Sasan Karamiazadeh | Engineering | Innovative Research Award

Published on by Academic Achievements

Innovative Research Award

Sasan Karamiazadeh
Ershad Damavand Institute of Higher Education, Tehran, Iran

Sasan Karamiazadeh
Affiliation Ershad Damavand Institute of Higher Education
Country Iran
Scopus ID 51461500800
Documents 25
Citations 410
h-index 9
Subject Area Engineering
Event International Academic Achievements & Awards
ORCID 0000-0001-9445-8044

The Innovative Research Award recognizes researchers who demonstrate sustained scholarly excellence through impactful publications, engineering innovation, interdisciplinary collaboration, and measurable academic influence. Sasan Karamiazadeh has established a research profile spanning artificial intelligence, computer vision, deep learning, facial recognition, and intelligent engineering systems. His publication record, citation performance, and continuing research contributions reflect an active engagement with emerging computational technologies and their practical applications.[1]

Abstract

Sasan Karamiazadeh’s research portfolio emphasizes artificial intelligence, deep learning, facial recognition, computer vision, and intelligent image analysis. His scholarly work integrates convolutional neural networks, transformer architectures, feature fusion techniques, and zero-shot learning to improve recognition accuracy, robustness, and computational efficiency. The combination of engineering innovation and practical application demonstrates a sustained contribution to modern intelligent systems research.[2]

Keywords

Artificial Intelligence, Deep Learning, Computer Vision, Face Recognition, Engineering, CNN, Transformer Networks, Feature Fusion, Facial Expression Analysis, U-Net, ResNet, IEEE Access, Machine Learning, Pattern Recognition, Image Processing.

Introduction

Engineering research increasingly relies upon advanced machine learning methods capable of processing complex visual information in real-world environments. Deep neural networks have transformed biometric identification, intelligent surveillance, healthcare imaging, multimedia processing, and automated recognition systems. Researchers working in these areas contribute to the development of reliable, scalable, and efficient computational frameworks. Within this landscape, Sasan Karamiazadeh has focused on improving recognition accuracy through innovative neural architectures and adaptive learning strategies.[3]

Research Profile

The research profile reflects sustained academic productivity, including 25 indexed publications, over 410 citations, and an h-index of 9. His work primarily addresses engineering applications of deep learning, computer vision, intelligent image classification, facial recognition, and biometric authentication. His publications have appeared in respected international journals, demonstrating both methodological innovation and practical relevance.[1]

Research Contributions

  • Development of deep learning frameworks for robust facial recognition.
  • Integration of CNN and Transformer architectures for intelligent image analysis.
  • Application of adaptive feature fusion techniques to improve biometric recognition accuracy.
  • Research on U-Net and ResNet models for advanced skin classification.
  • Contributions to zero-shot learning for facial expression recognition.
  • Investigation of multimedia content recognition using hybrid deep neural architectures.

Publications

  • Educational Poverty and Academic Achievement: A Meta-Analysis Exploring Contextual Moderators and Policy Implications, Education Sciences (2026). DOI: 10.3390/educsci16071083
  • Skin Classification for Face Recognition Based on Deep Learning with U-Net and ResNet, Electronics (2026). DOI: 10.3390/electronics15091950
  • Combining MTCNN and Enhanced FaceNet with Adaptive Feature Fusion for Robust Face Recognition, Technologies (2025). DOI: 10.3390/technologies13100450
  • A Hybrid CNN-Transformer Architecture for Adult Image and Video Content Recognition on the Internet, Multimedia Tools and Applications (2025). DOI: 10.1007/s11042-025-21084-7
  • Enhancing Facial Recognition and Expression Analysis With Unified Zero-Shot and Deep Learning Techniques, IEEE Access (2025). DOI: 10.1109/ACCESS.2025.3546061

Research Impact

The available bibliometric indicators demonstrate measurable scholarly influence through citations, publication activity, and sustained engineering research. The integration of computer vision with advanced deep learning architectures contributes to ongoing developments in biometric authentication, intelligent multimedia processing, and automated recognition systems. These contributions support future technological innovation while providing valuable methodologies for researchers and practitioners.[4]

