Lukas Petersson | Artificial Intelligence | Best Researcher Award

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Mr. Lukas Petersson | Artificial Intelligence | Best Researcher Award

Founder, Vectorview, United States

Lukas Petersson is a passionate AI and robotics researcher, currently serving as the CTO and Co-founder of Vectorview in San Francisco. With a strong background in software engineering, machine learning, and robotics, Lukas has contributed significantly to AI safety evaluations for major labs such as Anthropic. He has a track record of successful funding, securing $2.2M in capital, and conducting groundbreaking research on agentic capabilities of LLMs. 🌟🤖💡

Publication Profile

Google Scholar

Education:

Lukas is pursuing his M.Sc. and B.Sc. in Engineering Physics and Engineering Mathematics at Lund University, where he has achieved an impressive GPA of 4.9/5 and 5.0/5. He also spent a year at ETH Zurich focusing on Machine Learning and Robotics. 🎓📚

Experience:

Lukas has gathered diverse experience across top organizations such as Google, Disney Research, CommaAI, and the European Space Agency. He has contributed to AI research, robotics, and autonomy engineering, with notable achievements like developing RL algorithms for social robotic interaction and automating data analysis at Google. He has also been part of impactful projects like the viral robot developed at Disney Research. 🏢🧑‍💻🚀

Research Interests:

Lukas’s research interests lie at the intersection of AI Safety, Machine Learning, Robotics, and Autonomous Systems. His work focuses on improving agentic capabilities of large language models (LLMs) and exploring the application of Reinforcement Learning (RL) for social robots. 🤖🔬🌍

Awards:

Lukas’s work has been recognized in the fields of robotics and AI, contributing to significant advancements in safety and performance. He has excelled in competitive programming and autonomous vehicle development, receiving awards and recognition for his innovative approach to solving real-world challenges. 🏆🌟

Publications:

“Taming the Machine” (2023): Contributed research on AI Safety for a book discussing the future of machine learning and its societal impacts. 📚🧠

“MBSE” (2021): Published and presented a paper on Model-Based Systems Engineering at a conference, focusing on advanced methodologies in systems engineering. 📄🔧

 

Carolina Magalhães | Machine Learning | Best Researcher Award

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Dr. Carolina Magalhães | Machine Learning | Best Researcher Award

Investigadora, INEGI – Instituto de Ciência e Inovação em Engenharia Mecânica e Industrial, Portugal

👩‍🔬 Carolina Magalhães is a dedicated biomedical engineer and PhD candidate with expertise in applying AI and imaging technologies to healthcare challenges. Based in Porto, Portugal, she combines her passion for modern technology with a problem-solving mindset to develop innovative solutions in skin cancer diagnostics. Carolina has worked collaboratively with clinical experts to bridge research and practical applications, contributing significantly to advancing imaging-based decision support systems.

Publication Profile

ORCID

Education

🎓 Carolina holds a PhD in Biomedical Engineering from the Faculdade de Engenharia da Universidade do Porto (2020–2024). She also completed her MSc in Biomedical Engineering at the same institution (2016–2018) and earned her Bachelor’s in Bioengineering – Biomedical Engineering from Universidade Católica Portuguesa (2013–2016).

Experience

💼 Carolina has a rich research background, currently serving as a Graduate Research Fellow at INEGI, focusing on skin lesion diagnosis using multispectral imaging. Her work spans from leveraging machine learning models for skin cancer classification to thermal and UV imaging techniques. Previously, she contributed to projects on hyperhidrosis diagnosis, prosthetic device design, and thermal image analysis for musculoskeletal disorders, collaborating with leading hospitals and research centers in Portugal.

Research Interests

🔬 Carolina is passionate about exploring artificial intelligence, machine learning, and advanced imaging technologies for healthcare applications. Her interests include developing multispectral imaging systems, improving diagnostic tools for skin cancer, and advancing infrared thermography for clinical support systems.

