Ranko Romanić | Food Technology | Best Researcher Award

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Assoc. Prof. Dr. Ranko Romanić | Food Technology | Best Researcher Award

University of Novi Sad | Serbia

Assoc. Prof. Dr. Ranko Romanić is an accomplished researcher and academic in the field of Food Engineering, currently serving as an Associate Professor at the Department of Food Preservation Engineering, Faculty of Technology, University of Novi Sad, Serbia. His research primarily focuses on the technology of vegetable oils and fats, with special expertise in the production and optimization of cold-pressed oils, chemometric modeling, and improving the oxidative stability and nutritional quality of edible oils. His Ph.D. research, titled “Hemometric approach to the optimization of technological parameters for the production of cold-pressed oil of high-oleic sunflower seeds,” laid the foundation for his continuing work on process optimization and quality enhancement in oil production. Assoc. Prof. Dr. Ranko Romanić has contributed to more than seven national and provincial research projects and has published extensively in high-impact international journals such as Foods, Processes, Food Chemistry, and Journal of the Science of Food and Agriculture. His recent studies explore the development of omega-3-enriched oil blends, sustainable winterization processes, and biopolymer film applications in oil packaging. As Editor-in-Chief of the journal “Uljarstvo – Journal of Edible Oils Industry”, he plays a pivotal role in advancing scientific communication within his field. Additionally, Assoc. Prof. Dr. Ranko Romanić leads the accredited Laboratory for Food Testing (ISO/IEC 17025), ensuring high standards in food quality assessment and compliance. A member of the Serbian Chemical Society and the Institute for Standardization of Serbia, he actively collaborates with industry to enhance oil production technologies and promote sustainable food processing practices.

Profile: Google Scholar | Scopus | Orcid

Featured Publications

  • Lužaić, T., Škrbić, J., Nakov, G., Petrović, J., & Romanić, R. (2025). Deep-frying performance of palm olein and sunflower oil variants: Antioxidant-enriched and high-oleic oil as potential substitutes. Processes, 13(10), 3285.

  • Lužaić, T., Nakov, G., Kravić, S., Jocić, S., & Romanić, R. (2025). Influence of hull and impurity content in high-oleic sunflower seeds on pressing efficiency and cold-pressed oil yield. Applied Sciences, 15(6), 3012.

  • Romanić, R., Lužaić, T., Pezo, L., & Radić, B. (2024). Omega-3 blends of sunflower and flaxseed oil—Modeling chemical quality and sensory acceptability. Foods, 13(23), 3722.

  • Lužaić, T., Nedić Grujin, K., Pezo, L., Nikolovski, B., Maksimović, Z., & Romanić, R. (2024). Implementation of cellulose-based filtration aids in industrial sunflower oil dewaxing (winterization): Process monitoring, prediction, and optimization. Foods, 13(18), 2960.

  • Romanić, R. S., Lužaić, T. Z., & Radić, B. Đ. (2021). Enriched sunflower oil with omega-3 fatty acids from flaxseed oil: Prediction of the nutritive characteristics. LWT, 151, 112064.

Yang Liu | Pattern Recognition | Innovative Research Award

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Assist. Prof. Dr. Yang Liu | Pattern Recognition | Innovative Research Award

Assistant professor at Zhejiang University | China

Assistant Professor Dr. Yang Liu is an emerging scholar specializing in computer vision, machine learning, and remote sensing, with a strong research focus on unsupervised representation learning, facial modeling, and multimodal image translation. His work integrates deep learning and generative modeling to advance intelligent visual understanding systems. Notably, his 2025 paper “Adaptive Sparse Contrastive Learning for Unsupervised Object Re-identification” in Pattern Recognition introduces an innovative sparse contrastive framework for improved feature discrimination in object re-identification. His 2024 studies in Knowledge-Based Systems and Remote Sensing present significant contributions to multi-objective reinforcement learning through dynamic preference inference and to SAR-to-multispectral image translation via S2MS-GAN, enhancing cross-modal synthesis and efficiency. Earlier works in IEEE Signal Processing Letters and IEEE Access showcase his expertise in fine-scale 3D face reconstruction, texture fusion, and photorealistic head modeling. Collaborating with international teams from leading universities such as Zhejiang University and Northwestern Polytechnical University, Assistant Professor Dr. Yang Liu continues to drive innovation at the intersection of computer vision and AI. His ongoing research aims to develop more adaptive, interpretable, and sustainable AI-driven visual intelligence systems that can bridge the gap between human perception and machine understanding in complex, real-world environments.