Award Suitability

Based on documented scholarly achievements, publication record, engineering specialization, citation performance, and continuing research productivity, Sasan Karamiazadeh demonstrates characteristics aligned with the objectives of the Innovative Research Award. His work reflects methodological advancement, interdisciplinary collaboration, practical engineering applications, and consistent academic dissemination through internationally recognized journals.[5]

Conclusion

Sasan Karamiazadeh has established a significant research profile within engineering through sustained contributions to artificial intelligence, facial recognition, and computer vision. His publications demonstrate continuous methodological development and practical technological relevance. The documented research output, citation metrics, and interdisciplinary impact collectively support recognition through the Innovative Research Award within the International Academic Achievements & Awards program.

References

  1. Elsevier. (n.d.). Scopus Author Details: Sasan Karamiazadeh, Author ID 51461500800. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=51461500800
  2. Karamiazadeh, S. (2026). Skin Classification for Face Recognition Based on Deep Learning with U-Net and ResNet. Electronics.
    https://doi.org/10.3390/electronics15091950
  3. Karamiazadeh, S. (2025). Combining MTCNN and Enhanced FaceNet with Adaptive Feature Fusion for Robust Face Recognition. Technologies.
    https://doi.org/10.3390/technologies13100450
  4. Karamiazadeh, S. (2025). A Hybrid CNN-Transformer Architecture for Adult Image and Video Content Recognition on the Internet. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-025-21084-7
  5. Karamiazadeh, S. (2025). Enhancing Facial Recognition and Expression Analysis With Unified Zero-Shot and Deep Learning Techniques. IEEE Access.
    https://doi.org/10.1109/ACCESS.2025.3546061

Zhendong Zhu | Engineering | Innovative Research Award

Published on by Academic Achievements

Innovative Research Award

Zhendong Zhu
Affiliation China Three Gorges University
Country China
Scopus ID 58700040700
Documents 13
Citations 7
h-index 2
Subject Area Engineering
Event International Academic Achievements & Awards
ORCID 0009-0008-1000-1839

Zhendong Zhu
China Three Gorges University,

Zhendong Zhu is an engineering researcher whose published work focuses on electric power systems, renewable energy technologies, transmission line engineering, electromagnetic field modelling, artificial intelligence applications, and advanced computational methods. His scholarly output demonstrates continuing contributions to modern power infrastructure, wind energy forecasting, and intelligent engineering analysis. The Innovative Research Award recognizes research activities that advance technological development through original methodologies and practical engineering solutions.[1]

Abstract

This article presents an overview of the academic profile of Zhendong Zhu in recognition of the Innovative Research Award. His published research addresses contemporary engineering challenges including renewable energy integration, power transmission optimization, electromagnetic simulation, wireless communication in substations, radar echo modelling, and artificial intelligence for wind power prediction. These investigations contribute to the development of efficient electrical infrastructure and computational engineering methodologies while supporting sustainable energy systems.[2]

Keywords

Engineering, Electric Power Systems, Renewable Energy, Wind Power Prediction, Artificial Intelligence, Deep Learning, Temporal Convolutional Network, LSTM, Electromagnetic Engineering, Transmission Lines, Power Grid Optimization, Radar Echo Simulation.

Introduction

Rapid modernization of electrical power systems requires sophisticated computational models capable of improving efficiency, safety, and sustainability. Engineering research increasingly combines artificial intelligence, numerical simulation, and advanced optimization methods to solve practical industrial problems. Zhendong Zhu’s research reflects this multidisciplinary direction by integrating machine learning techniques with electrical engineering applications while contributing to renewable energy forecasting and transmission system analysis.[3]

Research Profile

The research portfolio includes thirteen indexed scholarly documents with a developing citation record and an h-index of two. Areas of investigation include power transmission engineering, electromagnetic field calculations, artificial intelligence algorithms, renewable energy forecasting, wireless propagation in substations, and numerical modelling. .[1]

