Harry Moongela | Computer Science | Young Scientist Award

Posted on by Academic achievements

Dr. Harry Moongela | Computer Science | Young Scientist Award

Wits University, South Africa

Harry Moongela is a skilled IT professional with a strong background in Information Technology, including teaching, research, and development roles. He currently serves as a Postdoctoral Researcher at the University of Pretoria in South Africa, where he focuses on Artificial Intelligence and Critical Thinking research projects. Fluent in English and with basic knowledge of German, Harry has a rich academic and professional background that spans over a decade in various IT roles, ranging from academic positions to industry-specific consultancy.

Profile

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Education🎓

Harry holds a PhD in Information Technology from the University of Pretoria (2022), following a Master’s Degree in Information Systems from Rhodes University (2017). His undergraduate studies in Computer Science & Information Technology were completed at the University of Namibia in 2013. Additionally, Harry holds several certifications, including Cisco CCNA and a Certificate in Basic Telkom Network from Huawei University, providing him with a broad technical skill set.

Experienceđź’Ľ

Harry’s professional career began with roles such as a Web Developer and IT Technician. He has since advanced to academic roles, serving as a Lecturer, Course Coordinator, and Researcher at the University of Pretoria. He has also held various IT consultancy and development positions, including at Derivco Pty Ltd and Kanuga Auto Parts SA. Harry has extensive experience in IT teaching, course design, software development, and system administration.

Research Interests🔬

Harry’s research interests lie in the intersection of Artificial Intelligence, Critical Thinking, and Information Technology. His work includes publishing academic research in AI applications, computer networks, and the integration of innovative technologies in IT systems. His goal is to continue advancing research in AI to solve real-world problems and improve educational systems in IT.

Awards🏆

Harry has received multiple accolades throughout his academic journey, including being named Best IT Student at the University of Namibia (2010-2013), Best Postgraduate Student at Rhodes University (2017), and being a member of the prestigious Future Africa Futures Literacy Masterclass (2023). These awards reflect his dedication to excellence in both his studies and professional life.

Publicationsđź“š

Harry has contributed to various conference and journal publications in the field of Information Technology. Some of his notable works include research on AI in education, network security, and critical thinking in IT systems.
For detailed reading, please refer to the following articles:

A framework for using social media for organisational learning: An empirical study of South African companies”

  • Authors: Moongela, H., Hattingh, M.
  • Journal: African Journal of Science, Technology, Innovation and Development
  • Year: 2024
  • Volume: 16
  • Issue: 6
  • Pages: 761–773
  • Citations: 0

“Healthcare Supply Chain Efficacy as a Mechanism to Contain Pandemic Flare-Ups: A South Africa Case Study”

  • Authors: Maramba, G., Smuts, H., Hattingh, M., Mawela, T., Enakrire, R.
  • Journal: International Journal of Information Systems and Supply Chain Management
  • Year: 2023
  • Volume: 17
  • Issue: 1
  • Article ID: 333713
  • Citations: 3

“Perceptions of social media on students’ academic engagement in tertiary education”

  • Authors: Moongela, H., McNeill, J.
  • Conference: ACM International Conference Proceeding Series
  • Year: 2017
  • Part: F130806
  • Article ID: a23
  • Citations: 2

Conclusion🚀

Harry Moongela’s combination of academic excellence, significant research contributions, leadership in both research and teaching, and dedication to professional development make him an outstanding candidate for the Best Researcher Award. His wide-ranging expertise in information technology and commitment to advancing knowledge through research and teaching solidify his qualifications for this prestigious recognition.

Rashmi S | Machine Learning Techniques | Best Researcher Award

Posted on by Academic achievements

Mrs. Rashmi S | Machine Learning Techniques | Best Researcher Award

Rashmi S – Machine Learning Techniques | Senior Research Fellow at JSS Science and Technology University, India

Rashmi S. is an accomplished Ph.D. research scholar specializing in Computer Vision and Machine Intelligence. Her academic focus is particularly on medical image analysis, with a concentration on radiographic image annotation using AI and deep learning techniques. With approximately five years of experience in the tech industry as a Core Java Developer, Rashmi brings a unique blend of software development expertise and advanced research skills. She is currently working at the Pattern Recognition & Image Processing Lab at JSS Science and Technology University, Mysuru. Rashmi is driven by the ambition to enhance healthcare systems through innovative AI solutions, and her research contributions aim to create more accurate, automated systems for interpreting medical imagery.

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Education

Rashmi S. completed her Bachelor of Engineering (B.E.) in Computer Science and Engineering from SJCE, Mysore, graduating with a CGPA of 9.05. She then pursued her Master’s degree in Computer Engineering (M.Tech) from the same institution, achieving an outstanding CGPA of 9.77. Currently, she is pursuing her Ph.D. in Computer Science and Engineering at JSS S&TU, where she is expected to submit her thesis in September 2024. Her academic journey has been marked by a strong commitment to research excellence, particularly in Machine Learning and Deep Learning, both of which she applies in her medical image analysis research.

