Dr. Amir Ali Mokhtarzadeh | Nanomedicine | Best Academic Researcher Award

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Dr. Amir Ali Mokhtarzadeh | Nanomedicine | Best Academic Researcher Award

Dr. Amir Ali Mokhtarzadeh | Assistant Professor of Pharmaceutical Biotechnology | Tabriz University of Medical Sciences | Iran

Dr. Amir Ali Mokhtarzadeh is an accomplished Assistant Professor of Pharmaceutical Biotechnology renowned for his pioneering research in nanomedicine, cancer gene therapy, drug delivery systems, and biosensor development. His scientific pursuits center on the application of advanced nanomaterials for gene and drug delivery, with a special emphasis on microRNAs, siRNAs, and shRNAs in targeted cancer therapy, as well as innovative biosensing techniques for cancer biomarker detection. His interdisciplinary work bridges cellular and molecular biology, genetic engineering, and pharmaceutical biotechnology, contributing significantly to translational medical research. Dr. Mokhtarzadeh has published over 270 peer-reviewed articles, authored multiple book chapters, and co-edited several Persian scientific textbooks in biotechnology and molecular biology. His outstanding scholarly impact is evidenced by more than 12,574 citations and an h-index of 66 on Scopus, alongside 14,742 citations and an h-index of 72 on Google Scholar, establishing him among the world’s top two percentage of scientists as recognized by Stanford University. His research outputs are widely cited across fields such as materials science, molecular biology, and nanotechnology, underscoring his global influence in biomedical innovation and pharmaceutical applications.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications

  1. Oroojalian, F., Beygi, M., Baradaran, B., Mokhtarzadeh, A., & Shahbazi, M. A. (2021). Immune cell membrane‐coated biomimetic nanoparticles for targeted cancer therapy. Small, 17(12), 2006484.

  2. Eivazzadeh-Keihan, R., Maleki, A., De La Guardia, M., Bani, M. S., Chenab, K. K., & Mokhtarzadeh, A. (2019). Carbon-based nanomaterials for tissue engineering of bone: Building new bone on small black scaffolds: A review. Journal of Advanced Research, 18, 185–201.

  3. Mokhtarzadeh, A., Eivazzadeh-Keihan, R., Pashazadeh, P., Hejazi, M., & Baradaran, B. (2017). Nanomaterial-based biosensors for detection of pathogenic virus. TrAC Trends in Analytical Chemistry, 97, 445–457.

  4. Yousefi, M., Dadashpour, M., Hejazi, M., Hasanzadeh, M., Behnam, B., & Mokhtarzadeh, A. (2017). Anti-bacterial activity of graphene oxide as a new weapon nanomaterial to combat multidrug-resistant bacteria. Materials Science and Engineering: C, 74, 568–581.

  5. Alamdari, S. G., Amini, M., Jalilzadeh, N., Baradaran, B., Mohammadzadeh, R., & Mokhtarzadeh, A. (2022). Recent advances in nanoparticle-based photothermal therapy for breast cancer. Journal of Controlled Release, 349, 269–303.

Dr. Jeonghoon Moon | Power Electronics | Best Researcher Award

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Dr. Jeonghoon Moon | Power Electronics | Best Researcher Award

Dr. Jeonghoon Moon | Visiting Professor in the Department of Electronic Engineer | Chosun University | South Korea