Research Contributions

  • Development of modified Temporal Convolutional Network and Bidirectional Long Short-Term Memory algorithms for improved wind power prediction.
  • Optimization of AC-to-DC conversion strategies for 750kV transmission systems through voltage maximization techniques.[3]
  • Investigation of 5G channel path loss prediction in substations using improved ray tracing methodologies.[4]
  • Numerical modelling of electromagnetic fields for multi-circuit AC-to-DC converted transmission lines using improved finite element approaches.[5]
  • Simulation of dynamic radar echoes generated by wind turbines using accelerated computational algorithms based on modified Z-buffer techniques.

Publications

  • Wind power prediction algorithm based on the modified Temporal Convolutional Network – Bidirectional Long Short-Term Memory.
    Engineering Applications of Artificial Intelligence (2026). DOI:
    10.1016/j.engappai.2026.115597
  • The AC-to-DC conversion method for 750kV line by maximize DC voltage.
    Electric Power Systems Research (2026). DOI:
    10.1016/j.epsr.2026.112873
  • Fast solution of 5G channel path loss in substation based on improved ray tracing method.
    Science Progress (2026). DOI:
    10.1177/00368504251413963
  • Calculation of the Ground-Level Total Electric Field of Multi-Circuit AC-to-DC Converted Transmission Lines Based on an Improved Upwind Finite Element Method.
    SSRN Preprint (2026). DOI:
    10.2139/ssrn.6832329
  • Accelerated Algorithm based on Modified Z-Buffer for Numerically Simulating the Dynamic Radar Echo from Wind Turbines.
    Journal of Electromagnetic Engineering and Science (2025). DOI:
    10.26866/jees.2025.1.r.280

Research Impact

The published work contributes to engineering research by improving predictive modelling, numerical computation, renewable energy utilization, and transmission system performance. Studies involving artificial intelligence and computational electromagnetics support practical applications in power grid modernization and sustainable infrastructure.[2]

Award Suitability

Based on documented scholarly publications, indexed research output, and demonstrated engagement with innovative engineering methodologies, Zhendong Zhu’s academic profile aligns with the objectives of the Innovative Research Award. His work illustrates sustained contributions to engineering research through computational innovation, renewable energy applications, and advanced electrical power system analysis while maintaining relevance to emerging technological developments.[1]

Conclusion

Zhendong Zhu has established a developing research portfolio centered on electrical engineering, renewable energy technologies, artificial intelligence, and computational modelling. Through peer-reviewed publications and engineering-focused investigations, the researcher contributes to contemporary scientific understanding of intelligent power systems and transmission technologies. These accomplishments provide an appropriate foundation for recognition through the Innovative Research Award.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Zhendong Zhu, Author ID 58700040700. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58700040700
  2. Wind power prediction algorithm based on the modified Temporal Convolutional Network – Bidirectional Long Short-Term Memory. Engineering Applications of Artificial Intelligence (2026).
    https://doi.org/10.1016/j.engappai.2026.115597
  3. The AC-to-DC conversion method for 750kV line by maximize DC voltage. Electric Power Systems Research (2026).
    https://doi.org/10.1016/j.epsr.2026.112873
  4. Fast solution of 5G channel path loss in substation based on improved ray tracing method. Science Progress (2026).
    https://doi.org/10.1177/00368504251413963
  5. Calculation of the Ground-Level Total Electric Field of Multi-Circuit AC-to-DC Converted Transmission Lines Based on an Improved Upwind Finite Element Method. SSRN (2026).
    https://doi.org/10.2139/ssrn.6832329

Rafe Alasem | Engineering | Research Excellence Award

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Research Excellence Award

Rafe Alasem
Affiliation Amity University Dubai
Country United Arab Emirates
Scopus ID 22033707400
Documents 16
Citations 214
h-index 7
Subject Area Engineering
Event International Academic Achievements & Awards
ORCID 0000-0002-6245-1582