Awards

🏆 Carolina’s innovative work has been recognized with prestigious research grants from the Foundation for Science and Technology (SFRH/BD/144906/2019) and other funding organizations. These awards have supported her impactful contributions to biomedical engineering and healthcare innovation.

Publications

“Systematic Review of Deep Learning Techniques in Skin Cancer Detection”
BioMedInformatics, 11/2024
Read here

“Skin Cancer Image Classification with Artificial Intelligence Strategies: A Systematic Review”
Journal of Imaging, 10/2024
Read here

“Use of Infrared Thermography for Abdominoplasty Procedures in Patients with Extensive Subcostal Scars: A Preliminary Analysis”
Plast Reconstr Surg Glob Open, 06/2023
Read here

“Classic Versus Scarpa-Sparing Abdominoplasty: An Infrared Thermographic Comparative Analysis”
J Plast Reconstr Aesthet Surg, 06/2023
Read here

“Towards an Effective Imaging-Based Decision Support System for Skin Cancer”
Handbook of Research on Applied Intelligence for Health and Clinical Informatics, 10/2022
Read here

Syed Ijaz Ul Haq | Machine Learning | Best Researcher Award

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Dr. Syed Ijaz Ul Haq | Machine Learning | Best Researcher Award 

Research associate, Shandong University of Technology, China

Syed Ijaz Ul Haq is a dedicated Research Assistant in Agronomy at Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan, since September 2021. Currently pursuing a Ph.D. in Agriculture Engineering and Food Science at Shandong University of Technology, China, he is passionate about advancing research in remote sensing, artificial intelligence, and deep learning. With a commitment to excellence and professional development, Syed aims to explore innovative solutions in agriculture. 🌱📚

Publication Profile

ORCID

Strengths for the Award

  1. Specialized Research Interests: Syed has a clear focus on Remote Sensing, AI, and Deep Learning, which are critical areas in modern agricultural research. His work on machine learning techniques for pest detection and weed analysis demonstrates innovative applications of technology in agriculture.
  2. Academic Background: Currently pursuing a Ph.D. in Agricultural Engineering and Food Science at Shandong University of Technology, Syed is in an excellent position to contribute cutting-edge research to the field.
  3. Professional Experience: His role as a Research Assistant at Pir Mehr Ali Shah Arid Agriculture University allows him to gain practical experience and engage with ongoing research projects, enhancing his research skills.
  4. Publication Record: With multiple publications in reputable journals, including articles on trace elements’ effects on crop growth and the use of AI for weed detection, he demonstrates the ability to conduct and disseminate impactful research.
  5. Peer Review Engagement: His involvement as a reviewer for the American Society of Plant Biologists reflects recognition by peers and contributes to his professional development.

Areas for Improvement

  1. Broader Research Impact: While Syed has several publications, expanding his research to include interdisciplinary collaborations or more diverse agricultural challenges could enhance his visibility and impact in the field.
  2. Networking and Collaboration: Actively seeking collaborations with other researchers or institutions could provide Syed with additional insights and resources, fostering a more extensive research network.
  3. Professional Development: Attending more international conferences and workshops could enhance his skills and provide opportunities for exposure to global trends in agricultural research and technology.
  4. Outreach and Application of Research: Engaging with local communities or agricultural practitioners to apply his findings could bridge the gap between research and real-world application, leading to significant societal impacts.

Education

Syed is currently enrolled in a Ph.D. program in Agriculture Engineering and Food Science at Shandong University of Technology, Zibo, Shandong, China, where he has been studying since July 2022. His academic focus revolves around integrating advanced technologies to enhance agricultural practices. 🎓🌾

Experience

Since September 2021, Syed has served as a Research Assistant in Agronomy at Pir Mehr Ali Shah Arid Agriculture University, where he contributes to various agricultural research projects, gaining valuable experience and insights into the field. His role involves collaborating with researchers to explore sustainable agricultural practices and technologies. 🧑‍🔬🌍

Research Focus

Syed’s research primarily focuses on the application of remote sensing, AI, and deep learning techniques in agriculture. His work aims to improve crop yield, pest detection, and weed management, making significant contributions to sustainable farming practices. 🤖🌿

