Category: Biography

🏆 Suitability for the Young Scientist Award

Fareeha Fatima exemplifies the spirit of the Young Scientist Award through her academic brilliance, research potential, and proactive engagement in the scientific community. Her M.Phil research applying advanced DFT techniques to novel LiZnZ compounds, and her M.Sc work on nanocomposite applications in theragnostics, reflect an impressive breadth and depth of understanding. At a young age, she has combined strong theoretical knowledge with practical teaching and laboratory skills. Her active participation in academic environments and strong interpersonal abilities further highlight her leadership qualities. Fareeha demonstrates innovation, discipline, and a forward-thinking approach that are critical for impactful scientific progress. She represents the future of physics research in Pakistan and is a deserving nominee for the Young Scientist Award.

🎓 Education

Fareeha Fatima has consistently excelled in her academic journey, marked by high achievements in physics. She completed her M.Phil in Physics (2022–2024) at The Islamia University of Bahawalpur with a CGPA of 3.88, conducting advanced theoretical research on LiZnZ (Z = Sb, Bi) compounds using GGA, WC, and mBJ methods. Her M.Sc in Physics (2019–2021), also from the same university with a CGPA of 3.68, involved exploring bismuth-based nanocomposites for theragnostic applications. Her B.Sc (2017–2019) established a strong scientific base. She previously completed her F.Sc (Pre-Engineering) and Matriculation with distinction. Her education reflects a consistent commitment to excellence and a growing focus on computational and materials science in the context of physics.

🏢 Work Experience

Fareeha Fatima has gained valuable experience in both academic and educational settings. She served as a Teaching Assistant at The Islamia University of Bahawalpur, Bahawalnagar Campus (Jan–May 2023), where she assisted in delivering lectures, managing student queries, and supporting practical sessions. Earlier, she worked as a science teacher at First Moon Kindergarten School and Pioneer Science Academy (Nov 2021–May 2022), demonstrating her ability to communicate scientific concepts effectively at different academic levels. These roles have enhanced her capabilities in team collaboration, time management, and active listening—key skills for any researcher. Her hands-on exposure to both theoretical instruction and practical demonstration underlines her readiness for more advanced research roles and makes her a well-rounded young academic professional.

🏅 Awards and Honors

Fareeha Fatima’s academic achievements speak volumes about her dedication and potential. While still early in her career, she has earned distinction through outstanding CGPAs in both her M.Phil (3.88) and M.Sc (3.68) programs. Her performance has consistently placed her among the top students in her department. She has been commended by faculty for her critical thinking, dedication to research, and strong academic ethics. Her work has attracted attention for its innovative approach to theoretical materials science and nanotechnology. Though formal national awards are still forthcoming, she is widely regarded by her peers and mentors as a future leader in physics research. Her academic accomplishments and potential make her a strong candidate for prestigious recognitions like the Young Scientist Award.

🔬 Research Focus

Fareeha Fatima’s research centers on theoretical condensed matter physics, particularly using Density Functional Theory (DFT) to study the structural and electronic properties of emerging compounds. Her M.Phil thesis focuses on LiZnZ (Z = Sb, Bi) semiconducting materials analyzed using GGA, WC, and mBJ schemes, offering insights relevant to next-generation electronic applications. Earlier, her M.Sc research explored bismuth-based nanocomposites for dual-use in medical diagnostics and therapy—highlighting an interdisciplinary approach that blends physics with nanomedicine. Fareeha is also proficient in digital tools like Origin Lab and MS Office for data analysis and scientific reporting. Her focus on simulation-driven prediction of material behaviors aligns with global research trends, making her contributions timely, impactful, and promising for future scientific development.

📊 Publication Top Notes:

Fatima, F., & Co-authors. (2024). Exploring the physical attributes of NaCaX (X = As, Sb) semiconductors by first principles calculations. The European Physical Journal Plus, W Category Journal.
Fatima, F., & Co-authors. (2024). Detailed DFT based exploration on spin-gapless features of IrCoNbX (X = Al, Ga, In) alloys. Journal of Magnetism and Magnetic Materials, W Category Journal.
Fatima, F., & Co-authors. (2024). Unveiling the robustness of half-metallicity under strain effects of CoVYZ (Z = Si, Ge, Sn) alloys. Journal of Magnetism and Magnetic Materials, W Category Journal.
Fatima, F., & Co-authors. (2024). Comparative Density Functional Theory Based Study of LiZnZ (Z = Sb, Bi) Compounds via GGA, WC and mBJ Schemes. Indian Journal of Physics, W Category Journal.

Mohd Usama | Machine Learning | Best Researcher Award

Posted on 17/05/202517/05/2025 by sciencefather

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:

🏆 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

Huakun Bi | Electrical Engineering | Best Researcher Award

Posted on 15/05/202515/05/2025 by sciencefather

Dr. Huakun Bi| Electrical Engineering
|Best Researcher Award

Lecturer at School of Electrical and Automation Engineering, Shandong University of Science and Technology, Qingdao, China.

