Tomohiro Hayashida | Machine Learning | Best Researcher Award

Published on 10/07/202510/07/2025 by Engineering Scientist

Prof. Tomohiro Hayashida | Machine Learning
| Best Researcher Award

Professor at Hiroshima University , Japan.

Professor Tomohiro Hayashida is a distinguished scholar in decision-making, machine learning, and optimization, currently serving at Hiroshima University. After earning his Master’s and Ph.D. in Engineering from the same institution, he joined the university as a Research Associate in 2006 and steadily rose to Full Professor by 2024. With over 80 academic publications, Prof. Hayashida has led competitive national research grants and worked on practical innovations in transportation and scheduling algorithms. His interdisciplinary collaborations span across academia and industry, reflecting his commitment to both theoretical advancements and real-world applications. His citation record, leadership in JSPS-funded projects, and role in applied AI solutions exemplify his deep impact in computational engineering and operations research.

🌍 Professional Profile:

Scopus

🏆 Suitability for the Best Researcher Award :

Prof. Tomohiro Hayashida exemplifies the qualities deserving of the Best Researcher Award. He has produced over 50 peer-reviewed journal papers, many in top-tier SCI/Scopus-indexed journals. As Principal Investigator of multiple JSPS KAKEN-funded projects, including those in evolutionary computing and dynamic systems, he has shown consistent research leadership. His collaborative work with industry, such as optimizing dispatch algorithms with SmartRyde Inc., demonstrates strong translational research. With an h-index around 11–12 and 442+ citations, he balances scholarly excellence and societal impact. His active role in multi-disciplinary collaborations—both domestic and international—further reinforces his stature as an innovative and impactful researcher, making him highly suitable for the Best Researcher Award.

🎓 Education :

Prof. Hayashida received his entire higher education from Hiroshima University. He completed his Master’s degree in Engineering in 2006 and subsequently pursued and earned his Ph.D. in Engineering. His academic training focused on optimization theory, intelligent systems, and operations research, equipping him with a robust foundation in both theoretical and applied research. The university’s focus on computational intelligence and engineering sciences helped shape his research vision early in his career. His seamless transition from student to researcher within the same academic institution showcases his consistent excellence and growth as a scholar. This strong academic grounding laid the basis for his long-term contributions to machine learning, decision sciences, and interdisciplinary engineering research.

🏢 Work Experience :

Prof. Tomohiro Hayashida began his academic career in 2006 as a Research Associate at Hiroshima University, immediately after earning his Master’s degree. He was promoted to Assistant Professor in 2007, Associate Professor in 2015, and Full Professor in 2024. He has over 18 years of academic experience in teaching, research, and supervision. Beyond academic duties, he is active in government-funded research and industry collaborations, such as the ride-hailing optimization project with SmartRyde Inc. He also contributes to the Digital Manufacturing Education and Research Center at Hiroshima University. His extensive experience in both research project leadership and educational innovation showcases a balanced, impactful academic career with national and international influence.

🏅Awards and Honors

While specific award titles are not publicly listed, Prof. Hayashida’s selection as Principal Investigator for multiple highly competitive JSPS KAKEN Grants—including Young Researcher awards and Scientific Research (C) projects—reflects significant national recognition of his research excellence. His promotion to Full Professor at Hiroshima University, a top-tier Japanese institution, itself is a mark of academic distinction. He has been entrusted with strategic roles in collaborative projects, some of which have gained media coverage, such as the SmartRyde dispatch algorithm. These achievements, combined with a strong citation record and presence in high-impact journals, serve as implicit acknowledgment of his contributions to AI, optimization, and applied decision-making sciences within both academic and practical domains.

🔬 Research Focus :

Prof. Hayashida’s research centers on decision-making, machine learning, optimization, and evolutionary computation. His work addresses complex real-world problems like multi-objective scheduling, group decision analysis, and dynamic system optimization. Through JSPS-funded projects, he has developed algorithms for adaptive agents, cooperative enterprises, and evolutionary scheduling. His recent collaborations include intelligent systems for ride-hailing and dynamic dispatching. He integrates mathematical modeling with practical applications, focusing on AI-driven solutions for industries such as energy systems and transportation. His interdisciplinary approach merges operations research, computer science, and systems engineering. With over 80 publications and national/international partnerships, his research advances both academic knowledge and technological innovation, particularly in adaptive, data-driven decision systems.

