Mr. Angelos Athanasiadis | Hardware Acceleration | Research Excellence Award

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Mr. Angelos Athanasiadis | Hardware Acceleration | Research Excellence Award

Aristotle University of Thessaloniki | Greece

Mr. Angelos Athanasiadis is a researcher in Electrical and Computer Engineering whose work centers on high-performance FPGA architectures, hardware acceleration of Convolutional Neural Networks, and advanced emulation methodologies for heterogeneous computing systems. His research focuses on enabling full-precision, non-quantized deep learning inference on reconfigurable hardware, addressing challenges in energy efficiency, throughput optimization, and deployment in accuracy-critical environments such as aerial monitoring, autonomous systems, and embedded intelligence. He has contributed to the development of parameterizable high-level synthesis (HLS) IP libraries and FPGA-optimized computational kernels, including a fully customizable matrix multiplication framework that supports architectural exploration, resource scalability, and integration with modern AMD FPGA toolchains. Beyond acceleration frameworks, he has designed FUSION, an innovative open-source emulation platform that synchronizes QEMU and OMNeT++ using HLA/CERTI to achieve deterministic, timing-accurate, multi-node experimentation with sub-microsecond synchronization and complete observability of system-level interactions. His work expands the boundaries of distributed embedded system prototyping by combining CPUs, GPUs, and FPGAs into unified hybrid simulation environments. He has participated in collaborative research projects and contributed to publications in embedded systems, power/timing modeling, and FPGA computing. His citation record reflects an emerging academic profile, with metrics documented through Google Scholar and Scopus, including citation counts, h-index values, and related research indicators. Supporting documents, citations, and publication evidence can be verified through his academic profiles as required. His research continues to advance the intersection of hardware design, machine learning acceleration, and distributed system emulation, contributing tools and methods that strengthen reproducibility, scalability, and efficiency in modern computing research.

Publication Profile

ORCID | Google Scholar

Featured Publications 

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2025). An efficient open-source design and implementation framework for non-quantized CNNs on FPGAs. Integration, 102625. (Citations: 1)

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2025). Energy-efficient FPGA framework for non-quantized convolutional neural networks. arXiv:2510.13362. (Citations: 1)

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2024). An open-source HLS fully parameterizable matrix multiplication library for AMD FPGAs. WiPiEC Journal-Works in Progress in Embedded Computing, 10(2). (Citations: 2)

Katselas, L., Jiao, H., Athanasiadis, A., Papameletis, C., Hatzopoulos, A., & colleagues. (2017). Embedded toggle generator to control the switching activity during test of digital 2D-SoCs and 3D-SICs. Proceedings of the International Symposium on Power and Timing Modeling, Optimization. (Citations: 2)

Katselas, L., Athanasiadis, A., Hatzopoulos, A., Jiao, H., Papameletis, C., & colleagues. (2017). Embedded toggle generator to control the switching activity. Conference publication. (Citations: 2)

Mr. Sajjad Naseri | AI in Designing | Research Excellence Award

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Mr. Sajjad Naseri | AI in Designing | Research Excellence Award

Ball State University | United States

Sajjad Naseri is an emerging interdisciplinary researcher working at the intersection of sustainable architecture, urban design, and green building performance, with a strong emphasis on integrating artificial intelligence into the built environment. His work spans data-driven evaluation of LEED-certified buildings, hospitality sustainability outcomes, climate-responsive design performance, and AI-enabled planning applications. He has contributed to impactful research exploring how certification levels influence user satisfaction, how climatic variables shape sustainability metrics, and how emerging digital tools can support more resilient, livable, and equitable urban settings. His cross-continental academic and research background—covering environmental design, architectural processes, and urban planning—strengthens his ability to synthesize global perspectives with applied analytical methods. Naseri has co-authored multiple journal articles, conference papers, and collaborative planning documents, demonstrating a strong commitment to evidence-based, practice-oriented research. His scholarly presence continues to expand, supported by measurable impact indices that reflect sustained and growing citations across platforms. His Google Scholar profile reports 60 citations, an h-index of 5, and an i10-index of 3, while Scopus citation metrics (when indexed) complement his publication visibility in architecture, sustainability, and built-environment research. With contributions published in recognized journals and conferences—including Buildings, Advances in Civil Engineering, Current Opinion, and the American Planning Association—his research helps bridge academic theory with real-world environmental planning and performance assessment. Naseri’s current work continues to advance sustainability-focused design and intelligent urban systems, positioning him as a promising scholar in green building analytics, AI-driven spatial design, and climate-responsive architectural strategies.