Dr. Huakun Bi, a Lecturer at the School of Electrical and Automation Engineering, Shandong University of Science and Technology, is a prolific researcher in power electronics. He earned his Ph.D. from Tianjin University in 2020 and has since demonstrated significant academic and industrial contributions. With 17 research papers—12 SCI and 15 EI indexed—and multiple prestigious research projects, he has become a leading expert in DC-DC converters, electric vehicles, and DC microgrids. Dr. Bi’s innovative designs have enhanced voltage regulation and energy efficiency in electric vehicle systems. His high citation count, active role as a journal reviewer, and leadership in government- and industry-funded projects reflect outstanding research impact and make him an excellent candidate for the Best Researcher Award.

🌍 Professional Profile:

🏆 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. Huakun Bi earned his Ph.D. in Electrical Engineering from Tianjin University, Tianjin, China, in 2020, where he conducted advanced research in power conversion technologies and electric transportation systems. His doctoral work focused on the design and optimization of DC-DC converters, especially for fuel cell and electric vehicle applications. His academic foundation includes rigorous training in circuit theory, control systems, and energy systems. During his doctoral studies, he led one independent innovation project and contributed to several national-level research initiatives. His education equipped him with a strong theoretical and practical understanding of modern electrical systems, laying the groundwork for his impactful contributions as a researcher and lecturer at the Shandong University of Science and Technology.

🏢 Work Experience

Dr. Huakun Bi currently serves as a Lecturer at the School of Electrical and Automation Engineering, Shandong University of Science and Technology, Qingdao. Since earning his Ph.D. in 2020, he has taken on leadership roles in academic and industrial research, including projects funded by the Shandong Natural Science Foundation, State Grid Corporation of China, and Huaneng Group. He has contributed to three additional school-enterprise cooperation projects. Dr. Bi also acts as a dedicated reviewer for five international journals in electrical engineering. His dual involvement in academia and applied industrial projects demonstrates a versatile and impactful career. His research work is not only academically significant but also practically relevant, bridging the gap between theoretical advancements and real-world implementation.

🏅 Awards and Honors

Dr. Huakun Bi’s research excellence is reflected in the high citation of his publications, notably in top-tier journals such as IEEE Transactions on Industrial Electronics and IEEE Transactions on Vehicular Technology. His 2019 publication has been cited 77 times, showcasing his work’s global impact. He has received recognition for his leadership in innovation projects at Tianjin University and has earned competitive funding from the Shandong Provincial Natural Science Foundation. Dr. Bi’s sustained contribution to national and industry-funded projects highlights the practical value of his research. His selection as a peer reviewer for multiple international journals further underscores his expertise and reputation in the field of power electronics and DC microgrids. These honors position him strongly for research excellence awards.

🔬 Research Focus

Dr. Huakun Bi’s research primarily targets high-performance DC-DC converters, energy-efficient electric vehicles, and stable DC microgrid systems. His work addresses core challenges in power electronics, including input current ripple reduction, wide voltage-gain range, and voltage stress mitigation. By introducing advanced converter topologies such as capacitor-clamped and dual-mode negative output converters, he enhances performance for fuel cell and EV applications. Dr. Bi has also contributed significantly to government- and industry-funded projects, applying theoretical innovations to practical systems. His papers are frequently cited in top journals, and his designs serve critical roles in electric transportation and energy systems. Through pioneering control methods and hardware design strategies, Dr. Bi is driving forward the next generation of clean, efficient energy solutions.

📊 Publication Top Notes:

Huakun Bi, Zonglei Mou, Yu Chen
“Common Grounded Wide Voltage-Gain Range DC–DC Converter With Zero Input Current Ripple and Reduced Voltage Stresses for Fuel Cell Vehicles”
Journal: IEEE Transactions on Industrial Electronics
Year: 2023
DOI: 10.1109/TIE.2022.3172767
Citations: 23

Huakun Bi, Bo Li, Ping Wang, Zhishuang Wang, Xiaochen Ma
“A New Coupled-Inductor-Based High-Gain Interleaved DC-DC Converter With Sustained Soft Switching”
Journal: IEEE Transactions on Vehicular Technology
Year: 2021
DOI: 10.1109/TVT.2021.3083317
Citations: Not specified

Huakun Bi, Cong Jia
“Common Grounded Wide Voltage‐Gain Range DC–DC Converter for Fuel Cell Vehicles”
Journal: IET Power Electronics
Year: 2019
DOI: 10.1049/iet-pel.2018.6234
Citations: 16

Guidan Li, Zhe Yang, Bin Li, Huakun Bi
“Power Allocation Smoothing Strategy for Hybrid Energy Storage System Based on Markov Decision Process”
Journal: Applied Energy
Year: 2019
DOI: 10.1016/j.apenergy.2019.03.001
Citations: Not specified

Huakun Bi, Ping Wang, Yu Che
“A Capacitor Clamped H-Type Boost DC–DC Converter With Wide Voltage-Gain Range for Fuel Cell Vehicles”
Journal: IEEE Transactions on Vehicular Technology
Year: 2019
DOI: 10.1109/TVT.2018.2884890
Citations: 77

Akibul Islam Chowdhury | Public Health Nutrition | Best Researcher Award

Posted on 12/05/202512/05/2025 by sciencefather

Mr. AkibulIslam Chowdhury |Public Health Nutrition |Best Researcher Award

Senior Lecturer at Daffodil International University,Bangladesh.