📊 Publication Top Notes:

📘 Integrated Optimization Method for Task Allocation and Hierarchical Reinforcement Learning in Cargo Transport Robots
🗓️ Year: 2025 | 📚 Journal: IEEJ Transactions on Electronics Information and Systems |

📄 Constrained-multiobjective Evolutionary Algorithm for Distribution System Reconfiguration under Severe Constraints
🗓️ Year: 2025 | 📚 Conference Paper |

🚚 Integrating Task Allocation and Hierarchical Reinforcement Learning for Optimized Cargo Transport Routing
🗓️ Year: 2025 | 📚 Conference Paper |

⚡ Distribution System Reconfiguration by an Evolutionary Algorithm using Constraint-Guided Dominance and Archive-Based Individual Preservation Strategy
🗓️ Year: 2024 | 📚 IEEJ Transactions on Power and Energy |

📊 Expectation and Fractile Models for Decentralised Distribution Systems under Demand Uncertainty and their Computational Methods
🗓️ Year: 2024 | 📚 International Journal of Operational Research |

🎓 WIP: Machine Learning Models for Predicting Student Performance in IoT-Enhanced Education
🗓️ Year: 2024/2025 | 📚 Conference Paper |

📈 WIP: Study on a Data-Driven Adaptive Learning Support System Design for Individualized Optimal Learning
🗓️ Year: 2024/2025 | 📚 Conference Paper |

Hao Zhang | Artificial Intelligence | Best Researcher Award

Published on 27/06/202527/06/2025 by Engineering Scientist

Dr. Hao Zhang | Artificial Intelligence
| Best Researcher Award

Associate professor at Carnegie Mellon University, United States.

Hao Zhang is a Research Associate at Carnegie Mellon University (CMU), conducting postdoctoral research at the Safe AI Lab under Prof. Ding Zhao. He also serves as the Associate Director of the ETAIC Research Lab at the University of Texas at Arlington, led by Prof. Eric Tseng (NAE Member). He holds a Ph.D. from Tsinghua University, co-advised by Prof. Zhi Wang and Prof. Shengbo Eben Li. With over 35 SCI/EI publications and 17 patents, his research advances multi-agent reinforcement learning and closed-loop LLMs for real-world AI deployment in autonomous vehicles, robotics, and smart energy systems. He collaborates globally with academic and industrial leaders such as BYD, SAIC, Dongfeng Motor, and UCL, making impactful contributions to intelligent mobility.

🌍 Professional Profile:

ORCID

Google Scholar

Scopus

🏆 Suitability for the Best Researcher Award :

Dr. Hao Zhang is an exceptional candidate for the Best Researcher Award due to his groundbreaking work at the intersection of artificial intelligence and real-world applications. His achievements in scalable AI for micro-mobility and autonomous vehicles have led to industrial deployments across leading automotive manufacturers. With a strong publication record, global collaborations, and 17 patents, he exemplifies innovation, impact, and leadership. He bridges theory and practice, pushing the boundaries of safe and trustworthy AI agents. His dual appointments at CMU and UTA and contribution to both academia and industry reflect his versatile excellence. Dr. Zhang’s work not only enhances technological advancement but also fosters a responsible and intelligent future for mobility and energy systems.

🎓 Education :

Hao Zhang received his Ph.D. in Mechanical Engineering from Tsinghua University, one of China’s most prestigious institutions, where he was co-advised by renowned scholars Prof. Zhi Wang and Prof. Shengbo Eben Li. During his Ph.D., he focused on reinforcement learning and its applications to intelligent vehicle systems. Prior to that, he completed his undergraduate and master’s studies with distinction, developing a strong foundation in robotics, automation, and control systems. His education also included collaborative learning experiences with industry, which laid the groundwork for his multidisciplinary approach to research. Currently, he is expanding his expertise through postdoctoral research at Carnegie Mellon University, contributing to the development of safe AI systems under the mentorship of Prof. Ding Zhao.

🏢 Work Experience :

Dr. Zhang has a rich portfolio of academic and industrial experience. As a Research Associate at Carnegie Mellon University, he works at the forefront of AI safety, while simultaneously serving as Associate Director at the ETAIC Lab at UTA. He has led or participated in five major government-funded research projects and four OEM-sponsored industry projects. His efforts have directly supported intelligent system development for companies such as BYD Auto, SAIC Motor, and Dongfeng. His engineering solutions have real-world applications in autonomous driving, energy management, and mobile robotics. His academic roles, coupled with his industrial consultancy, enable him to effectively translate research into practice. Dr. Zhang’s interdisciplinary experience sets him apart as a leader in applied AI and automation.