Publication Profile

Google Scholar

Featured Publications

Naseri, S., Talebian, S., Golkarieh, A., Eshraghi, S., & Naseri, M. (2025). Artificial intelligence impacts on architecture and smart built environments: A comprehensive review. Advances in Civil Engineering, 2(1). (Cited: 15)

Ghorashi, S. M., Ezzatfar, M., Hatami, R., Bagheri, A., Najafabadi, R. N., & Naseri, S. (2024). The role of subcultures in creating new social issues. Current Opinion, 4(3), 679–696. (Cited: 14)

Naseri, S., Eshraghi, S., & Talebian, S. (2024). Innovative sustainable architecture: A lesson learned from amphibious house in the UK. Current Opinion, 4(4), 766–777. (Cited: 10)

Naseri, S. (2024). AI in architecture and urban design and planning: Case studies on three AI applications. GSC Advanced Research and Reviews. (Cited: 8)

Najafabadi, R. N., Avar, S., Karimi, M., Anbari, M., & Naseri, S. (2024). Ecological restoration of historical monuments: Focus on the restoration of Chogha Zanbil. World Journal of Advanced Research and Reviews, 23(2), 240–250. (Cited: 8)

Assoc. Prof. Dr. Essa M. Saied | Computer-Aided Drug Design | Research Excellence Award

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Assoc. Prof. Dr. Essa M. Saied | Computer-Aided Drug Design | Research Excellence Award

Humboldt University of Berlin | Germany

Dr. Essa M. Saied is a distinguished bioorganic and biochemistry researcher whose work bridges synthetic medicinal chemistry, lipid biology, and small-molecule drug discovery. His research focuses on designing and synthesizing bioactive molecules, stereoselective synthetic methodologies, heterocyclic compounds, and lipid-based probes to investigate fundamental biological processes. He has made landmark contributions to understanding sphingolipid metabolism, ceramidase activity, lipid transfer mechanisms, and the structure–function relationships of bioactive lipids. His expertise spans synthetic chemistry, combinatorial library design, high-throughput screening, molecular modelling, virtual screening, enzymatic assay development, and structure–activity relationship optimization. Dr. Saied’s research has advanced knowledge of lipid-associated diseases, anticancer agents, antimicrobial strategies, and molecular mechanisms underlying metabolic and neurological disorders. He has authored around 70 peer-reviewed publications with strong global impact, reflected in a Scopus h-index of 31 with over 2,229 citations and a Google Scholar h-index of 35 with more than 2,872 citations. His body of work is widely referenced across the fields of lipidomics, medicinal chemistry, structural biology, and biochemical pharmacology. Dr. Saied’s publications include pioneering structural studies of adiponectin receptors, mechanistic analysis of ceramide-related pathways, discovery of small-molecule enzyme inhibitors, and the development of innovative analytical and spectroscopic tools to characterize lipid isomers. His research output demonstrates a consistent record of high-impact contributions, shaping modern understanding of lipid chemistry and its role in human disease.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications 

Vasiliauskaite-Brooks, I., Sounier, R., Rochaix, P., Bellot, G., Fortier, M., Hoh, F., Bechara, C., Saied, E. M., Arenz, C., Leyrat, C., De Colibus, L., & Granier, S. (2017). Structural basis for the ceramidase activity of adiponectin receptors. Nature, 544, 120–123. Citations: 230.

Vasiliauskaité-Brooks, I., Healey, R. D., Rochaix, P., Saint-Paul, J., Sounier, R., Grison, C., Waltrich-Augusto, T., Fortier, M., Hoh, F., Saied, E. M., Arenz, C., Leyrat, C., & Granier, S. (2018). Structure of a human intramembrane ceramidase explains enzymatic dysfunction found in leukodystrophy. Nature Communications, 9, 5437. Citations: 51.

Lone, M. A., Huelsmeier, A. J., Saied, E. M., Karsai, G., Arenz, C., von Eckardstein, A., & Hornemann, T. (2020). Subunit composition of the mammalian serine-palmitoyltransferase defines the spectrum of straight and methyl-branched long-chain bases. Proceedings of the National Academy of Sciences, 117, 15591–15598. Citations: 85.