Awards and Honours

Syed has been recognized for his contributions to agricultural research, including serving as a Reviewer for the American Society of Plant Biologists since 2021. His academic excellence is reflected in his ongoing Ph.D. studies, showcasing his dedication to advancing the field. 🏆📜

Publications

Influence of Trace Elements (Co, Ni, Se) on Growth, Nodulation and Yield of Lentil
Published in Polish Journal of Environmental Studies, 2024
Cited by: Crossref

Identification of Pest Attack on Corn Crops Using Machine Learning Techniques
Published in 2023
Cited by: Crossref

Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
Published in Applied Sciences, 2023
Cited by: Crossref

Conclusion

Syed Ijaz Ul Haq shows strong potential as a candidate for the Research for Best Researcher Award due to his focused research interests, current academic pursuits, publication record, and peer engagement. To further enhance his candidacy, he should consider broadening his research scope, expanding his professional network, and increasing the real-world applicability of his research findings. If he continues on this trajectory, he has the potential to make substantial contributions to agricultural research, making him a deserving recipient of this award.

 

Isabel de la Torre | Computer Science | Women Researcher Award

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Prof Dr. Isabel de la Torre | Computer Science | Women Researcher Award

Catedrática, Universidad de Valladolid, Spain

Isabel de la Torre Díez, born in 1979 in Zamora, Spain, is a renowned Full Professor at the University of Valladolid. She received her M.S. and Ph.D. degrees in Telecommunication Engineering from the same university in 2003 and 2010, respectively. Isabel’s expertise lies in telemedicine, e-health, m-health, and related fields. She has authored over 250 papers and played a significant role in numerous research projects. Isabel leads the GTe Research Group and is a key figure in the field of telemedicine and e-health. 🌐👩‍🏫

Publication Profile

 

Strengths for the Award

  1. Significant Research Contributions: Isabel de la Torre Díez has published over 250 papers in SCI journals, peer-reviewed conferences, and books. This extensive publication record highlights her impactful research in telemedicine, e-health, and related fields.
  2. Leadership and Innovation: She leads the GTe Research Group at the University of Valladolid and has been involved in creating and coordinating innovative software. Her leadership in advancing telemedicine and e-health applications demonstrates her commitment to improving healthcare through technology.
  3. Research Impact and Recognition: She has been involved in over 100 international conference program committees and has participated in numerous funded research projects. Her involvement as a reviewer for well-known SCI journals further underscores her expertise and influence in her field.
  4. Research and Teaching Excellence: With two research sexenios, she has demonstrated consistent research excellence. Her role in guiding doctoral theses and her contributions to high-impact journals and conferences reflect her high standing in the academic community.
  5. International Collaboration: Her postdoctoral research experiences in Portugal, Spain, and France highlight her international collaboration and mobility, enhancing her global research network and exposure.

Areas for Improvement

  1. Broader Recognition: While her research is extensive, further highlighting any awards or recognitions she has received could strengthen her application. Emphasizing awards or honors related to her research could enhance her candidacy.
  2. Diversity of Research Interests: While her focus is on telemedicine and e-health, demonstrating how her research contributes to a broader range of applications or interdisciplinary areas might strengthen her profile.
  3. Detailed Impact Metrics: Providing specific metrics, such as citation counts, h-index, and impact factors of the journals where she has published, could offer a clearer picture of her research impact.

Conclusion

Isabel de la Torre Díez is a highly qualified candidate for the Research for Women Researcher Award. Her extensive research contributions, leadership in innovative projects, and active participation in international research communities position her as a leading figure in her field. Enhancing her application with additional recognitions and detailed impact metrics could further bolster her candidacy. Overall, her achievements and ongoing contributions to the field of telemedicine and e-health make her a strong contender for the award.