🏅Awards and Honors

Hao Zhang’s research excellence has earned him notable recognition across academia and industry. He has published over 35 SCI/EI-indexed journal articles, authored a technical book (ISBN: 9780443329845), and holds 17 patents related to intelligent control and autonomous systems. His work has been cited nearly 500 times, demonstrating significant influence. His research contributions have been integrated into industrial platforms at BYD and Dongfeng, marking a rare crossover between lab and large-scale deployment. Although he is still early in his postdoctoral career, his consistent innovation and impact have made him a rising leader in AI-powered mobility. His contributions position him for prestigious honors such as the Best Researcher Award and similar recognitions for scientific leadership.

🔬 Research Focus :

Dr. Zhang’s research focuses on scalable and trustworthy AI for autonomous systems and smart energy applications. His core expertise includes multi-agent reinforcement learning, closed-loop large language models (LLMs), and intelligent motion control. He develops AI algorithms that can be safely deployed in micro-mobility devices (assistive and mobile robots), connected vehicles, and distributed energy platforms. His work contributes to both algorithmic innovation and real-world adoption, ensuring AI agents are reliable, interpretable, and responsive to dynamic environments. He is particularly interested in bridging theory with practice by collaborating with top-tier institutions and OEMs. Dr. Zhang’s interdisciplinary approach merges robotics, automotive systems, control engineering, and deep learning to create adaptive, secure, and energy-efficient intelligent agents.

📊 Publication Top Notes:

📘 Impact of ammonia addition on knock resistance and combustion performance in a gasoline engine with high compression ratio
📅 Year: 2023 | 📊 Cited by: 75 | 🛠️ Energy efficiency, combustion

📘 Hierarchical energy management strategy for plug-in hybrid electric powertrain integrated with dual-mode combustion engine
📅 Year: 2021 | 📊 Cited by: 42 | ⚡ Hybrid vehicles, control systems

📘 Data-driven predictive energy consumption minimization strategy for connected plug-in hybrid electric vehicles
📅 Year: 2023 | 📊 Cited by: 40 | 📡 Connected vehicles, optimization

📘 Experimental study on combustion and emission characteristics of ethanol-gasoline blends in a high compression ratio SI engine
📅 Year: 2023 | 📊 Cited by: 36 | 🔬 Fuel science, engine performance

📘 Learning-based supervisory control of dual mode engine-based hybrid electric vehicle with reliance on multivariate trip information
📅 Year: 2022 | 📊 Cited by: 34 | 🤖 AI control, mobility systems

📘 Integrated thermal and energy management of connected hybrid electric vehicles using deep reinforcement learning
📅 Year: 2023 | 📊 Cited by: 30 | 🧠 Deep learning, hybrid energy systems

Mohd Usama | Machine Learning | Best Researcher Award

Published on 17/05/202517/05/2025 by Engineering Scientist

Assist. Prof. Dr. MohdUsama|MachineLearning
|Best Researcher Award

Postdoctoral Researcher at Umea University, Sweden Sweden.

Dr. Mohd Usama is a Postdoctoral Researcher at the Department of Diagnostics and Intervention, Umea University, Sweden. He holds a Ph.D. in Computer Science from Huazhong University of Science and Technology, China, focusing on deep learning for disease prediction and sentiment analysis. His research bridges artificial intelligence and medical imaging, particularly using GANs for domain adaptation and plaque detection in ultrasound imagery. With a solid teaching and research background across reputed institutions in India, he has significantly contributed to developing AI-based clinical decision support systems. His scholarly work, practical innovation, and interdisciplinary expertise make him highly suitable for the Best Researcher Award, exemplifying excellence in research, innovation, and educational service in the domains of biomedical engineering and artificial intelligence.

🌍 Professional Profile:

Scopus

Orcid

🏆 Suitability for the Best Researcher Award

Dr. Mohd Usama exemplifies the qualities of a top-tier researcher through his impactful contributions to AI-driven medical imaging and clinical decision support systems. Currently a Postdoctoral Researcher at Umea University, Sweden, his work on generative adversarial networks for ultrasound-based atherosclerosis risk assessment addresses critical challenges in healthcare diagnostics. His strong academic foundation, interdisciplinary approach, and global research collaborations demonstrate exceptional innovation and dedication. Dr. Usama’s ability to translate deep learning research into real-world clinical applications, alongside a consistent record of teaching, publishing, and mentoring, positions him as a leader in his field. His scientific rigor, creativity, and societal impact make him a highly deserving candidate for the Best Researcher Award.