Akibul Islam Chowdhury is a dedicated academic and researcher at Daffodil International University, serving in the Department of Nutrition and Food Engineering. He holds both B.Sc. and M.Sc. degrees in Food Technology and Nutrition Science from Noakhali Science and Technology University (NSTU), where he achieved top honors. With over 15 publications in peer-reviewed journals, his work explores nutritional biochemistry, public health, food allergies, metabolic disorders, and diet-disease correlations. Akibul’s passion for food science is reflected in his commitment to student-centered education and evidence-based research. Recognized for his academic excellence and communication skills, he received the Best PowerPoint Presentation Award at a national seminar. His balanced profile of teaching, research, and publication makes him a strong contender for the Best Researcher Award.

🌍 Professional Profile:

🏆 Suitability for the Best Researcher Award

Akibul Islam Chowdhury is an outstanding young researcher with significant contributions in the fields of nutrition, public health, and food science. With over 15 peer-reviewed publications, including works in Food Chemistry: Molecular Sciences and Obesity Medicine, he has tackled topics ranging from allergen identification to metabolic disease management. He earned top academic honors during his M.Sc. and B.Sc. at NSTU and actively guides students through research-based learning at Daffodil International University. His strong publication record, academic excellence, and impactful research on health-related nutritional issues position him as a leading candidate for the Best Researcher Award. Akibul combines analytical rigor with social relevance, embodying the qualities of a dedicated scholar and innovator in nutrition science.

🎓 Education

Akibul Islam Chowdhury earned his M.Sc. in Food Technology and Nutrition Science from Noakhali Science and Technology University in 2020 (held in 2021), graduating with a perfect GPA of 4.00 and securing first position in his class. He also completed his B.Sc. from the same department and university in 2019 (held in 2020), earning a CGPA of 3.65 and ranking third. His earlier academic journey included an HSC from Govt. Science College, Dhaka (2015), and an SSC from A.K. High School (2013), both with outstanding results—GPA 5.00 in each. His academic record reflects consistent excellence and dedication to nutrition and food science, laying a solid foundation for his teaching and research contributions in the field.

🏢 Work Experience

Akibul Islam Chowdhury has gained academic and research experience as a Research Assistant at the Department of Food Technology and Nutrition Science, NSTU. His responsibilities included conducting literature reviews, designing nutritional studies, data collection and analysis, and co-authoring scientific papers. Currently, he serves as a faculty member in the Department of Nutrition and Food Engineering at Daffodil International University, Savar, Dhaka. In this role, he integrates teaching with applied research, guiding students in nutritional science, public health, and dietary practices. His experience bridges both classroom education and lab-based research, providing students with a work-based learning approach. His involvement in multiple collaborative studies has sharpened his analytical skills and nurtured his academic leadership.

🏅 Awards and Honors

Akibul Islam Chowdhury has been recognized for his academic performance and presentation skills. Most notably, he received the Best PowerPoint Presentation Award at the 2nd National Food Science Seminar organized at Noakhali Science and Technology University. This recognition reflects his clarity in scientific communication and ability to present complex data effectively to both academic and non-specialist audiences. In addition to this award, his consistent academic excellence—ranking first in his master’s program and third in his undergraduate class—demonstrates high academic merit. His early academic success in HSC and SSC examinations (GPA 5.00) further confirms a solid educational foundation. These achievements underscore his dedication and suitability for honors like the Best Researcher Award.

🔬 Research Focus

Akibul Islam Chowdhury’s research is anchored in the intersections of nutrition, food science, and public health. His work investigates metabolic disorders, gene-nutrient interactions, micronutrient supplementation, food-borne allergies, and the role of traditional plants like Stevia and Aloe vera in managing diabetes and obesity. He has conducted meta-analyses, systematic reviews, and clinical evaluations, contributing to journals such as Food Chemistry: Molecular Sciences and Obesity Medicine. His current interests include the molecular identification of lentil allergens, dietary impacts on COVID-19 severity, and public health nutrition interventions in Bangladesh. By integrating biostatistics and nutritional biochemistry, Akibul’s research aims to inform public health policy and dietary guidelines, emphasizing evidence-based solutions for lifestyle diseases and community health.

📊 Publication Top Notes:

Chowdhury, A. I., Ghosh, S., Hasan, M. F., Khandakar, K. A. S., & Azad, F. (2021). Prevalence of insomnia among university students in South Asian Region: a systematic review of studies. Journal of Preventive Medicine and Hygiene, 61(4), E525. Citations: 67
Chowdhury, A. I., Rahanur Alam, M., Raihan, M. M., Rahman, T., Islam, S., et al. (2022). Effect of stevia leaves (Stevia rebaudiana Bertoni) on diabetes: A systematic review and meta‐analysis of preclinical studies. Food Science & Nutrition, 10(9), 2868–2878. Citations: 34
Chowdhury, A. I., Alam, M. R., Rabbi, M. F., Rahman, T., & Reza, S. (2021). Does higher body mass index increase COVID-19 severity? A systematic review and meta-analysis. Obesity Medicine, 23, 100340. Citations: 31
Sultana, M., Dhar, S., Hasan, T., Shill, L. C., Purba, N. H., Chowdhury, A. I., et al. (2022). Knowledge, attitudes, and predictors of exclusive breastfeeding practice among lactating mothers in Noakhali, Bangladesh. Heliyon, 8(10). Citations: 28
Chowdhury, A. I. (2020). Role and effects of micronutrients supplementation in immune system and SARS-Cov-2 (COVID-19). Asian Journal of Immunology, 4(2), 47–55. Citations: 26
Chowdhury, A. I., Shill, L. C., Raihan, M. M., Rashid, R., Bhuiyan, M. N. H., Reza, S., et al. (2024). Human health risk assessment of heavy metals in vegetables of Bangladesh. Scientific Reports, 14(1), 15616. Citations: 22
Ghosh, S., Kabir, M. R., Alam, M. R., Chowdhury, A. I., & Al Mamun, M. A. (2022). Balanced diet related knowledge, attitude and practices (KAP) among adolescent school girls in Noakhali district, Bangladesh: A cross sectional study. International Journal of Adolescent Medicine and Health, 34(5), 319–325. Citations: 22
Chowdhury, A. I., & Alam, M. R. (2024). Health effects of heavy metals in meat and poultry consumption in Noakhali, Bangladesh. Toxicology Reports, 12, 168–177. Citations: 16
Halima, O., Najar, F. Z., Wahab, A., Gamagedara, S., Chowdhury, A. I., Foster, S. B., et al. (2022). Lentil allergens identification and quantification: An update from omics perspective. Food Chemistry: Molecular Sciences, 4, 100109. Citations: 16
Islam, M. S., Chowdhury, A. I., Shill, L. C., Reza, S., & Alam, M. R. (2023). Heavy metals induced health risk assessment through consumption of selected commercially available spices in Noakhali district of Bangladesh. Heliyon, 9(11). Citations: 14
Habib, M. A., Alam, M. R., Rahman, T., Chowdhury, A. I., & Shill, L. C. (2023). Knowledge, attitudes, and practices (KAP) of nutrition among school teachers in Bangladesh: A cross-sectional study. PLOS ONE, 18(3), e0283530. Citations: 13
Habib, M. A., Chowdhury, A. I., Alam, M. R., & Rahman, T. (2023). Commercially available iodized salts in Noakhali, Bangladesh: estimation of iodine content, stability, and consumer satisfaction level. Food Chemistry Advances, 2, 100294. Citations: 10
Shill, L. C., Alam, M. R., Chowdhury, A. I., & Alam, S. (2021). Association of FTO gene (rs9939609) with obesity and type-2 diabetes mellitus: Review from current studies. Romanian Journal of Diabetes Nutrition and Metabolic Diseases, 28(4), 447–452. Citations: 8
Reza, S., Alam, M. R., Chowdhury, A. I., Mamun, M. A. A., Akhter, M., & Habib, M. A. (2023). Assessing nutritional status and functionality in geriatric population of Bangladesh: the hidden epidemic of geriatric malnutrition. Gerontology and Geriatric Medicine, 9, 23337214231172663. Citations: 6
Habib, M. A., Dey, M., Chowdhury, A. I., Rahman, T., & Kundu, R. K. (2022). Current knowledge, attitude, and practice (KAP) towards physical activity (PA) and its impact on obesity management in Bangladesh: A cross‐sectional study. Health Science Reports, 5(6), e960. Citations: 6
Alam, M. R., Hossain, M. S., Chowdhury, A. I., Akhter, M., Mamun, A. A., & Reza, S. (2020). Relationship between malnutrition and functional disability in selected community-dwelling geriatric population in Bangladesh. medRxiv, 2020.08.02.20167049. Citations: 4
Chowdhury, A. I., Habib, M. A., & Rahman, T. (2022). Factors Influencing the Utilization of Antenatal Care, Institutional Delivery, and Postnatal Care Services Among Women in Bangladesh. Makara Journal of Health Research, 26(3), 1. Citations: 3
Chowdhury, A. I., Habib, M. A., & Ghosh, S. (2021). Effect of Saffron (Crocus sativus L.) on Common Non-Communicable Disease: Review from Current Clinical Findings. Journal of Ayurvedic and Herbal Medicine, 7(2), 93–108. Citations: 3
Habib, M. A., Chowdhury, A. I., Hossen, K., Kibria, T., & Hossain, M. (2020). Fast food intake and prevalence of overweight/obesity in students: do eating habits have a differential impact on gender. Journal of Contemporary Medical Research, 7(6), F4–F9. Citations: 3

Javier Ramírez | Computational Mechanics | Best Researcher Award

Posted on 09/05/202509/05/2025 by sciencefather

Dr. Javier Ramírez | Mechanics |Best Researcher Award

Professor at Universidad de Chile, Chile.