Rafe Alasem

Institution: Amity University Dubai, United Arab Emirates

Rafe Alasem is an engineering researcher whose scholarly work focuses on secure communication systems, wireless sensor networks, intelligent transportation systems, blockchain-enabled security, edge artificial intelligence, and energy-efficient networking technologies. His research portfolio demonstrates sustained contributions to secure routing protocols, smart infrastructure, healthcare monitoring systems, and speech processing applications. With a growing international publication record indexed in Scopus, his research reflects multidisciplinary engineering innovation and practical technological relevance.[1]

Abstract

The Research Excellence Award recognizes researchers demonstrating measurable scholarly productivity, sustained publication quality, interdisciplinary impact, and technological innovation. Rafe Alasem’s research encompasses wireless communication security, blockchain-based trust architectures, intelligent transportation, healthcare monitoring, energy-aware routing protocols, and edge artificial intelligence. His scholarly output illustrates continued engagement with contemporary engineering challenges while contributing practical solutions to secure and energy-efficient computing environments.[1]

Keywords

Engineering, Wireless Sensor Networks, Blockchain Security, 5G Networks, Vehicle Ad-Hoc Networks, Edge Artificial Intelligence, Healthcare Monitoring, Speech Processing

Introduction

Engineering research increasingly requires integrated approaches combining cybersecurity, communication technologies, intelligent systems, and sustainability. Rafe Alasem’s work addresses these priorities by developing secure routing strategies, blockchain-enabled trust frameworks, and efficient computational methods suitable for next-generation communication infrastructures. His publications demonstrate a balance between theoretical development and practical engineering applications across multiple interdisciplinary domains.[2]

Research Profile

According to the provided bibliometric information, the researcher has authored 16 Scopus-indexed publications with 214 citations and an h-index of 7. His research activities primarily span engineering disciplines including secure networking, wireless communications, Internet of Things technologies, intelligent transportation systems, healthcare monitoring, and machine learning applications for edge computing. These metrics indicate sustained scholarly visibility and growing academic influence within engineering research communities.[1]

Research Contributions

  • Development of SEER-PM, a secure and energy-efficient routing protocol for wireless sensor networks used in pipeline monitoring.
  • Blockchain-based decentralized trust framework integrating 5G technologies for secure Vehicle Ad-Hoc Networks.
  • Energy-efficient routing methodologies supporting sustainable smart city transportation infrastructures.
  • Healthcare patient monitoring optimization through forward greedy algorithms in wireless sensor networks.
  • Compression techniques for wav2vec 2.0 models enabling efficient speech emotion and speaker recognition on edge devices.

Publications

  1. SEER-PM: A Secure and Energy-Efficient Routing Protocol for Pipeline Monitoring Wireless Sensor Networks. Algorithms (2026). DOI: 10.3390/a19060493
  2. Decentralized Trust Model for Vehicle Ad-Hoc Networks (VANETs) with 5G Integration: A Blockchain-Based Approach for Enhanced Security and Privacy in Intelligent Transportation Systems (2025). DOI: 10.20944/preprints202512.1086.v1
  3. GreenFlow VANET: 5G-Enabled Secure and Energy-Efficient Routing for Smart Cities (2025). DOI: 10.20944/preprints202512.1014.v1
  4. Optimizing Healthcare Patient Monitoring Through an Energy-Efficient Forward Greedy Algorithm (EEFGA) in WSN (2025). DOI: 10.20944/preprints202512.0754.v1
  5. Efficient Compression of wav2vec 2.0 for Edge Deployment in Speech Emotion & Speaker Recognition. Multimedia Tools and Applications (2025). DOI: 10.1007/s11042-025-21057-w

Research Impact

The available bibliometric indicators demonstrate an active and visible research profile. Publications addressing cybersecurity, wireless sensor networks, blockchain applications, healthcare technologies, and edge artificial intelligence contribute to emerging engineering research directions. The combination of citation performance, interdisciplinary publication topics, and practical engineering applications illustrates measurable scholarly influence within contemporary technology research.[1]

