Arron Carter | Plant Breeding | Best Researcher Award  

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Dr. Arron Carter | Plant Breeding | Best Researcher Award

Professor | Wasington State University | United States

Dr. Arron Carter is a leading expert in plant breeding and genetics with a focus on winter wheat cultivar development, gene discovery, molecular markers, and high-throughput phenotyping to enhance global food security. He earned his PhD in Crop and Soil Sciences from Washington State University, where his doctoral work identified key quantitative trait loci and molecular markers for disease and agronomic traits in spring wheat. His professional journey includes serving as Professor and O.A. Vogel Endowed Chair of Wheat Breeding and Genetics at Washington State University, where he has made significant contributions to advancing sustainable agriculture and mentoring the next generation of researchers. His research interests span genomic selection, disease resistance, drought and heat tolerance, and integrating remote sensing and artificial intelligence into crop improvement. Over his career, he has been honored with multiple outstanding paper awards and recognition from professional societies for impactful research that combines innovation with practical agricultural applications. His research skills include advanced genomics, quantitative genetics, data-driven breeding strategies, UAV-based crop monitoring, and interdisciplinary collaborations at both national and international levels. His impact on the scientific community is reflected in 3,889 citations, 132 documents, and an h-index of 30.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate | LinkedIn

Featured Publications

  1. Cavanagh, C. R., Chao, S., Wang, S., Huang, B. E., Stephen, S., Kiani, S., Forrest, K., … Carter, A. H., … & Akhunov, E. (2013). Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars. Proceedings of the National Academy of Sciences, 110(20), 8057–8062.

  2. Sankaran, S., Khot, L. R., Espinoza, C. Z., Jarolmasjed, S., Sathuvalli, V. R., Carter, A. H., … Miklas, P. N. (2015). Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. European Journal of Agronomy, 70, 112–123.

  3. Carter, A. H., Chen, X. M., Garland-Campbell, K., & Kidwell, K. K. (2009). Identifying QTL for high-temperature adult-plant resistance to stripe rust (Puccinia striiformis f. sp. tritici) in the spring wheat (Triticum aestivum L.) cultivar ‘Louise’. Theoretical and Applied Genetics, 119(6),

  4. Sandhu, K. S., Lozada, D. N., Zhang, Z., Pumphrey, M. O., & Carter, A. H. (2021). Deep learning for predicting complex traits in spring wheat breeding program. Frontiers in Plant Science, 11, 613325.

  5. Naruoka, Y., Garland-Campbell, K. A., & Carter, A. H. (2015). Genome-wide association mapping for stripe rust (Puccinia striiformis f. sp. tritici) in US Pacific Northwest winter wheat (Triticum aestivum L.). Theoretical and Applied Genetics, 128(6), 1083–1101.

Emmanuel Ukekwe | Data Analytics | Best Researcher Award

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Dr. Emmanuel Ukekwe | Data Analytics | Best Researcher Award

Senior Lecturer | University of Nigeria | Nigeria

Dr. Emmanuel Ukekwe is a dedicated researcher and academic with expertise in artificial intelligence, expert systems, data science, computational programming, and software engineering, with a focus on applying intelligent technologies to solve societal problems. He obtained his Bachelor of Science in Computer/Statistics, Master of Science, and Ph.D. in Computer Science from the University of Nigeria, Nsukka, where he has grown into a respected lecturer and researcher. His professional journey includes roles as Senior Lecturer, Lecturer, and Instructor, as well as administrative positions such as Acting Head of Department and Acting Dean, demonstrating both academic and leadership excellence. His research interests span the application of machine learning and Python programming in data-driven problem solving, optimization models, recommender systems, and educational technologies. He has published extensively in recognized journals and conferences indexed in Scopus, covering healthcare systems, telecommunications, student performance, and COVID-19 analytics. He has been actively involved in university committees, curriculum development, and community-based research projects, and is a member of organizations such as the National Biotechnology Development Agency and the Technical Committee on UNESCO-HP projects. His skills include statistical analysis, software development, and advanced computational modeling, reflecting strong technical and analytical capabilities. His academic and research contributions have been recognized with professional memberships and community service engagements, marking him as an influential contributor to both academia and society. His research profile records 4 citations, 8 documents, and an h-index of 1.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. Okereke, G. E., Bali, M. C., Okwueze, C. N., Ukekwe, E. C., Echezona, S. C., & Ugwu, C. I. (2023). K-means clustering of electricity consumers using time-domain features from smart meter data. Journal of Electrical Systems and Information Technology, 10(1), 2.

