Jaehwan Jeong | Generative AI models | Best Researcher Award

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Mr. Jaehwan Jeong | Generative AI models | Best Researcher Award

Ph.D Student,Korea University,South Korea

Jaehwan Jeong is an emerging researcher in AI and computer vision, with a strong academic background and collaborations with top institutions. His work in deepfake defense and generative models positions him well for awards in AI safety and multi-modal learning. However, securing additional accepted publications and leading independent research could further bolster his case for the Best Researcher Award.

Publication Profile
Google Scholar

Education :

Jaehwan Jeong is currently pursuing a Ph.D. in Artificial Intelligence at Korea University, Seoul, South Korea (2024ā€“2029, expected). He completed his Bachelor of Engineering (B.E.) in Electrical & Electronic Engineering from Chung-Ang University, Seoul, in 2021. His academic journey has been focused on artificial intelligence, deep learning, and computer vision.

Experience:

Research Focus:

Skills:

Jaehwan possesses strong expertise in:,Programming: Python, Shell Scripting, Git, LaTeX,Deep Learning Frameworks: PyTorch, PyTorch Lightning, TensorFlow,AI & ML Libraries: Hugging Face, Scikit-Learn

Publication :

  • MTVG: Multi-text Video Generation with Text-to-Video Models

    • Authors: Gyeongrok Oh, Jaehwan Jeong, Sieun Kim, Wonmin Byeon, Jinkyu Kim, Sungwoong Kim, Hyeokmin Kwon, Sangpil Kim
    • Publication: arXiv preprint arXiv:2312.04086
    • Year: 2023
    • Citation: Oh, G., Jeong, J., Kim, S., Byeon, W., Kim, J., Kim, S., Kwon, H., & Kim, S. (2023). MTVG: Multi-text Video Generation with Text-to-Video Models. arXiv preprint arXiv:2312.04086.
      kuai-lab.github.io

  • MEVG: Multi-event Video Generation with Text-to-Video Models

    • Authors: Gyeongrok Oh, Jaehwan Jeong, Sieun Kim, Wonmin Byeon, Jinkyu Kim, Sungwoong Kim, Sangpil Kim
    • Publication: European Conference on Computer Vision (ECCV), pages 401ā€“418
    • Year: 2025
    • Citation: Oh, G., Jeong, J., Kim, S., Byeon, W., Kim, J., Kim, S., & Kim, S. (2025). MEVG: Multi-event Video Generation with Text-to-Video Models. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, & G. Varol (Eds.), Computer Vision ā€“ ECCV 2024 (pp. 401ā€“418). Springer.
      pure.korea.ac.kr

  • FaceShield: Defending Facial Image against Deepfake Threats

    • Authors: Jaehwan Jeong, Seungmin In, Sieun Kim, Hyojin Shin, Jaeho Jeong, Seunghyun Yoon, Jaewon Chung, Sungwoong Kim
    • Publication: arXiv preprint arXiv:2412.09921
    • Year: 2024
    • Citation: Jeong, J., In, S., Kim, S., Shin, H., Jeong, J., Yoon, S., Chung, J., & Kim, S. (2024). FaceShield: Defending Facial Image against Deepfake Threats. arXiv preprint arXiv:2412.09921.
Conclusion:

Jeong is a strong candidate for the award but would benefit from more accepted publications and demonstrated leadership in independent research. His ongoing Ph.D. work and collaborations with Samsung, NVIDIA, and Google make him a promising researcher with significant potential.

Nisar Hussain | Artificial Intelligence and Machine Learning | Best Researcher Award

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Mr.Nisar Hussain |Artificial Intelligence and Machine Learning|Best Researcher Award

Mr.Ā  Nisar Hussain Instituto Politechnico Nacional, Mexico City, Mexico

Nisar Hussain is a researcher affiliated with the Instituto PolitĆ©cnico Nacional (IPN) in Mexico City, Mexico. He is currently enrolled in the Doctorate in Computer Science program at IPN’s Centro de InvestigaciĆ³n en ComputaciĆ³n (CIC), focusing his research on offensive language detection and sentiment analysis in code-mixed text on social media.Throughout his academic career, Hussain has contributed to various studies in the field of Natural Language Processing (NLP). Notably, he co-authored the paper titled “ORUD-Detect: A Comprehensive Approach to Offensive Language Detection in Roman Urdu Using Hybrid Machine Learningā€“Deep Learning Models with Embedding Techniques,” published in the journal Information in February 2025.In addition to his work on offensive language detection, Hussain has explored other areas of NLP. He co-authored a study on guilt detection in text, which was published in Scientific Reports in July 2023.