Experience

Rashmi S. has held various roles in both academic and industry settings, which have enriched her research and technical skills. She began her career in software engineering, working with Cisco Video Technology in Bengaluru, where she was involved in the development of Java-based software for Set-Top Boxes. She later moved on to Oracle India Pvt. Ltd. as an Application Engineer, working on software maintenance and the development of Oracle Projects Fusion, a project management tool. Rashmi’s academic career includes positions as a Junior Research Fellow and Senior Research Fellow at JSS Science and Technology University, where she currently conducts her doctoral research. Her professional journey in both the software industry and academia gives her a unique edge in developing and implementing cutting-edge research in healthcare.

Research Interests

Rashmi S. is primarily focused on Machine Learning, Deep Learning, and Image Processing, especially in the context of medical image analysis. Her research interests revolve around improving diagnostic tools through AI-powered systems. Specifically, her work addresses cephalometric landmark annotation in radiographs using both traditional machine learning algorithms and deep learning techniques. Rashmi has explored applications of EEG signal processing and computer vision in healthcare, striving to develop solutions that can automate the annotation of medical images for more accurate diagnoses. Her research aims to bridge the gap between artificial intelligence and clinical practices, potentially revolutionizing medical imaging and diagnostic procedures.

Awards

Rashmi S. has received several prestigious awards throughout her academic and professional career. She was awarded the UGC-NET Junior Research Fellowship in November 2021, which has enabled her to pursue her doctoral research in depth. She was also recognized with the Senior Research Fellowship by the University Grants Commission in February 2024. Additionally, Rashmi has been the recipient of several scholarships, including the MHRD & GATE Scholarships during her undergraduate and postgraduate studies. Her commitment to research excellence has also earned her multiple accolades for her academic performance, including being recognized for her outstanding contributions to machine learning in the medical field.

Publications

“Cephalometric Skeletal Structure Classification Using Convolutional Neural Networks and Heatmap Regression“, co-authored with P. Murthy, V. Ashok, and S. Srinath, published in SN Computer Science (2022). This study leverages convolutional neural networks (CNNs) and heatmap regression for advanced skeletal structure classification in cephalometric radiographs, with a focus on enhancing the accuracy of diagnostic tools in orthodontics.

“Extended Template Matching Method for Region of Interest Extraction in Cephalometric Landmarks Annotation“, co-authored with S. Srinath, R. Rakshitha, and B.V. Poornima, presented at the 2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical… This paper introduces an extended template matching method aimed at improving the extraction of regions of interest (ROIs) in cephalometric image annotation, a crucial step for automatic landmark detection.

“Lateral Cephalometric Landmark Annotation Using Histogram Oriented Gradients Extracted from Region of Interest Patches“, co-authored with S. Srinath, K. Patil, P.S. Murthy, and S. Deshmukh, published in Journal of Maxillofacial and Oral Surgery (2023). This research presents a novel approach for lateral cephalometric landmark annotation by extracting histogram-oriented gradients from ROIs, advancing the methods for more precise orthodontic assessments.

“A Novel Method for Cephalometric Landmark Regression Using Convolutional Neural Networks and Local Binary Pattern“, co-authored with V. Ashok, presented at the 5th International Conference on Computer Vision and Image Processing (2021). This paper explores a novel technique for landmark regression in cephalometric images using a combination of CNNs and local binary patterns, enhancing the automation of cephalometric analysis.

“Landmark Annotation Through Feature Combinations: A Comparative Study on Cephalometric Images with In-depth Analysis of Model’s Explainability“, co-authored with S. Srinath, S. Murthy, and S. Deshmukh, published in Dentomaxillofacial Radiology (2024). This comparative study examines various feature combinations for landmark annotation and provides an explainability analysis of the models used, aiming to make machine learning-based medical imaging more transparent and understandable.

“Recognition of Indian Sign Language Alphanumeric Gestures Based on Global Features“, co-authored with B.V. Poornima, S. Srinath, and R. Rakshitha, presented at the 2023 IEEE International Conference on Distributed Computing, VLSI… This paper investigates the use of global features for recognizing Indian Sign Language gestures, contributing to the development of gesture recognition systems in communication technologies.

“ISL2022: A Novel Dataset Creation on Indian Sign Language“, co-authored with R. Rakshitha, S. Srinath, and S. Rashmi, presented at the 2023 10th International Conference on Signal Processing and Integrated…. This paper presents the creation of the ISL2022 dataset, a significant step toward improving machine learning models for Indian Sign Language recognition, highlighting the importance of datasets in advancing language recognition research.

“Cephalometric Landmark Annotation Using Transfer Learning: Detectron2 and YOLOv8 Baselines on a Diverse Cephalometric Image Dataset“, co-authored with S. Srinath, S. Deshmukh, S. Prashanth, and K. Patil, published in Computers in Biology and Medicine (2024). This work leverages transfer learning techniques, using Detectron2 and YOLOv8 models, to annotate cephalometric landmarks on a diverse dataset, pushing the envelope for automated medical image analysis.

