Puranam Revanth Kumar | Artificial Intelligence and Machine Learning | Best Researcher Award Posted on 06/07/202406/07/2024 by Academic achievements Dr.Puranam Revanth Kumar | Artificial Intelligence and Machine Learning | Best Researcher Award Assistant Professor Malla Reddy University, Hyderabad, India. Dr. Puranam Revanth Kumar is an Assistant Professor at Malla Reddy University, Hyderabad, specializing in Artificial Intelligence and Machine Learning. His research focuses on image processing, deep learning, and biomedical imaging, with significant contributions to neuroimaging. He holds a Ph.D. in Electronics and Communication Engineering from ICFAI University, Hyderabad. profile Google Scholar 📚 Education Ph.D., Electronics and Communication Engineering, ICFAI University, Hyderabad, India, 2019-2024. M.Tech, Control and Instrumentation, AMRITA University, Coimbatore, India, 2017-2019. B.Tech, Electronics and Instrumentation, GITAM University, Hyderabad, India, 2012-2016. 👨💼 Experience Assistant Professor, Department of Artificial Intelligence and Machine Learning, Malla Reddy University, Hyderabad, India (2024-present). LabVIEW Software Trainee, Optomech Engineers Pvt. Ltd., Hyderabad, India (2018-2019). 🔍 Research Interests Dr. Kumar’s research interests include image processing, deep learning, machine learning, neuroimaging, biomedical imaging, and artificial intelligence applications in healthcare. 🏆 Award Deep Learning Award, NPTEL. Machine Learning Award, Coursera. MATLAB on-ramp Award, MathWorks. 📄 Publications Journal Articles: Spatial Attention UNet Model with Harris Hawks Optimization for Retinal Blood Vessel and Optic Disc Segmentation in Fundus Images, International Ophthalmology, 2024. Automated Human Brain Tissue Segmentation from Clinical MRI Images, Intelligent Medicine, 2024. BrainHyperintensities: Automatic Segmentation of White Matter Hyperintensities in Clinical Brain MRI Images, The Journal of Supercomputing, 2024. Inpainting Non-Anatomical Objects in Brain Imaging using Enhanced Deep Convolutional Autoencoder Network, Sadhana, 2024. Brain disorders: Impact of mild SARS-CoV-2 may shrink several parts of the brain, Neuroscience & Biobehavioral Reviews, 2023. Conference Proceedings: Segmentation of White Matter Lesions in MRI Images Using Optimization-based Deep Neural Network, 4th International Conference on Image Processing and Capsule Networks, 2023. Spreading Factor Optimization for Interference Mitigation in Dense Indoor LoRa Networks, IEEE IAS Global Conference on Emerging Technologies, 2023.
Monika Nagy-Huber | Machine Learning | Best Researcher Award Posted on 28/06/202428/06/2024 by Academic achievements Mrs. Monika Nagy-Huber | Machine Learning | Best Researcher Award Ms. Universität Basel, Switzerland Monika Timea Nagy-Huber is currently pursuing a PhD in Computer Science at the University of Basel, specializing in Physics-informed Machine Learning Algorithms under the supervision of Prof. Dr. Volker Roth in the Biomedical Data Analysis research group. She earned her Master of Science in Mathematics from the same university, focusing on Numerics (Partial Differential Equations for Wave Equations) and Algebra-Geometry-Number Theory (Elliptic Curves). Her master’s thesis, titled “The Local Discontinuous Galerkin Method with Local Time Stepping Method for solving the Wave Equation,” received a grade of 5.5. Monika also holds a Bachelor of Science in Mathematics from the University of Basel, completed in 2016. Profile Google scholar Personal Data 🌐 Last Name: Nagy-Huber First Name: Monika Timea E-Mail: monika.nagy@unibas.ch Nationality: Swiss University Education 🎓 PhD in Computer Science, University of Basel 09/2019 – Present Supervisor: Prof. Dr. Volker Roth Research Group: Biomedical Data Analysis Specialization: Physics-informed Machine Learning Algorithms Master of Science in Mathematics, University of Basel 02/2016 – 02/2019 Areas of Specialization: Numerics (Partial Differential Equations for Wave Equations), Algebra-Geometry-Number Theory (Elliptic Curves) Master’s Thesis: “The Local Discontinuous Galerkin Method with Local Time Stepping Method for solving the Wave Equation” Grade: 5.5 Supervisor: Prof. Dr. Marcus J. Grote Bachelor of Science in Mathematics, University of Basel 09/2011 – 02/2016 Publications: The effect of lysergic acid diethylamide (LSD) on whole-brain functional and effective connectivity CITED BY 17 Learning invariances with generalised input-convex neural networks CITED BY 5 Mesh-free Eulerian Physics-Informed Neural Networks CITED BY 1 Mesh-free Eulerian physics-informed neural networks CITED BY 1