Jeonghoon Moon is a distinguished researcher in power electronics and AI-based control, with a focus on EMI-aware predictive control of DC–DC converters, sensor-level CPS security, and battery balancing strategies. His research integrates advanced machine learning techniques, including physics-informed LSTM models, with practical hardware implementations on DSP platforms for real-time disturbance prediction, ripple reduction, and system stability. He has made significant contributions to predictive and robust control, developing lightweight controllers that approximate LSTM outputs for deterministic execution on embedded systems, enabling faster detection latency and improved DC-rail performance. Moon has proposed novel safety envelopes unifying efficiency deviation with time- and frequency-domain ripple metrics to guide safe derating under dynamic operating conditions and potential spoofing scenarios. His work also encompasses EMI-aware PWM shaping and battery module balancing, validated through rigorous MATLAB/Simulink simulations and reproducible hardware experiments. Moon maintains multi-institutional collaborations with academic and industry partners to advance power electronics and AI integration. His research outputs include four SCI/SCIE journal publications, multiple consultancy projects, and one patent, reflecting both academic rigor and industrial relevance. His research impact is evidenced by 25 Scopus-indexed documents with 25 citations and an h-index of 2. Moon’s contributions extend to ultrasonic piezo resonance tracking and high-speed resonant frequency detection using AI-guided methodologies, demonstrating the applicability of machine learning in real-time control systems and intelligent energy management.

Publication Profile

Scopus | ORCID

Featured Publications

Moon, J.-H., Kim, J.-H., & Lee, J.-H. (2025). Sensor-Level Anomaly Detection in DC–DC Buck Converters with a Physics-Informed LSTM: DSP-Based Validation of Detection and a Simulation Study of CI-Guided Deception. Applied Sciences.

Moon, J., Lim, S., Kim, J., Kang, G., & Kim, B. (2024). A Study on the Mechanical Resonance Frequency of a Piezo Element: Analysis of Resonance Characteristics and Frequency Estimation Using a Long Short-Term Memory Model. Applied Sciences.

Moon, J., Park, S., & Lim, S. (2022). A Novel High-Speed Resonant Frequency Tracking Method Using Transient Characteristics in a Piezoelectric Transducer. Sensors.

Moon, J. H. (2021). A Study on Resonance Tracking Method of Ultrasonic Welding Machine Inverter. Journal of the Korean Society of Industry Convergence.

Moon, J. H. (2021). Fast and Stable Synchronization Between the Grid and Generator by Virtual Coordinates and Feed-Forward Compensation in Grid-Tied Uninterruptible Power Supply System. IEEE Access.

Prof. Dr. Cesar Hernando Valencia Niño | Evolutionary Computation | Best Researcher Award

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Prof. Dr. Cesar Hernando Valencia Niño | Evolutionary Computation | Best Researcher Award

Prof. Dr. Cesar Hernando Valencia Niño | Director of Master on Data Analytics and Intelligent Systems | Santo Tomas University Bucaramanga | Colombia

Cesar Hernando Valencia Niño is a distinguished researcher in artificial intelligence, robotics, mechatronics, and intelligent control systems. His work integrates machine learning algorithms with mechanical and electrical engineering to develop predictive, inferential, and adaptive systems applied to robotics, biomedical devices, industrial automation, and human–machine interaction. As leader of a Category A research group, he has contributed significantly to interdisciplinary applications of AI in areas such as prosthetics, echo state networks, autonomous systems, and biomedical forecasting. His portfolio includes contributions to the advancement of industrial robotics, machine design, neuroevolutionary computation, magnetorheological systems, and control architectures for UAVs and prosthetics. With active participation in 25 research and innovation projects, he has produced 17 peer-reviewed journal articles, 5 book chapters, 12 industrial prototypes, 7 documented innovations, and 5 patents. He is also a recognized reviewer of top-tier indexed journals and has directed theses across undergraduate to doctoral levels. Valencia Niño has presented his work in more than 30 knowledge dissemination events, demonstrating strong engagement in academic and scientific communities. His citation impact reflects growing international recognition: Scopus reports 45 citations from 44 documents with 17 indexed publications and an h-index of 4, while Google Scholar attributes 96 citations, an h-index of 6, and an i10-index of 2. His research continues to bridge artificial intelligence with engineering solutions for complex, real-world challenges, emphasizing innovation, automation, and intelligent system design.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications

  • Valencia, C. H., Vellasco, M. M. B. R., & Figueiredo, K. (2023). Echo State Networks: Novel reservoir selection and hyperparameter optimization model for time series forecasting. Neurocomputing, 545, 126317.

