Ms. Priyanka Manchegowda | Computer Vision | Women Researcher Award

Posted on by Global Sensor Awards

Ms. Priyanka Manchegowda | Computer Vision | Women Researcher Award

Ms. Priyanka Manchegowda | Computer Vision | Amrita Vishwa Vidyapeetham | India

Ms. Priyanka Manchegowda is a results-driven Assistant Professor and researcher, currently pursuing her Ph.D., with over 12 years of combined experience in teaching computer science, academic leadership, and curriculum development. She holds an M.Sc. in Computer Science from Pooja Bhagavat Memorial Mahajana Post Graduate Centre, affiliated with the University of Mysore, Mysuru, India, and has actively contributed to higher education by delivering advanced courses in Exploratory Data Analysis using Python, Digital Image Processing, Design and Analysis of Algorithms, Data Structures, Problem Solving and Programming, Operations Research, Numerical Analysis, Statistical Techniques, Programming in C/C++, and Database Management Systems, consistently achieving strong student satisfaction. Professionally, she has served as an Assistant Professor at SBRR Mahajana First Grade College, Mysuru, from 2013 to 2020, and currently at Amrita Vishwa Vidyapeetham, School of Computing, Mysuru Campus since 2020, where she also contributes as a member of the Board of Studies and has developed curricula in alignment with university standards. In addition to teaching, she has guided Bachelor’s and Master’s students on research projects, focusing her research on computer vision-based human age estimation tailored for Indian medico-legal scenarios, demonstrating expertise in analytical methods, quantitative aptitude, image processing, and programming with Python, C, and C++, alongside database management using MS SQL Server and tools such as MATLAB and Anaconda. Ms. Manchegowda has actively contributed to institutional initiatives and student development, serving as the SWAYAM MOOC Nodal Officer, and as convener for the Rotaract Club and SARANTHA, while also engaging in faculty evaluations for the Internal Quality Assurance Cell (IQAC). She brings strong leadership, teamwork, administrative, and communication skills, alongside a commitment to lifelong learning and academic engagement. Her professional recognition includes citations in Scopus with an h-index reflecting the impact of her scholarly contributions.

Professional Profiles: ORCID | Scopus

Selected Publications

  • Priyanka, M., Divyashree, M., & Madhu, V. (2022). Computer Vision-Based Approach for Estimating Age and Gender using Wrist X-Ray Images.

  • Priyanka, M., Sreekumar, S., & Arsh, S. (2022). Detection of Covid-19 from the Chest X-Ray Images: A Comparison Study between CNN and Resnet-50.

Prof. Zhang Wenli | Computer Vision | Excellence in Research Award

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Prof. Zhang Wenli | Computer Vision | Excellence in Research Award 

Prof. Zhang Wenli | Computer Vision | Beijing University of Technology | China

Dr. Wenli Zhang is a distinguished scholar and innovative technology leader currently serving as a Professor in the Faculty of Information Technology at Beijing University of Technology, recognized for impactful contributions in signal and information processing, artificial intelligence, computer vision, 3D point cloud processing, unmanned aerial vehicle inspection technology, and brain-computer interfaces, positioning Dr. Wenli Zhang as a key figure advancing intelligent sensing and human-machine interaction research in China and globally. Building a strong academic foundation through advanced studies in computer science and informatics in both China and Japan, Dr. Wenli Zhang earned a Ph.D. in Engineering from the University of Tokyo, where a passion for applied research and innovation in intelligent systems was further strengthened. Prior to joining academia in China, Dr. Wenli Zhang developed extensive industrial innovation experience as Chief Researcher at Panasonic Corporation’s Tokyo Research Institute, driving real-world AI and vision-based solutions for next-generation automated applications. In her current role, Dr. Wenli Zhang leads interdisciplinary research that spans multiple sectors including smart agriculture, UAV-based intelligent inspection, and medical rehabilitation, effectively bridging fundamental theories with emerging societal needs and technological transformation. With strong collaboration networks and a commitment to promoting scientific excellence, Dr. Wenli Zhang serves actively in influential professional roles, including council member of the Beijing Interdisciplinary Science Society and committee member of the Innovation Engineering Branch of China Creative Studies Institute, contributing leadership within China’s innovation and engineering communities. Skilled in advanced algorithm development, intelligent visual perception, sensor network data fusion, and neural signal decoding, Dr. Wenli Zhang empowers her research team to develop practical systems that enhance automation, sustainability, and accessibility across industries. Her exceptional commitment to teaching and mentorship has earned her the prestigious “Distinguished Teacher” recognition at Beijing University of Technology, reflecting her dual dedication to academic excellence and student success.

Professional Profiles: ORCID  

Selected Publications:

  • Jiang, K., Guo, W., & Zhang, W. (2025). Amodal Segmentation and Trait Extraction of On-Branch Soybean Pods with a Synthetic Dual-Mask Dataset. Sensors.

  • Zhang, W., Peng, X., Bai, T., Wang, H., Takata, D., & Guo, W. (2024). A UAV-Based Single-Lens Stereoscopic Photography Method for Phenotyping the Architecture Traits of Orchard Trees. Remote Sensing.

  • Zhang, W., Peng, X., Cui, G., Wang, H., Takata, D., & Guo, W. (2023). Tree Branch Skeleton Extraction from Drone-Based Photogrammetric Point Cloud. Drones.

  • Li, Y., Liu, B., & Zhang, W. (2024). Driving-Related Cognitive Abilities Prediction Based on Transformer’s Multimodal Fusion Framework. Sensors.

  • Pang, G., Liu, B., & Zhang, W. (2025). Cloud Rehabilitation System Based on Automatic sEMG Signal Processing. Book Chapter.

  • Zhai, R., Gao, Y., Li, G., Ding, Q., Zhang, Y., & Zhang, W. (2025). Control System for Rehabilitation Bionic Hand Based on Precise Control Algorithms.

  • Wang, Y., Pang, G., Liu, B., Li, Y., & Zhang, W. (2025). Gesture Recognition Method Based on Hybrid Classifier Under Non-ideal Conditions.