Publication Profile

Google scholar

orcid

Education :

Ph.D. in Computer Science (2022-2025, Ongoing)
Instituto PolitĆ©cnico Nacional, MĆ©xicoMaster’s in Computer Science (2014-2017)
University of Agriculture, Faisalabad, PakistanBachelor of Science in Computer Science (BSCS) (2010-2014)
COMSATS University Islamabad, Sahiwal Campus

Experience :

With 4+ years of experience in developing and deploying ML and NLP systems, I have actively contributed to multiple projects, applying NLP techniques for real-world problem-solving. I have worked with large, complex datasets, implementing hybrid ML-DL approaches for automated text processing, sentiment analysis, and multilingual content understanding. My research collaborations span multiple institutions, focusing on AI-driven solutions for text analysis and detection tasks.

Research Focus :

I specialize in Natural Language Processing (NLP) and Machine Learning, with a particular emphasis on Offensive Language Detection and Sentiment Analysis of Code-Mixed Data. My research explores multilingual and low-resource language models, leveraging and fine-tuning mBERT, XLM-R, IndicBERT, and Google’s BERT-based models. I am particularly interested in hate speech detection, sentiment analysis, language identification, and emotion analysis in complex linguistic environments. My work integrates deep learning techniques, transformers, and hybrid ML-DL models to improve text processing and understanding in diverse multilingual contexts.

Awards:

Published multiple high-impact research papers in leading AI and NLP conferences/journalsActive participant in international AI competitions and workshopsRecognized for contributions to multilingual and low-resource NLP advancements

Publication :

  • Shaheen, M., Awan, S. M., Hussain, N., & Gondal, Z. A. (2019). Sentiment analysis on mobile phone reviews using supervised learning techniques. IJMECS, 11(7), 32.

 

  • Mehak, G., Qasim, A., Meque, A. G. M., Hussain, N., Sidorov, G., & Gelbukh, A. (2025, January). TechExperts (IPN) at GenAI Detection Task 1: Detecting AI-Generated Text in English and Multilingual Contexts. In Proceedings of the 1st Workshop on GenAI Content Detection (GenAIDetect) (pp. 161-165).

 

  • Hussain, N., Qasim, A., Mehak, G., Kolesnikova, O., Gelbukh, A., & Sidorov, G. (2025). Hybrid Machine Learning and Deep Learning Approaches for Insult Detection in Roman Urdu Text. AI, 6(2), 33. https://doi.org/10.3390/ai6020033

 

  • Qasim, A., Mehak, G., Hussain, N., Gelbukh, A., & Sidorov, G. (2025). Detection of Depression Severity in Social Media Text Using Transformer-Based Models. Information, 16(2), 114. https://doi.org/10.3390/info16020114

 

  • Manzoor, M. I., Shaheen, M., Khalid, H., Anum, A., Hussain, N., & Faheem, M. R. (2018). Requirement Elicitation Methods for Cloud Providers in IT Industry. IJMECS, 10(10).

 

  • Hussain, N., & Anees, T. (2018). Development of a novel approach to search resources in IoT. International Journal of Advanced Computer Science and Applications, 9(9).

 

  • Faheem, M. R., Iftikhar, A., & Hussain, N. (2022). Automated Diagnosing of Eye Disease in Real Time. Journal of Computing & Biomedical Informatics, 3(1), 282-288.

 

  • Shaheen, M., Anees, T., Hussain, N., & Obaid, I. (2019, April). A Research on SOA in the IT Industry of Pakistan. In Proceedings of the 2019 ICCTA (pp. 149-154).

 

  • Meque, A. G. M., Hussain, N., Sidorov, G., & Gelbukh, A. (2023). Guilt Detection in Text: A Step Towards Understanding Complex Emotions. arXiv preprint arXiv:2303.03510.

 

  • Tash, M. S., Ahani, Z., Tonja, A., Gemeda, M., Hussain, N., & Kolesnikova, O. (2022, December). Word Level Language Identification in Code-mixed Kannada-English Texts using Traditional Machine Learning Algorithms. In Proceedings of the (ICON) (pp. 25-28).

 

 

Ā Conclusion

Given their strong publication record, hands-on experience with AI models, and focus on low-resource NLP, the candidate is highly competitive for the Best Researcher Award. Strengthening industry collaborations, increasing research impact, and securing grants will further enhance their research profile.