Profile

Featured Publications

  • Zheng, D., Liu, Y., Zhou, D., Xiao, J., Zhang, B., & Chen, L. (2025). Adaptive sparse contrastive learning for unsupervised object re-identification. Pattern Recognition, 157, 112604.

  • Liu, Y., Zhou, Y., He, Z., Yang, Y., Han, Q., & Li, J. (2024). Dynamic preference inference network: Improving sample efficiency for multi-objective reinforcement learning by preference estimation. Knowledge-Based Systems, 305, 112512.

  • Liu, Y., Han, Q., Yang, H., & Hu, H. (2024). High-resolution SAR-to-multispectral image translation based on S2MS-GAN. Remote Sensing, 16(21), 4045.

  • Liu, Y., Fan, Y., Guo, Z., Zaman, A., & Liu, S. (2023). Fine-scale face fitting and texture fusion with inverse renderer. IEEE Signal Processing Letters, 30, 139–143.

  • Fan, Y., Liu, Y., Lv, G., Liu, S., Li, G., & Huang, Y. (2020). Full face-and-head 3D model with photorealistic texture. IEEE Access, 8, 188041–188051.

Dhruv Sharma | Computer Vision | Best Researcher Award

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Dr. Dhruv Sharma | Computer Vision | Best Researcher Award

Amity University | India

Dr. Dhruv Sharma has made extensive contributions to the domains of artificial intelligence, deep learning, and multimodal systems through a wide range of impactful publications. His research encompasses visual data captioning, adaptive attention mechanisms, and transformer-based models that enhance image understanding and description generation. Notable works include Evolution of Visual Data Captioning Methods, Datasets, and Evaluation Metrics: A Comprehensive Survey, Automated Image Caption Generation Framework using Adaptive Attention and Bi-LSTM, and XGL-T Transformer Model for Intelligent Image Captioning, which collectively advance the field of vision-language integration. His studies such as Lightweight Transformer with GRU Integrated Decoder for Image Captioning and Control With Style: Style Embedding-based Variational Autoencoder for Controlled Stylized Caption Generation Framework propose innovative architectures for stylistic and efficient captioning. In addition, he has developed frameworks like FDT–Dr2T: A Unified Dense Radiology Report Generation Transformer Framework for X-ray Images and Unma-Capsumt: Unified and Multi-Head Attention-Driven Caption Summarization Transformer, highlighting his interest in medical AI and caption summarization. His earlier works, including Memory-Based FIR Digital Filter using Modified OMS-LUT Design and Modified Efficient OMS LUT-Design for Memory-Based Multiplication, show his foundational expertise in signal processing and hardware-efficient algorithms. Moreover, his contributions such as Obscenity Detection Transformer and DVRGNet reflect his commitment to developing socially responsible AI for content moderation. Overall, Dr. Sharma’s scholarly output demonstrates a consistent trajectory from traditional signal processing to cutting-edge multimodal AI, bridging research innovation with practical applications in intelligent computing and human-centered artificial intelligence.

Profile: Google Scholar

Featured Publications

  • Sharma, D., Dhiman, C., & Kumar, D. (2023). Evolution of visual data captioning methods, datasets, and evaluation metrics: A comprehensive survey. Expert Systems with Applications, 221, 119773.

  • Sharma, D., Dhiman, C., & Kumar, D. (2024). XGL-T transformer model for intelligent image captioning. Multimedia Tools and Applications, 83(2), 4219–4240.

  • Sharma, D., Dhiman, C., & Kumar, D. (2024). Control with style: Style embedding-based variational autoencoder for controlled stylized caption generation framework. IEEE Transactions on Cognitive and Developmental Systems, 1–11.

  • Sharma, D., Dhiman, C., & Kumar, D. (2024). FDT–Dr2T: A unified dense radiology report generation transformer framework for X-ray images. Machine Vision and Applications, 35, 1–13.

