Ali Reza Keivanimehr | AI in healthcare | Best Researcher Award

Posted on by 21b4bddf87157652317827146367558f

Mr.Ali Reza Keivanimehr | AI in healthcare
| Best Researcher Award

Mr.  Ali RezaKeivanimehr ,  Amirkabir University of Technology (Tehran’s Polytechnic), Iran.

Ali Reza Keivanimehr is an exceptional early-career researcher with a solid academic foundation, a promising research trajectory in machine learning applications for healthcare, and strong technical expertise. His combination of research, teaching, and technical projects highlights a well-rounded profile. His contributions, especially in the use of TinyML for cardiovascular diagnosis, are commendable and align with global health priorities.

Publication Profile

Google scholar

Education :

Master of Science in Information Technology Engineering – Internet of Things (IoT) (2022 – 2025)Amirkabir University of Technology (Tehran Polytechnic), Tehran, IranRanked 403rd in QS World University Rankings 2024GPA: 3.53/4 (17.48/20) – 3rd highest in 2022 faculty entranceThesis: Applications of TinyML in Prediction and Diagnosis of Cardiovascular DiseasesSupervisor: Dr. Mohammad Akbari | Advisor: Dr. Abbas AhmadiBachelor of Science in Computer Engineering – Software Engineering (2018 – 2021)Imam Khomeini International University of Qazvin, Qazvin, IranProject: Designing a Software Interface for Industrial Machinery Maintenance

Experience :

Research Assistant (2022 – Present)
Data Science Lab (DSLab), Amirkabir University of Technology, Tehran, IranConducting research on TinyML and edge intelligence applications in cardiovascular disease prediction.Teaching Assistant — Machine Learning and Pattern Recognition (2024 – 2025)Amirkabir University of Technology, Tehran, IranAssisted in course instruction, project supervision, and student evaluations under Dr. Alireza Rezvanian.Teaching Assistant — Data Structure and Algorithms (2019 – 2020)
Imam Khomeini International University of Qazvin, Qazvin, IranSupported coursework delivery, assignments, and exam preparations under Morteza Mohammadi Zanjireh.

Research Focus :

Natural Language Processing (NLP)Graph Neural NetworksEdge IntelligenceExplainable Artificial Intelligence (XAI)Generative Adversarial Networks (GANs)Dr. Keivanimehr’s research centers on Tiny Machine Learning (TinyML) and edge intelligence, with a specific emphasis on their applications in cardiovascular disease monitoring. He explores the deployment of machine learning models on low-power, resource-limited devices to facilitate real-time analytics and pervasive monitoring for patients with cardiac anomalies.

Skills and Expertise:

As a research assistant, Dr. Keivanimehr has developed expertise in machine learning, classification, and supervised learning. His technical proficiency includes a focus on computational health and biomedical applications, particularly in the context of resource-constrained devices.Programming: PythonMachine Learning Frameworks: PyTorch, TensorFlowBig Data Tools: Apache SparkLanguages: TOEFL iBT (Score: 109 | Reading: 28 | Listening: 30 | Speaking: 26 | Writing: 25)

Awards:

 

48th Rank among 5000+ participants, National Entrance Exam for Master Studies in IT Engineering (2022)3rd Rank in IT Engineering Master’s cohort based on GPA (2022 – Present)Full Master’s Scholarship: Awarded for excellence in national entrance exams; covers tuition, dormitory, and partial food expenses (2022 – Present)Full Bachelor’s Scholarship: Granted for top performance in national entrance exams; included tuition, accommodation, and meal support (2018 – 2021)

 

Publication 

 

  • Keivanimehr, A., & Akbari, M. (2024). TinyML and Edge Intelligence Applications in Cardiovascular Disease: A Survey. Computers in Biology and Medicine. DOI: 10.1016/j.compbiomed.2025.109653

 

Conclusion

Ali Reza Keivanimehr is a suitable candidate for the Best Researcher Award. His strong academic record, impactful research, and consistent growth in machine learning and edge intelligence demonstrate his potential as a leading researcher in his field. With further international exposure and expanded publication efforts, he is poised to make significant contributions to both academia and industry.

 

Dongsong Zhang | Mobile health | Best Researcher Award

Posted on by 21b4bddf87157652317827146367558f

 Dr.Dongsong Zhang |Mobile health|Best Researcher Award

 Dr.  Dongsong Zhang UNC Charlotte,United States.

 

Dr. Dongsong Zhang is a Professor in the Department of Business Information Systems and Operations Management at the University of North Carolina at Charlotte. He earned his Ph.D. in Management Information Systems from the University of Arizona. Dr. Zhang’s research interests include mobile and intelligent health systems, human-computer interaction, and business analytics. He has published extensively in top-tier journals and conferences, contributing significantly to the fields of information systems and health informatics.