“Crack SAM: Enhancing Crack Detection Utilizing Foundation Models and Detectron2 Architecture“, co-authored with R. Rakshitha, S. Srinath, N. Vinay Kumar, and B.V. Poornima, published in Journal of Infrastructure Preservation and Resilience (2024). This research explores advanced crack detection techniques, using foundation models and Detectron2, to improve the detection of cracks in infrastructure.

“Enhancing Crack Pixel Segmentation: Comparative Assessment of Feature Combinations and Model Interpretability”, co-authored with R. Rakshitha, S. Srinath, N. Vinay Kumar, and B.V. Poornima, published in Innovative Infrastructure Solutions (2024). This paper focuses on crack pixel segmentation, offering insights into the comparative performance of various feature combinations and the interpretability of machine learning models used in infrastructure monitoring.

Conclusion

Rashmi S. has demonstrated exceptional skill and dedication to the field of Computer Vision and Machine Intelligence. With her substantial industry experience and strong academic background, Rashmi has contributed significantly to AI research in healthcare. Her work has the potential to revolutionize medical image analysis, offering more efficient and accurate diagnostic tools. Through her awards, publications, and ongoing research, Rashmi S. stands as an exemplary candidate for the Best Researcher Award, with the promise of continuing to make groundbreaking advancements in her field.

Zhiqiang He | Artificial Intelligence | Best Researcher Award

Posted on by Academic achievements

Dr. Zhiqiang He | Artificial Intelligence | Best Researcher Award 

Ph.D. at The university of Electro-Communications, China

Zhiqiang He is an emerging researcher specializing in reinforcement learning and artificial intelligence (AI), with a focus on developing and optimizing control algorithms for complex systems. He has made significant contributions to both academic research and industrial applications, demonstrating expertise in designing innovative AI solutions for real-world problems. His educational background in control science and engineering, combined with practical experiences at leading tech companies, has shaped his career and led to several impactful publications in renowned journals. Zhiqiang’s accomplishments, recognized through various academic awards and industry achievements, make him a strong candidate for the “Best Researcher Award.”

Profile

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Education

Zhiqiang pursued his Master of Science in Control Science and Engineering at Northeastern University (NEU), Shenyang, China, from September 2019 to June 2022, where he maintained a commendable GPA of 3.29/4. During his master’s program, he specialized in the development of reinforcement learning algorithms, which formed the cornerstone of his research. Prior to this, he earned his Bachelor of Science in Automation at East China Jiaotong University (ECJTU), Nanchang, China, from September 2015 to June 2019, with a GPA of 3.42/4. His undergraduate studies laid a strong foundation in automation and control systems, providing the technical skills and knowledge that fueled his passion for AI and intelligent decision-making.

Experience

Throughout his academic journey, Zhiqiang actively engaged in research and industry roles that enriched his experience in the field of AI. He served as a team leader at the Institute of Deep Learning and Advanced Intelligent Decision-Making at NEU, where he worked on the development of reinforcement learning algorithms. Leading projects from September 2020 to June 2021, he conducted research on model-based reinforcement learning, optimized algorithm performance, and supervised students in their projects. Additionally, his early experience as a team leader at the Jiangxi Province Advanced Control and Key Optimization Laboratory involved applying reinforcement learning to control problems from 2016 to 2019, where he gained hands-on skills in analyzing system behaviors and establishing Markov Decision Process (MDP) models.

In the industry, Zhiqiang took on roles that deepened his technical expertise. He was an intern at Baidu, Beijing, China, where he pioneered the development of the Expert Data-Assisted Multi-Agent Proximal Policy Optimization (EDA-MAPPO) algorithm, an innovative approach to multi-agent cooperative adversarial AI. Later, as a reinforcement learning algorithms engineer at InspirAI in Hangzhou, he led the development of AI strategies for popular card games, showcasing his ability to apply AI solutions to commercial projects and enhance algorithmic performance.

Research Interest

Zhiqiang’s research interests are centered on reinforcement learning, AI, and control systems. He focuses on designing algorithms that improve the efficiency and accuracy of AI models in decision-making tasks. His work involves exploring new methods for multi-agent reinforcement learning, optimizing algorithms for real-time applications, and addressing challenges in intelligent control. By bridging theoretical research with practical applications, he aims to push the boundaries of AI, making it more adaptable and applicable to various industries. His dedication to advancing reinforcement learning techniques aligns with the future trajectory of AI research, where automation and intelligent decision-making are key drivers of innovation.

Awards

Zhiqiang has received recognition for his academic excellence and research contributions throughout his career. He was honored as an “Outstanding Graduate” by East China Jiaotong University in 2019, acknowledging his academic achievements and leadership potential. In addition, he secured the Third Prize in the 15th “Challenge Cup” Jiangxi Division in 2017 and the Second Prize in the International Mathematical Modeling Competition for American College Students in 2018, demonstrating his problem-solving skills and competitive spirit. His active engagement in professional development is further highlighted by his certifications in network technology and programming languages, which add to his multidisciplinary skill set.

Publications

He Z, Qiu W, Zhao W, et al. Understanding World Models through Multi-Step Pruning Policy via Reinforcement Learning. Information Sciences, 2024: 121361. – Cited by 32 articles.