Award Suitability

Based on the available scholarly record, Rafe Alasem demonstrates characteristics commonly associated with recognition for research excellence, including peer-reviewed publications, citation impact, interdisciplinary engineering contributions, and research addressing contemporary technological challenges. His work in secure networking, intelligent transportation, healthcare monitoring, and edge computing aligns with the objectives of international academic recognition programs that emphasize innovation, scientific quality, and societal relevance.[3]

Conclusion

Rafe Alasem has established a research portfolio centered on secure communication systems, intelligent networking technologies, and energy-efficient engineering solutions. His documented publication record, citation performance, and multidisciplinary contributions provide evidence of sustained academic activity and continued engagement with emerging engineering challenges. These accomplishments support consideration for recognition through the Research Excellence Award within the International Academic Achievements & Awards program.

References

  1. Elsevier. (n.d.). Scopus Author Details: Rafe Alasem, Author ID 22033707400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=22033707400
  2. Alasem, R. (2026). SEER-PM: A Secure and Energy-Efficient Routing Protocol for Pipeline Monitoring Wireless Sensor Networks. Algorithms.
    DOI: https://doi.org/10.3390/a19060493
  3. Alasem, R. (2025). Efficient Compression of wav2vec 2.0 for Edge Deployment in Speech Emotion & Speaker Recognition. Multimedia Tools and Applications. DOI: https://doi.org/10.1007/s11042-025-21057-w
  4. Alasem, R. (2025). Decentralized Trust Model for Vehicle Ad-Hoc Networks (VANETs) with 5G Integration: A Blockchain-Based Approach for Enhanced Security and Privacy in Intelligent Transportation Systems. Preprints.
    DOI: https://doi.org/10.20944/preprints202512.1086.v1

Jafar Abdollahi | Engineering | Innovative Research Award

Published on by Academic Achievements

Innovative Research Award

Jafar Abdollahi
Affiliation Islamic Azad University
Country Iran
Scopus ID 57222869366
Documents 25
Citations 444
h-index 11
Subject Area Engineering
Event International Academic Achievements & Awards

Jafar Abdollahi
Islamic Azad University, Iran

Jafar Abdollahi is an Artificial Intelligence researcher and Ph.D. student at the Department of Computer Engineering, Islamic Azad University, Central Tehran Branch, Iran. His research integrates machine learning, deep learning, computer vision, biomedical image analysis, medical informatics, IoT-enabled healthcare, and predictive analytics. His work has contributed to healthcare decision-support systems, intelligent diagnosis, and clinical outcome prediction using advanced computational models.[1]

Abstract

Jafar Abdollahi has established an active research profile in Artificial Intelligence with emphasis on medical image analysis, disease prediction, explainable AI, healthcare informatics, and intelligent clinical decision support. His publications span leading journals including Expert Systems with Applications, Biomedical Signal Processing and Control, SN Computer Science, and Archives of Breast Cancer. His research demonstrates practical implementation of deep learning, ensemble learning, transformer architectures, and optimization algorithms for healthcare applications.[2]

Keywords

Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Biomedical Image Analysis, Medical Informatics, IoT Healthcare, Disease Prediction, Data Science, Neural Networks.

Introduction

His academic career focuses on developing intelligent computational models capable of improving healthcare delivery through automated diagnosis and predictive analytics. His interdisciplinary collaborations involve researchers from the United States, Italy, Japan, Nigeria, Turkey, and the United Arab Emirates, illustrating the international relevance of his research activities.[3]

Research Profile

  • Machine Learning and Deep Learning
  • Medical Image Processing
  • Computer Vision
  • Biomedical AI
  • Healthcare Data Science
  • Predictive Analytics

Research Contributions

His research has produced advanced AI models for breast cancer detection, wound classification, diabetes prediction, heart disease diagnosis, COVID-19 detection, lung cancer analysis, pharmacological outcome prediction, and smart healthcare systems integrating IoT technologies. His work combines transformer architectures, ensemble learning, genetic algorithms, and explainable AI methods for clinically relevant applications.[4]

Publications

The researcher has authored more than 120 scientific publications including ISI, Scopus-indexed journals, IEEE conference papers, international conference proceedings, arXiv publications, book chapters, and translated academic books. His citation metrics include approximately 1,095 citations, an h-index of 18, and an i10-index of 22.[5]

Research Impact

His scientific contributions have influenced healthcare AI, intelligent diagnostics, and biomedical engineering. Recognition by the AD Scientific Index among Iran’s highly cited researchers further reflects the visibility of his research within the international scientific community.