  • Valencia Niño, C. H. (2011). Modelo de optimización en la gestión de inventarios mediante algoritmos genéticos. ITECKNE: Innovación e Investigación en Ingeniería, 8(2), 156–162.

  • Valencia, C. H., Vellasco, M. M. B. R., & Figueiredo, K. T. (2014). Trajectory tracking control using echo state networks for the CoroBot’s arm. In Robot Intelligence Technology and Applications 2.

  • Valencia, C. H., Vellasco, M., Tanscheit, R., & Figueiredo, K. T. (2015). Magnetorheological damper control in a leg prosthesis mechanical. In Robot Intelligence Technology and Applications 3.

  • Valencia Niño, C. H., & Dutra, M. S. (2010). Estado del arte de los vehículos autónomos sumergibles alimentados por energía solar. ITECKNE, 7(1), 46–53.

 

Mr. Cenyu Liu | Hybrid Architecture | Best Researcher Award

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Mr. Cenyu Liu | Hybrid Architecture | Best Researcher Award

Mr. Cenyu Liu | Master Student | Shanghai Jiaotong University | China

Academic Background

Cenyu Liu is a Master’s student at Shanghai Jiao Tong University, specializing in biomedical engineering and deep learning. His academic training encompasses advanced neural network design, signal processing, and wearable health technologies. Cenyu has focused on developing efficient deep learning models for automatic sleep stage classification from single-channel EEG signals. His work has been recognized in top journals and conferences, with citations across Google Scholar and Scopus, demonstrating the reach and influence of his research. He maintains an active researcher profile with indexed publications and a growing h-index, reflecting consistent contributions to biomedical AI and wearable device applications. Documentation of his research, including articles, patents, and profiles, is publicly accessible through ORCID and research profile links.

Research Focus

Cenyu Liu’s research centers on the intersection of artificial intelligence and healthcare technology. He develops compact hybrid deep learning models that enable accurate and efficient sleep stage classification for real-time monitoring using wearable devices. His work aims to bridge computational neuroscience and practical health applications, making AI solutions deployable on edge devices.

Work Experience

Cenyu has primarily conducted research in academic settings, collaborating with multidisciplinary teams at Shanghai Jiao Tong University. He has worked closely with experts in wearable sensor technology and biomedical signal processing, contributing to projects that integrate machine learning with portable health monitoring systems. His experience includes designing experiments, implementing deep learning pipelines, and validating models on benchmark datasets.

Key Contributions

Mr. Cenyu Liu has made significant contributions to AI-driven healthcare through the development of MultiScaleSleepNet, a hybrid CNN–BiLSTM–Transformer model that leverages multi-scale feature extraction and attention mechanisms for EEG-based sleep stage classification. His model demonstrates robustness across datasets and is optimized for computational efficiency, making it suitable for real-time applications on wearable devices. Additionally, he has contributed to mobile-based health monitoring patents and co-authored research on continuous core body temperature monitoring, enhancing the safety and efficiency of health-tracking systems.

Awards & Recognition

Cenyu Liu has been recognized for his research excellence and innovation in biomedical AI, particularly in wearable health technologies. His work has gained attention through peer-reviewed publications and citations in both Scopus and Google Scholar, reflecting its scientific impact. He has been invited to collaborate on high-profile projects that advance the practical application of AI in healthcare.

Professional Roles & Memberships

Mr. Cenyu is an active member of IEEE, participating in professional communities focused on artificial intelligence, biomedical engineering, and signal processing. He engages in collaborative research projects and maintains a profile of professional development through scholarly networks, contributing to the global scientific community.

Publication Profile

ORCID

Featured Publications

Liu, C., Guan, Q., Zhang, W., Sun, L., Wang, M., Dong, X., Xu, S. MultiScaleSleepNet: A Hybrid CNN–BiLSTM–Transformer Architecture with Multi-Scale Feature Representation for Single-Channel EEG Sleep Stage Classification. Sensors.

Zhang, W., Li, L., Wang, Y., Dong, X., Liu, C., Sun, L., Xu, S. Continuous Core Body Temperature Monitoring for Heatstroke Alert via a Wearable In-Ear Thermometer. ACS Sensors.

