Belkacem Bekhiti | Engineering | Best Researcher Award

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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.

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.

Mohsen Hatami | Electrical and Computer | Best Researcher Award

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Mohsen Hatami | Electrical and Computer | Best Researcher Award

PhD candidate at Binghamton University, United states

Mohsen Hatami is a highly motivated and accomplished Ph.D. candidate in Electrical and Computer Engineering at Binghamton University, SUNY. With a strong foundation in IoT systems, smart technologies, and AI/ML, his work focuses on advancing sustainable computing and cybersecurity within emerging technologies such as smart grids and metaverse applications. Throughout his academic and professional journey, Mohsen has led innovative projects, particularly in IoT solar cell systems, smart grid management, and cyber-physical defense systems, contributing significantly to the field through his published works.

Profile:

Google scholar

Education:

Mohsen Hatami’s educational background reflects a robust commitment to the advancement of electrical and computer engineering. He is currently pursuing a Ph.D. in Electrical and Computer Engineering at Binghamton University, where he has achieved a remarkable GPA of 3.94/4.0. His research explores the intersection of IoT, AI, machine learning, and smart grid technologies, with an expected completion date in May 2026. Mohsen holds a Master’s degree in Electrical and Electronic Engineering from Kashan University, Iran, where he was recognized as a top student and researcher. His academic journey began with a Bachelor of Science in Applied Science Electronics from Bahar Higher Education Institute of Mashhad and an Associate degree from Shahrekord All Boys Vocational College, both in Iran.

Experience:

Mohsen’s professional experience spans multiple roles where he applied his technical expertise in both hardware and software engineering. At Genoptic (Canada) and Tavanmand (Iran), he led the design and implementation of IoT systems for solar cell monitoring, enhancing energy efficiency through real-time data collection. He also worked on industrial IoT solutions, including an IoT-based failure management system for industrial use, leveraging 4G/5G networks for robust connectivity. Further, Mohsen contributed to the development of smart farm IoT systems at Paya Chip Co., Iran, optimizing water usage and soil monitoring for enhanced agricultural productivity. In addition, he designed fiber optic networks and power systems for the smart grid at Diaco Co. and Pars Kavian Niroo, respectively, demonstrating his versatility across various technical domains.

Research Interests:

Mohsen’s research interests cover a broad spectrum of cutting-edge fields within electrical engineering, including AI and machine learning, embedded systems, network security, blockchain technology, and the metaverse. His work primarily focuses on the integration of IoT with emerging technologies such as 5G/6G communication, edge computing, and digital twins. He is particularly interested in exploring the role of AI in enhancing the security of cyber-physical systems, especially in smart grid environments, and the potential applications of the metaverse in smart grid management.

Awards:

Throughout his academic career, Mohsen Hatami has earned several honors recognizing his research contributions and academic excellence. As a top student and researcher at Kashan University, he was awarded for his outstanding performance in his Master’s program. Additionally, Mohsen has been acknowledged for his leadership in research projects and his dedication to advancing knowledge in fields such as IoT systems and smart technologies.

Publications:

Mohsen Hatami’s research has been widely recognized in top-tier journals and conferences. Some of his key publications include:

  1. Hatami, M., Nasab, M. A., Chen, Y., Mohammadi, J., Ardiles-Cruz, E., & Blasch, E. (2024). ELOCESS: An ESS Management Framework for Improved Smart Grid Stability and Flexibility. IEEE Transactions on Consumer Electronics.

  2. Hatami, M., Qu, Q., Chen, Y., Kholidy, H., Blasch, E., & Ardiles-Cruz, E. (2024). A Survey of the Real-Time Metaverse: Challenges and Opportunities. Future Internet, 16(10), 379.

  3. Hatami, M., Nasab, M. A., Zand, M., Padmanaban, S. (2024). Demand Side Management Programs in Smart Grid Through Cloud Computing. Renewable Energy Focus, 51, 100639.

  4. Hatami, M., Khan, M., Zhao, W., Chen, Y. (2024). A Novel Trusted Hardware-Based Scalable Security Framework for IoT Edge Devices. Discover Internet of Things, 4(1), 4.

  5. Hatami, M., Qu, Q., Chen, Y., Mohammadi, J., Blasch, E., Ardiles-Cruz, E. (2024). ANCHOR-Grid: Authenticating Smart Grid Digital Twins Using Real World Anchors.

  6. Hatami, M., Qu, Q., Xu, R., Nagothu, D., Chen, Y., Li, X., Blasch, E., Ardiles-Cruz, E. (2024). The Microverse: A Task-Oriented Edge-Scale Metaverse. Future Internet, 16(2), 60.

  7. Hatami, M., Nikoufard, M. (2018). Analysis of Ultra-Compact TE to TM Polarization Rotator in InGaAsP and SOI Technologies. Optik-International Journal for Light and Electron Optics, 153, 9-15.

Conclusion:

Mohsen Hatami is a promising researcher and engineer in the field of Electrical and Computer Engineering, with a focus on IoT systems, AI/ML, and cybersecurity. His academic achievements and professional experience reflect a strong commitment to advancing technology in the fields of smart grids, metaverse applications, and embedded systems. With numerous published works in leading journals and his continuous contributions to innovative projects, Mohsen stands out as a dedicated researcher and an emerging expert in his field. His ongoing work in the smart grid and cybersecurity domains holds significant potential for addressing future challenges in these rapidly evolving areas.