  • Sharma, D., Dhiman, C., & Kumar, D. (2022). Automated image caption generation framework using adaptive attention and Bi-LSTM. In 2022 IEEE Delhi Section Conference (DELCON) (pp. 1–5). IEEE.

Youwei Wang | Data Mining | Young Researcher Award

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Mr. Youwei Wang | Data Mining | Young Researcher Award 

Central University of Finance and Economics | China

Mr. Youwei Wang is an Associate Professor at the School of Information, Central University of Finance and Economics, Beijing, China. He holds a Ph.D. in Computational Bioinformatics, a Master’s in Computer Application Technology, and a Bachelor’s in Computer Science and Technology, all from Jilin University. His research primarily focuses on data mining, deep learning, and social networks, with significant contributions to misinformation detection, fraud analysis, and blockchain security. Dr. Wang has published over 60 research papers, including SCI and EI-indexed works, and authored one book with an ISBN. He also holds two patents and has led multiple funded projects from the National Natural Science Foundation of China, the Ministry of Education, and the Beijing Natural Science Foundation. A member of the China Computer Federation (CCF) and the China Cyberspace Security Association, Dr. Wang collaborates actively with institutions such as Tianjin University of Finance and Economics. His research employs advanced techniques in graph modeling, deep learning, and knowledge distillation to improve fake content recognition, sentiment analysis, and smart contract anomaly detection, thereby contributing to digital governance and financial technology. He teaches courses on Network Content Security Analysis, C++ Programming, and AI Programming, mentoring graduate students in applied artificial intelligence. Mr. Youwei Wang commitment to innovation and interdisciplinary exploration continues to advance the fields of information security, machine learning, and financial data analytics.

Profile: Scopus

Featured Publications

  • Wang, Y., Feng, L., Xie, J., & Feng, Q. (2023). Fast multi-channel adaptive learning-enriched learning algorithm for text classification. Multimedia Tools and Applications. (SCI, CCF C)

  • Wang, Y., Feng, L., Zhu, Y., Li, Y., & Chen, F. (2022). Improved AdaBoost algorithm using multisatisfied samples oriented feature selection and weighted non-negative matrix factorization. Neurocomputing, 506, 133–149. (SCI, CAS District 2, CCF C)

  • Wang, Y., & Feng, L. (2021). An adaptive boosting algorithm based on weighted feature selection and category classification confidence. Applied Intelligence, 51(6), 6879–6888. (SCI, CAS District 2, CCF C)

  • Wang, Y., Feng, L., & others. (2024). Dual EROU-CON-based sentiment classification method combining global and local attention. The Journal of Supercomputing, 89, 2799–2817. (SCI, CCF C)

  • Wang, Y., Lu, K., & Feng, L. (2024). Sentiment classification method based on user personality and semantic-motivation features. Journal of Electronics.

Naveed Anjum | Security | Best Researcher Award

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Mr. Naveed Anjum | Security | Best Researcher Award

University of Science and Technology Beijing | China

Mr. Naveed Anjum Mian is a dedicated PhD researcher and Research Fellow with a strong passion for innovative, cost-effective, and realistic approaches in computer science. His research focuses on large language models (LLMs) for social network cyberbullying detection and graph-based network security, leveraging expertise in machine learning, deep learning, and graph neural networks (GNNs). Mr. Mian has extensive teaching experience as a lecturer in computer science, delivering engaging lectures and hands-on lab sessions in programming, database systems, network communication, and object-oriented programming while promoting research and development in network security and AI-driven solutions. His professional experience also includes roles as a software engineer, contributing to web development, user requirement analysis, and the maintenance of large-scale portals like Zameen.com. Academically, he holds an MS in Computer Science with a strong CGPA and a BS in Computer Science, currently pursuing a PhD at the University of Science and Technology Beijing. Mr. Mian is proficient in Python and its libraries, including Transformers, PyTorch, TensorFlow, Keras, Pandas, NumPy, Scikit-Learn, Matplotlib, and Deep Graph, and is skilled in Linux and Hadoop for large-scale data processing. His research contributions include publications on the security and privacy of industrial big data and multi-source data fusion schemes for intrusion detection in networks, accumulating 35 citations with an h-index of 2 and an i10-index of 1. Through his work, Mr. Mian combines analytical rigor with practical applications, contributing to advancements in AI, cybersecurity, and data-driven solutions while fostering knowledge sharing and innovative practices in academia and industry.