Dr. Javier Ramírez Ganga is an Adjunct Professor at the Universidad de Chile’s Department of Mathematical Engineering and a Project Engineer at the Center for Mathematical Modeling (CMM). With a Ph.D. in Engineering Sciences specializing in Mathematical Modeling, his research bridges numerical methods and real-world applications in mining, hydrology, and inverse problems. He has co-authored impactful publications in prestigious journals and actively contributes to national research projects. His international research visits and collaborations, especially in France, highlight his global engagement. Dr. Ramírez’s innovative work in gradient damage models and control theory positions him as a leader in applied mathematics, making him a highly deserving candidate for the Best Researcher Award.

🌍 Professional Profile:

🏆 Suitability for the Best Researcher Award

Dr. Javier Ramírez Ganga is a strong contender for the Best Researcher Award due to his significant contributions to computational mechanics, inverse problems, and applied mathematics. His academic path from a B.Sc. in Mathematics to a Ph.D. in Engineering Sciences with a focus on mathematical modeling demonstrates a deep commitment to interdisciplinary and application-driven research. His current roles as Adjunct Professor and Project Engineer at Universidad de Chile and the Center for Mathematical Modeling reflect leadership in impactful research environments.

🎓 Education

Javier Ramírez Ganga earned his Ph.D. in Engineering Sciences with a focus on Mathematical Modeling from Universidad de Chile in 2021. His doctoral thesis addressed the numerical reconstruction of inverse problems for partial differential equations under the supervision of Jaime H. Ortega and Gino Montecinos. He previously completed a Mathematical Engineering degree in 2016 at Universidad de Santiago de Chile, where he developed numerical approximations for exact controls in the 2D heat equation. His academic journey began with a B.Sc. in Mathematics from the same institution in 2015. This strong mathematical foundation supports his interdisciplinary research, blending advanced theory with real-world computational modeling. His training reflects both academic excellence and practical problem-solving skills.

🏢 Work Experience

Dr. Ramírez currently serves as an Adjunct Professor at the Universidad de Chile’s Department of Mathematical Engineering and as a Project Engineer at the CMM. Since 2020, he has contributed to several major national research projects, including FONDEF IDEA initiatives and the Advanced Center for Water Technologies (CAPTA), working on numerical methods for engineering applications. His supervisors include prominent researchers such as Jaime H. Ortega and James Mc Phee. Internationally, he conducted two research stays at Institut Fourier, Université Grenoble-Alpes, France. His expertise spans numerical modeling, applied mathematics, and inverse problems, enabling collaborations across engineering and environmental sciences. His experience demonstrates versatility and a sustained commitment to high-impact, interdisciplinary research.

🏅 Awards and Honors

While specific awards are not listed, Dr. Javier Ramírez Ganga’s scholarly output and participation in prestigious research projects demonstrate a high level of academic recognition. His publications in Applied Mathematical Modelling and Mathematical Reports, along with presentations at major conferences like MassMin 2020, highlight the academic impact of his work. His repeated invitations for international research visits to the Institut Fourier, Université Grenoble-Alpes, signal his growing global reputation. His continued selection for competitive national projects such as FONDEF IDEA and CAPTA also reflects the confidence of Chile’s research funding bodies in his expertise. These accomplishments collectively suggest a trajectory of excellence and make him a strong candidate for future honors and distinctions.

🔬 Research Focus

Dr. Javier Ramírez Ganga’s research centers on numerical analysis, control theory, and inverse problems in partial differential equations (PDEs), with strong applications in engineering and environmental modeling. His recent work includes gradient damage models for underground mining, CGO solutions for coupled conductivity equations, and inverse modeling for water technologies. He applies computational tools like Python, FreeFem++, and Matlab to simulate complex systems and propose efficient solutions for practical challenges. His interdisciplinary collaborations bridge applied mathematics, geophysics, and hydrology, contributing to innovation in sustainable mining and water resource management. By integrating mathematical rigor with engineering relevance, his work enhances the predictive power of simulations and informs policy and design in critical sectors.

📊 Publication Top Notes:

Journal Articles

Bonnetier, E., Gaete, S., Jofré, A., Lecaros, R., Montecinos, G., Ortega, J. H., Ramírez-Ganga, J., & San Martín, J. S. (2025). Gradient damage models for studying material behavior in underground mining. Applied Mathematical Modelling, 116171.

Lecaros, R., Montecinos, G., Ortega, J. H., & Ramírez-Ganga, J. (2022). CGO solutions for coupled conductivity equations. Mathematical Reports, 24(1–2), 217–220.

Conference Proceedings

Gaete, S., Jofré, A., Lecaros, R., Montecinos, G., Ortega, J. H., Ramírez-Ganga, J., & San Martín, J. S. (2020). A gradient damage model applied to underground mining methods. In MassMin 2020: Proceedings of the Eighth International Conference & Exhibition on Mass Mining. University of Chile.

Preprints

Bonnetier, E., Gaete, S., Jofré, A., Lecaros, R., Montecinos, G., Ortega, J. H., Ramírez-Ganga, J., & San Martín, J. S. (2020). A shear-compression damage model for the simulation of underground mining by block caving. arXiv preprint, arXiv:2012.11118.