Impact Statement / Vision

Mr. Cenyu Liu envisions advancing artificial intelligence for personalized and portable healthcare. His research seeks to enable real-time, low-complexity AI models for wearable devices, empowering continuous health monitoring and improving preventive care through innovative computational solutions.

Dr. Mohamed Hegab | Scheduling Automation | Best Researcher Award

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Dr. Mohamed Hegab | Scheduling Automation | Best Researcher Award

Dr. Mohamed Hegab | Professor | California State University | United States

Academic Background

Dr. Mohamed Hegab holds a PhD in Civil Engineering and is a licensed Professional Engineer with certifications in Project Management and Construction Management. His academic journey encompasses extensive training and research in infrastructure systems, project controls, and construction technology. With over three decades of experience in both academia and industry, he has contributed to advancing knowledge in construction planning, public-private partnerships, and AI-enabled construction automation. His scholarly impact is demonstrated through a robust portfolio of publications, books, and peer-reviewed research Citation Index: Google Scholar Citations ≈ 480 | h-index = 11 | i10-index = 12 reflecting his influence in the field. All supporting documents and credentials are verifiable upon request.

Research Focus

Dr. Hegab’s research centers on integrating artificial intelligence with construction planning and management. His work focuses on ontology-based frameworks for automated scheduling, digital twin integration, and smart infrastructure monitoring. He explores innovative approaches to construction productivity modeling, risk assessment, and project controls that bridge academic theory with industry practice.

Work Experience

Dr. Hegab has served as a Professor and Department Chair, leading civil engineering and construction management programs. His professional experience spans consulting for large-scale infrastructure projects, including metropolitan water systems and state transportation authorities. He has overseen multi-disciplinary teams, managed project budgets, and provided expert advisory services to public and private organizations. Beyond academia, he has held leadership positions in businesses supporting construction operations, demonstrating a unique blend of academic rigor and practical expertise.

Key Contributions

Dr. Hegab has pioneered the use of AI-driven semantic frameworks in construction planning, enabling automated project scheduling and constraint validation. His work has improved decision-making processes, minimized data fragmentation in digital models, and enhanced the implementation of Construction 4.0 practices. He has significantly influenced industry standards, academic curricula, and international research collaborations, bridging the gap between emerging technologies and practical infrastructure delivery.

Awards & Recognition

Dr. Hegab has been widely recognized for his contributions to construction engineering and management. His research and industry leadership have garnered national and international attention, earning accolades for innovation in project delivery, risk assessment, and AI integration in construction processes. His work continues to inspire academic peers and industry professionals globally.

Professional Roles & Memberships

Dr. Hegab actively contributes to professional organizations, including the American Society of Civil Engineers, Construction Management Association of America, Project Management Institute, and the Dispute Resolution Board Foundation. He serves as a senior evaluator for accreditation bodies and participates in multidisciplinary research collaborations with universities and research institutions worldwide, supporting the advancement of construction engineering education and practice.

Publication Profile

Scopus | Google Scholar

Featured Publications

Hegab, M., Smith, G. R. (2007). Delay time analysis in microtunneling projects. Journal of Construction Engineering and Management, 133, 191-195.

Nassar, K., Gunnarsson, H. G., Hegab, M. (2005). Using Weibull analysis for evaluation of cost and schedule performance. Journal of Construction Engineering and Management, 131, 1257-1262.

Hegab, M., Salem, O. M. (2010). Ranking of the factors affecting productivity of microtunneling projects. Journal of Pipeline Systems Engineering and Practice, 1, 42-52.

Ali, S., Zayed, T., Hegab, M. (2007). Modeling the effect of subjective factors on productivity of trenchless technology. Journal of Construction Engineering and Management, 133, 743-748.
Elwakil, E., Hegab, M. (2018). Risk management for power purchase agreements. IEEE Conference on Technologies for Sustainability, 1-6.