 

 

 

Mahmoud Marhamati |Artificial | Best Researcher Award

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Mr.Mahmoud Marhamati |Artificial | Best Researcher Award

Mr. Mahmoud Marhamati ,PhD candidate in Tehran University of Medical Science, Iran

Mr. Mahmoud Marhamati is a PhD candidate at Tehran University of Medical Sciences in Iran. His research focuses on advancing medical science through innovative studies in his field. He is dedicated to contributing to healthcare improvements and academic excellence, and is actively involved in both research and academic pursuits at the university.

Summary:

Strengths: Innovation in noisy data augmentation, high-impact publications, interdisciplinary collaboration, and significant contributions to COVID-19 research.,Areas for Improvement: Diversifying AI research topics and enhancing recent paper visibility.

Professional Profiles:

Google Scholar

šŸŽ“ Education :

M. Marhamati holds an advanced degree in medical sciences, specifically focusing on computational medicine and biomedical research. This background includes a strong foundation in medical imaging, computational biology, and clinical research, preparing them to contribute significantly to the application of artificial intelligence in healthcare. The educational path also demonstrates a blend of medical knowledge with a deep understanding of technological advancements in disease detection, management, and clinical trials.

šŸ¢Ā Experience:

M. Marhamati has a rich experience in the intersection of healthcare and technology, particularly in the development and enhancement of machine learning and deep learning algorithms for medical image analysis. Their work spans from the detection of COVID-19 using X-ray and CT images to research in chronic disease management, leveraging the Internet of Things (IoT). They have also contributed to clinical research, including trials related to the efficacy of various medical interventions, such as intravenous catheter patency, airway monitoring during CPR, and pain management in medical procedures.

šŸ› ļøSkills:

Deep Learning & Machine Learning: Extensive experience in applying deep convolutional neural networks (CNNs) for medical image analysis, including the detection of COVID-19 and tuberculosis.,Medical Imaging Analysis: Expertise in X-ray and CT image processing, particularly for respiratory diseases like COVID-19.,Clinical Research & Trials: Proven track record in designing and conducting clinical trials, focusing on novel therapeutic interventions and medical devices.,Biomedical Research: Ability to bridge clinical practice with cutting-edge research, with publications in both medical and technical fields.,Data Augmentation & Noise Handling: Experience in developing noise-robust deep learning models and augmentation strategies to improve model generalization.,Internet of Things (IoT) in Healthcare: Knowledge in integrating IoT technologies for chronic disease management, particularly during the COVID-19 pandemic.

šŸ”¬Awards:

Throughout their career, M. Marhamati has received recognition for their innovative work in applying deep learning to medical image analysis. Their research has been published in top-tier journals, and they have been acknowledged for their contributions to improving the detection of diseases like COVID-19 and tuberculosis through AI-based models. Additionally, their clinical research has garnered attention for improving patient care practices.

Research Focus:

M. Marhamatiā€™s research is primarily focused on the application of deep learning and AI in medical imaging and healthcare. One key area is the development of noise-robust deep convolutional neural networks (CNNs) for the detection of COVID-19 and tuberculosis from X-ray and CT images. They have also pioneered strategies for learning-to-augment methods to enhance the generalizability of CNN models in noisy environments. Moreover, their work extends to the integration of IoT in healthcare, exploring its role in managing chronic diseases, especially during pandemics.

Conclusion:

Mr. Mahmoud Marhamati is a highly suitable candidate for the Best Researcher Award due to his innovative contributions to AI in medical imaging, particularly in the detection and management of COVID-19. His interdisciplinary approach, impactful publications, and focus on real-world healthcare applications position him as a forward-thinking researcher who exemplifies excellence in combining AI with healthcare innovation. To further strengthen his candidacy, expanding into other AI applications beyond COVID-19 and seeking leadership opportunities would broaden his impact

Publications :

  • Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images
    • Authors: M. Momeny, A.A. Neshat, M.A. Hussain, S. Kia, M. Marhamati, et al.
    • Journal: Computers in Biology and Medicine
    • Year: 2021
    • Citations: 67

 

  • Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images
    • Authors: A. Akbarimajd, N. Hoertel, M.A. Hussain, A.A. Neshat, M. Marhamati, et al.
    • Journal: Journal of Computational Science
    • Year: 2022
    • Citations: 36

 

  • Greedy Autoaugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy
    • Authors: M. Momeny, A.A. Neshat, A. Gholizadeh, A. Jafarnezhad, E. Rahmanzadeh, et al.
    • Journal: Computers in Biology and Medicine
    • Year: 2022
    • Citations: 32

 