Education 🎓

Isabel de la Torre Díez earned her M.S. and Ph.D. degrees in Telecommunication Engineering from the University of Valladolid, Spain, in 2003 and 2010, respectively. Her education laid a strong foundation for her prolific career in telemedicine and e-health. 🏫📜

Experience 👩‍💼

Isabel de la Torre Díez is a Full Professor in the Department of Signal Theory and Communications and Telematics Engineering at the University of Valladolid. She has authored over 250 papers and coauthored 16 registered innovative software. Isabel has been involved in more than 100 international conference program committees and has participated in 44 funded research projects. She is also a reviewer for renowned journals like the International Journal of Medical Informatics. 🏫📚

Research Focus 🔬

Isabel’s research focuses on the development and evaluation of telemedicine applications, e-health, m-health, EHRs (Electronic Health Records), machine and deep learning, privacy and security, biosensors, QoS (Quality of Service), and QoE (Quality of Experience) in the health field. She has significantly contributed to these areas, particularly in telepsychiatry, teleophthalmology, and telecardiology. 🧠💻

Awards and Honors 🏆

Isabel de la Torre Díez has received numerous accolades throughout her career. She has two research sexenios and coordinates the GTe Research Group and the GIR “Society of Information” group. She has also been recognized for her contributions as a reviewer for prestigious journals and her leadership in various research projects and collaborations. 🌟🏅

Publications 📄

  1. Novel model to authenticate role-based medical users for blockchain-based IoMT devices
    PLOS ONE
    2024-07-10
    DOI: 10.1371/journal.pone.0304774
  2. A Digital Mental Health Approach for Supporting Suicide Prevention: A Qualitative Study
    International Journal of Mental Health and Addiction
    2024-06-21
    DOI: 10.1007/s11469-024-01347-4
  3. A deep learning approach for Named Entity Recognition in Urdu language
    PLoS ONE
    2024
    DOI: 10.1371/journal.pone.0300725
    Cited by 1 article
  4. A Detectability Analysis of Retinitis Pigmentosa Using Novel SE-ResNet Based Deep Learning Model and Color Fundus Images
    IEEE Access
    2024
    DOI: 10.1109/ACCESS.2024.3367977
    Cited by 1 article

 

Avirup Roy | Machine Learning |Machine Learning Research Award

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Mr. Avirup Roy | Machine Learning |Machine Learning Research Award

PhD Student, Michigan State University, United States

Dr. Avirup Roy is a dedicated researcher and engineer specializing in networked embedded and wireless systems. Currently pursuing his PhD at Michigan State University, his work focuses on developing self-learning mechanisms for embedded hardware systems with limited computational resources. With a solid foundation in electronics and communication engineering, Avirup has gained extensive experience in both academia and industry, contributing to projects ranging from smart malaria detection to automated power management systems. His technical skills span machine learning, embedded systems, cloud computing, and web development. Beyond his professional life, Avirup is passionate about Indian classical music, photography, and swimming. 🌟📚🎵📷🏊‍♂️

Profile

ORCID

 

Education🎓

Michigan State University, East Lansing, MI, US PhD in Electrical and Computer Engineering (2020-Present). Dissertation: Self-learning mechanisms for Embedded hardware systems with limited computational resources. GPA: 3.75/4Maulana Abul Kalam Azad University of Technology, Kolkata, WB, India Bachelor of Technology (BTech) in Electronics and Communication Engineering (2013-2017)

Experience💼

Graduate Research Assistant, Michigan State University (Sep 2020 – Jul 2023),Developed an android and website application for smart malaria detection involving cloud database integration. Graduate Teaching Assistant, Michigan State University (Aug 2023 – Present), Instructed and graded labs for Embedded Cyber-physical Systems, VLSI Systems, and Digital Control courses. ICER Cloud Computing Fellow, Michigan State University (Sep 2023 – Present), Implemented Azure cloud resources in semi-supervised federated learning for embedded devices. Programmer Analyst, Cognizant Technology Solutions (Dec 2017 – Jul 2020), Developer and support analyst for ASP.NET based applications of MetLife Inc. Intern, Calcutta Electric Supply Corporation (CESC) Limited (Jul 2016 – Aug 2016), Worked on automated power management systems using SCADA communication. Intern, Bharat Sanchar Nigam Limited (BSNL) (Jun 2015 – Aug 2015), Explored general trends in wireless communication. Undergraduate Researcher, Maulana Abul Kalam Azad University of Technology (2015-2016), Presented research at various international conferences and served as the vice-president of SPIE Student Chapter.

