Praveen Kumar Chandaliya
Sardar Vallabhbhai National Institute of Technology Surat, Norwegian University of Science and Technology, Malaviya National Institute of Technology
About
Praveen Kumar Chandaliya is an Assistant Professor in the Department of Artificial Intelligence at SVNIT, Surat. Previously, he was a post-doctoral research associate on the iMARS EU-funded project at NTNU, Norway. He earned his Ph.D. in Computer Science and Engineering from MNIT Jaipur under the supervision of Prof. Neeta Nain, with a focus on Child Face Aging and Rejuvenation with Generative Adversarial Networks. He also served as a visiting scholar at Michigan State University, USA, under the mentorship of Distinguished Prof. Anil Kumar Jain.
His research interests include biometrics, face recognition, face image quality, Generative AI, synthetic dataset generation, medical image analysis
Employment
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Sardar Vallabhbhai National Institute of Technology Surat Assistant Professor2024 - Present
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Norwegian University of Science and Technology Postdoctoral Researcher2023 - Present
Education
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Malaviya National Institute of Technology PhD2017 - 2022
Projects & Funding
Projects & funding information is unavailable.
Publications (16)
- Mobile Sensing-Based PM2.5 Prediction along Urban-Transport Corridors using Low-Rank-Adapted Spatially-Attentive-Graph-Neural-Network Framework Save
- Toward Explainable Ethnicity Altered Synthetic Images Save
- WaveletFusion: enhancing plant leaf disease classification with multi-scale feature extraction and explainable AI Save
- Dual-Model Synergy for Fingerprint Spoof Detection Using VGG16 and ResNet50 Save
- AW-GAN: face aging and rejuvenation using attention with wavelet GAN Save
- Longitudinal Analysis of Mask and No-Mask on Child Face Recognition Save
- ChildGAN: Face aging and rejuvenation to find missing children Save
- PlasticGAN: Holistic generative adversarial network on face plastic and aesthetic surgery Save
- Longitudinal Analysis of Mask and No-Mask on Child Face Recognition Save
- Child Face Age Progression and Regression using Self-Attention Multi-Scale Patch GAN Save