🎓 Education

Dr. Usama earned his Ph.D. in Computer Science from Huazhong University of Science and Technology, China (2016–2020), with a dissertation on “Recurrent Deep Learning for Text Processing with Application to Disease Prediction and Sentiment Analysis,” supervised by Prof. Min Chen. He completed his Master’s in Computer Science and Applications (71.78%, First Division) from Aligarh Muslim University (2013–2016), focusing on cloud-based electric vehicle charging management. His undergraduate degree is a B.Sc. (Hons) in Statistics (71.07%, First Division), also from Aligarh Muslim University (2009–2012), with a thesis on students’ opinions on the Ombudsman Bill in India. His academic journey reflects a blend of statistical foundations, computing applications, and interdisciplinary insights, crucial for innovative AI research in biomedical domains.

🏢 Work Experience

Dr. Mohd Usama has served as a Postdoctoral Researcher at Umea University, Sweden (Dec 2022–Present), contributing to AI-powered clinical decision support systems and generative models for carotid ultrasound imaging. Previously, he worked as an Assistant Professor at the University of Petroleum and Energy Studies (2022), Kalasalingam Academy of Research and Education (2021–2022), and Madanapalle Institute of Technology and Science (2020–2021). He taught various courses including Deep Learning, Algorithms, Programming, and Information Security. His work spans both academia and research, with a deep engagement in curriculum development and applied machine learning. His experience in medical imaging research and teaching demonstrates a strong integration of theoretical and practical knowledge, making him a well-rounded and impactful scholar.

🏅 Awards and Honors

Dr. Mohd Usama has been recognized for his innovative interdisciplinary research contributions at the intersection of artificial intelligence and healthcare. He received prestigious academic scholarships for his doctoral studies in China and earned consistent recognition throughout his academic career. He has been invited to deliver expert lectures and guest talks on AI, deep learning, and statistical computing at various institutions. His role in international collaborative projects on ultrasound imaging and disease prediction further demonstrates his global impact. As a frequent reviewer for reputed journals and contributor to academic forums, he maintains high standards of scholarly excellence. These achievements, coupled with his dedication to knowledge dissemination and impactful research, position him as a strong candidate for the Best Researcher Award.

🔬 Research Focus

Dr. Mohd Usama’s research lies at the convergence of artificial intelligence, deep learning, and medical imaging. His work primarily involves the use of generative adversarial networks (GANs) to address domain adaptation, noise reduction, and feature interpolation in carotid ultrasound images. He develops AI-powered clinical decision support systems to enhance subclinical atherosclerosis risk prediction and ultrasound diagnostics. His doctoral research explored recurrent deep learning for text analysis in healthcare applications. He is also keenly interested in disease modeling, natural language processing, and sentiment analysis within clinical contexts. Dr. Usama’s work emphasizes real-world application of machine learning in healthcare, contributing to early diagnosis and precision medicine through robust, data-driven solutions, reinforcing his value as a research innovator.

📊 Publication Top Notes:

Usama, M., Nyman, E., Näslund, U., & Grönlund, C. (2025).
A domain adaptation model for carotid ultrasound: Image harmonization, noise reduction, and impact on cardiovascular risk markers.
Computers in Biology and Medicine.
https://doi.org/10.1016/j.compbiomed.2025.110030
Usama, M., & Grönlund, C. (2023).
Carotid Ultrasound Image Denoising Using Low-to-High Image Quality Domain Adaptation.
The Medical Technology Days (MTD), 2023, Stockholm.
Singh, A. P., Kumar, S., Kumar, A., & Usama, M. (2022).
Machine Learning based Intrusion Detection System for Minority Attacks Classification.
2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES).
https://doi.org/10.1109/cises54857.2022.9844381
Ahmad, B., Usama, M., Ahmad, T., Khatoon, S., & Alam, C. M. (2022).
An ensemble model of convolution and recurrent neural network for skin disease classification.
International Journal of Imaging Systems and Technology, 32(1), 15–24.
https://doi.org/10.1002/ima.22661
Ahmad, B., Usama, M., Huang, C. M., Hwang, K., Hossain, M. S., & Muhammad, G. (2020).
Discriminative Feature Learning for Skin Disease Classification Using Deep Convolutional Neural Network.
IEEE Access, 8, 39098–39110.
https://doi.org/10.1109/ACCESS.2020.2975198
Qamar, S., Jin, H., Zheng, R., Ahmad, P., & Usama, M. (2020).
A variant form of 3D-UNet for infant brain segmentation.
Future Generation Computer Systems, 108, 618–628.
https://doi.org/10.1016/j.future.2019.11.021
Usama, M., Ahmad, B., Song, E., Hossain, M. S., Alrashoud, M., & Muhammad, G. (2020).
Attention-based sentiment analysis using convolutional and recurrent neural network.
Future Generation Computer Systems, 106, 336–347.
https://doi.org/10.1016/j.future.2020.07.022
Usama, M., Ahmad, B., Xiao, W., Hossain, M. S., & Muhammad, G. (2020).
Self-attention based recurrent convolutional neural network for disease prediction using healthcare data.
Computer Methods and Programs in Biomedicine, 187, 105191.
https://doi.org/10.1016/j.cmpb.2019.105191
Ahmad, P., Jin, H., Qamar, S., Zheng, R., Jiang, W., Ahmad, B., & Usama, M. (2019).
3D Dense Dilated Hierarchical Architecture for Brain Tumor Segmentation.
Proceedings of the 2019 4th International Conference on Big Data and Computing (ICBDC).
https://doi.org/10.1145/3335484.3335516
Ahmad, B., Usama, M., Lu, J., Xiao, W., Wan, J., & Yang, J. (2019).
Deep Convolutional Neural Network Using Triplet Loss to Distinguish the Identical Twins.
2019 IEEE Globecom Workshops (GC Wkshps).
https://doi.org/10.1109/GCWkshps45667.2019.9024704
Usama, M., Xiao, W., Ahmad, B., Wan, J., Hassan, M. M., & Alelaiwi, A. (2019).
Deep Learning Based Weighted Feature Fusion Approach for Sentiment Analysis.
IEEE Access, 7, 140361–140373.
https://doi.org/10.1109/ACCESS.2019.2940051
Usama, M., Ahmad, B., Yang, J., Qamar, S., Ahmad, P., Zhang, Y., Lv, J., & Guna, J. (2019).
Equipping recurrent neural network with CNN-style attention mechanisms for sentiment analysis of network reviews.
Computer Communications, 149, 111–121.
https://doi.org/10.1016/j.comcom.2019.08.002
Hao, Y., Usama, M., Yang, J., Hossain, M. S., & Ghoneim, A. (2019).
Recurrent convolutional neural network based multimodal disease risk prediction.
Future Generation Computer Systems, 98, 296–304.
https://doi.org/10.1016/j.future.2018.09.031

Shahrzad Falahat | Deep learning | Best Researcher Award

Published on 05/05/202505/05/2025 by Engineering Scientist

Dr. Shahrzad Falahat| Deep learning |Best Researcher Award

Lecturer at Shahid Bahonar University of Kerman,Iran.

Dr. Shahrzad Falahat is a visionary researcher specializing in computer vision and remote sensing with a proven track record in academic and industrial AI. Holding a Ph.D. in Computer Vision, she has led interdisciplinary AI projects for over five years, collaborating across sectors such as electrical, medical, and railway industries. Her innovations include software for fault detection in power lines, cutting electricity outages by 70%, and automatic cartography tools that significantly improve mapping efficiency. Dr. Falahat’s technical proficiency spans Python, PyTorch, TensorFlow, and embedded AI systems, making her a versatile leader in AI development. Her outstanding contributions, impactful publications, and real-world implementations make her an exceptional candidate for the Best Researcher Award.

🌍 Professional Profile:

Google scholar

Orcid

🏆 Suitability for the Best Researcher Award

Dr. Shahrzad Falahat exemplifies excellence in applied AI research, making her a highly suitable candidate for the Best Researcher Award. With a Ph.D. in Computer Vision and over five years of impactful industrial experience, she has led innovative projects that address critical real-world challenges. Her development of AI-powered fault detection software for power transmission lines reduced outages by 70%, while her automated cartography system cut map production time by 80%. She combines deep technical expertise in Python, PyTorch, TensorFlow, and embedded AI with strong project management and cross-sector collaboration. Her work integrates research and practice, resulting in scalable, intelligent solutions with tangible societal benefits, positioning her as a leader in the field of AI and computer vision.