Saied, E. M., El-Maradny, Y. A., Osman, A. A., Darwish, A. M. G., Abo Nahas, H. H., & El-Seedi, H. R. (2021). A comprehensive review about the molecular structure of SARS-CoV-2: Insights into natural products against COVID-19. Pharmaceutics, 13, 1759. Citations: 66.

Kirschbaum, C., Saied, E. M., Greis, K., Mucha, E., Gewinner, S., Schöllkopf, W., Meijer, G. J. M., von Helden, G., Poad, B. L. J., Blanksby, S. J., Arenz, C., & Pagel, K. (2020). Resolving sphingolipid isomers using cryogenic infrared spectroscopy. Angewandte Chemie, 132, 13740. Citations: 51.

Dr. Gu Shan | Power Systems | Women Researcher Award

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Dr. Gu Shan | Power Systems | Women Researcher Award

Associate Professor | Zhejiang University of Water Resources and Electric Power | China

Dr. Shan Gu is a researcher in the fields of energy engineering, sustainable power systems, and environmental technology, with a strong focus on biomass utilization, air-pollutant mitigation, and life cycle assessment. Her work integrates engineering experimentation, process optimization, and environmental impact evaluation to advance the development of clean energy technologies. She has contributed significantly to the study of biomass pyrolysis, nanosilica extraction from agricultural waste, and the operational behavior of circulating fluidized bed gasifiers. Her research on biomass CFB gasification systems, including the coupling of gasifiers with industrial steam boilers, has generated important insights into practical challenges such as slagging, ash deposition, and system optimization. These contributions have provided evidence-based guidance for the scaling, operation, and environmental performance improvement of biomass-based energy systems. Dr. Gu has authored more than 30 research publications, including multiple SCI-indexed articles, with several featured in high-impact journals. Her scholarly work demonstrates strong visibility, with measurable academic influence across citation databases. According to Scopus, she has 14 indexed documents, 16 citations by 15 documents, and an h-index of 2. Her Google Scholar profile shows significantly higher engagement, with over 250 citations across her most influential works, including widely referenced studies on nanosilica production and biomass gasification, each exceeding 100 citations. Her publications continue to inform ongoing research in sustainable materials, renewable energy pathways, and the optimization of energy–environment systems, positioning her as an active contributor to advancing cleaner technologies and carbon-reduction strategies.

Publication Profile

Scopus

Featured Publications

Gu, S., Zhou, J., Luo, Z., Wang, Q., & Ni, M. (2013). A detailed study of the effects of pyrolysis temperature and feedstock particle size on the preparation of nanosilica from rice husk. Industrial Crops and Products. Citations: 121.

Gu, S., Zhou, J., Yu, C., & Shi, Z. (2015). A novel two-staged thermal synthesis method of generating nanosilica from rice husk via pre-pyrolysis combined with calcination. Industrial Crops and Products. Citations: 105.

Gu, S., Zhou, J., Luo, Z., & Shi, Z. (2015). Kinetic study on the preparation of silica from rice husk under various pretreatments. Journal of Thermal Analysis and Calorimetry. Citations: 25.

Gu, S., Zhou, J., Lin, B., & Luo, Z. (2015). Life cycle greenhouse gas impacts of biomass gasification-exhausted heat power generation technology in China. Journal of Biobased Materials and Bioenergy..

Li, R., Gu, S., Ye, Y., Li, Z., Zhou, L., & Xu, C. (2025). System optimization and primary electrical design of a 50 MW agrivoltaic power station: A case study in China.

Mr. Abir Das | Artificial Intelligence | Research Excellence Award

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Mr. Abir Das | Artificial Intelligence | Research Excellence Award