Research Interests🔍

Embedded Machine Learning: Focused on developing efficient learning algorithms for resource-constrained devices.
Networked Embedded Systems: Exploring self-learning mechanisms and their applications in real-world scenarios.
Cloud Computing: Leveraging cloud resources for semi-supervised federated learning.
VLSI Systems: In-depth study and teaching of Very-Large-Scale Integration systems.
Cyber-Physical Systems: Research on embedded systems interacting with physical processes.

Awards🏆

National Social Entrepreneurship Programme (2014): Secured 2nd position for the ‘Hand-Made Paper Industry’ project.
SPIE Smart Structures and Non-destructive Evaluation Conference (2016): Presented research in Las Vegas, Nevada.
EAPE Conference (2015): Presented research on emerging areas of photonics and electronics.
Graduate Fellowships: Awarded multiple fellowships during PhD for research and teaching excellence.

Publications

Semi-Supervised Learning Using Sparsely Labelled Sip Events for Online Hydration Tracking Systems
A. Roy, H. Dutta, A. K. Bhuyan, and S. K. Biswas, 2023, International Conference on Machine Learning and Applications (ICMLA).
Cited by: 3 articles.

An On-Device Learning System for Estimating Liquid Consumption from Consumer-Grade Water Bottles and Its Evaluation
Roy, A., Dutta, H., Griffith, H., & Biswas, S., 2022, Sensors.
Cited by: 5 articles.

HaiTian Chen | Computer Science | Best Researcher Award

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Ms. HaiTian Chen | Computer Science | Best Researcher Award

College of Science, North China University of Science and Technology, China

Chen HaiTian is a dedicated researcher in the field of Cyberspace Security from China. Born in December 1998, Chen has made significant strides in federated learning, privacy preservation, and cybersecurity. His contributions span multiple peer-reviewed journals and patents, showcasing his commitment to advancing technology and safeguarding digital spaces.

Profile

ORCID

 

Education

Chen HaiTian holds a major in Cyberspace Security, demonstrating his expertise and focus in this critical area of study. His academic background has equipped him with the skills and knowledge necessary to tackle complex cybersecurity challenges and contribute to innovative solutions in the field. 🎓

Research Interests

Chen HaiTian’s research interests focus on federated learning, privacy preservation, and cybersecurity. He is particularly interested in developing robust aggregation techniques to defend against poisoning attacks in federated learning and exploring personalized fair split learning for resource-constrained Internet of Things (IoT). 🔍

Awards

Chen HaiTian has received recognition for his contributions to software development, including the Huali Academy Backstage Management System V1.0 and the DC Early Warning System V1.0. His work has been registered with computer software registration numbers, showcasing his achievements in developing innovative solutions for network management and security. 🏆

Publications

Chen, H.; Chen, X.; Peng, L. (2023). FLRAM: Robust Aggregation Technique for Defense Against Byzantine Poisoning Attacks in Federated Learning. Electronics. Cited by Electronics.

Chen, H.; Chen, X.; Peng, L. (2024). Personalized Fair Split Learning for Resource-Constrained Internet of Things. Sensors, 24, 88. Cited by Sensors.

Chen, H., Chen, X., Ma R., et al. (2024). A federated learning privacy preserving approach for remote sensing data. Computer Applications. Cited by Computer Applications.

Chen, H., Chen, X. (2023). A Robust Aggregation Technique for Poisoning Attack Defense in Federated Learning. Cited by Journal.

Xu C., Zhang S., Chen H., et al. (2024). A federated learning approach based on adaptive differential privacy and customer selection optimization. Computer Applications. Cited by Computer Applications.