Gaete, S., Jofré, A., Lecaros, R., Montecinos, G., Ortega, J. H., Ramírez-Ganga, J., & San Martín, J. S. (2020). A fast algorithm of the shear-compression damage model for the simulation of block caving. arXiv preprint, arXiv:2012.14776.

Winny Andalia | Renewable energy | Best Researcher Award

Posted on 08/05/202508/05/2025 by sciencefather

Assist.Prof.Dr. Winny Andalia| Renewable energy |Best Researcher Award

Assist prof at Universitas Tridinanti , Indonesia.

Winny Andalia, S.T., M.T., an Assistant Professor at the University of Tridinanti, exemplifies excellence in applied research and academic leadership. Her multidisciplinary expertise spans chemical engineering, quality control, supply chain management, and public health innovations. She has led over ten funded research projects, published in reputable journals including Scopus-indexed platforms, and authored textbooks that enhance engineering education. Her research impact is reflected in her Scopus H-index of 3 and Google Scholar H-index of 9. As a certified professional in ISO 21008 auditing and supply chain management, and an active reviewer and editor for national and international journals, Winny contributes significantly to scientific advancement and industrial relevance. Her dynamic contributions make her a compelling candidate for the Best Researcher Award.

🌍 Professional Profile:

🏆 Suitability for the Best Researcher Award

Winny Andalia, S.T., M.T., Assistant Professor at the University of Tridinanti, stands out as a dedicated academic and impactful researcher. Her interdisciplinary projects—ranging from sulfuric acid recovery and biodiesel catalyst selection to e-commerce decision modeling—demonstrate innovation and relevance to both industry and society. She has successfully secured funding from national agencies including DIPA UNSRI and LPPM Tridinanti, reflecting confidence in her research capabilities. With certification in ISO 21008 auditing and supply chain management from BNSP, she bridges theory and practice effectively. Her roles as journal editor and reviewer amplify her influence in scientific communication. Through consistent scholarly output, national recognition, and commitment to sustainable development, Winny exemplifies the excellence deserving of the Best Researcher Award.

🎓 Education

Winny Andalia earned her Bachelor of Engineering (S.T.) and Master of Engineering (M.T.) degrees in fields aligned with chemical and industrial systems, fostering a solid foundation in applied engineering and scientific inquiry. Her academic journey has been marked by a commitment to both theoretical knowledge and practical application, enhanced by professional certifications such as BNSP in supply chain management and ISO 21008 auditing. These credentials complement her formal education and empower her research in quality control, environmental systems, and production analysis. Her continuous learning approach and multidisciplinary academic exposure have equipped her to lead impactful research initiatives and contribute meaningfully to curriculum development and scientific literature within the University of Tridinanti and broader academic community.

🏢 Work Experience

With extensive experience in academia and research, Winny Andalia has been serving as an Assistant Professor at the University of Tridinanti. Her professional journey includes managing numerous research projects funded by national agencies such as RISTEK DIKTI and KEMDIKBUD-DIKTI, addressing issues from biodiesel production to COVID-19 immunity boosters. She has authored and co-authored textbooks and reference works on topics including calculus, quality control, and reaction kinetics. Her active involvement in academic publishing includes roles as a journal reviewer and editor, further reflecting her expertise and leadership. Certified in ISO auditing and supply chain management, she bridges academia and industry effectively. Winny’s contributions span education, research, and consultancy, positioning her as a respected figure in Indonesia’s scientific and engineering communities.

🏅 Awards and Honors

Winny Andalia has received multiple forms of recognition for her academic and professional excellence. Her certification by BNSP in supply chain management and ISO 21008 auditing underscores her practical expertise, while numerous research grants from RISTEK DIKTI, LPDP, and KEMDIKBUD-DIKTI highlight her capability to secure and lead impactful national projects. She has been entrusted with editorial and reviewer roles in national and international journals, acknowledging her subject matter authority. Her published works in Scopus and Sinta-accredited journals, along with widely-used textbooks, further validate her academic contributions. Though specific named awards may not be listed, her continuous funding success, publication record, and role in knowledge dissemination signify prestigious professional achievements in Indonesia’s research and higher education landscape.

🔬 Research Focus

Winny Andalia’s research focuses on sustainable engineering solutions, biodiesel optimization, environmental management, quality control, and public health analytics. Her early work addressed sulfuric acid recovery and catalyst selection for biodiesel production, advancing green chemical processes. She has developed decision-support models for e-commerce and inventory systems, integrating methods like AHP and Statistical Process Control. During the COVID-19 pandemic, she pivoted to public health, researching the effects of immunomodulators and bovine colostrum as immunity boosters. Her recent work investigates converting household waste into bio-oil via pyrolysis. This multidisciplinary approach demonstrates her commitment to solving real-world problems through rigorous data analysis and engineering innovation. Her published research, national funding success, and applied methodologies illustrate a strong impact on both academia and society.