Siliguri Government Polytechnic College | India

Abir Das is an emerging AI/ML researcher whose work spans deep learning, computer vision, medical imaging, and explainable AI. With a strong foundation in developing end-to-end AI systems, his research focuses on Vision Transformers, self-supervised learning, noisy-label correction, and interpretable models for high-stakes applications such as healthcare, EEG signal analysis, and industrial fault diagnosis. He has contributed as the first author to multiple international journals, working extensively on hybrid deep learning models, CLIP-based zero-shot learning, EEG motor imagery classification, and sensor-driven diagnostic pipelines. His research integrates expertise in PyTorch, TensorFlow, and modern transformer architectures, emphasizing human-centered, reliable, and transparent AI solutions. He has actively explored the intersection of computer vision and embedded systems, enhancing drone autonomy, depth estimation, and real-time object detection, while also contributing to speech technologies through accent-conversion and multimodal learning. His scientific output includes publications in reputable venues such as Scientific Reports, MDPI Sensors, and Computers, Materials & Continua. His growing scholarly impact is reflected in Scopus metrics: 11 citations from 11 documents with an h-index of 1, and Google Scholar metrics: 12 citations, h-index 1, i10-index 1. His work continues to advance practical and theoretically grounded AI methodologies, blending deep learning innovations with real-world applications across biomedical imaging, EEG analysis, and industrial AI systems.

Publication Profile

Scopus | Google Scholar

Featured Publications

Das, A., Singh, S., Kim, J., Ahanger, T. A., & Pisa, A. A. (2025). Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models. Scientific Reports, 15(1), 27161.

Zereen, A. N., Das, A., & Uddin, J. (2024). Machine fault diagnosis using audio sensor data and explainable AI techniques: LIME and SHAP. Computers, Materials & Continua, 80(3).

Das, S. S. A. (2025). Few-shot and zero-shot learning for MRI brain tumor classification using CLIP and Vision Transformers. Sensors, 25(23), 7341.

Prof. Mengmeng Liao | Computer Vision | Research Excellence Award

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Prof. Mengmeng Liao | Computer Vision | Research Excellence Award

Associate Professor | Shanghai University | China

Prof. Mengmeng Liao is an accomplished researcher in artificial intelligence, computer vision, pattern recognition, and image processing, with a strong record of contributions to both foundational and applied aspects of visual computing. His work focuses on developing robust algorithms for face recognition, multi-resolution modeling, adaptive subspace learning, and representation learning, addressing complex challenges in real-world environments such as noise interference, limited samples, and multi-pose variation. He has authored more than 20 SCI/EI-indexed research papers, including publications in leading international journals such as Information Sciences, Neurocomputing, Expert Systems with Applications, Electronics, and IEEE Signal Processing Letters. His research impact is reflected in Scopus metrics, with 170 citations across 159 citing documents and an h-index of 6, alongside a growing presence on Google Scholar. Prof. Liao has also contributed to several major national research initiatives, securing competitive funding from programs such as the National Natural Science Foundation and the Postdoctoral Innovative Talent Support Program. His active engagement with the global academic community includes serving as a technical committee member, session chair, and program chair for numerous international conferences. Through his interdisciplinary approach and sustained research output, Prof. Liao continues to advance the field of artificial intelligence, particularly in intelligent visual perception, pattern learning, and computational recognition systems.

Publication Profile

Scopus

Publications

Fan, X., Liao, M., Chen, L., & Hu, J. (2023). Few-shot learning for multi-POSE face recognition via hypergraph de-deflection and multi-task collaborative optimization. Electronics.

Liao, M., Fan, X., Li, Y., & Gao, M. (2023). Noise-related face image recognition based on double dictionary transform learning. Information Sciences.

Fan, X., Liao, M., Xue, J., Wu, H., Jin, L., Zhao, J., & Zhu, L. (2023). Joint coupled representation and homogeneous reconstruction for multi-resolution small sample face recognition. Neurocomputing.

Liao, M., Li, Y., & Gao, M. (2022). Graph-based adaptive and discriminative subspace learning for face image clustering. Expert Systems with Applications.

Jiang, W., Li, Y., Liao, M., & Wang, S. (2021). An improved LPI radar waveform recognition framework with LDC-Unet and SSR-Loss. IEEE Signal Processing Letters.