Award Suitability

Considering his publication record, international collaborations, interdisciplinary research, citation impact, invited keynote presentations, industrial AI projects, and continuous innovation in intelligent healthcare technologies, Jafar Abdollahi demonstrates strong qualifications for recognition under the Innovative Research Award category.

Conclusion

Jafar Abdollahi represents a new generation of Artificial Intelligence researchers combining methodological innovation with practical healthcare applications. His contributions to machine learning, medical imaging, and intelligent decision-support systems continue to advance computational healthcare research while supporting international scientific collaboration.

External Links

References

  1. Abdollahi, J., & Aref, S. (2024). Early Prediction of Diabetes Using Feature Selection and Machine Learning Algorithms. SN Computer Science, 5(2). Springer. https://link.springer.com/article/10.1007/s42979-023-02545-y
  2. Mousa, R., Rezaei, B., Mahmoudi, L., & Abdollahi, J. (2025). Multi-modal wound classification using wound image and location by Swin Transformer and Transformer. Expert Systems with Applications.https://doi.org/10.1016/j.eswa.2025.127077
  3. Abdollahi, J., & Nouri-Moghaddam, B. (2022). Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction. Iran Journal of Computer Science. https://link.springer.com/article/10.1007/s42044-022-00100-1
  4. Abdollahi, J., Nouri-Moghaddam, B., & Ghazanfari, M. (2021). Deep Neural Network Based Ensemble Learning Algorithms for the Healthcare System (Diagnosis of Chronic Diseases). arXiv.https://arxiv.org/abs/2103.08182
  5. DBLP Computer Science Bibliography. Jafar Abdollahi – Publication Profile.
    https://dblp.org/pid/197/3784.html

Muhammad Noman Shahid | Mechanical Engineering | Best Researcher Award

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Mr.Muhammad Noman Shahid | Mechanical Engineering | Best Researcher Award

MS Scholar Capital University of Science and Technology Pakistan

Muhammad Noman Shahid is a dedicated Mechanical Engineer currently pursuing an MS in Mechanical Engineering at CUST, Islamabad. With a CGPA of 4.00/4.00 and a solid foundation in mechanical engineering principles, Muhammad’s expertise spans FEA, CFD, topological optimization, and CAD modeling. His academic and professional journey reflects his commitment to innovation and excellence in the engineering field.

Profile

ORCiD

Education

🎓 Muhammad Noman Shahid is completing his MS in Mechanical Engineering at Capital University of Science and Technology (CUST), Islamabad, with an expected graduation date of July 2025 and a perfect CGPA of 4.00/4.00. He also holds a BS in Mechanical Engineering from the same institution, achieved from 2019 to 2023, where he worked on the “Design and Development of Continuous Passive Motion (CPM) Machine for Post Knee Surgery Rehabilitation” as his final year design project.

Experience

💼 Muhammad’s professional experience includes an internship at SABRO Air Conditioning Pakistan in Islamabad, where he gained over 200 hours of hands-on experience in various HVAC manufacturing processes. His contributions included optimizing production time, ensuring product integrity, and enhancing overall HVAC system efficiency. Muhammad has also demonstrated leadership in numerous extracurricular roles, such as Focal Person at Pakistan Nuclear Society and President Media at Al-Muhandis Society, CUST.

Research Interests

🔬 Muhammad’s research interests lie in mechanical engineering, focusing on fluid dynamics, computational modeling, topological optimization, and biomechanics. He is particularly passionate about developing innovative solutions in tissue engineering and energy storage systems.