📊 Publication Top Notes:

Kinerja Katalis NaOH dan KOH ditinjau dari Kualitas Produk Biodiesel yang dihasilkan dari Minyak Goreng Bekas
W. Andalia, I. Pratiwi
Jurnal Tekno Global 7 (2), 36
Citations: 36 | Year: 2018
Taguchi experiment design for DES K2CO3-glycerol performance in RBDPO transesterification
S. Arita, L.N. Komariah, W. Andalia, F. Hadiah, C. Ramayanti
Emerging Science Journal 7 (3), 917–927
Citations: 31 | Year: 2023
Analisis karakteristik dan potensi logam pada limbah padat fly ash dan bottom ash di PLTU industri pupuk
M. Asof, S. Arita, L. Luthfia, W. Andalia, M. Naswir
Jurnal Teknik Kimia 28 (1), 44–50
Citations: 12 | Year: 2022
Pengendalian Kualitas Pada Produksi Karet Menggunakan Metode Six Sigma (Studi Kasus: PT. Sri Trang Lingga Indonesia)
E. Parianti, I. Pratiwi, W. Andalia
Integrasi: Jurnal Ilmiah Teknik Industri 5 (1), 24–28
Citations: 12 | Year: 2020
Penentuan pola distribusi optimal menggunakan metode saving matrix untuk meningkatkan fleksibilitas pemesanan
W. Andalia, D. Oktarini, S. Humairoh
Journal Industrial Servicess 7 (1), 23–26
Citations: 11 | Year: 2021
Identifikasi Perawatan Mesin Press Hidrolik Dengan Menggunakan Metode FMEA dan FTA (Studi Kasus di Bengkel Cahaya Ilahi)
J. Sidik, W. Andalia, T. Tamalika
Jambura Industrial Review (JIREV) 2 (2), 57–64
Citations: 10 | Year: 2022
Analisis Pemilihan Supplier Menggunakan Metode Analytical Hierarchy Process (Studi Kasus PT. Perkasa Sejahtera Mandiri)
W. Andalia, I. Pratiwi
Integrasi: Jurnal Ilmiah Teknik Industri 3 (1), 40–50
Citations: 9 | Year: 2018
Perancangan model keputusan multikriteria pemilihan layanan e-commerce untuk kepuasan pelanggan
I. Pratiwi, W. Andalia
Prosiding Semnastek
Citations: 9 | Year: 2018
Recovery of H2SO4 from spent acid waste using bentonite adsorbent
M. Asof, S.A. Rachman, W.A. Nurmawi, C. Ramayanti
MATEC Web of Conferences 101, 02007
Citations: 9 | Year: 2017
Pelatihan Pengembangan Produk UMKM di Kecamatan Sako Palembang
I. Pratiwi, S. Aprilyanti, W. Andalia
Jurnal Abdimas Mandiri 8 (1), 1–6
Citations: 8 | Year: 2024

Shahrzad Falahat | Deep learning | Best Researcher Award

Posted on 05/05/202505/05/2025 by sciencefather

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:

🏆 Suitability for the Best Researcher Award

🎓 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

Jingge Wang |Transfer Learning | Best Researcher Award

Posted on 05/05/202505/05/2025 by sciencefather

Ms. Jingge Wang| Transfer Learning
|Best Researcher Award

PhD Candidate at Tsinghua University, China .

Jingge Wang is a Ph.D. student in Computer Science at Tsinghua University, under the supervision of Prof. Yang Li. He focuses on robust machine learning, generative models, and medical image analysis. Jingge has published in leading journals and conferences such as IEEE JSTSP, MICCAI, and the Journal of the Franklin Institute. He has collaborated internationally, including a visiting research stint at the University of Texas at Austin with Prof. Qixing Huang. His research contributions emphasize generalization in unseen domains and progression-aware medical imaging, showing high innovation and societal relevance. With an exceptional academic record and growing impact, Jingge Wang is highly deserving of the Best Researcher Award, demonstrating both technical depth and practical application in AI for healthcare.

🌍 Professional Profile:

🏆 Suitability for the Best Researcher Award

Jingge Wang demonstrates exceptional promise and productivity as a young researcher in artificial intelligence and medical imaging. His work bridges robust machine learning and healthcare, with impactful contributions in domain generalization, generative modeling, and progression-aware disease prediction. He has published in top venues including IEEE JSTSP, Journal of the Franklin Institute, and MICCAI. Jingge excels academically, with near-perfect GPAs in both undergraduate and postgraduate studies, and has international research exposure from a collaborative visit to UT Austin. His innovative use of diffusion models and longitudinal data in medical imaging addresses pressing real-world challenges. With a record of high-impact publications, interdisciplinary work, and global collaboration, Jingge Wang stands out as an outstanding candidate for the Best Researcher Award.

🎓 Education

Jingge Wang is currently pursuing his Ph.D. in Computer Science at Tsinghua University (2022–2026), advised by Prof. Yang Li. He completed a short-term research visit to the University of Texas at Austin (2023–2024), collaborating with Prof. Qixing Huang. He holds a Master of Science in Data Science from Tsinghua University (2019–2022), graduating with an outstanding GPA of 3.98/4.0. Jingge completed his undergraduate studies in Artificial Intelligence and Automation at Huazhong University of Science and Technology (2015–2019), where he also excelled academically with a GPA of 3.97/4.0. This strong interdisciplinary foundation, combining AI, automation, and data science, positions him well to conduct impactful research at the intersection of machine learning and medical imaging.