Impact Statement / Vision

Dr. Hegab envisions a future where AI-driven methodologies and digital integration transform construction management, enabling smarter, safer, and more efficient infrastructure systems. His work continues to advance knowledge, inform policy, and inspire innovation across academia and industry globally.

Dr. Amyrul Azuan Mohd Bahar | Microwave Engineering | Best Researcher Award

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Dr. Amyrul Azuan Mohd Bahar | Microwave Engineering | Best Researcher Award

Dr. Amyrul Azuan Mohd Bahar | Platform Application Engineer | Intel Microelectronics | Malaysia

Amyrul Azuan Mohd Bahar is a Malaysian electronics engineer and researcher with notable contributions in microwave sensors, antenna systems, and material characterization. He has authored multiple high-impact publications and achieved strong citation metrics across major platforms, including 742 citations on Google Scholar with an h-index of 12, 598 citations on Scopus with an h-index of 12, and 639 citations with an h-index of 13 on ResearchGate. His work has influenced both academic and industrial research, particularly in RF sensor technology and dielectric measurement systems. His scholarly output spans journals indexed in ISI and Scopus, and he remains active in collaborative research and innovation.

Publication Profile

Scopus 

ORCID

Google Scholar

Education Background

He completed his Docto of Philosophy in Electronics Engineering with a concentration in sensor design at Universiti Teknikal Malaysia Melaka . Prior to that, he earned a Bachelor of Electronics Engineering with an emphasis on wireless communication at the same institution in under a conversion program. His academic path included involvement in funded research projects and technical development, supported by scholarships such as the UTeM Zamalah Scheme and the MyBrain UTeM scholarship. His doctoral studies centered on microwave and RF-based biochemical sensors, leading to prototypes and publications in high-quality journals and conferences.

Professional Experience

He currently serves as a Senior Platform Application Engineer at Intel Microelectronics in Penang, where he supports the Edge Computing Group through hardware enablement, technical validation, and customer-focused engineering solutions. His responsibilities include schematic reviews, system debugging, reference platform testing, and technical documentation. He represents technical requirements during product planning and delivers training to both internal teams and clients. Previously, he worked as a Graduate Research Assistant and Project Research Assistant at Universiti Teknikal Malaysia Melaka, focusing on microwave sensor design, fabrication, and antenna development. His academic roles also included tutoring under the Zamalah Scheme with teaching exposure in electronic and computer engineering.

Awards and Honors

His achievements include recognition for academic excellence, research innovation, and industrial contributions. He received gold, silver, and bronze medals across numerous innovation competitions such as PECIPTA, UTeMEX, and MTE for his microwave sensor prototypes and high-Q resonator advancements. He earned Best Presentation Awards at Intel ICETC and internal technical forums, as well as Best Paper Awards at conferences including ICTEC and PReCON. Intel’s IOTG PMCE Division awarded him multiple recognition titles, while his university honored him with the Chancellor’s Award. Scholarships during his academic journey further acknowledged his research potential and scholarly performance.

Research Focus

His primary research centers on microwave and RF resonator sensor technology for dielectric characterization, bio-sensing, and microfluidic applications. He has developed high-sensitivity split-ring resonator structures, circular SIW-based biochemical sensors, and miniaturized antenna systems tailored to advanced detection environments. His work extends into electromagnetic material measurement, high-resolution waveguide sensors, and substrate-integrated waveguide techniques. Collaborative publications showcase interdisciplinary efforts in biomedical exposure studies, wireless applications, and material permittivity sensing. His research output is indexed in Scopus and ISI, demonstrating impact through technical innovation, practical validation, and adoption of emerging methodologies in sensing and measurement.

Publications

Bahar, A. A. M., Zakaria, Z., Md Arshad, M. K., Isa, A. A. M., Dasril, Y., et al. (2019). Real time microwave biochemical sensor based on circular SIW approach for aqueous dielectric detection. Scientific Reports, 9(1), 5467. Cited by 116.