  2. Ukekwe, E. C., Obayi, A. A., Johnson, A., Musa, D. A., & Agbo, J. C. (2025). Optimizing data and voice service delivery for mobile phones based on clients’ demand and location using affinity propagation machine learning. Journal of the Nigerian Society of Physical Sciences, 7(2), 2109.

  3. Ukekwe, E. C., Ezeora, N. J., Obayi, A. A., Asogwa, C. N., Ezugwu, A. O., Adegoke, F. O., Raiyetumbi, J., & Tenuche, B. (2025). Examining the impact of mathematics ancillary courses on computational programming intelligence of computer science students using machine learning techniques. Computer Applications in Engineering Education, 33(4), e70054.

  4. Ukekwe, E. C., Ogbonna, G. U. G., Adegoke, F. O., Okereke, G. E., & Asogwa, C. N. (2023). Clustering Nigeria’s IDP camps for effective budgeting and re-settlement policies using an optimized K-means approach. African Conflict & Peacebuilding Review, 13(2), 60–85.

  5. Okereke, G. E., Azegba, O., Ukekwe, E. C., Echezona, S. C., & Eneh, A. (2023). An automated guide to COVID-19 and future pandemic prevention and management. Journal of Electrical Systems and Information Technology, 10(1), 16.

Madhuri Rao | Machine Learning | Best Researcher Award

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Dr. Madhuri Rao | Machine Learning | Best Researcher Award

Senior Assistant Professor | MIT World Peace University | India

Dr. Madhuri Rao is a dedicated researcher and academic in computer science with expertise in wireless sensor networks, Internet of Things, artificial intelligence, blockchain, and cybersecurity, with her current work focusing on deep learning, cloud security, and healthcare applications. She earned her Ph.D. in Computer Science and Engineering from Biju Patnaik University of Technology, where her research emphasized energy-efficient object tracking in wireless sensor networks. Over her career, she has gained extensive professional experience as a faculty member, academic coordinator, research supervisor, and editorial board member, contributing significantly to both teaching and research. She has authored and co-authored numerous publications in reputed journals and conferences, including IEEE, Springer, Elsevier, and Scopus-indexed platforms, along with patents and book chapters that highlight her innovative approach. Her research interests span interdisciplinary applications of advanced technologies to address challenges in security, healthcare, and sustainability, with ongoing involvement in collaborative projects and international initiatives. She has received recognition through awards such as best paper honors and a best research scholar award, underscoring her contributions to the academic community. Her research skills include problem-solving, experimental design, data analysis, and guiding students at undergraduate, postgraduate, and doctoral levels, coupled with active roles as session chair, track chair, and guest lecturer in international conferences. She is also a life member of professional societies and holds certifications that strengthen her academic profile. Her impactful contributions are reflected in 116 citations and an h-index of 7.

Profile: Google Scholar | ORCID | ResearchGate | LinkedIn

Featured Publications

  1. Rao, M., & Kamila, N. K. (2021). Cat swarm optimization based autonomous recovery from network partitioning in heterogeneous underwater wireless sensor network. International Journal of System Assurance Engineering and Management, 1–15.

  2. Rao, M., Kamila, N. K., & Kumar, K. V. (2016). Underwater wireless sensor network for tracking ships approaching harbor. 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 1098–1102. IEEE.

  3. Rao, M., & Kamila, N. K. (2018). Spider monkey optimisation based energy efficient clustering in heterogeneous underwater wireless sensor networks. International Journal of Ad Hoc and Ubiquitous Computing, 29(1–2), 50–63.

  4. Chaudhury, P., Rao, M., & Kumar, K. V. (2009). Symbol based concatenation approach for text to speech system for Hindi using vowel classification technique. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 1393–1396. IEEE.

  5. Kumar, K. V., Kumari, P., Rao, M., & Mohapatra, D. P. (2022). Metaheuristic feature selection for software fault prediction. Journal of Information and Optimization Sciences, 43(5), 1013–1020.