Chen P, He Z, Chen C, et al. Control strategy of speed servo systems based on deep reinforcement learning. Algorithms, 2018, 11(5): 65. – Cited by 15 articles.

Wang J, Zhang L, He Z, et al. Erlang planning network: An iterative model-based reinforcement learning with multi-perspective. Pattern Recognition, 2022, 128: 108668. – Cited by 27 articles.

Zhang L, He Z, Zhao Y, et al. Reinforcement Learning-based Control of Robotic Manipulators. Journal of Robotics, 2023, 12(3): 112-121. – Cited by 19 articles.

He Z, Zhao W, Zhang L, et al. Multi-Agent Deep Reinforcement Learning in Dynamic Environments. Artificial Intelligence Review, 2022, 55(2): 456-472. – Cited by 24 articles.

Chen C, He Z, Qiu W, et al. Optimal Control for Nonlinear Systems Using Reinforcement Learning. Control Theory and Applications, 2021, 59(4): 553-566. – Cited by 18 articles.

Conclusion

Zhiqiang He’s contributions to AI and reinforcement learning, coupled with his practical experience and research output, position him as a promising researcher in the field. His work not only advances the academic understanding of intelligent control but also finds applications in industry, where AI solutions are critical to technological development. By consistently pushing for excellence in his projects, he demonstrates qualities that make him a deserving candidate for the “Best Researcher Award.” His trajectory reflects a commitment to innovation, making him an asset to the research community and a potential leader in future AI advancements.

Preeti Sharma | Deep Learning | Women Researcher Award

Posted on by Academic achievements

Mrs . Preeti Sharma | Deep Learning | Women Researcher Award 

Assistant Professor , DIT University, Dehradun, Uttrakhand , India

Preeti Sharma is a dedicated researcher and educator currently pursuing a Ph.D. in Computer Science and Engineering at the University of Petroleum and Energy Studies, Dehradun. With a distinguished academic background including gold medals and high honors in her MTech and MCA degrees, Preeti has demonstrated excellence in her field. She is passionate about advancing the field of artificial intelligence and machine learning, focusing on generative adversarial networks (GANs) and deepfake detection.

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Education 

Preeti Sharma is pursuing a Ph.D. in Computer Science and Engineering at the University of Petroleum and Energy Studies, Dehradun, with her thesis submitted. She holds an MTech in Computer Science and Engineering from Uttarakhand Technical University, where she graduated as a gold medalist with an impressive 85%. Preeti completed her M.C.A. from M.D.U. (Campus), Rohtak, with a strong academic record of 82%.

Experience 

Preeti Sharma currently serves as a Junior Research Fellow and Teaching Assistant at the University of Petroleum and Energy Studies, Dehradun, where she has been contributing since April 2021. Prior to this, she was a Non-Teaching Staff member at the same university from September 2015 to March 2021. She also gained valuable experience as a Guest Lecturer at Arihant Institute of Technology, Haldwani, and an intern at the National Informatics Center (NIC).

Research Interests 

Preeti Sharma’s research interests include the application of Generative Adversarial Networks (GANs) in image and deepfake detection, robust CNN models, and advancements in digital forensics. Her work explores innovative methods for deepfake detection and image forgery using GAN-based models, contributing significantly to the field of multimedia tools and applications.

Awards 

Preeti Sharma has been recognized for her exceptional research and presentations. She received a certification for the best oral presentation at the International Young Researcher Conclave (IYRC-2024). Her paper on generative adversarial networks won first prize in the Research Conclave IYRC 2024 at UPES.

Publications 

  • Sharma, P., Kumar, M., Sharma, H.K. et al. Generative adversarial networks (GANs): Introduction, Taxonomy, Variants, Limitations, and Applications. Multimedia Tools and Applications (2024). Link
  • Sharma, P., Kumar, M., & Sharma, H.K. Robust GAN-Based CNN Model as Generative AI Application for Deepfake Detection, EAI Endorsed Trans IoT, vol. 10 (2024).
  • Sharma, P., Kumar, M., & Sharma, H.K. A generalized novel image forgery detection method using a generative adversarial network. Multimedia Tools and Applications (2023). Link
  • Sharma, P., Kumar, M., & Sharma, H.K. A GAN-based model of deepfake detection in social media. Procedia Computer Science, 218, 2153-2162 (2023).
  • Sharma, P., Kumar, M., & Sharma, H.K. Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation. Multimedia Tools and Applications, 82(12), 18117-18150 (2023).
  • Sharma, P., Kumar, M., & Sharma, H.K. A Guide to Digital Forensic: Theoretical to Software-Based Investigations. Perspectives on Ethical Hacking and Penetration Testing, IGI Global (2023). Link
  • Sharma, P., Kumar, M., & Sharma, H.K. CNN-based Facial Expression Recognition System Using Deep Learning Approach. Conference on Computational Intelligence and Information Retrieval CIIR (2021).
  • Sharma, P. Real Time Tracking System for Object Tracking using the Internet of Things (IoT). Conference on Computational Intelligence and Information Retrieval CIIR (2021).
  • Sharma, P. Leach and Improved Leach: A Review. International Journal of Advanced Research in Computer Science, Vol 10 (2019).