🎓 Education

Dr. Shahrzad Falahat earned her Ph.D. in Computer Vision, focusing on advanced deep learning techniques and remote sensing applications. Her academic journey equipped her with a robust foundation in machine learning, optimization, and AI-driven image processing. Throughout her doctoral studies, she published influential research on automated systems in industrial and environmental monitoring. Her educational background is enriched by expertise in embedded systems, GPU computing, and multi-platform AI development. With a blend of theoretical insight and practical execution, Dr. Falahat continues to bridge academia and industry, pushing the frontiers of computer vision and applied AI technologies.

🏢 Work Experience

Dr. Shahrzad Falahat currently serves as a Researcher at Shahid Bahonar University of Kerman, where she leads AI projects across industries including energy, agriculture, and transportation. She has spearheaded projects that translated complex research into deployable AI solutions. Previously, she was a Medical Imaging Data Scientist at Azin Eye Surgery Center, developing real-time diagnostic systems for eye diseases. Her contributions extend to edge AI deployments using NVIDIA Jetson Nano and STM32 AI, and leading product management from conception to deployment. She excels in dataset design, stakeholder collaboration, and technical documentation. Dr. Falahat’s blend of academic depth and real-world implementation underscores her excellence in delivering innovative, scalable AI solutions.

🏅 Awards and Honors

Dr. Shahrzad Falahat’s contributions to AI-driven innovation have earned her recognition in both research and industrial domains. She has been honored for her work on reducing electricity outages through intelligent fault detection systems and for her impactful software tools that enhance mapping and diagnosis. Her projects have received institutional support, including collaborations with the Islamic Republic of Iran Railways and Azin Eye Surgery Center. She has been an invited presenter at several national workshops and conferences and is respected for her role in bridging AI research with industrial applications. Her consistent excellence in technical leadership, publication, and applied innovation positions her as a distinguished candidate for research excellence awards.

🔬 Research Focus

Dr. Falahat’s research centers on the intersection of computer vision, deep learning, and remote sensing. She develops intelligent systems for infrastructure monitoring, medical diagnostics, and geospatial mapping. Her key focus lies in real-time AI deployment, optimization of deep learning models, and embedded system integration. Notable projects include automatic fault detection in power lines and real-time eye disease detection using medical imaging, both demonstrating high accuracy and operational efficiency. She is also actively involved in railway infrastructure monitoring. Her work leverages edge computing, cloud AI platforms, and domain-specific datasets to deliver practical, scalable solutions. Dr. Falahat’s applied research addresses real-world challenges, making significant contributions to both technological advancement and societal needs.

📊 Publication Top Notes:

Maize tassel detection and counting using a Yolov5-based model
Cited by: 16
Author(s): S Falahat, A Karami
Year: 2023
Influence of thickness on the structural, optical and magnetic properties of bismuth ferrite thin films
Cited by: 15
Author(s): H Maleki, S Falahatnezhad, M Taraz
Year: 2018
Synthesis and study of structural, optical and magnetic properties of BiFeO3–ZnFe2O4 nanocomposites
Cited by: 9
Author(s): S Falahatnezhad, H Maleki
Year: 2018
Deep fusion of hyperspectral and LiDAR images using attention-based CNN
Cited by: 7
Author(s): S Falahatnejad, A Karami
Year: 2022
PTSRGAN: Power transmission lines single image super-resolution using a generative adversarial network
Cited by: 5
Author(s): S Falahatnejad, A Karami, H Nezamabadi-pour
Year: 2024
Influence of synthesis method on the structural, optical and magnetic properties of BiFeO3–ZnFe2O4 nanocomposites
Cited by: 5
Author(s): S Falahatnezhad, H Maleki, AM Badizi, M Noorzadeh
Year: 2019
A comparative study on predicting the characteristics of plasma activated water: artificial neural network (ANN) & support vector regression (SVR)
Cited by: 2
Author(s): S Karimian, S Falahat, ZE Bakhsh, MJG Rad, A Barkhordari
Year: 2024
A Spectral-Spatial Augmented Active Learning Method for Hyperspectral Image Classification
Cited by: 2
Author(s): S Falahatnejad, A Karami
Year: 2023
PTSRDet: End-to-End Super-Resolution and object-detection approach for small defect detection of power transmission lines
Cited by: 0
Author(s): S Falahatnejad, A Karami, H Nezamabadi-pour
Year: 2025
Building Footprint Segmentation Using the Modified YOLOv8 Model
Cited by: 0
Author(s): S Falahatnejad, A Karami, R Sharifirad, M Shirani, M Mehrabinejad, …
Year: 2024

Zhiying Mu| Neural Networks | Best Researcher Award

Published on 02/05/202502/05/2025 by Engineering Scientist

Dr. Zhiying Mu| Neural Networks
|Best Researcher Award

Dr . Zhiying Mu Northwestern Polytechnical University, China .