Shijie Li | Embodied AI | Best Researcher Award

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Dr. Shijie Li | Embodied AI | Best Researcher Award

Scientist | A*STAR Institute for Infocomm Research | Singapore

Dr. Shijie Li is a computer vision researcher with expertise in 3D perception, embodied AI, and vision-language models, contributing to the development of intelligent systems for real-world applications. He earned his Ph.D. in Computer Science from Bonn University under the supervision of Prof. Juergen Gall, following a master’s degree from Nankai University and a bachelor’s degree in Automation Engineering from the University of Electronic Science and Technology of China. His professional experience includes research positions and internships at A*STAR Singapore, Qualcomm AI Research in Amsterdam, Intel Labs in Munich, Alibaba DAMO Academy in China, and Technische Universität München in Germany, showcasing strong international collaborations and applied research expertise. His research interests lie in 3D scene understanding, motion forecasting, vision-language integration, semantic segmentation, and novel view synthesis. He has published in leading journals and conferences such as ICCV, CVPR, IEEE TPAMI, IEEE TNNLS, WACV, BMVC, ICRA, and IROS, reflecting impactful and consistent contributions. His academic excellence has been recognized through scholarships and awards including the Fortis Enterprise Scholarship, National Inspirational Scholarship, First Class Scholarship, and Outstanding Graduate Award. He has also served as a reviewer for top journals and conferences such as IEEE TPAMI, IJCV, CVPR, ICCV, ECCV, NeurIPS, and AAAI, reflecting his active role in the research community. His skills include deep learning, diffusion models, semantic and motion forecasting, vision-language modeling, and embodied AI, with a focus on interdisciplinary innovation. His research impact is reflected in 183 citations, 10 documents, and an h-index of 7.

Profiles: Google Scholar | Scopus | ORCID | LinkedIn

Featured Publications

Li, S., Abu Farha, Y., Liu, Y., Cheng, M., & Gall, J. (2023). MS-TCN++: Multi-stage temporal convolutional network for action segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6), 6647–6658.

Chen, X., Li, S., Mersch, B., Wiesmann, L., Gall, J., Behley, J., & Stachniss, C. (2021). Moving object segmentation in 3D LiDAR data: A learning-based approach exploiting sequential data. IEEE Robotics and Automation Letters, 6(4), 6529–6536.

Qiu, Y., Liu, Y., Li, S., & Xu, J. (2020). MiniSeg: An extremely minimum network for efficient COVID-19 segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(11), 13180–13187.

Li, S., Chen, X., Liu, Y., Dai, D., Stachniss, C., & Gall, J. (2021). Multi-scale interaction for real-time LiDAR data segmentation on an embedded platform. IEEE Robotics and Automation Letters, 7(2), 738–745.

Li, S., Zhou, Y., Yi, J., & Gall, J. (2021). Spatial-temporal consistency network for low-latency trajectory forecasting. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 10737–10746.

Huaiqu Feng | Agricultural Robotics | Best Researcher Award

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Dr. Huaiqu Feng | Agricultural Robotics | Best Researcher Award

Huaiqu Feng | Zhejiang University | China

Huaiqu Feng is a skilled researcher with expertise in robotics and electromechanical intelligent equipment, focusing on computer vision, deep learning, and image processing for agricultural automation. He holds a Master of Engineering in Agricultural Mechanization Engineering from Northeast Agricultural University and a Bachelor of Engineering in Automation from Hubei Normal University. Throughout his academic and professional career, he has participated in multiple research projects, including provincial science and technology programs and industrial transformation initiatives, demonstrating strong capability in applying AI and robotics to practical agricultural problems. He has contributed to several high-impact publications, patents, and software developments, showcasing his innovative approach and technical proficiency. His professional experience includes leading research teams, mentoring students, and managing projects that integrate advanced technologies into real-world applications. His research interests span robotics, precision agriculture, intelligent equipment, and AI-based image analysis. He is proficient in Matlab for algorithm development, microcontroller programming with STM32, and 3D modeling and simulation using Creo and Pro/E. Huaiqu Feng also actively engages in community and leadership roles through student organizations, innovation competitions, and volunteer initiatives, highlighting his commitment to fostering collaboration and advancing the research community. 426 Citations, 20 Documents, 8 h-index.

Profiles: Google Scholar | Scopus | ORCID

Featured Publications

  1. Quan, L., Feng, H., Lv, Y., Wang, Q., Zhang, C., Liu, J., & Yuan, Z. (2019). Maize seedling detection under different growth stages and complex field environments based on an improved Faster R–CNN. Biosystems Engineering, 184, 1-23.

