Mr. ViekashVinoth Kumar, SambaNova Systems Inc, United States
Viekash Vinoth Kumar is a highly skilled researcher with over three years of domain experience and more than eight publications to his name. His expertise lies in AI, Computer Vision, Machine Learning, and Deep Learning. He has worked on advanced robotics and AI projects, demonstrating a strong ability to innovate and optimize complex systems. His experience spans top institutions like Carnegie Mellon University, the National University of Singapore, and the Indian Institute of Technology Madras.
Professional Profile:
Suitability for the Research for Best Researcher Award
Viekash holds a Master of Science in Robotics from Carnegie Mellon University (CMU), where he has maintained an outstanding QPA of 4.06/4.00. He was awarded the prestigious NSF PG Scholarship, positioning him among the top 15 scholars in a highly competitive pool. He also has a Bachelor of Technology in Instrumentation and Control Engineering from the National Institute of Technology Tiruchirappalli (NITT), where he excelled academically, securing the B.Tech Institute Award for the highest GPA in his 2nd and 3rd years.
Academic Background 📚
Currently, Viekash is a Graduate Student Researcher at CMU’s Biorobotics and BIG Labs. He has interned at JBT Corporation and the University of Waterloo, where he contributed to significant advancements in AI and computer vision. His work includes developing AI-driven clinical tools, enhancing medical imaging techniques, and optimizing autonomous vehicle navigation systems. His role as a Teaching Assistant and reviewer for prestigious journals further reflects his academic excellence.
Professional Experience 💼
Currently, Viekash is a Graduate Student Researcher at CMU’s Biorobotics and BIG Labs. He has interned at JBT Corporation and the University of Waterloo, where he contributed to significant advancements in AI and computer vision. His work includes developing AI-driven clinical tools, enhancing medical imaging techniques, and optimizing autonomous vehicle navigation systems. His role as a Teaching Assistant and reviewer for prestigious journals further reflects his academic excellence.
Research Interests and Contributions 🔬
Viekash’s research interests are deeply rooted in AI, specifically in Computer Vision, Machine Learning, Deep Learning, and Robotics. His projects often explore innovative solutions to complex challenges, such as developing AI architectures for medical imaging, optimizing neural networks for robotics applications, and pioneering methods for data-driven prediction and analysis. His work has resulted in several high-impact publications, reflecting his commitment to advancing the field.
Awards and Recognitions 🏆
Viekash Vinoth Kumar has earned notable recognition for his research, including the prestigious NSF PG Scholarship at Carnegie Mellon University, awarded to top 15 scholars from over 10,000 applicants. He has also received the Best Paper Award from IEEE for his contributions to deep learning. His academic excellence was highlighted by the B.Tech Institute Award for highest GPA during his undergraduate studies. Additionally, Viekash’s work has been acknowledged through several international conference awards and prestigious journal publications, reflecting his significant impact in his field.
Innovations and Impact 🚀
Viekash Vinoth Kumar’s innovations in AI and robotics have significantly advanced medical imaging and autonomous systems. His development of AI-driven tools for real-time medical diagnostics and enhanced imaging techniques has improved accuracy and efficiency in clinical settings. His work on autonomous vehicle navigation and optimized neural network models has advanced robotics, offering new solutions for complex challenges. These contributions not only push technological boundaries but also provide practical, impactful solutions across multiple domains.
Publication Top Notes:
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“Paced Curriculum Distillation with Prediction and Label Uncertainty for Image Segmentation”
Year: 2024
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“CADSketchNet – An Annotated Sketch Dataset for 3D CAD Model Retrieval with Deep Neural Networks”
Year: 2023
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“FAZSeg: A New Software for Quantification of the Foveal Avascular Zone”
Year: 2022
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“CLEARNESS: Cross-scaLe tEmporAl gRaph NEtwork for Super-reSolutions”
Year: 2023
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“Deep Learning Based Muscle Intent Classification in CPM for Knee OA Rehabilitation”
Year: 2023