Alahnomi, R. A., Zakaria, Z., Ruslan, E., Ab Rashid, S. R., & Bahar, A. A. M. (2017). High-Q sensor based on symmetrica

l split ring resonator with spurlines for solids material detection. IEEE Sensors Journal, 17(9), 2766–2775. Cited by 159.

Bahar, A. A. M., Zakaria, Z., Ab Rashid, S. R., Isa, A. A. M., & Alahnomi, R. A. (2017). High-efficiency microwave planar resonator sensor based on bridge split ring topology. IEEE Microwave and Wireless Components Letters, 27(6), 545–547. Cited by 77.

Bahar, A. A. M., Zakaria, Z., Ab Rashid, S. R., Isa, A. A. M., & Alahnomi, R. A. (2017). Dielectric analysis of liquid solvents using microwave resonator sensor for high efficiency measurement. Microwave and Optical Technology Letters, 59(2), 367–371. Cited by 39.

Alahnomi, R. A., Zakaria, Z., Ruslan, E., Bahar, A. A. M., & Ab Rashid, S. R. (2016). High sensitive microwave sensor based on symmetrical split ring resonator for material characterization. Microwave and Optical Technology Letters, 58(9), 2106–2110. Cited by 39.

Conclusion

Dr. Amyrul Azuan Mohd Bahar has built a strong profile through combined industrial leadership and research productivity in microwave engineering. His citation performance on Google Scholar, Scopus, and ResearchGate reflects sustained academic influence. His professional role at Intel aligns with his research background, enabling him to apply theoretical insights to practical engineering challenges. The range of awards and publications underscores his contributions to sensor design and characterization technologies. His continued involvement in development, validation, and technical training positions him as a key figure in advanced electronics applications across industry and academia.

Mr. Jae Min Lee | Architectural Engineering | Best Researcher Award

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Mr. Jae Min Lee | Architectural Engineering | Best Researcher Award

Mr. Jae Min Lee | Ph.D student | Chungbuk National University | South Korea

Academic Background

Jae Min Lee has established a strong academic foundation in Architectural Engineering with a focus on concrete mechanics and computational modeling. His scholarly record reflects measurable research engagement, with Scopus indexing multiple scholarly outputs, Google Scholar citations indicating growing influence, and an h-index demonstrating early-career research impact. His academic journey combines experimental material science and data-driven modeling, positioning him at the intersection of civil engineering and artificial intelligence.

Research Focus

His research centers on predicting and characterizing the behavior of concrete through machine learning and data-informed techniques. He integrates artificial neural networks and physics-informed neural networks to study thermal, mechanical, and moisture-related characteristics in complex concrete systems.

Work Experience

He has contributed to academic research environments through active involvement in laboratory-based investigations and computational analysis. His role includes developing data-driven methodologies for understanding heterogeneous concrete behavior and bridging experimental findings with predictive modeling. He has also participated in collaborative research that links advanced simulations with material characterization, enhancing interdisciplinary insight into structural performance.

Key Contributions

His contributions significantly advance the understanding of thermal and mechanical behavior in large-scale concrete structures. By implementing inverse estimation approaches using neural network frameworks, he has improved the accuracy of predicting internal temperature rise and moisture diffusion in mass concrete. His work introduces efficient methods for quantifying behavioral parameters even when physical observations are limited or affected by noise, reducing experimental dependency. These developments support sustainable and intelligent engineering practices and promote cost-efficient evaluation of material properties through computational innovation.

Awards & Recognition

His academic achievements and growing research influence have led to nomination for the Best Researcher Award. His work has drawn attention for combining civil engineering principles with artificial intelligence to solve emerging challenges in structural materials research.

Professional Roles & Memberships

He is an active member of major technical organizations, including the Korea Concrete Institute and the Korea Institute for Structural Maintenance and Inspection. His involvement reflects commitment to professional development and knowledge dissemination within the concrete engineering community. He also participates in collaborative initiatives involving machine learning applications in material sciences, contributing to interdisciplinary research networks.

Publication Profile

Scopus

Featured Publications

Lee, J. M., & Lee, C. J. Inverse estimation of moisture diffusion model for concrete using artificial neural network.

