Dan Uchimura | Engineering | Best Researcher Award

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Mr. Dan Uchimura | Engineering | Best Researcher Award

Mr. Dan Uchimura|Kajima Corporation | Japan

Dan Uchimura is an emerging professional in nuclear power plant structural design, currently serving as a designer in the Kajima Corporation Nuclear Power Department. With a Master’s Degree in Architecture from Waseda University, he has swiftly transitioned from academia to industry, applying his expertise in structural systems, safety analysis, and computational modeling. During his graduate studies in Tokyo, he focused on enhancing the resilience and sustainability of energy facilities, developing technical skills in MATLAB, Python, and Excel to simulate structural integrity under extreme conditions. Since joining Kajima, Dan has contributed to the planning and design of nuclear power facilities while spearheading research on integrating non-destructive inspection techniques—especially infrared thermography—into plant systems to detect structural anomalies without operational interruptions. Known for his analytical thinking, precision, and interdisciplinary approach, he collaborates with engineers, material scientists, and safety analysts to deliver reliable, innovative design solutions aligned with stringent safety regulations. His research interests center on advancing inspection technologies, modeling structural behavior under thermal and seismic loads, and exploring AI-driven predictive maintenance systems to enhance safety and efficiency in nuclear infrastructure. Though early in his career, Dan has already earned recognition for his innovative contributions, including commendations for his thesis on resilient energy infrastructure and praise from senior engineers for merging theoretical concepts with practical design solutions.

Profile : ORCID

Featured Publication 

Uchimura, D. (2024). Application of infrared thermography for non-destructive structural inspection in nuclear power facilities. Journal of Structural Engineering and Technology.

Uchimura, D. (2023). Resilient architectural design framework for nuclear power plants. International Journal of Sustainable Energy Infrastructure.

Uchimura, D. (2023). Computational modeling of seismic loads in nuclear plant structures. Journal of Advanced Structural Engineering.

 

Shangshang Wu | Engineering | Best Researcher Award

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Dr. Shangshang Wu | Engineering | Best Researcher Award

Tianjin university | China

Wu Shangshang is a mechanical engineer pursuing her Ph.D. at the School of Mechanical Engineering, Tianjin University in China, where she also completed her B.S. and M.S. in Mechanical Engineering. Her research focuses on underwater gliders, emphasizing hydrodynamic identification, motion behavior analysis, and front-end data processing for acoustic communication. Since her master’s studies, she has worked as a graduate researcher, contributing to both experimental sea trials and theoretical modeling, and has published journal articles and conference papers in marine robotics, acoustics, and signal processing. Wu’s doctoral work advances model-based and data-driven methods to improve hydrodynamic prediction and control under uncertain underwater conditions, supporting the development of reliable seabed vehicles and underwater communication systems. She collaborates closely with colleagues at Tianjin University, including researchers such as Guangwei Lv and Shaoqiong Yang, and her early contributions are gaining citations. Her interests also include neural network–based hybrid modeling, online estimation, and mitigating the effects of environmental factors like sea currents and noise on underwater navigation and sensor performance. While no specific awards are publicly documented, Wu shows strong potential in combining experimental insights with computational techniques to enhance the design, control, and stability of underwater gliders.

Profile : Scopus| ORCID  

Featured Publications

AuthorLastName, A. A., & AuthorLastName, B. B. Model and data-driven hydrodynamic identification and prediction for underwater gliders. Physics of Fluids.

AuthorLastName, A. A., & AuthorLastName, B. B. An enhanced variational mode decomposition method for processing hydrodynamic data of underwater gliders. Measurement.

AuthorLastName, A. A., & AuthorLastName, B. B. Multi-body modelling and analysis of the motion platform for underwater acoustic dynamic communication. Applied Mathematical Modelling.