🏢 Work Experience

Jingge Wang has diverse and impactful research experience. He is leading projects on progression-aware disease prediction and medical image generation, with a focus on exploiting temporal clinical data. He developed domain generalization models using probabilistic and Wasserstein-based methods, resulting in publications in ICLR (RobustML workshop), IEEE JSTSP, and Journal of the Franklin Institute. Jingge’s collaboration with international experts, such as Prof. Qixing Huang at UT Austin, has enriched his research vision. He has hands-on experience with latent diffusion models, generative AI, and domain-robust training techniques. His technical depth spans theoretical development and real-world applications, especially in healthcare AI. This combination of academic rigor and practical relevance exemplifies his readiness and suitability for advanced research roles.

🏅 Awards and Honors

While specific named awards are not listed, Jingge Wang’s academic and research accomplishments reflect award-worthy excellence. He graduated with top GPAs in both his B.S. (3.97/4.0) and M.S. (3.98/4.0), indicating consistent high performance. His peer-reviewed publications in competitive venues like IEEE JSTSP, MICCAI, and ISBI highlight recognition from the international academic community. He has contributed first-author work in key areas such as domain generalization and medical image synthesis, and was selected for a prestigious visiting position at the University of Texas at Austin. These achievements collectively demonstrate his scholarly distinction and strong candidacy for research excellence awards. His current work under review at MICCAI and other venues further signals continued impactful contributions.

🔬 Research Focus

Jingge Wang’s research focuses on robust machine learning, generative modeling, and medical imaging. He specializes in domain generalization, tackling the challenge of adapting models to unseen or evolving environments. His recent work integrates temporal dynamics and longitudinal data into predictive models, improving diagnostic accuracy in healthcare. He also explores controllable generative models using diffusion techniques for medical image synthesis, contributing to data augmentation and disease progression modeling. Jingge’s research bridges theory and practice—leveraging distributional robustness and probabilistic modeling for real-world generalization. Through collaboration with global researchers and publication in leading journals, he is advancing AI applications in medicine, particularly for progressive disease prediction and modality synthesis, aligning perfectly with future-facing research in AI-driven healthcare.

📊 Publication Top Notes:

Ji, H., Tong, H., Wang, J., Yan, D., Liao, Z., & Kong, Y. (2021). The effectiveness of travel restriction measures in alleviating the COVID-19 epidemic: evidence from Shenzhen, China. Environmental Geochemistry and Health, 1–18.
Citations: 16
Wang, Y.X. Jingge, Li, Y., & Xie, L. (2021). Class-conditioned Domain Generalization via Wasserstein Distributional Robust Optimization. RobustML Workshop at ICLR 2021.
Citations: 10
Liu, H., Wang, J., Zhang, X., Guo, Y., & Li, Y. (2024). Enhancing Continuous Domain Adaptation with Multi-path Transfer Curriculum. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 286–298.
Citations: 3
Xie, Y., Wang, J., Feng, T., Ma, F., & Li, Y. (2024). CCIS-Diff: A Generative Model with Stable Diffusion Prior for Controlled Colonoscopy Image Synthesis. arXiv preprint arXiv:2411.12198.
Citations: 2
Yang, J., Wang, J., Zhang, G., & Li, Y. (2024). Selecting the Best Sequential Transfer Path for Medical Image Segmentation with Limited Labeled Data. arXiv preprint arXiv:2410.06892.
Citations: 1
Wang, J., Xie, L., Xie, Y., Huang, S.L., & Li, Y. (2024). Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge. IEEE Journal of Selected Topics in Signal Processing.
Citations: 1
Yang, J., Zhang, G., Wang, J., & Li, Y. (2025). Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially. arXiv preprint arXiv:2502.01000.
Citations: Not yet cited
Yang, J., Zhang, G., Wang, J., & Li, Y. (2024). Graph-guided Source Selection with Sequential Transfer for Medical Image Segmentation. In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
Citations: Not yet cited

Zhiying Mu| Neural Networks | Best Researcher Award

Posted on 02/05/202502/05/2025 by sciencefather

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:

🏆 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)

Xin Bai | Mechanical Engineering | Best Researcher Award

Posted on 29/04/202529/04/2025 by sciencefather

Assist. Prof. Dr. Xin Bai | Mechanical Engineering
|Best Researcher Award

Assist. Prof. Institute of Metal Research, Chinese Academy of Sciences, China.

Assoc. Prof. Dr. Xin Bai is a distinguished researcher at the Institute of Metal Research, Chinese Academy of Sciences, and a member of the Youth Innovation Promotion Association. Renowned for his pioneering work in fatigue fracture and structural reliability, Dr. Bai has significantly advanced methods for predicting fatigue performance from minimal experimental data. His research is both innovative and impactful, addressing critical needs in materials engineering and structural integrity. His commitment to developing cost-effective and efficient reliability assessment tools and software has garnered recognition across academia and industry. Dr. Bai’s sustained research excellence, leadership, and contributions to cutting-edge reliability science make him a compelling candidate for the Best Researcher Award.

🌍 Professional Profile: