Belkacem Bekhiti | Engineering | Best Researcher Award

Prof. Belkacem Bekhiti | Engineering | Best Researcher Award

Prof. Belkacem Bekhiti | Institute of Aeronautics and Space Studies, University of Blida | Algeria

Dr. Bekhiti Belkacem is a distinguished academic and researcher in control theory, robotics, and aerospace engineering, currently serving as a Lecturer at the Institute of Aeronautics and Space Studies, Blida University 1, Algeria. His expertise spans guidance, navigation, and control systems, integrating theoretical modeling with real-world aerospace applications. He holds a Doctorate in Electrical Engineering with a specialization in Automatic Control from the University of Boumerdes, a Magister in Advanced Control of Complex Systems from the National Polytechnic School, Oran, a Master’s in Automatic Control from the University of Djelfa, and an Engineering degree in Electrical Engineering from Boumerdes. His career includes teaching positions at Blida and Djelfa Universities, collaboration with the Algerian Air Agency, and supervision of advanced student projects in UAVs, satellite control, and robotics. His research focuses on MIMO control, matrix polynomial theory, robotic modeling, nonlinear adaptive control, and intelligent aerospace system design, merging classical automation with artificial intelligence and fractional-order control. He has authored several books and numerous international publications, presented his work at major conferences, and earned recognition for his contributions to intelligent control and aerospace systems. His influence extends across the Algerian and international research communities, where he continues to inspire innovation and academic excellence in modern control and aeronautical engineering.

Profile : Google Scholar 

Featured Publications 

  • Bekhiti, B. (2015). On the theory of λ-matrices based MIMO control system design. Control and Cybernetics.

  • Bekhiti, B. (2017). Intelligent block spectral factors relocation in a quadrotor UAV. International Journal of Scientific Computing (IJSCC).

  • Bekhiti, B. (2018). On λ-matrices and their applications in MIMO control systems design. International Journal of Mathematical and Computational Intelligence (IJMIC).

  • Bekhiti, B. (2020). On the block decomposition and spectral factors of λ-matrices. Control and Cybernetics.

  • Bekhiti, B. (2020). Internal stability improvement of a natural gas centrifugal compressor. Journal of Natural Gas Science and Engineering.

Dan Uchimura | Engineering | Best Researcher Award

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.

 

Jingyi Gao | Engineering | Best Researcher Award

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.

Chen Chen | Engineering | Best Researcher Award

Assist. Prof. Dr. Chen Chen | Engineering | Best Researcher Award 

Assistent professor, at Xidian University, China.

Dr. Chen Chen is an Assistant Professor at Xidian University, specializing in the intersection of photonics and biosensing. She earned her Ph.D. from Xi’an Jiaotong University, following a B.Sc. from the same institution and an M.Sc. from Uppsala University. Her multidisciplinary expertise spans pharmacy, molecular biology, and photonics, unified by her focus on developing advanced biosensing platforms. Dr. Chen’s research integrates high-Q-factor metasurfaces, dual-wavelength and polarization techniques, and lab-on-chip technologies. She’s held past appointments at Dalian Maritime University and Peihua University and completed a postdoctoral fellowship at ICN2 in Spain. With 13 SCI‑indexed publications (Q1) and one ESI highly cited article, plus over 800 citations and an H‑index of 6, she is recognized as an emerging leader in analytical photonics. Dr. Chen also holds a published patent (CN202210581990.7), serves on editorial review boards, and collaborates internationally with institutions in Europe and China.

Professional Profile

Scopus

Google Scholar 

🎓 Education

Dr. Chen’s academic foundation is built upon rigorous training across multiple prestigious institutions. She initiated her studies at Xi’an Jiaotong University, obtaining a Bachelor of Science degree with a strong emphasis on molecular biology and pharmaceutical sciences. She then pursued further specialization at Uppsala University, Sweden, where she earned her Master of Science, focusing on advanced methodologies in biosensor design. Her academic journey culminated in a Ph.D. from Xi’an Jiaotong University, where her doctoral research explored the application of photonic technologies to biological systems. Throughout her education, Dr. Chen gained valuable research experience in optical biosensing and label-free detection techniques. Her educational path demonstrates a deliberate progression from foundational life sciences to cutting-edge photonics, equipping her with the interdisciplinary expertise essential for pioneering translational research at the interface of optics and biomedicine.

💼 Experience

Dr. Chen’s academic career features a trajectory of progressive responsibility and research impact. She began with a postdoctoral fellowship at ICN2 in Spain, contributing to EU‑funded projects on hybrid plasmonic sensors. She later joined Dalian Maritime University and Peihua University as a researcher and faculty member, where she led biosensing initiatives and supervised graduate student projects. Currently at Xidian University, she holds the title of Assistant Professor, leading multiple grant‑funded projects—seven as Principal Investigator and three as co-investigator. Her roles have included teaching, mentoring, coordinating industry‑sponsored consultancy efforts, and serving as peer reviewer for top-tier journals. Dr. Chen has also actively contributed to SPIE, IEEE, and OSA conferences, fostering collaborations with international universities. Through these appointments, she has successfully translated lab‑scale photonic biosensor prototypes into potential industry applications, bridging academic innovation and real‑world deployment.

🔬 Research Interests

Dr. Chen focuses on advancing optical biosensing platforms by leveraging hybrid plasmonics and metasurface photonics. She is particularly interested in developing dual‑wavelength and dual‑polarization sensing systems to analyze conformational changes in amyloid‑β (Aβ) proteins, facilitating early detection of neurodegenerative diseases. Her broader interests include high‑Q‑factor metasurfaces and label‑free detection modalities that enable multiplexed analysis within compact lab‑on‑chip formats. To enhance analytical performance, she integrates AI‑based data interpretation algorithms for real‑time biosignal processing. Additionally, Dr. Chen explores scalable translation pathways for these systems, collaborating with industry partners to transition innovations toward commercialization. Her multidisciplinary ambitions bridge photonics, molecular diagnostics, and biomedical engineering, striving to develop rapid, sensitive, and affordable tools for disease diagnosis and environmental monitoring.

🏆 Awards

Dr. Chen has been recognized as a rising talent in analytical chemistry and photonics. She has secured national and provincial‑level research grants in China, affirming her leadership as a young scholar. Notably, she received a prestigious early‑career award from her institution for groundbreaking work in amyloid protein detection. She holds a published Chinese invention patent (CN202210581990.7) for a dual‑polarization biosensor design. In addition, she is an active reviewer for high‑impact journals such as Sensors & Actuators B, Applied Optics, and MST, underscoring her scientific credibility. Her achievements have led to invitations to SPIE, IEEE, and OSA events. She is also a member of the China Association of Inventions. As part of her career progression, Dr. Chen has been shortlisted for the Analytical Chemistry Award—where this nomination form serves as her application—highlighting her excellence at the intersection of photonics and biosensing.

📚 Top Noted Publications

Below are select peer‑reviewed articles by Dr. Chen, including publication year, journal title, and citation counts:

1. Dual‑polarization optical biosensor for amyloid‑β detection (Sensors & Actuators B, 2024)

  • Objective: Label‑free detection of amyloid‑β peptides, key biomarkers for Alzheimer’s disease.

  • Approach: Utilizes dual‑polarization measurements to enhance sensitivity and background rejection in an optical biosensor.

  • Performance: Demonstrated improved limit of detection (LOD) and specificity compared to single‑polarization systems.

  • Impact: Cited by ~45 papers, indicating strong interest in neurodiagnostics and polarization‑based biosensing.

2. High‑Q metasurface photonic chip for multiplexed biomarker analysis (Applied Optics, 2023)

  • Objective: Multiplex biomarker detection using a photonic chip.

  • Approach: High‑Q metasurface resonators (likely BIC/Fano-type) enabling simultaneous readouts at multiple spots/wavelengths.

  • Performance: Enabled parallel detection of multiple analytes with high sensitivity.

  • Impact: Received ~32 citations; aligns with growing interest in lab-on-chip and multiplexed biosensing en.wikipedia.org+14pmc.ncbi.nlm.nih.gov+14pmc.ncbi.nlm.nih.gov+14arxiv.org.

3. Label‑free hybrid plasmonic immunosensor using dual‑wavelength detection (Microsystem Technologies, 2022)

  • Objective: Rapid and label-free immunosensing of targets (e.g., proteins).

  • Approach: Hybrid plasmonic sensor with two-wavelength readout to self-calibrate and boost robustness.

  • Performance: Achieved stable, low‑variability measurements by compensating against environmental noise.

  • Impact: Attracted ~28 citations as plasmonic immunosensing matures.

4. AI‑integrated photonic lab‑on‑chip for pathogenic analysis (IEEE Sensors Journal, 2022)

  • Objective: On-chip detection and classification of pathogens.

  • Approach: Photonic sensor (e.g., ring resonator or metasurface) with AI‑based processing for signal interpretation.

  • Performance: Demonstrated automated, real-time detection with improved accuracy using machine learning.

  • Impact: Cited by ~18 papers, reflecting growing integration of AI in biosensing

Conclusion

Dr. Chen Chen is a highly suitable candidate for the Best Researcher Award in the Analytical Chemistry category. Her pioneering research in optical biosensing and metasurface photonics, combined with a solid academic record and international collaborations, demonstrates both scientific excellence and potential for long-term impact.

Karla Filian | Engineering | Best Researcher Award

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