Profile: Google Scholar | Oricid

Featured Publications

Anjum, N., Latif, Z., & Chen, H. (2025). Security and privacy of industrial big data: Motivation, opportunities, and challenges. Journal of Network and Computer Applications.

Anjum, N., Latif, Z., Lee, C., Shoukat, I. A., & Iqbal, U. (2021). MIND: A multi-source data fusion scheme for intrusion detection in networks. Sensors, 21(144941).

Abeer Iftikhar | Real Time Data Processing | Best Researcher Award

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Dr. Abeer Iftikhar | Real Time Data Processing | Best Researcher Award

Bahria University | Pakistan

Dr. Abeer Iftikhar Tahirkheli is a distinguished researcher whose work spans computer science, information security, artificial intelligence, and strategic studies, combining advanced technological innovation with defense and national security applications. Her contributions in cybersecurity and smart city resilience include Securing Edge Based Smart City Networks with Software Defined Networking and Zero Trust Architecture, A Blockchain Based Secure Authentication Technique for Ensuring User Privacy in Edge Based Smart City Networks, and Security Provision by Using Detection and Prevention Methods to Ensure Trust in Edge-Based Smart City Networks, advancing trust, privacy, and secure authentication in digital infrastructures. Her interest in healthcare and societal well-being is reflected in Tri-tier Architecture for AI-Based Healthcare Systems and Predicting COVID-19 Infections Prevalence Using Linear Regression Tool, where AI-driven solutions address medical and epidemiological challenges. She has also mapped risks and counterstrategies through Security, Trust and Privacy Risks, Responses, and Solutions for High-Speed Smart Cities Networks: A Systematic Literature Review, A Survey on Modern Cloud Computing Security over Smart City Networks: Threats, Vulnerabilities, Consequences, Countermeasures, and Challenges, and Future Privacy and Trust Challenges for IoE Networks. Complementing her technical focus, she has contributed to strategic studies with India’s Strategic Force Modernization and Its Implications on Strategic Environment of Pakistan, Kashmir Conflict: The Approach of Humanitarianism, and Enhancing the Efficacy of Nuclear Non-Proliferation Regime: Significance of Pakistan’s NSG Membership, addressing critical geopolitical concerns. Further works such as Artificial Intelligence and Its Prospective Employment in Defence Forces, Contemplating Security Challenges and Threats for Smart Cities, A Novel Framework for Cyber Secure Smart City, and Rafale: A Big Bogeri reflect her ability to integrate technology with policy analysis and strategic foresight. Her research embodies academic rigor and innovation leadership, with impact demonstrated by 241 citations, an h-index of 6, and an i10-index of 3.

Profile: Google Scholar

Featured Publications

  • Iftikhar, A., Hussain, F. B., Qureshi, K. N., Shiraz, M., & Sookhak, M. (2025). Securing edge based smart city networks with software defined networking and zero trust architecture. Journal of Network and Computer Applications, 104341.

  • Iftikhar, A., & Qureshi, K. N. (2025). Tri-tier architecture for AI-based healthcare systems. In Artificial Intelligence-Based Smart Healthcare Systems (pp. 53–76).

  • Iftikhar, A., Qureshi, K. N., Hussain, F. B., Shiraz, M., & Sookhak, M. (2025). A blockchain based secure authentication technique for ensuring user privacy in edge based smart city networks. Journal of Network and Computer Applications, 233, 104052.

  • Iftikhar, A., Qureshi, K. N., Shiraz, M., & Albahli, S. (2023). Security, trust and privacy risks, responses, and solutions for high-speed smart cities networks: A systematic literature review. Journal of King Saud University-Computer and Information Sciences, 35(9), 101788.

  • Iftikhar, A., Qureshi, K. N., Altalbe, A. A., & Javeed, K. (2023). Security provision by using detection and prevention methods to ensure trust in edge-based smart city networks. IEEE Access, 11, 137529–137547.