Peng L., Zhang S., Chen H., et al. (2023). Clustered federated learning based on improved CFSFDP algorithm. Journal of North China University of Science and Technology (Natural Science Edition). Cited by NCUST.

Qiang Li | Computer Science | Best Researcher Award

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Mr. Qiang Li | Computer Science | Best Researcher Award

Lecturer, Qingdao University, China

Dr. Li Qiang is an experienced lecturer in computer science with a PhD in Engineering. He specializes in high-performance computing and has a strong background in both teaching and research. Committed to fostering academic excellence and technological innovation, Dr. Li has been a dedicated educator and researcher at Qingdao University since 2015.

Profile

ORCID

 

Education 🎓

PhD in Engineering: University of the Chinese Academy of Sciences, Computer Network Information Center (2010-2014), Advisor: Lu Zhonghua. Master’s in Information Science and Engineering: Shandong University of Science and Technology (2007-2010), Advisor: Zhao Maoxian. Bachelor’s in Education: Qingdao University (2003-2007).

Experience 👨‍🏫

Lecturer at Qingdao University, School of Computer Science and Technology (January 2015-Present). Teaching undergraduate and graduate courses in computer science. Supervising student research projects and theses. Conducting research in high-performance computing. Published 12 research papers in journals and conferences. Granted 2 patents.

Research Interests 🔬

Dr. Li Qiang’s research interests lie in high-performance computing, particularly in the optimization and parallel implementation of numerical simulations and the development of new computational frameworks. His work focuses on enhancing computational efficiency and scalability in large-scale scientific computations.

Awards 🏆

Dr. Li Qiang has been recognized for his contributions to the field of high-performance computing through multiple publications and patents. His innovative work has led to advancements in computational methods and has garnered attention in the academic community.

Publications 📄

Optimization Research of Heterogeneous 2D-Parallel Lattice Boltzmann Method Based on Deep Computing Unit. Appl. Sci. 2024, 14, 6078.

Heterogeneous Parallel Implementation of Large-Scale Numerical Simulation of Saint-Venant Equations. Appl. Sci. 2022, 12, 5671. Cited by 6

The Study of Parallelization of SWAT Hydrology Cycle. The 32nd ACM International Conference on Supercomputing, Beijing, 2018. [Cited by 3]

A New Parallel Framework of Distributed SWAT Calibration. Journal of Arid Land, 2015, 7(1): 122-131. [Cited by 7]

Parallel Simulation of High-Dimensional American Option Pricing Based on CPU VS MIC. Concurrency and Computation: Practice and Experience, 2014, 27(5): 1110-1121. [Cited by 5]

Jihyeon Ryu | Computer Science | Best Researcher Award

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Assist Prof Dr. Jihyeon Ryu | Computer Science | Best Researcher Award

Professor, Kwangwoon University, South Korea

👩‍🏫 Jihyeon Ryu is an Assistant Professor at the School of Computer and Information Engineering, Kwangwoon University. She specializes in split learning, convolutional neural networks, and user authentication, contributing significantly to the field of computer science with her extensive research and numerous publications.

Profile

Google Scholar

 

Education

Ph.D. in Software (Integrated Master’s and Doctorate Course) from Sungkyunkwan University (Mar. 2018 – Feb. 2023), advised by Prof. Dongho Won and Prof. Hyoungshick Kim. B.S. in Mathematics and Computer Science and Engineering from Sungkyunkwan University (Mar. 2013 – Feb. 2018). Early Graduation from Sejong Science High School (Mar. 2011 – Feb. 2013).