Conclusion

Preeti Sharma’s profile shows a strong foundation in research and technical expertise, with notable contributions to GANs and deepfake detection. Her academic achievements, innovative patents, and recognition in the field underscore her qualifications. To strengthen her candidacy for the Research for Women Researcher Award, she could emphasize the broader impact of her research and highlight her leadership or mentorship roles. Overall, her qualifications and achievements make her a strong contender for the award.

Natasha Christabelle Santosa | Artificial Intelligence | Best Researcher Award

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Mrs Natasha Christabelle Santosa | Artificial Intelligence | Best Researcher Award

Mrs Natasha Christabelle Santosa , Tokyo Institute of Technology , Japan

Natasha Christabelle Santosa is a dedicated artificial intelligence researcher with a passion for advancing machine learning technologies. Fluent in four languages, she has honed her expertise over two years of part-time work and PhD studies. Natasha is currently a research assistant at Tokyo Institute of Technology, where she investigates dynamic ontology applications in scientific paper recommendations. Her experience spans diverse areas including natural language processing, information retrieval, and computer vision. She is actively seeking opportunities in Tokyo, preferably in remote or hybrid roles, to leverage her skills in a global or English-Japanese environment.

Publication Profile

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Strengths for the Award

  1. Diverse Expertise: Natasha has a strong background in AI, machine learning, and data analysis, covering the full machine learning cycle from data construction to model deployment. Her experience spans various domains, including information retrieval, natural language processing, and computer vision.
  2. Advanced Research: Her PhD research at Tokyo Institute of Technology on dynamic ontology for scientific paper recommendations shows a commitment to advancing AI methodologies and practical applications. Her work on graph neural networks for paper recommendations, published in reputable journals, highlights her ability to tackle complex problems in cutting-edge research.
  3. Multilingual Capabilities: Being quadrilingual (Indonesian, Javanese, English, and intermediate Japanese) enhances her ability to collaborate in diverse environments, particularly beneficial in global research settings.
  4. Recognition and Funding: Receiving the prestigious Japanese government MEXT scholarship for both master’s and PhD studies underscores her exceptional academic capabilities and potential.

Areas for Improvement

  1. Broader Impact: While her research is advanced, expanding her work to include more interdisciplinary applications or collaborations could broaden its impact and applicability.
  2. Professional Experience: Gaining more industry experience or leading larger-scale projects could further enhance her practical skills and visibility in the field.
  3. Networking and Outreach: Increasing her presence in international conferences and workshops could provide additional opportunities for collaboration and recognition.

Education

Natasha is pursuing a PhD in Artificial Intelligence at Tokyo Institute of Technology, with an expected completion in September 2024. Her research focuses on scientific paper recommendation using dynamic ontology and neural networks. She holds a Master’s in Artificial Intelligence from the same institution, with a thesis on ontology-based personalized recommendation systems. Her academic journey began with a Bachelor’s in Computer Science from Gadjah Mada University, where she graduated with honors, focusing on adaptive neuro-fuzzy inference systems for cancer diagnosis.

Experience

Natasha’s professional experience includes part-time research roles at Tokyo Institute of Technology and the Advanced Institute of Science and Technology. At Tokyo Tech, she explores dynamic ontology for scientific paper recommendations. Previously, at AIST, she worked on using graph neural networks for end-to-end paper recommendations, contributing to a preprint publication. Her roles involved extensive research and practical applications in machine learning, enhancing her expertise across various domains including NLP and computer vision.

Research Focus

Natasha’s research concentrates on enhancing scientific paper recommendation systems through dynamic ontology and neural network approaches. Her PhD work involves developing advanced methods to assist in paper writing, while her earlier research explored ontology-based personalized recommendations. She has applied her skills in machine learning, data analysis, and graph neural networks to improve information retrieval and recommendation systems, aiming to advance the field of AI with innovative solutions.

Publications Top Notes

đź“„ N. C. Santosa, X. Liu, H. Han, J. Miyazaki. 2023. S3PaR: Section-Based Sequential Scientific Paper Recommendation for Paper Writing Assistance. In Knowledge Based Systems [in press]

đź“„ N. C. Santosa, J. Miyazaki, H. Han. 2021. Automating Computer Science Ontology Extension with Classification Techniques. In IEEE Access, Vol. 9, pp.161815-161833.

đź“„ N. C. Santosa, J. Miyazaki, H. Han. 2021. Flat vs. Hierarchical: Classification Approach for Automatic Ontology Extension. In Proceedings of Data Engineering and Information Management (DEIM).

Conclusion

Natasha Christabelle Santosa is a highly qualified candidate for the Best Researcher Award due to her extensive expertise in AI, strong research contributions, and multilingual capabilities. Her innovative work on scientific paper recommendations and advanced machine learning techniques demonstrates her potential to make significant contributions to the field. By addressing areas for improvement, such as expanding her interdisciplinary impact and gaining further industry experience, she can enhance her profile and increase her chances of receiving the award.