Lee, J. M., Zhang, W., Lee, D., & Lee, C. Residual strength of concrete subjected to fatigue based on a machine learning technique.

Impact Statement / Vision

His long-term vision is to develop intelligent frameworks that enhance predictive accuracy and reduce experimental burden in concrete engineering. By combining deep learning, physics-based modeling, and structural material science, his work aspires to advance next-generation concrete technologies. He aims to contribute solutions that support sustainability, efficiency, and innovation in civil and structural engineering research.

Prof. Chunlei Guo | Energy Technologies | Best Researcher Award

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Prof. Chunlei Guo | Energy Technologies | Best Researcher Award

Prof. Chunlei Guo | Professor | University of Rochester | United States

Academic Background

Chunlei Guo received his undergraduate education in Optical Physics and Fine Mechanics at the Changchun Institute of Optics in China. He then pursued his Ph.D. in Physics at the University of Connecticut, followed by postdoctoral training in Materials Science at Los Alamos National Laboratory. He has established a strong foundation in laser physics, optics, and materials science, contributing to his recognition as a leading researcher in photonics. According to Scopus, his work includes over four hundred publications cited nearly ten thousand times, with an h-index indicating substantial influence in his field. His Google Scholar profile further reflects his widespread impact across laser material processing, femtosecond laser applications, and nanostructuring.

Research Focus

Guo’s research primarily focuses on femtosecond laser interactions with materials, including the creation of superhydrophobic surfaces and laser-induced nanostructures. His work integrates ultrafast laser techniques with material science, aiming to advance applications in energy, imaging, and nanotechnology. His studies emphasize precise control of surface properties and functionalization at the micro- and nanoscale.

Work Experience

Guo has held a variety of academic and research positions, starting as an Assistant Professor and later Associate Professor at the Institute of Optics at the University of Rochester. He is currently a Professor at the Institute of Optics and holds joint appointments in the Department of Physics and Astronomy and the Laboratory for Laser Energetics. He has also served as the founding director of the GPL Photonics Lab in China, further establishing his international research presence.

Key Contributions

Guo has made significant contributions to laser-induced surface structuring, development of superhydrophobic and superwicking surfaces, and femtosecond laser applications in imaging and material processing. His work has enabled new methods for nanostructuring metals, improving energy management, and advancing optical technologies. He has been widely recognized for developing techniques that combine laser precision with novel material functionalities.

Awards & Recognition

Guo’s research excellence has earned him multiple prestigious awards, including honors for innovation in defense and design, recognition by professional societies, and fellowships in the Optical Society of America, American Physical Society, and International Academy of Photonics and Laser Engineering.

Professional Roles & Memberships

He has served in numerous editorial and advisory roles, including Editor-in-Chief of the CRC Handbook of Laser Technology and Applications, associate editor for leading optics journals, and program committee membership for major international conferences. He has also chaired conferences and technical groups, contributing to shaping the field of laser science and engineering globally.

Publication Profile

Scopus | ORCID

Featured Publications

Guo, C., Vorobyev, A. Y., & Singh, S. C. (2023). Imaging Dynamics of Femtosecond Laser-Induced Surface Nanostructuring. In Ultrafast Laser Nanostructuring. Springer Series in Optical Sciences, 239, 355–375.

Guo, C., & Singh, S. C. (2021). CRC Handbook of Laser Technology and Applications. CRC Press.

Vorobyev, A. Y., & Guo, C. (2015). Superwicking Surfaces Produced by Femtosecond Laser. In Advanced Lasers, 193, 101–120.

Guo, C. (2016). Using femtosecond lasers to create new material properties. SPIE Newsroom.

Guo, C. (2010). Surface-plasmon-enhanced photoelectron emission. SPIE Newsroom.

Impact Statement / Vision

Guo envisions leveraging ultrafast laser technologies to design materials with unprecedented properties for industrial, environmental, and energy applications. His work continues to inspire innovations in nanofabrication, surface engineering, and photonics, bridging fundamental research and practical applications for global scientific advancement.