Awards and Funding

🏅 Muhammad has received several accolades for his academic excellence and innovative projects. In 2024, he achieved the Chancellor’s Honor Roll and secured the 3rd position in Mechanical Engineering (Entrepreneurship) at the 2nd Federal Engineering Capstone Expo. He also received IGNITE funding under the National Technology Fund’s Grossroot ICT Research Initiative for his final year design project.

Publications

📚 Muhammad has published significant research work, including:

  1. “Computational Investigation of the Fluidic Properties of Triply Periodic Minimal Surface (TPMS) Structures in Tissue Engineering,” Designs, vol. 8, no. 4, 2024. Link
    • Cited by: Articles in tissue engineering and fluid dynamics journals.
  2. “A Biomechanical Approach for Computational Assessment of Heavy Payload Robots in Human-Robot Accident Scenarios for Industry 4.0,” Nanotechnology Reviews, 2023. [In Review]

 

Tanaya Mandal | Engineering | Best Researcher Awards

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Ms. Tanaya Mandal | Engineering | Best Researcher Awards

PhD Candidate | Texas A&M University | United States

Short Bio 🌟

Tanaya Mandal is a dynamic materials engineer and Ph.D. candidate at Texas A&M University, with over four years of experience in researching the impact of material temperature on product performance. She has worked with prestigious institutions such as GE and TRI, and she actively chairs the Materials for Extreme Environments Technical Committee at SAMPE North America.

Profile

SCOPUS

Education 🎓

Tanaya Mandal is currently pursuing a Ph.D. in Materials Science and Engineering at Texas A&M University, maintaining a perfect GPA of 4.00. She previously earned her M.E. in the same field with a Corrosion Certificate from Texas A&M University in December 2020. Before that, she received her M.HSc from Trinity School of Medicine in May 2019, and her B.S. in Biochemistry and Molecular Biology from Houston Baptist University in May 2013.

Experience 🛠️

Texas Research Institute, Austin, TX
Application Engineering/Research & Development Intern (May 2023 – August 2023)
Tanaya collaborated with customers to develop prototypes for aerospace applications and engaged in the development of wear protection coatings. She worked closely with the sales team and analyzed high-temperature adhesion applications.

Texas A&M University, College Station, TX
PhD Research Student/Graduate Teaching Assistant (January 2021 – Present)
She led a project for the Air Force Office of Scientific Research, creating and analyzing self-healing vitrimer composites for aerospace. She also taught and assessed courses in materials science and engineering.

General Electric Global Research, Niskayuna, NY
Edison Technical Research Intern (June 2020 – August 2020)
Tanaya designed multilayer nitride coatings, evaluated hardness testing of various alloys, and participated in electrochemistry testing for accident tolerant fuel projects.

Research Interest 🔬

Tanaya’s research interests include the development and characterization of high-performance materials for extreme environments, particularly focusing on self-healing composites, high-temperature adhesion applications, and advanced nuclear reactors.

Awards 🏆

  • Best Oral Presentation in Advanced Materials and Nanotechnology at the Chemical Engineering Graduate Student Association (ChEGSA) Research Symposium (2024)
  • Moderator for Non-Destructive Evaluation & Materials Testing Technical Presentations at CAMX (2023)
  • SAMPE Student Chapter Grant Award (2021-2023)
  • Semifinalist for SAMPE University Research Symposium (URS) Program Competition (2021)
  • Women in 3D Printing (Wi3DP) Next Gen Mentorship Program (2021-present)
  • Judge for Senior Division of Materials Science at the Texas Science & Engineering Fair (2021)
  • SAMPE University Leadership Experience Award (2020)
  • Judge for Undergraduate Research Symposium at TAMU (2019)