 

Research Interests:

Split Learning. Convolutional Neural Networks. User Authentication

Awards

Family Company Workshop Lecture, Hoseo University (Feb. 2022). Invited Lecturer, Mental Women’s High School (Nov. 2021). Excellence Prize, Software Department Excellence Research Awards (Feb. 2021). Graduate Merit Scholarship, Sungkyunkwan University (Mar. 2018 – Feb. 2021). SimSan Scholarship, Multiple instances (Mar. 2018 – Sep. 2020). Excellence Award, 국가 암호기술 전문인력 양성과정 (Nov. 2019). Grand Prize, Software Department Excellence Research Awards (Feb. 2019). Samsung Science Scholarship (Mar. 2013 – Feb. 2018)

Publications

Lightweight Hash-Based Authentication Protocol for Smart Grids. Sensors, 2024. Sangjin Kook, Keunok Kim, Jihyeon Ryu, Youngsook Lee, Dongho Won.

Enhanced Lightweight Medical Sensor Networks Authentication Scheme Based on Blockchain. IEEE ACCESS, 2024. Taewoong Kang, Naryun Woo, Jihyeon Ryu.

Secure and Anonymous Authentication Scheme for Mobile Edge Computing Environments. IEEE Internet of Things Journal, 2024. Hakjun Lee, Jihyeon Ryu, Dongho Won.

Distributed and Federated Authentication Schemes Based on Updatable Smart Contracts. Electronics, 2023. Keunok Kim, Jihyeon Ryu, Hakjun Lee, Youngsook Lee, Dongho Won.

An Improved Lightweight User Authentication Scheme for the Internet of Medical Things. Sensors, 2023. Keunok Kim, Jihyeon Ryu, Youngsook Lee, Dongho Won.

Nabi Mehri Khansari | Machine Learning | Best Researcher Award

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Dr. Nabi Mehri Khansari | Machine Learning | Best Researcher Award

University Professor, Sahand University of Technology, Iran

Dr. Nabi Mehri-Khansari is an esteemed Assistant Professor at the Sahand University of Technology. With a rich academic background in Mechanical and Aerospace engineering from prestigious institutions like Iran University of Science and Technology and the University of Tehran, he has made significant contributions to the field. His research spans failure analysis, damage and fracture mechanics in lightweight composite structures, leveraging machine learning and deep learning. Dr. Mehri-Khansari has collaborated with various international research centers and industries, enhancing his expertise and impact in the field.

Profile

Scopus

Education

🎓 Dr. Nabi Mehri-Khansari obtained his B.Sc. degree in Mechanical Engineering from the Iran University of Science and Technology in 2011. He pursued his M.Sc. and Ph.D. degrees in Aerospace Engineering from the University of Tehran, completing them in 2014 and 2018, respectively. His academic excellence is marked by being ranked 2nd in M.Sc. and 1st in Ph.D., earning acceptance with quotas for talented students. He also served as a research fellow at NTNU University, Trondheim, Norway, further broadening his academic horizons.

Experience

🔧 Dr. Mehri-Khansari has an extensive professional background. He has been a faculty member at the Sahand University of Technology since January 2019. Prior to this, he was a lecturer at the University of Tehran – North Branch, a research assistant at NTNU University in Norway, and a technical expert at the Iranian Space Institute. His diverse roles reflect his versatile expertise and commitment to advancing engineering education and research.

Research Interests

🔬 Dr. Mehri-Khansari’s research interests are vast and interdisciplinary. They include wind turbine technology, multi-scale fracture mechanics of composites and inhomogeneous media, multi-scale damage mechanics, aeroelasticity, and defect detection methods. His innovative work often incorporates machine learning and deep learning techniques, pushing the boundaries of traditional engineering research.

Awards

🏅 Dr. Mehri-Khansari has received numerous accolades throughout his career. These include the prestigious Ph.D. acceptance with quotas for talented students, being ranked 1st in his Ph.D. program at the University of Tehran, and the Best Teacher Award from the Sahand University of Technology in June 2024. His membership in professional organizations such as the American Society of Mechanical Engineering and the Iranian Composites Scientific Association further underscores his professional excellence.

Publications

Orthotropic failure criteria based on machine learning and micro-mechanical matrix adapting coefficient
Mixed-modes (I/III) fracture of aluminum foam based on micromechanics of damage
Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models
Numerical & experimental assessment of mixed-modes (I/II) fracture of PMMA/hydroxyapatite nanocomposite