Prof. Rafik Aguech | Probabilities | Best Researcher Award

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Prof. Rafik Aguech | Probabilities | Best Researcher Award

Prof. Rafik Aguech | Professor | King Saud University | Saudi Arabia

Academic Background

Rafik Aguech holds advanced qualifications in mathematics with a specialization in probability and statistics, achieved through progressive academic training in leading institutions in France and Tunisia. His doctoral research focused on stochastic algorithms, supervised at a renowned probability laboratory. His scholarly journey includes a habilitation degree in mathematical sciences, along with earlier postgraduate and undergraduate qualifications in probability, fundamental mathematics, and applications. His academic credentials led to formal recognition for university-level teaching and research roles in both French and Tunisian systems. With a strong publication footprint, his citation record reflects consistent scholarly influence. Scopus lists over 51 citations with an h-index of 5 across 21 indexed documents, while Google Scholar reports more than one 125 citations with an h-index of 7 and an i10-index of three. These metrics underscore his sustained contributions to stochastic processes, urn models, and random structures.

Research Focus

His research centers on stochastic algorithms, urn models, random walks, fragmentation processes, branching structures, and probabilistic analysis in theoretical and applied contexts. His work bridges probability theory with computer science, algorithmic modeling, and combinatorics, offering mathematical insights with cross-disciplinary relevance.

Work Experience

He has served in academic and research capacities across institutions in Europe, North Africa, and the Middle East. His roles have progressively advanced from adjunct and assistant professor positions to associate professorship at a leading Gulf institution in mathematical sciences. He has delivered lectures, supervised research, and contributed to graduate and postgraduate education in probability, statistics, stochastic modeling, and advanced mathematical theory. His teaching portfolio spans foundational mathematics, applied probability, nonparametric statistics, and statistical theory for both undergraduate and graduate cohorts across several universities.

Key Contributions

He has authored and coauthored numerous works on random structures, stochastic tracking algorithms, urn models, and algorithmic probability. His pioneering contributions to the study of elephant random walks, generalized urn schemes, and recursive tree behavior form a significant body of work influencing both theoretical development and applied probability. His collaborative research has connected probability with computer systems theory, combinatorial models, and asymptotic behavior of algorithms.

Awards & Recognition

His research influence is acknowledged through sustained collaboration with leading mathematicians, invitations to international research institutes, and repeated participation as an invited speaker at global probability and algorithmic meetings.

Professional Roles & Memberships

He has played a pivotal role in research communities through involvement in international working groups, collaborations with French and Tunisian research teams, and membership in organizations bridging informatics and mathematics. He has also chaired and organized thematic schools, probability colloquia, and international conferences, contributing to academic exchange and institutional visibility.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications 

Aguech, R., Moulines, E., & Priouret, P. (2000). On a perturbation approach for the analysis of stochastic tracking algorithms. SIAM Journal on Control and Optimization, 39(3), 872–899.

Aguech, R. (2009). Limit theorems for random triangular urn schemes. Journal of Applied Probability, 46(3), 827–843.

Aguech, R., Lasmar, N., & Mahmoud, H. (2006). Limit distribution of distances in biased random tries. Journal of Applied Probability, 43(2), 377–390.

Aguech, R., Jedidi, W. (2019). New characterizations of completely monotone functions and Bernstein functions. Arab Journal of Mathematical Sciences, 25(1), 57–82.

Aguech, R., El Machkouri, M. (2024). Gaussian fluctuations for the elephant random walk with gradually increasing memory. Journal of Physics A: Mathematical and Theoretical, 57(6), 065203.

Impact Statement / Vision

His academic path reflects a commitment to advancing probability theory through rigorous analysis and collaborative inquiry. By linking abstract stochastic models with algorithmic structures, his research enhances understanding of random processes in computation, information sciences, and applied mathematics. He aims to continue driving innovation in probabilistic modeling and mentoring emerging scholars in mathematical research.