Anurag Mallik
Doctoral Researcher
Deep learning, and Image processing.
AI for Education
LLM-based formative feedback and exemplar selection for school mathematics, evaluated for feasibility before deployment.
LLM feedback
Math tutoring
Publications
2
Citations
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Researchers
1
Alumni
0
Open positions
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01 / WHO WORKS ON THIS
Researchers
Members of the lab whose work sits in this area.
02 / WHAT CAME OUT OF IT
2 publications
Everything the lab has published in this area, newest first.
2026
2026
JOURNAL
Semantic Clustering for Automated Few-Shot Exemplar Selection in LLM-Based Formative Feedback for Middle-School Mathematics: A Feasibility Study
Y. R. Pant, H. Y. Adarbah, A. Noore, D. Che, A. Ahmed, R. Ayala
AI, vol. 7, no. 8, Art. no. 280
2022
2022
JOURNAL
Key demand factors in professional business courses: A mixed-methods study
H. Y. Adarbah, M. M. H. Goode
Journal of Business, Communication & Technology, vol. 1, no. 2, pp. 44–53
ELSEWHERE IN THE LAB
Other research areas
AREA 01
Federated & Edge Intelligence
Training and unlearning across distributed clients, with Byzantine robustness and privacy budgets that survive contact with real edge hardware.
Federated learningEdge AIFederated unlearning
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AREA 02
Vehicular Networks & Security
V2X and MANET security: authentication protocols, digital-twin traffic environments, and defences against routing and forwarding attacks.
V2XRPLDigital twin
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AREA 03
AI for Healthcare
Clinical and assistive models built to be checked — ECG arrhythmia detection, breast-ultrasound diagnosis, and multi-sensor elderly-care monitoring.
ECGUltrasoundElderly care
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AREA 04
Wireless Sensor networks
A Wireless Sensor Network (WSN) consists of a spatially distributed collection of dedicated, autonomous devices equipped with sensors to cooperatively monitor physical or environmental conditions. These compact nodes track data—such as temperature, pressure, motion, or acoustics—and leverage radio signals to pass their measurements through the network infrastructure to a central location, known as a base station or sink, where the data is analyzed. Because these networks typically operate in remote or hazardous environments, they rely heavily on low-power, ad-hoc wireless routing protocols and localized battery power to maintain long-term, self-healing communication without human intervention.
AREA 05
Applied Machine Learning & Signal Processing
Signal-level models for sensing and interaction, from sEMG recognition to energy forecasting on constrained devices.
sEMGForecastingSignals
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AREA 06
Networking & Systems
Wireless sensor networks, protocol performance under noise, and the systems work that makes distributed intelligence deployable.
WSNProtocolsSimulation
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