Jie Li | Artificial Intelligence | Best Researcher Award

Posted on by Academic achievements

Assoc Prof Dr. Jie Li | Artificial Intelligence | Best Researcher Award 

Assoc Prof Dr. Jie Li, Chongqing University of Science & Technology, China

Profile

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Dr. Jie Li is an Associate Professor at the School of Computer Science and Engineering, Chongqing University of Science and Technology. With a PhD from Chongqing University (2011), she has held roles as an assistant researcher at the Chongqing Green Intelligent Technology Research Institute and a post-doctoral fellow at Chongqing Qingshan Industrial Company Limited. Her research has led to numerous patents and influential publications in top journals like IEEE Transactions. Dr. Li has also been involved in significant university-enterprise cooperative projects, highlighting her leadership and innovation in artificial intelligence and machine learning.

Strengths for the Award:

  1. Significant Research Contributions: Dr. Jie Li has made substantial contributions to artificial intelligence, machine learning, and fault diagnosis. Her work, published in top-tier journals like IEEE Transactions, demonstrates high-impact research in these fields.
  2. Extensive Patent Portfolio: With over 40 invention patents applied for and 18 authorized, Dr. Li’s innovative approaches are translating into practical technologies and solutions, showcasing her role as a leading inventor and researcher.
  3. Leadership in Projects: She has successfully led 16 national and provincial research projects and 7 enterprise-level projects. Her leadership in university-enterprise cooperative projects further underscores her ability to bridge academia and industry effectively.
  4. Academic and Industry Impact: Her book “Artificial Intelligence” has received industry praise, and her publications, totaling over 40 papers, reflect a broad and impactful research portfolio.

Areas for Improvement:

  1. Broader Citation Metrics: While Dr. Li has a respectable citation count, expanding her citation index could enhance her visibility and recognition in the global research community. Increasing collaboration with international researchers might help achieve this.
  2. Research Dissemination: Although Dr. Li has published extensively, further dissemination through high-impact conferences and workshops could elevate her work’s visibility and influence, potentially leading to more collaborative opportunities.
  3. Diverse Research Areas: Diversifying her research focus beyond her core areas could open new avenues for innovation and impact. Exploring emerging trends in AI and machine learning might strengthen her research portfolio.

Education🎓

Dr. Jie Li completed her PhD in Computer Science at Chongqing University in December 2011. Her doctoral studies laid the foundation for her extensive research in artificial intelligence and machine learning. During her academic career, she has broadened her expertise through postdoctoral research and academic visits to prestigious institutions like Tsinghua University and the University of Rhode Island. These experiences have enriched her academic perspective and research capabilities, significantly contributing to her professional achievements.

Experienceđź’Ľ

Dr. Jie Li began her career as an assistant researcher at the Chongqing Green Intelligent Technology Research Institute from February 2012 to April 2014. She later worked as a post-doctoral fellow at Chongqing Qingshan Industrial Company Limited from April 2017 to January 2020. Her academic tenure at Chongqing University of Science and Technology includes significant roles, such as being rated as an associate professor in September 2019. Additionally, she has led numerous national and provincial research projects and has been actively involved in university-enterprise cooperation initiatives.

Research Focus🔬

Dr. Jie Li’s research encompasses Deep Learning, Machine Learning, Fault Diagnosis, and Artificial Intelligence. Her work focuses on advancing these fields through innovative algorithms and practical applications. She has led and participated in several high-impact projects funded by national and provincial bodies. Her research has significantly contributed to the development of new technologies and solutions, reflected in her extensive patent portfolio and publications in prestigious journals such as IEEE Transactions.

Publications Top Notes

Polyacrylonitrile-based 3D N-rich activated porous carbon synergized with Co-doped MoS2 for promoted electrocatalytic hydrogen evolution (Huang, Z., Li, J., Guo, S., Zeng, J., Yuan, F., Separation and Purification Technology, 2025, 354, 129011) đź“„

In-situ construction of nano-multifunctional interlayer to obtain intimate Li/garnet interface for dendrite-free all solid-state battery (Yu, S., Gong, Z., Gao, M., Li, Y., Chen, Y., Journal of Materials Science and Technology, 2025, 206, pp. 248–256) 📄

Advanced cathode materials for metal ion hybrid capacitors: Structure and mechanisms (Li, J., Liu, C., Momen, R., Zou, G., Ji, X., Coordination Chemistry Reviews, 2024, 517, 216018) đź“–

Unraveling the delithiation mechanism of air-stabilized fluorinated lithium iron oxide pre-lithiation material (Wen, N., Li, J., Zhu, B., Guo, J., Zhang, Z., Chemical Engineering Journal, 2024, 497, 154536) đź“„

Dual ion regulation enables High-Coulombic-efficiency lithium metal batteries (Huang, X., Wang, M., Zhou, Y., Li, J., Lai, Y., Nano Energy, 2024, 129, 110031) đź“„

In-Situ Construction of Electronically Insulating and Air-Stable Ionic Conductor Layer on Electrolyte Surface and Grain Boundary to Enable High-Performance Garnet-Type Solid-State Batteries (Zhou, X., Liu, J., Ouyang, Z., Li, J., Jiang, L., Small, 2024, 20(34), 2402086) đź“„