Zhiying Mu, a Ph.D. candidate in Cyberspace Security at Northwestern Polytechnical University, is a distinguished young scholar dedicated to AI safety and intelligent system security. With a rigorous academic foundation and cross-disciplinary insight from her mathematics background at the University of Connecticut and the University of Nebraska–Lincoln, she has contributed to multiple national-level research projects, including the “New Generation Artificial Intelligence” initiative. Her publications in top-tier journals like IEEE IoT Journal and Neural Processing Letters demonstrate high academic impact. She leads multiple research and data-driven modeling projects with outstanding results. Her innovative mindset, strong leadership, and publication record make her an exceptional candidate for the Best Researcher Award.

🌍 Professional Profile:

Scopus

🏆 Suitability for the Best Researcher Award

🎓 Education

Zhiying Mu earned her Ph.D. in Cyberspace Security (2021–2025) from Northwestern Polytechnical University, under the supervision of Academician He Dequan. Her curriculum includes machine learning, optimization, complex networks, and academic ethics. She actively contributed to major national and industrial research projects related to AI safety and power systems. She previously earned a Master’s degree in Mathematics from the University of Connecticut (2017–2019), and a Bachelor’s degree in Mathematics from the University of Nebraska–Lincoln (2013–2017). Her academic performance has been consistently excellent, with a GPA of 3.8/4.0 during her Ph.D. Her multidisciplinary training bridges cybersecurity and data science, laying a robust foundation for her research excellence and interdisciplinary innovation.

🏢 Work Experience

Zhiying Mu has led and participated in various high-impact research projects involving AI safety, network attack modeling, and climate risk forecasting. Notable projects include storm damage prediction using regression models, ACI index modeling via time-series analysis, and optimal strategy evaluation in Tic-Tac-Toe using generalized linear models. She has also organized institutional reading programs, promoting interdisciplinary knowledge sharing. Her responsibilities typically involve end-to-end project management: problem formulation, data collection and preprocessing, statistical modeling, visualization, and outcome documentation. She is proficient in R and Python and applies advanced analytics and machine learning techniques. Her blend of theoretical depth and practical implementation reflects a versatile and impactful research profile in both academic and applied contexts.

🏅 Awards and Honors

Zhiying Mu has received significant recognition for her research and academic contributions. She was the top borrower of the year at her university library, reflecting her deep engagement with academic literature. She has been entrusted with leadership roles in national projects funded under China’s “New Generation Artificial Intelligence” program and major horizontal projects with the State Grid Corporation of China. Her peer-reviewed publications in top SCI-indexed journals such as IEEE IoT Journal, Neurocomputing, and Neural Processing Letters highlight her academic excellence. She has also served as a project lead in multiple interdisciplinary modeling initiatives. Her academic and extracurricular leadership underscores her status as an emerging thought leader in AI security and intelligent systems.

🔬 Research Focus

Zhiying Mu’s research centers on artificial intelligence security, multi-task learning, risk modeling, and network attack analysis. She investigates adversarial learning techniques, identity-preserving dialogue generation, and neural machine translation enhancement using syntactic features. Her work integrates mathematical modeling, machine learning, and cybersecurity to address challenges in intelligent power systems, social network robustness, and data-driven decision-making. She has explored both black-box and white-box vulnerabilities in AI systems and proposed defense mechanisms with theoretical grounding. Her interdisciplinary focus also includes time-series forecasting for insurance risk and strategic modeling in game theory. She actively contributes to national AI safety platforms and is committed to advancing secure, interpretable, and reliable AI technologies for critical infrastructures.

📊 Publication Top Notes:

Prompt-enhanced Neural Machine Translation with POS Tags

Authors:
Mu, Zhiying; Lin, Shengchuan; Guo, Sensen; Yu, Shanqing; Gao, Dehong

Journal:
Neurocomputing, 2025

Citations:
0 (as of now)

R Lakshman Naik |Computer Science and Engineering | Best Paper Award

Published on 06/03/2025 by Engineering Scientist

Mr.R Lakshman Naik|Computer Science and Engineering| Best Paper Award

Research Scholar at Indian Institute of Information Technology Sonepat , Haryana, India

Mr. R. Lakshman Naik is a Research Scholar at the Indian Institute of Information Technology (IIIT) Sonepat, Haryana, India. His research focuses on advanced topics in computer science, artificial intelligence, data science, or related fields. As a dedicated scholar, he actively contributes to academic research, publications, and innovative technological developments. IIIT Sonepat, recognized as an Institute of National Importance, provides a dynamic environment for cutting-edge research and interdisciplinary collaboration.