  2. Zhao, G., Quan, L., Li, H., Feng, H., Li, S., Zhang, S., & Liu, R. (2021). Real-time recognition system of soybean seed full-surface defects based on deep learning. Computers and Electronics in Agriculture, 187, 106230.

  3. Li, D., Li, B., Long, S., Feng, H., Xi, T., Kang, S., & Wang, J. (2023). Rice seedling row detection based on morphological anchor points of rice stems. Biosystems Engineering, 226, 71-85.

  4. Wei, C., Li, H., Shi, J., Zhao, G., Feng, H., & Quan, L. (2022). Row anchor selection classification method for early-stage crop row-following. Computers and Electronics in Agriculture, 192, 106577.

  5. Li, D., Li, B., Long, S., Feng, H., Wang, Y., & Wang, J. (2023). Robust detection of headland boundary in paddy fields from continuous RGB-D images using hybrid deep neural networks. Computers and Electronics in Agriculture, 207, 107713.

Tuğba Özge Onur | Image Reconstruction | Best Researcher Award

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Assoc. Prof. Dr. Tuğba Özge Onur | Image Reconstruction | Best Researcher Award

Associate Professor | Zonguldak Bülent Ecevit University | Turkey

Assoc. Prof. Dr. Tuğba Özge Onur is a distinguished researcher specializing in signal processing, image reconstruction, and optimization. She earned her Ph.D. in electrical and electronics engineering from a leading university, where she developed a strong foundation in computational imaging and algorithm design. Her professional experience includes leading research projects, coordinating international collaborations, and mentoring students in both academic and applied research settings. Her research interests span computer vision, optimization techniques, and advanced signal processing methods, with a focus on developing innovative solutions for real-world challenges. She possesses a diverse set of research skills, including algorithm development, data analysis, experimental design, and implementation of complex computational models. She is actively engaged in the scientific community through professional memberships and collaborative initiatives. Her work has been widely recognized and published in reputed journals and conferences, demonstrating both the depth and impact of her contributions. Her commitment to advancing knowledge, mentoring emerging researchers, and participating in collaborative projects underscores her influence in the field. 98 Citations, 23 Documents, 6 h-index.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. Onur, T. Ö. (2022). Improved image denoising using wavelet edge detection based on Otsu’s thresholding. Acta Polytechnica Hungarica, 19(2), 79–92.

  2. Onur, Y. A., İmrak, C. E., & Onur, T. Ö. (2017). Investigation on bending over sheave fatigue life determination of rotation resistant steel wire rope. Experimental Techniques, 41(5), 475–482.

  3. Narin, D., & Onur, T. Ö. (2022). The effect of hyperparameters on the classification of lung cancer images using deep learning methods. Erzincan University Journal of Science and Technology, 15(1), 258–268.

  4. Kaya, G. U., & Onur, T. Ö. (2022). Genetic algorithm based image reconstruction applying the digital holography process with the Discrete Orthonormal Stockwell Transform technique for diagnosis of COVID-19. Computers in Biology and Medicine, 148, 105934.

  5. Onur, T. (2021). An application of filtered back projection method for computed tomography images. International Review of Applied Sciences and Engineering, 12(2), 194–200.

Akanksha Dwivedi | Parallel Computing | Excellence in Research Award

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Ms. Akanksha Dwivedi | Parallel Computing | Excellence in Research Award

Research Scholar | Indian Institute of Technology Jodhpur | India

Ms. Akanksha Dwivedi is a doctoral research scholar in Computer Science and Engineering at the Indian Institute of Technology Jodhpur, where she works under the guidance of Dr. Dip Sankar Banerjee at the Systems for Performance, Analysis, and Data Engineering Lab. She holds a Master of Technology in Mechatronics, Robotics, and Automation from the Center for Advanced Studies, Lucknow, and a Bachelor of Technology in Electronics and Communication Engineering from Dr. APJ Abdul Kalam Technical University, Lucknow. She has served as a Teaching Assistant at IIT Jodhpur and RSVS Lucknow, as well as a Project Associate at the National Institute of Technology Uttarakhand, contributing to projects in deep learning for speech decoding and precision health technologies. Her research interests include high-performance computing, scalable parallel algorithms, data analytics, artificial intelligence for healthcare applications, robotics, and sensor technologies. She has published in reputed venues such as Future Generation Computer Systems and IEEE High Performance Extreme Computing, with additional contributions in AI-driven healthcare sensors and sustainable materials. Akanksha has received prestigious fellowships including the Anusandhan National Research Foundation project fellowship and the Ministry of Education doctoral fellowship. She has been honored with awards for innovative ideas, international travel grants, and recognition in hackathons and debate competitions, as well as achievements in sports at the state level. Her research skills span programming in C, Python, and CUDA, parallel computing with OpenMP, data analysis, robotics systems, and advanced tools such as MATLAB and Docker, reflecting her strong technical foundation and multidisciplinary expertise.

