Dr. Maria Muzamil Memon | Robotics | Research Excellence Award

Dr. Maria Muzamil Memon | Robotics | Research Excellence Award

Harbin Institute of Technology | China

Dr. Maria Muzamil Memon is a dedicated researcher and postdoctoral fellow at the Harbin Institute of Technology, China, specializing in micro-electro-mechanical systems , microfluidics, and flexible sensor technologies. she earned her Phd in electronic engineering from the university of electronic science and technology of China, where she focused on aln-based surface acoustic wave sensors and significantly improved pressure sensitivity through innovative structural design, validated via comsol-based finite element analysis. Her academic journey includes a master’s degree in mechanical engineering from Harbin institute of technology and a bachelor’s degree in electronics engineering from Mehran University of engineering and technology. Throughout her career, she has worked extensively on microfluidic chip fabrication, biomedical device development, and multiphysics simulations, Robotics while also supervising undergraduate and postgraduate students. Dr. Memon has received multiple prestigious awards, including several academic achievement and excellent performance awards from UESTC, and she is a two-time recipient of the Chinese government scholarship. her research impact is reflected in 141 citations, an h-index of 7, and 6 documented publications, i10-index, demonstrating her growing influence in the fields of mems sensors, sensing materials, and microfluidic systems.

Profile: Google Scholar

Featured Publications

Memon, M. M., Liu, Q., Manthar, A., Wang, T., & Zhang, W. (2023). Surface acoustic wave humidity sensor. Micromachines, 14(5), 945.

Memon, M. M., Hongyuan, Y., Pan, S., Wang, T., & Zhang, W. (2022). Surface acoustic wave humidity sensor based on hydrophobic polymer film. Journal of Electronic Materials, 51(10), 5627–5634.

Memon, M. M., Pan, S., Wan, J., Wang, T., & Zhang, W. (2021). Highly sensitive thick diaphragm-based surface acoustic wave pressure sensor. Sensors and Actuators A: Physical, 331, 112935.

Memon, M. M., Pan, S., Wan, J., Wang, T., Peng, B., & Zhang, W. (2022). Sensitivity enhancement of SAW pressure sensor based on the crystalline direction. IEEE Sensors Journal, 22(10), 9329–9335.

Memon, M. M., Pan, S., Wan, J., Wang, T., Peng, B., & Zhang, W. (2022). Sensitivity enhancement of SAW pressure sensor based on the crystalline direction. IEEE Sensors Journal, 22(10), 9329–9335.

Ms. Tamanna | Neural Networks for Robot Control | Best Researcher Award

Ms. Tamanna | Neural Networks for Robot Control | Best Researcher Award

Goethe University, Frankfurt | Germany

Tamanna is a Ph.d. researcher at Goethe University, Frankfurt, Germany, specializing in geochemistry with a focus on integrating data science and machine learning into geo-scientific research. Her work aims to bridge the gap between traditional geoscience and modern computational methodologies by leveraging data-driven approaches to analyze complex geochemical systems. She holds a bs-ms degree in earth and environmental sciences from the Indian institute of science education and research, Bhopal. her expertise spans elemental and isotopic geochemistry, geospatial analysis, statistical modeling, and predictive analytics for geochemical processes. She is proficient in programming and data visualization using python, Neural Networks for Robot Control, matlab, qgis, and arcgis. Tamanna has authored two research documents with approximately one citation and an h-index of 1 for those publications; overall, her author-level record includes 99 citations and an h-index of 6, according to google scholar. Beyond her technical skills, she is actively involved in interdisciplinary research that combines quantitative methods, laboratory work, and field investigations to enhance the understanding of earth’s chemical evolution. fluent in english and hindi, with working knowledge of german, she represents the next generation of data-driven geoscientists.

Profile: Orcid

Featured Publications

Tamanna, Hezel, D. C., & Marschall, H. R. (2025, October). MRMinerals and MineralTD: Machine‐Readable Mineral Formula and Compositions Data Set for Data‐Driven Research. Geoscience Data Journal.

Tamanna, Hezel, D. C., Srivastava, N., & Faber, J. (2025, August 13). Using machine learning for automatic rock classification. American Mineralogist.