Enhancing the Efficient Utilization of Li2S in Lithium-Sulfur Batteries via Functional Additive Diethyldiselenide (Li, Z., Wang, M., Yang, J., Lai, Y., Li, J., Energy and Fuels, 2024, 38(16), pp. 15762–15770) 📄

Emerging polyoxometalate clusters-based redox flow batteries: Performance metrics, application prospects, and development strategies (Han, M., Sun, W., Hu, W., Zhang, C., Li, J., Energy Storage Materials, 2024, 71, 103576) đź“–

Conductivity behavior of Na5YSi4O12 and its typical structural analogues by solution-assisted solid-state reaction for solid-state sodium battery (Liu, L., Xu, Y., Zhou, X., Guo, X., Jiang, Y., Journal of Solid State Chemistry, 2024, 336, 124781) đź“„

Preparation of Hard-Soft Carbon via Co-Carbonization for the Enhanced Plateau Capacity of Sodium-Ion Batteries (Li, J., Zheng, H., Du, B., Li, D., Chen, Y., Energy and Fuels, 2024, 38(14), pp. 13398–13406) 📄

Conclusion:

Dr. Jie Li’s exceptional achievements in artificial intelligence and machine learning, marked by a robust patent portfolio, significant publications, and leadership in high-impact projects, position her as a strong candidate for the Best Researcher Award. Her innovative contributions and ability to lead and execute complex research projects highlight her outstanding capabilities and potential for furthering advancements in her field. Addressing the areas for improvement could further enhance her already impressive research profile and global impact.

AHMADOU MUSTAPHA FONTON MOFFO | Machine Learning | Best Researcher Award

Posted on by Academic achievements

Dr. AHMADOU MUSTAPHA FONTON MOFFO | Machines Learning | Best Researcher Award 

Economist | UNESCO | Canada

Short Bio 🌟

Ahmadou Mustapha FONTON is a distinguished economist based in Montréal, Canada, with a Ph.D. in Economics from the Université du Québec à Montréal. Specializing in macroeconomics, financial economics, and applied econometrics, FONTON excels in leveraging machine learning and big data to inform policy decisions and develop robust risk models. His extensive professional experience includes roles at UNESCO and the Ministry of Scientific Research in Cameroon, reflecting his dedication to advancing economic research and policy.

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Strengths for the Award

  1. Extensive Expertise and Experience: Dr. Fonton brings a wealth of experience in both academic and non-academic settings. His role as an economist at UNESCO and previous positions demonstrate a solid track record in applied econometrics, macroeconomics, and financial economics. His contributions to data collection, statistical analysis, and policy evaluation underscore his broad expertise.
  2. Advanced Technical Skills: His proficiency with a diverse set of software tools (PYTHON, R, MATLAB, STATA, SPSS, etc.) and techniques, including machine learning and big data analysis, highlights his technical acumen. This expertise is critical for modern economic research, especially in forecasting and analyzing complex economic phenomena.
  3. Strong Research Output: Dr. Fonton’s publication record, including his recent work on machine learning in stress testing US banks, demonstrates his ability to contribute valuable insights to the field of economics. His working papers and conference presentations further reflect his active engagement in cutting-edge research.
  4. Academic and Teaching Experience: His roles as a research assistant and instructor at Université du Québec à Montréal and Institut Siantou Superieur show a strong background in teaching and mentoring. This experience is important for fostering new talent and advancing the field through education.
  5. International Perspective and Multilingual Skills: Dr. Fonton’s international experience, combined with his multilingual abilities (English, French, and Bamoun), provides him with a unique perspective on global economic issues. This is especially relevant in the context of UNESCO’s work and cross-border research collaborations.
  6. Policy Impact: His involvement in projects that influence policy, such as his work on forecasting time series for UNESCO and his previous consulting roles, indicates a strong capacity for translating research into practical recommendations. This aligns well with the goals of the Research for Best Researcher Award, which often emphasizes practical impacts of research.

Areas for Improvement

  1. Broader Publication Record: While Dr. Fonton has a notable publication in the International Review of Financial Analysis and several working papers, increasing his publication count in high-impact journals could strengthen his profile further. Broadening his research topics or collaborating on interdisciplinary studies might also enhance his visibility in different research circles.
  2. Increased Collaboration and Networking: Engaging in more collaborative research projects and expanding his network within the global research community could open up additional opportunities for impactful research and visibility. This could involve co-authoring papers with researchers from diverse backgrounds or participating in more international conferences.
  3. Focus on Long-term Projects: While Dr. Fonton’s work on various projects is commendable, focusing on longer-term research initiatives might yield more significant and sustained contributions to the field. Developing comprehensive research programs or longitudinal studies could be beneficial.
  4. Enhanced Public Engagement: Increasing efforts to communicate his research findings to the public and policymakers could amplify the impact of his work. This might include writing policy briefs, engaging in media outreach, or participating in public lectures and forums.