Publication Profile

Scopus

Education :

Lakshman Naik Ramavathu holds a Master of Technology (M.Tech) degree in Digital Communication from Kakatiya University, Warangal (2014-16), and another M.Tech in Computer Science and Engineering from JNT University, Hyderabad (2009-11). He completed his Bachelor of Technology (B.Tech) in Electronics and Communication Engineering from JNT University, Hyderabad (2001-05). His diverse educational background provides a strong foundation in computer science, digital communication, and information technology.

Experience :

Currently, he serves as an Assistant Professor (C) in the Department of Information Technology at KU College of Engineering & Technology, Warangal, where he has been teaching since 2016. His teaching expertise includes Operating Systems, Computer Architecture and Organization, Data Communication and Networking, Machine Learning, Python Programming, and Mobile Cloud Computing.

Prior to his academic career, he worked as a Part-time Lecturer in the Department of Computer Science at Kakatiya University (2012-2016), where he taught subjects like System Software, Cloud Computing, Mobile Communication, and Open-Source Software.

Research Focus :

Lakshman Naik Ramavathu’s research interests include Machine Learning, Cloud Computing, Data Mining, and Computer Networking. His work revolves around optimizing computational frameworks, developing intelligent predictive models, and improving networking protocols for enhanced system performance.

Skills:

Sun Certified System Administrator for Sun Solaris 9 (Part-I & II)Microsoft Certified Professional in Windows 2003 Enterprise Server Expertise in Cloud Computing, Machine Learning, Computer Networks, and Data Mining

Awards:

Recognized for impactful research contributions in cloud computing and machine learning Multiple research papers published in high-impact international journals Significant contributions to academia and industry in system administration and computing

Publication :

Comparison of Data Mining Versus Traditional Analysis in Textile Business”
- Publication: IFRSA International Journal of Data Warehousing & Mining
- ISSN (Online): 2249–2186
- ISSN (Print): 2249–7161
- Volume: 1, Issue 1
“DFFS: Detecting Fraud in Finance Sector”
- Authors: R. Lakshman Naik, Dr. Manjula Bairam
- Publication: International Journal of Advanced Engineering Sciences and Technologies
- ISSN: 2230-7818
- Volume: 9, Issue 2
“Study of Trends in Higher Education”
- Publication: International Journal of Computer Trends and Technology
- ISSN: 2231-2803
- Volume: 1, Issue 1
“Stock Prediction using Neural Network”
- Publication: International Journal of Advanced Engineering Sciences and Technologies
- ISSN: 2230-7818
- Volume: 10, Issue 1
“Session Data Protection Using Tree-Based Dependency”
- Publication: International Journal of Advances in Engineering & Technology
- ISSN: 2231-1963
- Volume: 2, No. 1
“Secure Authentication Scheme for Mobile Ad Hoc Networks”
- Publication: International Journal of Mobile & Adhoc Network
- ISSN (Online): 2231-6825
- ISSN (Print): 2249-202X
- Volume: 2, Issue 1
“Secure Scheme of Data Protection in Cloud Computing”
- Publication: International Journal of Computer Science and Technology
- ISSN: 0976-8491
- ISSN: 2229-4333
- Volume: 3, No. 1
“Cloud Computing: Research Issues and Implications”
- Publication: International Journal of Cloud Computing and Services Science
- ISSN: 2089-3337
- Volume: 2, No. 2
“Prediction of BSE Stock Data using MapReduce K-Mean Cluster Algorithm”
- Publication: International Journal of Current Engineering and Technology, INPRESSCO
- E-ISSN: 2277–4106
- P-ISSN: 2347–5161
- Volume: 5, No. 3
“Current Apprises of Opinion Mining Methods”
- Publication: International Journal of Engineering and Advanced Technology (IJEAT)
- ISSN: 2249–8958
- Volume: 9, Issue 2

Conclusion

Based on his research achievements, Lakshman Naik Ramavathu is well-suited for a Best Paper Award, provided the submission is among his most impactful and high-quality research works. Enhancing recent publications, collaborations, and practical implementations will further solidify his standing in the academic and research community.