 

Dr. Yingguo Lyu | Biological Sciences | Innovative Research Award

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Dr. Yingguo Lyu | Biological Sciences | Innovative Research Award

Henan University of Technology | China

Dr. Yingguo Lyu is a food scientist specializing in cereal chemistry, noodle technology, and the processing principles of traditional Chinese foods. His research focuses on understanding the biochemical, rheological, and structural behaviors of cereal-based products and developing innovative technologies for improving the quality, safety, and industrialization of staple foods such as noodles, steamed bread, dumpling wrappers, and instant products. He has contributed significantly to the study of frozen dough systems, moisture dynamics, gluten network formation, fermentation processes, and multi-grain formulations. A major part of his work explores rice bran glutamate decarboxylase, GABA production, and enzyme-modulated pathways that support functional food development. Dr. Lyu has led and contributed to multiple scientific projects on fresh noodle color stability, circular drying technology, noodle industrialization, and protein-matrix interactions, producing practical outcomes for China’s grain-processing sector. His research achievements include award-winning technological advancements and several patents related to frozen noodles and GABA-rich food innovations. He has authored numerous high-impact journal articles and books in the fields of food science and cereal technology. His scholarly contributions are widely cited, with measurable visibility across academic platforms. Based on publicly available citation databases, his Scopus and Google Scholar citation records indicate strong research influence, including an h-index that reflects his sustained contributions to cereal chemistry and food engineering. His body of work continues to bridge fundamental food science with industrial applications, supporting advancements in modern food processing technologies.

Publication Profile

ORCID

Featured Publications

Lyu, Y., Chen, J., Li, X. (2014). Study on processing and quality improvement of frozen noodles. LWT – Food Science and Technology, 59(1), 403-410.

Mr. Carlos Rodrigo Paredes Ocranza | Affective Computing | Machine Learning Research Award

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Mr. Carlos Rodrigo Paredes Ocranza | Affective Computing | Machine Learning Research Award

Zhejiang University of Science and Technology | China

Mr. Carlos Rodrigo Paredes Ocaranza is an emerging researcher in artificial intelligence with a strong focus on EEG-based emotion recognition, affective computing, and brain–computer interface (BCI) analytics. His work challenges conventional assumptions in neurotechnology by demonstrating that traditional machine learning pipelines, when paired with domain-specific feature engineering, outperform state-of-the-art deep learning models such as EEGNet for consumer-grade EEG devices. His research introduces advanced domain adaptation methods—such as anatomical channel mapping, CORAL, and TCA—that collectively achieve remarkable gains in cross-dataset generalization, including a reported 69-fold improvement in robustness. He has conducted large-scale validation experiments across hundreds of independent evaluations to ensure statistical reliability and real-world applicability. His contributions highlight significant computational advantages, including faster model training, reduced inference time, and lower memory requirements, advancing the feasibility of accessible BCI systems for mental-health monitoring and multimodal emotion-decoding research. His citation profile is currently emerging, with one indexed publication in Scopus and expanding coverage as new profiles on Google Scholar and ORCID are being established. His scholarly documents, publication records, and citation metrics continue to grow as his research outputs undergo indexing in major academic databases. His work reflects a dedication to developing practical, interpretable, and resource-efficient neuro-AI systems that can be deployed beyond laboratory environments, strengthening the intersection between cognitive science, statistical learning, and computational affective modeling.

Publication Profile

ORCID

Featured Publication

Paredes Ocaranza, C. R., Paredes Ocaranza, E. D., & Yun, B. (2025). Traditional machine learning outperforms EEGNet for consumer-grade EEG emotion recognition: A comprehensive evaluation with cross-dataset validation. Sensors, 25(23), 7262.

Prof. Dr. Beatriz Defez | Sensors | Research Excellence Award

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Prof. Dr. Beatriz Defez | Sensors | Research Excellence Award

Professor | Valencia Polytechnic University | Spain

Beatriz Defez García is an accomplished scholar whose work bridges engineering graphics, materials science, acoustic behavior, assistive technologies, and advanced computational modeling. Her research notably spans finite-element analysis of ceramic materials, acoustic characterization of engineered structures, fluid–solid interactions, and the development of innovative navigation systems to support individuals with visual impairments. She has made influential contributions to the fields of human–machine interaction, sensory guidance technologies, telemedicine, and computer-assisted image segmentation, particularly in dermatological applications. Her scholarship encompasses collaborative, interdisciplinary projects addressing societal and industrial needs through engineering innovation. With a strong record of productivity, she has authored 30 Scopus-indexed documents and accumulated 188 citations in Scopus, reflecting an h-index of 7, while her Google Scholar profile reports 486 citations, an h-index of 11, and an i10-index of 11, demonstrating the sustained visibility and relevance of her research. Her most widely cited studies include pioneering prototypes for real-time assistive navigation, analytical and experimental investigations of bubble and drop oscillations, and advanced techniques for usability evaluation in digital learning environments. Across her publications, Defez García integrates computational methods, experimental validation, and user-centered design principles to advance engineering solutions that improve material performance, enhance accessibility, and support health-related digital transformation. Her recent work also highlights growing contributions in telemedicine, sustainable digitalization, and biomedical image processing, reinforcing her role as a multidisciplinary researcher shaping both technological innovation and applied scientific practice.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications

Dunai, L., Fajarnes, G. P., Praderas, V. S., Defez Garcia, B., & Lengua, I. (2010). Real-time assistance prototype—A new navigation aid for blind people. IECON Annual Conference on IEEE Industrial Electronics Society, 1–6.

Milne, A. J. B., Defez, B., Cabrerizo-Vílchez, M., & Amirfazli, A. (2014). Understanding (sessile/constrained) bubble and drop oscillations. Advances in Colloid and Interface Science, 203, 22–36.

Dunai, L., Peris-Fajarnés, G., Lluna, E., & Defez, B. (2013). Sensory navigation device for blind people. The Journal of Navigation, 66(3), 349–362.

Fraile-Garcia, E., Ferreiro-Cabello, J., Defez, B., & Peris-Fajanes, G. (2016). Acoustic behavior of hollow blocks and bricks made of concrete doped with waste-tire rubber. Materials, 9(12), 962.

Maria, M. S., Silvia, A. N., Beatriz, D. G., Andrew, D., & Guillermo, P. F. (2022). Health care in rural areas: Proposal of a new telemedicine program assisted from the reference health centers, for sustainable digitization and its contribution to carbon reduction. Heliyon, 8(7).

Mr. Ikram Ullah | Biological Sciences | Research Excellence Award

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Mr. Ikram Ullah | Biological Sciences | Research Excellence Award

Northwest A&F University | China

Mr. Ikram Ullah is an emerging life science researcher specializing in plant genomics, molecular biology, and stress physiology, with a particular focus on horticultural crops. His research centers on genome-wide identification, characterization, and functional analysis of key gene and transcription factor families that regulate plant growth, defense pathways, and stress responses. He has contributed significantly to understanding bHLH, CRK, YABBY, callose enzyme, and Tubby-Like Protein gene families, providing new insights into their structural diversity, expression dynamics, and regulatory roles under biotic and abiotic stresses. His work extends to plant–pathogen interactions, root microbiota, cold stress management, heat stress mitigation, and hormonal modulation in economically important crops such as rose, cucumber, wheat, pepper, Brassica species, and tuberose. He is skilled in advanced molecular techniques, gene expression profiling, microbial metagenomics, and bioinformatics tools that support integrative genomic analyses. His research contributions are reflected in his citation metrics, with notable visibility across global indexing platforms. According to Scopus, his work has received around 1,900 citations from approximately 1,606 documents with an h-index of 23. His broader academic footprint on Google Scholar further highlights his growing impact within plant sciences and biotechnology. Mr. Ullah’s research continues to advance molecular breeding, stress-resilience strategies, and genomic understanding essential for sustainable horticulture and crop improvement.

Publication Profile

Scopus | ORCID

Featured Publications

Ullah, I., Yuan, W., Uzair, M., Li, S., Rehman, O., Nanda, S., & Wu, H. (2022). Genome-wide identification and expression analysis of the RcYABBYs reveals their potential functions in rose under Botrytis cinerea infection. Horticulturae, 8, 989.

Ullah, I., Ponsalven, A., Abbas, A., Hussain, S., & Nanda, S. (2022). Molecular characterization of bHLH transcription factor family in rose under Botrytis cinerea infection.

Ullah, I., et al. (2025). Molecular mechanisms and genomic strategies for enhancing stress resilience in pepper crop. Scientia Horticulturae, 352, 114403.

Tang, Y., Wu, J., Zhao, M., Guo, Y., Ullah, I., & Wu, H. (2020). Complete genome sequence of begonia flower breaking virus, a novel member of the genus Potyvirus. Archives of Virology, 165, 1915–1918.

Nanda, S., Rout, P., Ullah, I., Swapna, R., Velagala, V. R., Ritesh, K., & Wu, H. (2023). Genome-wide identification and molecular characterization of CRK gene family in cucumber under cold stress and sclerotium rolfsii infection. BMC Genomics, 24, 219.