Profiles: ORCID | ResearchGate | LinkedIn

Featured Publications

  1. Dwivedi, A., & Banerjee, D. S. (2024, December 4). MST in incremental graphs through tree contractions. In Proceedings of the 28th IEEE High Performance Extreme Computing Conference (HPEC), Boston, USA.

  2. Dwivedi, A., Sharma, S., & Banerjee, D. S. (2023, March 3). Efficient parallel algorithms for large tree contraction. In Proceedings of the Student Research Symposium, International Conference on High Performance Computing (HiPC).

Vesna Skrbinjek | Social Robots | Best Researcher Award

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Assist. Prof. Dr. Vesna Skrbinjek | Social Robots | Best Researcher Award

Vice-Dean | International School for Social and Business Studies | Slovenia

Dr. Vesna Skrbinjek is a dedicated academic and researcher specializing in higher education, economic sociology, quality assurance, and digital and employment skills. She earned her PhD in Economic Sociology from the School of Advanced Social Sciences in Nova Gorica, where her research focused on the effects of economic crises on higher education funding. Her professional experience spans teaching mathematics, statistics, and higher education topics, advancing from junior researcher to her current role as Vice Dean at the International School for Social and Business Studies in Celje, where she also serves as Associate Professor and Editor-in-Chief of the International Journal of Management in Education. She has contributed to international collaborations, including her role as an EACEA Expert for the European Commission in evaluating Erasmus+ projects, and has actively shaped academic quality processes, curriculum reforms, and student development initiatives. Her research interests include higher education policy, digital transformation in learning, and the sociology of education. She has been recognized for her leadership in academia and contributions to international research and publishing. Skilled in project management, quality assurance, digital platforms, and advanced data analysis tools such as SPSS and R, she combines research excellence with institutional leadership. She has achieved 102 citations, 11 documents and an h-index of 5.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate | LinkedIn

Featured Publications

  1. Breznik, K., & Skrbinjek, V. (2020). Erasmus student mobility flows. European Journal of Education, 55(1), 105–117.

  2. Skrbinjek, V., & Dermol, V. (2019). Predicting students’ satisfaction using a decision tree. Tertiary Education and Management, 25(2), 101–113.

  3. Krabonja, M. V., Kustec, S., Skrbinjek, V., Aberšek, B., & Flogie, A. (2024). Innovative professional learning communities and sustainable education practices through digital transformation. Sustainability, 16(14), 1–19.

  4. Skrbinjek, V., Lesjak, D., & Šušteršič, J. (2018). Impact of the recent economic crisis on tertiary education funding: A comparative study. International Journal of Innovation and Learning, 23(2), 123–144.

  5. Skrbinjek, V., Vičič Krabonja, M., Aberšek, B., & Flogie, A. (2024). Enhancing teachers’ creativity with an innovative training model and knowledge management. Education Sciences, 14(12), 1381.

Shuangke Liu | Energy Conversion | Best Researcher Award

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Mr. Shuangke Liu | Energy Conversion | Best Researcher Award

Associate Professor | National University of Defense Technology | China

Mr. Liu Shuangke is an associate professor and master’s supervisor at the National University of Defense Technology with extensive expertise in high-specific-energy lithium-sulfur and lithium-metal batteries as well as fiber-shaped batteries. He earned his Ph.D. from the National University of Defense Technology and has built a strong foundation in materials science and electrochemistry. Throughout his professional career, he has led numerous research projects, published over 40 papers in reputed journals such as Nature Communications, Energy & Environmental Science, Advanced Functional Materials, and Chem. Soc. Rev., and applied for 15 patents while authoring a monograph. His research interests focus on innovative battery materials, structural design of hollow carbon frameworks, gradient multicomponent solid electrolyte interphases, spin modulation, and orbital strengthening for high-voltage cathodes. He has mentored students who have won multiple awards, guided theses recognized at provincial and national levels, and contributed actively to the scientific community as a youth editorial board member for journals including Electrochemistry and Carbon Neutralization. Liu Shuangke has received various honors for teaching excellence and research achievements. His research skills include advanced electrochemical analysis, materials synthesis, battery design, and interphase engineering, reflecting both innovation and practical impact. His work has garnered 1,593 citations by 1,392 documents, with 47 documents and an h-index of 25, demonstrating high visibility and influence in the field of energy storage and battery research.