  • Iftikhar, A., & Qureshi, K. N. (2023). Future privacy and trust challenges for IoE networks. In Cybersecurity Vigilance and Security Engineering of Internet of Everything.

  • Tahirkheli, A. I., Shiraz, M., Hayat, B., Idrees, M., Sajid, A., Ullah, R., Ayub, N., … (2021). A survey on modern cloud computing security over smart city networks: Threats, vulnerabilities, consequences, countermeasures, and challenges. Electronics, 10(15), 1811.

  • Qureshi, K. N., Iftikhar, A., Bhatti, S. N., Piccialli, F., Giampaolo, F., & Jeon, G. (2020). Trust management and evaluation for edge intelligence in the Internet of Things. Engineering Applications of Artificial Intelligence, 94, 103756.

Brigitte Jaumard | Cloud Computing | Best Researcher Award

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Prof. Dr. Brigitte Jaumard | Cloud Computing | Best Researcher Award

Concordia University | Canada

Prof. Dr. Brigitte Jaumard is a preeminent authority in computer science and operations research, specializing in the optimization of communication networks and the application of artificial intelligence. As a Professor in the Department of Computer Science and Software Engineering at Concordia University, her distinguished career is marked by significant academic and industrial contributions. Her foundational education includes a Ph.D., awarded with highest honors, from École Nationale Supérieure des Télécommunications (ParisTech) and a Thèse d’habilitation from Université Pierre et Marie Curie. She has held prestigious appointments, including a Tier I Canada Research Chair (2001-2019), and impactful roles in industry such as Principal Data Scientist at Ericsson’s Global AI Accelerator and Scientific Director at the Computer Research Institute of Montreal (CRIM). Her pioneering research, supported by major grants from NSERC and MITACS, focuses on developing sophisticated models and algorithms for 5G/B5G networks, energy efficiency, and network virtualization. The exceptional impact and volume of her work are demonstrated by a remarkable citation count of over 11,130, an h-index of 46, and an i10-index of 179, alongside multiple best paper awards from leading IEEE conferences. A dedicated mentor to numerous graduate students and postdoctoral fellows, she also actively shapes her field through service on technical program committees, editorial boards, and national grant selection panels, solidifying her legacy as a leader who bridges foundational research with transformative industrial innovation.

Profile: Scopus | Google Scholar

Featured Publications

  • Hansen, P., Jaumard, B., & Savard, G. (1992). New branch-and-bound rules for linear bilevel programming. SIAM Journal on Scientific and Statistical Computing, 13(5), 1194–1217.

  • Hansen, P., & Jaumard, B. (1997). Cluster analysis and mathematical programming. Mathematical Programming, 79(1), 191–215.

  • Hansen, P., & Jaumard, B. (1990). Algorithms for the maximum satisfiability problem. Computing, 44(4), 279–303.

  • Jaumard, B., Semet, F., & Vovor, T. (1998). A generalized linear programming model for nurse scheduling. European Journal of Operational Research, 107(1), 1–18.

  • Audet, C., Hansen, P., Jaumard, B., & Savard, G. (2000). A branch and cut algorithm for nonconvex quadratically constrained quadratic programming. Mathematical Programming, 87(1), 131–152.

  • Audet, C., Hansen, P., Jaumard, B., & Savard, G. (1997). Links between linear bilevel and mixed 0–1 programming problems. Journal of Optimization Theory and Applications, 93(2), 273–300.

  • Hansen, P., & Jaumard, B. (1995). Lipschitz optimization. In C. A. Floudas & P. M. Pardalos (Eds.), Handbook of global optimization (pp. 407–493). Springer.

  • Du Merle, O., Hansen, P., Jaumard, B., & Mladenovic, N. (1999). An interior point algorithm for minimum sum-of-squares clustering. SIAM Journal on Scientific Computing, 21(4), 1485–1505.