Publications 📚

  • Mandal, T., Ozten, U., Vaught, L., Meyer, J.L., Amiri, A., Polycarpou, A., Naraghi, M. (2024). Processing and Mechanics of Aromatic Vitrimeric Composites at Elevated Temperatures and Healing Performance. J. Compos. Sci., 8, 252.
  • Mandal, T., Rodriguez-Melendez, D., Palen, B., Long, C.T., Chiang, H., Sarikaya, S., Naraghi, M., Grunlan, J.C. (2023). Heat Shielding Nanobrick Wall for Carbon Fiber Reinforced Polymer Composites. American Chemist Society Applied Polymer Materials, 5(5), 3270-3277.
  • Hoffman, A. K., Umretiya, R. V., Crawford, C., Spinelli, I., Huang, S., Buresh, S., Perlee, C., Mandal, T., Abouelella, H., Rebak, R. B. (2023). The relationship between grain size distribution and ductile to brittle transition temperature in FeCrAl alloys. Materials Letters, 331, 133427.

 

 

Abdellatif Sadeq | Mechanical | Best Researcher Award

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Assist Prof Dr. Abdellatif Sadeq | Mechanical | Best Researcher Award

Ph.D., Qatar University, Qatar

🌍 Dr. Abdellatif Mohammad Sadeq, born on April 30, 1993, in Jordan, currently resides in Doha, Qatar. Fluent in both Arabic and English, Dr. Sadeq is a distinguished mechanical engineer with a robust academic background and a dedication to teaching and research. He has over eight years of teaching experience and currently serves as the Dean of Academic Affairs and a mechanical engineering lecturer at Qatar Naval Academy. Dr. Sadeq’s research focuses on energy and automotive engineering, emphasizing sustainable energy solutions.

Profile

Google Scholar

 

Education🎓

🎓 Dr. Abdellatif Mohammad Sadeq holds a B.Sc. (2015), M.Sc. (2018), and Ph.D. (2022) in Mechanical Engineering from Qatar University. Additionally, in September 2023, he earned a second M.Sc. in Hybrid and Electric Vehicles Design and Analysis from Skill-Lync Online Platform in Chennai, India. His academic journey is marked by numerous accolades, including certificates of excellence and distinction.

Experience🏫

💼 Dr. Sadeq’s professional journey began in 2015 as a Graduate Teaching and Research Assistant at Qatar University, where he contributed significantly to teaching and research in mechanical engineering. Since October 2023, he has been serving as the Dean of Academic Affairs and a lecturer at Qatar Naval Academy. His roles have encompassed curriculum development, faculty training, and strategic academic planning, ensuring high standards of education and research excellence.

Research Interests📚

🔬 Dr. Sadeq’s research interests lie in energy and automotive engineering, with a particular focus on internal combustion engines, alternative fuels, hydrogen energy, renewable energy utilization, and energy storage techniques. He specializes in system modeling, simulation, and the design of hybrid and electric vehicles. Additionally, he contributes to heat transfer and HVAC systems, integrating management principles to tackle complex engineering challenges.

Awards🏆

🏅 Throughout his academic and professional career, Dr. Sadeq has received numerous awards and certificates, recognizing his excellence in both teaching and research. His commitment to academic and professional growth is evident through his continuous pursuit of knowledge and innovative solutions in mechanical engineering.

Publications

📚 Dr. Abdellatif Mohammad Sadeq’s extensive research work is well-documented in his publications, which are available on platforms like Google Scholar, ResearchGate, and ORCID. Here are some notable publications:

“Enhanced Synthesis and Performance Analysis of Castor Oil-Based Biolubricant for Two-Stroke Engines” [Submitted on May 2024].

“Advanced Demand Response Strategy and Development of Real-Time Data Acquisition Using The IoT-based Grasshopper Optimization Algorithm” [Submitted on May 2024].

“Optimization of In-Cylinder Pressure Prediction in Biodiesel Engines Using Deep Neural Networks” [Submitted on May 2024].

“Performance improvement of phase change material (PCM) based shell-and-tube type latent heat energy storage system utilizing different shaped fins” [Submitted on May 2024].

“Improved Hydrogen Production and Electrical Power in Photovoltaic-Thermal by Using Micro-jet Array Cooling System” [Submitted on May 2024].