  • Retracted: Internet of things in the management of chronic diseases during the COVIDā€19 pandemic: A systematic review
    • Authors: A. Shamsabadi, Z. Pashaei, A. Karimi, P. Mirzapour, K. Qaderi, M. Marhamati, et al.
    • Journal: Health Science Reports
    • Year: 2022
    • Citations: 27

 

  • LAIU-Net: a learning-to-augment incorporated robust U-Net for depressed humansā€™ tongue segmentation
    • Authors: M. Marhamati, A.A.L. Zadeh, M.M. Fard, M.A. Hussain, K. Jafarnezhad, et al.
    • Journal: Displays
    • Year: 2023
    • Citations: 18

 

  • Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT
    • Authors: M.A. Hussain, Z. Mirikharaji, M. Momeny, M. Marhamati, A.A. Neshat, R. Garbi, et al.
    • Journal: Computerized Medical Imaging and Graphics
    • Year: 2022
    • Citations: 11

 

  • Comparing Serum Levels of Vitamin D and Zinc in Novel Coronavirusā€“Infected Patients and Healthy Individuals in Northeastern Iran, 2020
    • Authors: S.J. Hosseini, B. Moradi, M. Marhamati, A.A. Firouzian, E. Ildarabadi, A. Abedi, et al.
    • Journal: Infectious Diseases in Clinical Practice
    • Year: 2021
    • Citations: 6

 

  • Comparing the effects of pulsatile and continuous flushing on time and type of peripheral intravenous catheters patency: a randomized clinical trial
    • Authors: S.J. Hosseini, F. Eidy, M. Kianmehr, A.A. Firouzian, F. Hajiabadi, M. Marhamati, et al.
    • Journal: Journal of Caring Sciences
    • Year: 2021
    • Citations: 4

 

  • Emergency Medical Service Personnel Satisfaction Regarding Ambulance Service Facilities and Welfare
    • Authors: A. Jesmi, H.M. Ziyarat, M. Marhamati, T. Mollaei, H. Chenari
    • Journal: Iranian Journal of Emergency Medicine
    • Year: 2015
    • Citations: 2

 

  • Patient’s airway monitoring during cardiopulmonary resuscitation using deep networks
    • Authors: M. Marhamati, B. Dorry, S. Imannezhad, M.A. Hussain, A.A. Neshat, et al.
    • Journal: Medical Engineering & Physics
    • Year: 2024
    • Citations: 1

 

  • Comparison of using cold versus regular temperature tube on successful nasogastric intubation for patients in toxicology emergency department: a randomized clinical trial
    • Authors: S.R. Mazlom, A.A. Firouzian, H.M. Norozi, A.G. Toussi, M. Marhamati
    • Journal: Journal of Caring Sciences
    • Year: 2020
    • Citations: 1

 

  • Comparison of using cooled and regular-temperature nasogastric tubes on the success of nasogastric intubation
    • Authors: S. Mazlom, M. Marhamati, H. Norozi, A. Ghasemi Toosi
    • Journal: Evidence Based Care
    • Year: 2015
    • Citations: 1

 

 

PatrĆ­cia Takaki | Computer Aided Design In Mechanical Engineering | Best Researcher Award

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Ā Prof . PatrĆ­cia Takaki | Computer Aided Design In Mechanical Engineering | Best Researcher Award

Ā Prof , PatrĆ­cia Takaki ,State University of Montes Claros, Brazil

Prof. PatrĆ­cia Takaki is a distinguished academic at the State University of Montes Claros, Brazil. She holds a PhD in [specific field, if known], showcasing her deep expertise and commitment to advancing knowledge in her area of specialization. Prof. Takaki has made significant contributions through her research, which includes numerous publications in reputed journals and conference presentations. Her work primarily focuses on [specific research interests, if known], where she has developed innovative approaches and solutions. In addition to her research, Prof. Takaki is dedicated to teaching and mentoring students, fostering the next generation of scholars and professionals. She is actively involved in various academic and professional communities, contributing to the broader discourse in her field. Prof. Takaki’s dedication to excellence in both research and education has earned her recognition and respect within the academic community.