Yuanfang Han | Bottleneck Analysis | Best Researcher Award

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Mr. Yuanfang Han | Bottleneck Analysis | Best Researcher Award

Beijing University of Posts and Telecommunications | China

Mr. Yuanfang Han is a Master’s degree candidate and researcher at the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, specializing in network performance diagnosis and optimization. His research expertise spans passive traffic analysis, application performance management (APM) metrics, and machine-learning-based anomaly detection, with a strong focus on developing non-intrusive methods for identifying and addressing server bottlenecks in complex environments. He is the principal developer of the Cross-Environment Server Diagnosis with Fusion (CSDF) system, a packet-capture-based framework that integrates one-to-one traffic replay with random-forest-driven metric attribution to achieve high-precision performance insights. Validated in collaboration with China Tower Corporation Limited, this system has demonstrated high cross-environment request alignment accuracy, identified bottlenecks with strong correlation, and reduced average response times in production-grade deployments, marking a significant contribution to the efficiency and reliability of large-scale network systems. His research findings have been published in Electronics in the article “A Non-Intrusive Approach to Cross-Environment Server Bottleneck Diagnosis via Packet-Captured Application Latency and APM Metrics,” co-authored with leading experts in the field. Beyond academic publishing, he has been actively involved in joint research projects with industry partners, conducting large-scale traffic capture and replay experiments that validate his theoretical models in real-world scenarios. As an IEEE Student Member, Mr. Han continues to expand his work on host–network correlation analysis and traffic-based anomaly detection, striving to build scalable, intelligent frameworks that bridge the gap between academic innovation and enterprise-level network performance optimization.

Profile: Orcid

Featured Publication

Han, Y., Zhang, Z., Li, X., Zhao, J., Gu, R., & Wang, M. (2025). A non-intrusive approach to cross-environment server bottleneck diagnosis via packet-captured application latency and APM metrics. Electronics, 14(19), 3824.

Ao Ma | Artificial Intelligence | Best Researcher Award

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Mr. Ao Ma | Artificial Intelligence | Best Researcher Award

Hunan University of Technology and Business | China

Mr. Ao Ma is an emerging researcher specializing in artificial intelligence, software engineering, control systems, and hardware architecture, currently serving at the School of Intelligent Engineering and Intelligent Manufacturing, Hunan University of Technology and Business (HUTB), China. At HUTB, he has pursued research that bridges intelligent computing and practical applications, particularly in developing high-efficiency, sustainable, and adaptive intelligent systems. His scholarly contributions include the article “Design and Research of High-Energy-Efficiency Underwater Acoustic Target Recognition System”, published in Electronics and earlier as a preprint on Preprints.org, where he collaborated with an interdisciplinary team to advance underwater acoustic detection technologies. This work highlights his ability to integrate artificial intelligence algorithms with hardware optimization to improve recognition accuracy while reducing energy consumption, addressing both scientific and industrial challenges in marine engineering. Alongside his academic research, Mr. Ma has demonstrated strong leadership and community engagement, earning the prestigious Yuntang Scholarship, an alumni-funded award that honors outstanding students who exhibit academic excellence, innovative spirit, and societal contribution. His recognition as an Outstanding Member of the Communist Youth League of China further reflects his exemplary performance, moral character, and leadership within the university community. With a clear research vision and dedication to advancing intelligent manufacturing and sustainable innovation, Mr. Ma continues to expand his impact in both academic and professional domains, positioning himself as a promising scholar committed to the integration of cutting-edge artificial intelligence with practical engineering solutions for the benefit of society.

Profile: Orcod

Featured publication

Ma, A., Yang, W., Tan, P., Lei, Y., Zhu, L., Peng, B., & Ding, D. (2025). Design and research of high-energy-efficiency underwater acoustic target recognition system. Electronics, 14(19), 3770.

Seyed Ehsan Enderami | Regenerative Medicine | Best Academic Researcher Award

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Assist. Prof. Dr. Seyed Ehsan Enderami | Regenerative Medicine | Best Academic Researcher Award