Education 🎓

  • 2023: Ph.D. in Economics, UniversitĂ© du QuĂ©bec Ă  MontrĂ©al, Canada
  • 2010: M.Sc. in Economics, UniversitĂ© Catholique de Louvain, Belgium
  • 2005: B.Sc. in Statistics, ISSEA YaoundĂ©, Cameroon
  • 2000: Certificate in Mathematics, Cameroon

Experience đź’Ľ

2023–Present: Economist-Statistician, UNESCO Institute of Statistics, Canada
Leading data collection and processing for Science and Culture Annual Surveys, developing new survey instruments, and producing statistical reports.

2012–2017: Coordinator of Statistical Projects, Ministry of Scientific Research, Cameroon
Directed national statistical surveys, analyzed data on Research and Development, and assisted in organizing expert meetings and seminars.

2009–2012: Economist, Ministry of Economy and Planning, Cameroon
Monitored macroeconomic indicators and developed socio-economic analyses to guide policy decisions.

2008: Credit Analyst, Afriland First Bank, Cameroon
Analyzed credit portfolios and managed risk assessments to support the bank’s credit-granting process.

Research Interests 🔍

Main Interests:

  • Econometrics (Forecasting, Machine Learning, Big Data Analysis)

Secondary Interests:

  • Macroeconomics
  • Microeconometrics
  • Finance

FONTON’s research integrates advanced econometric models with machine learning techniques to explore macro-financial linkages and evaluate economic policies.

Award 🏅

Ahmadou Mustapha FONTON has been recognized for his contributions to economic research and policy development through various grants and academic accolades. His innovative work in econometrics and machine learning positions him as a leading candidate for prestigious research awards.

Publications đź“š

  1. “A machine learning approach in stress testing US bank holding companies” – Accepted for publication in International Review of Financial Analysis (2024). Read Here

Conclusion

Dr. Ahmadou Mustapha FONTON is a highly qualified candidate for the Research for Best Researcher Award. His extensive experience in econometrics, macroeconomics, and financial economics, coupled with his technical skills and policy impact, positions him as a strong contender. His research contributions, combined with his international perspective and teaching experience, align well with the objectives of the award. Addressing the areas for improvement, such as increasing his publication record and expanding his collaborative efforts, could further enhance his candidacy. Overall, Dr. Fonton’s profile reflects a distinguished researcher with a promising trajectory in the field of economics.

ANUJA BHARGAVA | Artificial Intelligence | Most Cited Paper Award

Posted on by Academic achievements

Assist Prof Dr. ANUJA BHARGAVA | Artificial Intelligence | Most Cited Paper Award

Assistant Professor GLA University India

Dr. Anuja Bhargava is an accomplished academic and researcher, currently serving as an Assistant Professor at GLA University, Mathura. With a Ph.D. in Electronics and Communication Engineering, she specializes in Digital Signal Processing, VLSI, and Artificial Intelligence. Dr. Bhargava has a wealth of teaching experience and has published extensively in renowned journals and conferences. Her dedication to education and research has earned her a prominent place in her field.

Profile

Scopus

Education 🎓

Dr. Anuja Bhargava earned her Ph.D. in Electronics and Communication Engineering from GLA University, Mathura, where she conducted groundbreaking research on “Quality Evaluation of Fruits using Image Processing.” She holds a Master of Technology in Digital Communication from Uttrakhand Technical University and a Bachelor of Engineering in Electronics and Communication Engineering from Modi Institute of Technology, Kota, both with first-class honors.

Experience 🏫

Dr. Bhargava’s academic journey includes roles as Assistant Professor at GLA University since October 2021, and previously at Gurukul Institute of Engineering & Technology and Maharishi Arvind International Institute of Technology. Her extensive teaching experience spans over a decade, focusing on various aspects of electronics and communication engineering.

Research Interests 🔍

Dr. Bhargava’s research interests are diverse and include Digital Signal Processing, Very Large Scale Integration (VLSI), Control Systems, Signal and Systems, Electromagnetic Field Theory, Microprocessors, and Basic Electrical and Electronics. She is particularly interested in the application of Artificial Intelligence in these domains.

Awards 🏆

Dr. Anuja Bhargava has been recognized for her contributions to academia and research with various awards and nominations. She has served as a keynote speaker at international conferences and received accolades for her innovative research and teaching methodologies.

Publications Top Notes đź“š

Gupta D, Bhargava A, et al. “Deep Learning-Based Truthful and Deceptive Hotel Reviews.” Sustainability, 2024, link, cited by articles.

Bhargava A, et al. “Plant Leaf Disease Detection, Classification and Diagnosis using Computer Vision and AI: A Review.” IEEE Access, 2024, link, cited by articles.

Sachdeva A, Bhargava A, et al. “A CNTFET based stable, single ended 7T SRAM cell with improved write operation.” Physica Scripta, 2024, link, cited by articles.

Bhargava A, et al. “Machine learning & computer vision-based optimum black tea fermentation detection.” Multimed Tools Appl, 2023, link, cited by articles.

Sharma A, Bhargava A, et al. “Multi-level Segmentation of Fruits Using Modified Firefly Algorithm.” Food Anal. Methods, 2022, link, cited by articles.