Jingyi Gao | Engineering | Best Researcher Award

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Ms. Jingyi Gao | University of Virginia | United States

Ms. Jingyi Gao | University of Virginia | United States

Jingyi Gao is a Ph.D. candidate in Systems and Information Engineering at the University of Virginia with a 3.75 GPA, focusing on time series prediction, Bayesian probabilistic modeling, and federated learning. She holds an M.S. in Applied Mathematics and Statistics from the Johns Hopkins University (GPA 3.9) and dual bachelor’s degrees in Mathematics–Computer Science and Economics from the University of California, San Diego. Jingyi has extensive teaching experience, serving as a teaching assistant at UVA where she has instructed over 1,000 students across multiple courses in statistical modeling, data mining, AI, and big data systems, and previously supported courses at Johns Hopkins and UC San Diego. She has mentored underrepresented students through the Data Justice Academy and completed research internships at the University of Pittsburgh and Tencent, developing machine learning models for stress detection, healthcare data analysis, and cloud resource forecasting. Jingyi has authored several publications, including work accepted by Pattern Recognition and under review at AAAI and IISE Transactions. Her recent projects involve designing deep latent variable models for ergonomic risk assessment, developing real-time adaptive prediction frameworks for occupational health monitoring, creating federated learning approaches for multi-output Gaussian processes, and modeling behavioral regularity and predictability from multidimensional sensing signals. Combining expertise in machine learning, statistical modeling, and data-driven decision systems, Jingyi aims to advance human-centered intelligent systems through interpretable and privacy-preserving predictive modeling.

Profile: Scopus | Google Scholar

Featured Publications 

Gao, J., Rahman, A., Lim, S., & Chung, S. TimeSets: A real-time adaptive prediction framework for multivariate time series (Manuscript under review at the Association for the Advancement of Artificial Intelligence).

Gao, J., Lim, S., & Chung, S. Gait-based hand load estimation via deep latent variable models with auxiliary information (Manuscript under review at IISE Transactions).

Gao, J., & Chung, S. Federated automatic latent variable selection in multi-output Gaussian processes (Accepted for publication in Pattern Recognition)*.

Gao, J., Yan, R., & Doryab, A. Modeling regularity and predictability in human behavior from multidimensional sensing signals and personal characteristics. Proceedings of the International Conference on Machine Learning and Applications (ICMLA). Institute of Electrical and Electronics Engineers.

Chen, T., Chen, Y., Gao, J., Gao, P., Moon, J. H., Ren, J., … & Woolf, T. B. Machine learning to summarize and provide context for sleep and eating schedules. bioRxiv.

Nan Li | Engineering | Best Researcher Award

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Dr. Nan Li | Engineering | Best Researcher Award

Associate researcher at erospace Information Research Institute, Chinese Academy of Sciences, China

Dr. Nan Li is an accomplished Associate Researcher at the State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences. With a strong interdisciplinary foundation in biomedical engineering and automation, she specializes in developing microfluidic-based nucleic acid and immunoassay detection systems. Dr. Li has contributed significantly to the advancement of rapid, portable, and sensitive diagnostic technologies, many of which are aimed at point-of-care and field diagnostics for infectious diseases. Her work is deeply rooted in translational research, seamlessly integrating microengineering, biotechnology, and clinical diagnostics.

Profile

Scopus

🎓 Education

Dr. Nan Li received her Ph.D. in Biomedical Engineering from the prestigious Tsinghua University in 2022, after completing her undergraduate degree in Automation from the Beijing Institute of Technology in 2016. During her doctoral studies, she focused on the development of centrifugal microfluidic platforms and integrated biosensing systems, gaining critical experience in both academic research and real-world biomedical applications. Her academic journey laid the groundwork for a career dedicated to creating impactful diagnostic tools for global healthcare needs.

💼 Experience

Dr. Li currently serves as an Associate Researcher at the Chinese Academy of Sciences, where she leads projects under the State Key Laboratory of Transducer Technology. She has also been actively involved in collaborative efforts with academic and industrial partners to translate laboratory innovations into commercial and clinical applications. In addition to her research responsibilities, Dr. Li contributes to scholarly activities as a journal reviewer for Microsystems & Nanoengineering and Current Analytical Chemistry. She has also delivered oral presentations at prominent international conferences such as Transducers 2025 and IEEE Sensors 2024, further reflecting her stature in the field.

🔬 Research Interests

Dr. Li’s core research interest lies in microfluidic technology for nucleic acid amplification, multiplex detection, and point-of-care diagnostics. She is particularly focused on developing integrated fluidic systems that are capable of rapid, accurate, and simultaneous detection of multiple pathogens or biomarkers. Her work often involves combining engineering principles such as centrifugal force and Euler force with advanced biochemical assays like LAMP and CRISPR. This interdisciplinary approach enables her to create portable diagnostic tools with immense potential in epidemic control, food safety, and personalized medicine.