Publication Profile

scopus

Education :

Dr. Dongsong Zhang earned his Ph.D. in Management Information Systems from The Eller College of Management at the University of Arizona in 2002. He also holds an M.S. in Natural Language Processing from the Institute of Psychology, Chinese Academy of Sciences (1995) and a B.S. in Electrical & Computer Engineering from the Branch Campus of Peking University, Beijing, China (1990).

Experience :

Dr. Zhang has extensive experience in academia and research leadership. He is currently the Interim Executive Director of the School of Data Science at the University of North Carolina at Charlotte (UNCC). Previously, he served as the Director of Research at the same school (2020-2024). Since 2018, he has been the Belk Endowed Chair Professor in Business Analytics at the Belk College of Business and a Professor (Courtesy) in Computer Science at UNCC.Prior to joining UNCC, Dr. Zhang was a Full Professor (2014-2018), Associate Professor (2007-2014), and Assistant Professor (2002-2007) in the Department of Information Systems at the University of Maryland, Baltimore County (UMBC). His early career includes research roles at the Center for the Management of Information, University of Arizona (1997-2002), and the Institute of Psychology, Chinese Academy of Sciences (1990-1996).

Research Focus :

Dr. Zhang’s research revolves around data-driven decision-making and intelligent systems, with a particular focus on:AI and Machine Learning for Business IntelligenceFake News Detection & Online Misinformation AnalysisCybersecurity, Phishing Detection, and Behavioral AnalyticsNatural Language Processing in Social Media & Public HealthHuman-Computer Interaction (HCI) & Adaptive SystemsHis projects explore how emerging technologies can be leveraged to improve cybersecurity, healthcare, and business intelligence.

 

Awards:

Dr. Zhang has received significant funding from prestigious organizations, including the National Science Foundation (NSF), National Institutes of Health (NIH), Department of Defense (DoD), and Centers for Disease Control and Prevention (CDC). Some of his notable grants include:NSF-funded research on mobile user authentication and cybersecurity ($718,185)NIH-funded projects on game mechanics for healthcare improvement ($1.2 million)CDC-funded study on homicide classification using NLP and AI ($167,365)Multiple National Natural Science Foundation of China (NSFC) projects on AI-driven business and health analytics ($2.35 million total)In addition to research grants, Dr. Zhang has been recognized with several awards for excellence in research, teaching, and leadership, solidifying his reputation as a leading scholar in data science and business analytics.

Publication :

  • Yan, Z., Peng, F., & Zhang, D. (2025). DECEN: A Deep Learning Model Enhanced by Depressive Emotions for Depression Detection from Social Media Content. Decision Support Systems. Accepted for publication on Feb. 8, 2025.

  • Zhang, D., Shan, G., Lee, M., Zhou, L., & Fu, Z. (2025). MT-GPD: A Multimodal Deep Transfer Learning Model Enhanced by Auxiliary Mechanisms for Cross-domain Online Fake News Detection. Production and Operation Management. Accepted for publication in Jan. 2025.

  • Yu, L., Gong, W., Zhang, D., & Ding, Y. (2025). From Interaction to Prediction: A Multi-Interactive Attention-based Approach to Product Rating Prediction. INFORMS Journal on Computing. Forthcoming.

  • Zhang, D., Zhou, L., Tao, J., Zhu, T., & Gao, G. (2025). KETCH: A Knowledge-Enhanced Transformer-based Approach to Suicidal Ideation Detection from Social Media Content. Information Systems Research (ISR). Published online on May 31, 2024.

  • Peng, F., Zhang, D., & Yan, Z. (2024). Digital Phenotyping-based Depression Detection in the Presence of Comorbidity: An Uncertainty Reasoning Approach. Journal of Management Information Systems (FT 30), 41(4), 931-957.

  • Yu, L., Xing, W., & Zhang, D. (2024). Live Streaming Channel Recommendation Based on Viewers’ Interaction Behavior: A Hypergraph Approach. Decision Support Systems, 184.

  • Chen, S., Yin, S., Guo, Y., Ge, Y., Janies, D., Dulin, M., Brown, C., Robinson, P., & Zhang, D. (2023). Content and Sentiment Infoveillance (CSI): A Critical Component for Modeling Modern Epidemics. Frontiers in Public Health, 11, 1111661. https://doi.org/10.3389/fpubh.2023.1111661. PMID: 3700.

 

Conclusion 

Dr. Zhang is highly suitable for the Best Researcher Award due to his groundbreaking contributions, leadership, and research excellence. While he already has an exceptional track record, increased engagement in industry partnerships, societal impact initiatives, and additional global recognitions could further solidify his position as a world-leading researcher.