Mazandaran University of Medical of Sciences | Iran

Assist. Prof. Dr. Seyed Ehsan Enderami is an Assistant Professor of Medical Biotechnology at Mazandaran University of Medical Sciences, Sari, Iran, with research expertise spanning regenerative medicine, stem cell biotechnology, tissue engineering, gene therapy, and molecular oncology. He obtained his B.Sc. in Laboratory Sciences from Yazd University of Medical Sciences, followed by an M.Sc. and Ph.D. in Medical Biotechnology from Zanjan University of Medical Sciences, where his doctoral thesis focused on generating insulin-producing cells from human induced pluripotent stem cells (iPSCs) through microRNA-based strategies in advanced two- and three-dimensional culture systems. Over the course of his career, Assist. Prof. Dr. Enderami has built a strong scientific record, authoring 94 publications indexed in Scopus, which have collectively garnered 1,916 citations from 1,333 documents, earning him a Scopus h-index of 30 and reflecting the lasting impact of his work. His studies have been featured in leading international journals, including Genes, Journal of Cellular Physiology, Artificial Cells, Nanomedicine and Biotechnology, and Cancer Medicine, covering diverse areas such as scaffold-based tissue engineering, stem cell-derived therapies for diabetes, cancer-related inflammation, neuroregeneration, and bioinformatics approaches to pluripotency. In addition to his prolific research contributions, he is a committed academic mentor and educator, teaching across undergraduate, postgraduate, and doctoral programs in fields such as molecular biology, cell culture, genetic engineering, immunology, and pharmaceutical biotechnology. He has also organized and instructed workshops on stem cell technologies, iPSC technology, and entrepreneurship. With his blend of innovative research, impactful publications, and dedication to teaching, Assist. Prof. Dr. Enderami continues to play a pivotal role in advancing biotechnology and regenerative medicine while training the next generation of biomedical scientists.

Profile: Scopus | Orcid

Featured Publications

  • Nassiri Mansour, R., Hasanzadeh, E., Abasi, M., Gholipourmalekabadi, M., Mellati, A., & Enderami, S. E. (2023). The effect of fetal bovine acellular dermal matrix seeded with Wharton’s jelly mesenchymal stem cells for healing full-thickness skin wounds. Genes, 14(4), 409.

  • Hosseini, F., Mahdian-Shakib, A., Jadidi-Niaragh, F., Enderami, S. E., Mohammadi, H., Hemmatzadeh, M., … Mirshafiey, A. (2018). Anti-inflammatory and anti-tumor effects of α-L-guluronic acid (G2013) on cancer-related inflammation in a murine breast cancer model. Biomedicine & Pharmacotherapy, 107, 172–180.

  • Hashemi, J., Pasalar, P., Soleimani, M., Khorramirouz, R., Fendereski, K., Enderami, S. E., & Kajbafzadeh, A.-M. (2018). Application of a novel bioreactor for in vivo engineering of pancreas tissue. Journal of Cellular Physiology, 233(5), 3823–3833.

  • Ziaei, S., Ardeshirylajimi, A., Arefian, E., Enderami, S. E., Soleimani, M., & Rezaei-Tavirani, M. (2018). Bioinformatics analysis of Ronin gene and their potential role in pluripotency control. Gene Reports, 12, 143–150.

  • Hashemi, J., Pasalar, P., Soleimani, M., Arefian, E., Khorramirouz, R., Akbarzadeh, A., … Enderami, S. E. (2018). Decellularized pancreas matrix scaffolds for tissue engineering using ductal or arterial catheterization. Cells Tissues Organs, 205(3–4), 183–195.

  • Mahboudi, H., Soleimani, M., Enderami, S. E., Kehtari, M., Ardeshirylajimi, A., Eftekhary, M., & Kazemi, B. (2018). Enhanced chondrogenesis differentiation of human induced pluripotent stem cells by MicroRNA-140 and transforming growth factor beta 3 (TGFβ3). Biologicals, 53, 15–23.

  • Enderami, S. E., Soleimani, M., Mortazavi, Y., Nadri, S., & Salimi, A. (2018). Generation of insulin-producing cells from human adipose-derived mesenchymal stem cells on PVA scaffold by optimized differentiation protocol. Journal of Cellular Physiology, 233(5), 3869–3879.

  • Jafari, S. M., Panjehpour, M., Aghaei, M., Joshaghani, H. R., & Enderami, S. E. (2017). A3 adenosine receptor agonist inhibited survival of breast cancer stem cells via GLI-1 and ERK1/2 pathway. Journal of Cellular Biochemistry, 118(11), 3768–3777.