Profiles: Scopus | ORCID | ResearchGate        

Featured Publications

Liu, S., Yao, Z., Fu, T., Pan, T., Luo, C., Pang, M., Xiong, S., Guo, Q., Li, Y., Zheng, C., Sun, W., & Zhou, G. (2025). Dynamic doping and interphase stabilization for cobalt-free and high-voltage lithium metal batteries. Nature Communications.

Liu, S., [Authors as above]. (2025). In situ partial-cyclized polymerized acrylonitrile-coated NCM811 cathode for high-temperature ≥ 100 °C stable solid-state lithium metal batteries. Nano Micro Letters.

Liu, S., Pan, T., Li, Y., Yao, Z., Zhu, Y., Wang, X., Wang, J., Zheng, C., & Sun, W. (2025). Research advances on lithium-ion batteries calendar life prognostic models. Carbon Neutralization.

Integrated online identification of aerodynamic and thrust parameters for air-breathing aircraft. Conference Paper.

Mir Arman Mirzaaghaian Amiry | Human Lungs Health | Best Researcher Award

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Mr. Mir Arman Mirzaaghaian Amiry | Human Lungs Health | Best Researcher Award

Researcher | Western Sydney University | Australia

Mr. Mir Arman Mirzaaghaian Amiry is a dedicated researcher in mechanical engineering with expertise in computational modeling, 3D design, medical device development, and biomedical applications. He is pursuing a PhD in Mechanical Engineering at Western Sydney University with a thesis focused on computational simulation of particle transport and deposition in a 3D human lung model. He also holds a master’s degree in mechanical engineering from Babol Noshirvani University of Technology and a bachelor’s degree from Mazandaran University of Science and Technology. His professional experience includes serving as a tutor, lab demonstrator, and technical officer at Western Sydney University, as well as contributing to an internship at Metabolic Health Solutions where he designed and prototyped an innovative breath-by-breath calorimeter leading to a patent on a metabolic measurement system with sensors, machine learning, and digital health integration. He has also been a STEM student ambassador mentoring high school students. His research interests include computational fluid dynamics, biomedical device design, medical device regulatory affairs, and additive manufacturing. Among his awards and honors are the Australian Government postgraduate research scholarship, excellence in reviewing recognition from the Thermal and Fluids Engineering Conference, and high academic ranking during his master’s studies. He has earned professional certifications in Lean Six Sigma, ISO 13485 for medical devices, and AI in project management. His research skills span SOLIDWORKS, Ansys Fluent, COMSOL Multiphysics, 3D printing, data analysis, and regulatory management, complemented by strong teamwork, communication, and project leadership abilities. Citations 90 and h-index 3.

Profiles: Google Scholar | ORCID | LinkedIn | ResearchGate 

Featured Publications

  1. Mirzaaghaian, A., & Ganji, D. D. (2016). Application of differential transformation method in micropolar fluid flow and heat transfer through permeable walls. Alexandria Engineering Journal, 55(3), 2183–2191.

  2. Mirzaaghaian, A., Ramiar, A., Ranjbar, A. A., & Warkiani, M. E. (2020). Application of level-set method in simulation of normal and cancer cells deformability within a microfluidic device. Journal of Biomechanics, 112, 110066.

  3. Mirzaaghaian, A., Zhao, M., Rahman, M. M., & Dong, K. (2024). Numerical simulation of targeted drug delivery to different regions of realistic human lung model under realistic aerosol breathing condition. Powder Technology, 444, 120039.

  4. Mirzaaghaian, A., Zhao, M., & Dong, K. (2025). Patient-specific release of the aerosols from the spacer and its effect on the drug delivery to the human lungs. ASTFE Digital Library.

  5. Cebis, M. J. P., Smith, T. L., Waheed, M., Amiry, M. A. M., & Lian, Y. S. J. J. (2024). Metabolic measurement system with sensors, machine learning, and digital health database. Australian Patent Application No. 2024900118.