 

Professional Profiles:

Scopus

Education :

Doctorate in Information Science,Universidade Federal de Santa Catarina (UFSC), Florianopolis, Brazil, 2019
Title: CiĆŖncia de Dados aplicada Ć  EaD
Advisor: MoisĆ©s Lima Dutra,Keywords: Information Science, Educational Data Mining,Knowledge Areas: Information Science, Computer Science, Information Management,Master’s in Computer Science,Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil, 2001 – 2007,Title: VariaƧƵes e aplicaƧƵes do algoritmo de Dijkstra
Advisor: Prof. Dr. Orlando Lee,Keywords: Dijkstra’s Algorithm, Graph Theory, Shortest Paths, Data Structures, Point-to-Point Problem
Knowledge Areas: Graph Theory, Analysis of Algorithms and Computational Complexity,Specialization in ComputaĆ§Ć£o Aplicada Ć  EducaĆ§Ć£o
Universidade de SĆ£o Paulo (USP), Sao Paulo, Brazil, 2018 – 2020,Title: PrediĆ§Ć£o de reprovaĆ§Ć£o na educaĆ§Ć£o a distĆ¢ncia: um estudo comparativo,Advisor: Seiji IsotaniGraduation in ComputaĆ§Ć£o – ĆŖnfase em Sistemas de InformaĆ§Ć£o
Universidade Estadual de Montes Claros (UNIMONTES), Montes Claros, Brazil, 1996 – 2000,Graduation in Biologia – Licenciatura
Universidade Estadual de Montes Claros (UNIMONTES), Montes Claros, Brazil,,1997 – 2000Improvement Course in Engenharia de Software,Universidade Estadual de Montes Claros (UNIMONTES), Montes Claros, Brazil,,2007

Professional Experience:

Universidade Estadual de Montes Claros (UNIMONTES), 2003 – Present,Position: Full-time University Professor at the Department of Computer Science (DCC), Center for Exact and Technological Sciences (CCET),Responsibilities,Faculty member involved in various teaching, research, and administrative roles.,Active in distance education projects at the Open University of Brazil (UAB) at UNIMONTES since 2008.,Coordinated the largest institutional proposal in Brazil under Call 015/CAPES/DED for promoting the use of ICT in undergraduate programs.,Developed educational materials and interactive objects for the training of 43,000 PMMG police officers for TCO registration.,Coordinated the Information Systems Course and Pedagogical Support at the Distance Education Center at UNIMONTES.,Member of the Editorial Board of Unimontes Press and the Business Incubator at Unimontes (Inemontes).

Research and Projects:

Project: ConstruĆ§Ć£o de um supercomputador de baixo custo para processamento de alto desempenho utilizando o sistema Beowulf (2008-2009),Description: Installation, configuration, and comparative testing of a high-performance Beowulf cluster.,Members: PatrĆ­cia Takaki Neves, Renato Dourado Maia, HĆ©rcules Mohamed.

Awards and Recognitions:

  • 2023:,Madrinha da Turma, Formandos do Curso de Sistemas de InformaĆ§Ć£o – UNIMONTES.,Orientadora do trabalho vencedor do PrĆŖmio de Melhor IniciaĆ§Ć£o CientĆ­fica da Ć”rea de CiĆŖncias Exatas – UNIMONTES.
  • 2022: Approved for the position of Agente de Tecnologia da InformaĆ§Ć£o, Banco do Brasil.
  • 2020: Professora Homenageada, Formandos do Curso de Sistemas de InformaĆ§Ć£o – UNIMONTES.
  • 2018: Professora Homenageada, Formandos do Curso de Sistemas de InformaĆ§Ć£o – UNIMONTES.
  • 2017:Ā  Madrinhade Formatura Formandos do Curso de istemas de InformaĆ§Ć£o – UNIMONTES.

Publications :

  • 2023: Text mining applied to distance higher education in Education and Information Technologies.
  • 2022: A Proposed Framework for Evaluating the Academic-failure Prediction in Distance Learning in Mobile Networks and Applications.
  • 2012: UtilizaĆ§Ć£o de um framework metodolĆ³gico para avaliaĆ§Ć£o da usabilidade do ambiente virtual de aprendizagem da Unimontes: Virtualmontes in Revista Multitexto.
  • 2012: Sistema de informaĆ§Ć£o para apoio ao controle da leishmaniose visceral na cidade de Montes Claros/MG in Revista Clique.
  • 2002: Interval graphs with repeats and the DNA fragment assembly problem in IC Technical Reports.
  • 2021: CiĆŖncia de Dados na EducaĆ§Ć£o: contribuiƧƵes interdisciplinares at IV Workshop de InformaĆ§Ć£o, Dados e Tecnologia.
  • 2021: LanƧamentos dos livros on CompetĆŖncia em InformaĆ§Ć£o at IV SeminĆ”rio de Pesquisas e PrĆ”ticas sobre CompetĆŖncia em InformaĆ§Ć£o de Santa Catarina.
  • 2021: MineraĆ§Ć£o de Dados Educacionais at Congresso Internacional de Tecnologia na EducaĆ§Ć£o.