Vinit Katariya | Deep Learning | Best Researcher Award Posted on 02/07/202402/07/2024 by Academic achievements Dr. Vinit Katariya | Deep Learning | Best Researcher Award Post Doctoral Researcher | University of North Carolina at Charlotte | United States Short Bio 🌐 Vinit Katariya is a Machine Learning Researcher based in Charlotte, NC. His expertise spans machine learning modeling and algorithm design, with a focus on Python, C, and frameworks like PyTorch. He is dedicated to solving complex, large-scale problems with real-world impact. Profile Google Scholar Education 📚 Vinit Katariya earned his Doctor of Philosophy in Electrical Engineering from the University of North Carolina at Charlotte in December 2023, under the guidance of Dr. Hamed Tabkhi. His research focused on innovative applications of deep learning in transportation and IoT systems. Experience 💼 As a Postdoctoral Researcher at UNC Charlotte, Vinit leads projects on speed estimation and smart city solutions, collaborating with international teams to enhance real-world video analytics and transportation efficiency. Research Interest 🧠 Vinit Katariya’s research interests include deep learning applications in transportation safety, IoT edge computing, and anomaly detection using AI frameworks on embedded platforms. Awards 🏆 Best Demo Award – ICCPS 2023 UNC Charlotte Graduate School Summer Scholarship – May 2022 Publications 📄 Vinit Katariya has co-authored articles in high-impact journals and conferences, including: Deeptrack: Lightweight deep learning for vehicle trajectory prediction in highways – IEEE Transactions on Intelligent Transportation Systems, 2022 VegaEdge: Edge AI confluence for real-time IoT-applications in highway safety – Internet of Things, 2024 VT-Former: A Transformer-based Vehicle Trajectory Prediction Approach For Intelligent Highway Transportation Systems – CVPR, 2024
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