Effectiveness of various classification techniques on human face recognition,” in International Conference on High Performance Computing & Simulation (HPCS’14), Bologna, Italy, July 2014, pp. 651–655.
S. Nikan, M. Ahmadi, 2014, “Study of the effectiveness of various feature extractors for human face recognition for low-resolution images,” in International Conference on Artificial Intelligence and Software Engineering (AISE’14), Phuket, Thailand, Jan. 2014, pp. 1–6.
S. Nikan, M. Ahmadi, 2012, “Human face recognition under occlusion using LBP and entropy weighted voting,” in International Conference on Pattern Recognition (ICPR’12), Tsukuba, Japan, pp. 1699-1702.

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