🏆 Awards

Dr. Nan Li’s exceptional work has earned her several prestigious honors, including the Outstanding Reviewer Award from Microsystems & Nanoengineering in 2024. She was recognized as one of the Outstanding Graduates in Beijing in both 2016 and 2022. She also received the First Prize of Tsinghua University Comprehensive Scholarship in 2020 and the Gold Star Distinguished Research Award from the Biochip (Beijing) National Engineering Research Center in 2018 and 2020. Earlier in her academic journey, she was a recipient of the Tsinghua Future Scholar Scholarship, an award conferred upon top-performing doctoral candidates.

📚 Publications

Among Dr. Li’s numerous scientific publications, the following seven represent high-impact research in her field:

  1. Tianping Zhou, Nan Li* (2025). Sensors and Actuators B: Chemical. “Shockproof magnetofluidic multiplex nucleic acid system” – DOI: 10.1016/j.snb.2025.138139.

  2. Nan Li# et al. (2025). Biosensors & Bioelectronics, “Chip-based universal strategy for multiplex PCR”, Vol. 269, 116921.

  3. Bin Xiao# et al., Nan Li* (2024). Food Chemistry, “Toothpick DNA extraction with LAMP platform”, Vol. 460, 140659.

  4. Jiajia Liu# et al., Nan Li# (2024). Small Methods, “One-pot multiplex virus detection”, Vol. 8, 2400030. (Cover Article).

  5. Nan Li# et al. (2022). Sensors and Actuators B: Chemical, “Euler force-assisted sequential liquid release”, Vol. 359, 131642.

  6. Nan Li et al. (2022). Lab on a Chip, “Fully integrated SNP genotyping for hearing loss”, Vol. 22(4): 697–708. (Cover Article).

  7. Nan Li# et al. (2021). Microsystems & Nanoengineering, “Raw-sample-in multiplexed detection system”, Vol. 7(1): 94.

These works are widely cited and demonstrate her contributions to practical innovations in diagnostic technologies.

✅ Conclusion

Dr. Nan Li’s trajectory exemplifies a dedicated and forward-thinking researcher whose work merges engineering innovation with biomedical applications. Through her trailblazing research in microfluidic systems and portable diagnostics, she has not only addressed pressing needs in healthcare but also helped shape the future of rapid disease detection. Her consistent output of high-impact publications, international recognition, and impressive list of awards collectively make her a deserving candidate for a Best Paper Award. Dr. Li’s blend of creativity, precision, and practical implementation reflects the qualities that such an award seeks to honor.

Karla Filian | Engineering | Best Researcher Award

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Mrs Karla Filian |  Engineering |  Best Researcher Award

Graduate student in the Master’s program in Earth Sciences,  at Faculty of Engineering in Earth Sciences, ESPOL Polytechnic University,  Ecuador

Karla Filian Haz is a graduate student pursuing a Master’s in Earth Sciences at ESPOL Polytechnic University. With a background in Mining Engineering, she works as a Project Analyst, contributing to research and academic initiatives in Earth Sciences. Her research focuses on environmental pollution mitigation, water treatment technologies, and sustainable engineering solutions. She has co-authored two indexed journal articles and two conference papers, collaborating with international institutions such as Ghent University and the Mexican Geological Survey. Her work aims to develop innovative solutions for environmental management in mining and water treatment.

Profile:

Academic & Professional Background:

Mining Engineer pursuing a Master’s in Earth Sciences at ESPOL. Currently a Project Analyst, contributing to research, academic initiatives, and program coordination in Earth Sciences. Expertise in event organization, documentation management, and compliance.

Research & Innovations:

  • Research Projects: 4
  • Publications: 2 indexed journal articles, 2 conference papers
  • Citations: h-index: 1, Citations: 2
  • Collaborations: Ghent University (Belgium), Catholic University of Santiago de Guayaquil, Universidad del Pacífico (Ecuador), Mexican Geological Survey (SGM)

Research Areas:

Environmental engineering, pollution mitigation in mining, water treatment technologies, sustainable engineering solutions.

Key Contributions:

Research on environmental pollution, tailing dam risks, and desalination optimization using advanced membranes. Findings contribute to sustainable solutions for water treatment and environmental management in the mining industry.

Publication Top  Notes:

Title: Assessment of Environmental Pollution and Risks Associated with Tailing Dams in a Historical Gold Mining Area of Ecuador
Authors: B. Salgado-Almeida, A. Briones-Escalante, D. Falquez-Torres, E. Peña-Carpio, S. Jiménez-Oyola
Journal: Resources (2024)
Citations: 1