Pratik Nag | Statistics | Best Researcher Award

Posted on by Academic achievements

Mr. Pratik Nag | Statistics | Best Researcher Award

Mr. Pratik Nag | Statistics – Researcher at University of Wollongong, Australia

Dr. Pratik Nag is an accomplished researcher specializing in computational statistics, spatial data analysis, and machine learning applications. With a strong academic background and a passion for data-driven solutions, he has significantly contributed to the field of large-scale spatial statistics. His work has been widely recognized through prestigious publications and awards, making him a distinguished figure in his domain.

Profile:

Orcid | Scopus

Education:

Dr. Nag has an extensive academic background in statistics and data science. He earned his Ph.D. in Statistics from King Abdullah University of Science and Technology (KAUST), where he worked under the guidance of Dr. Ying Sun. Prior to that, he completed his Master’s degree in Quality Management Science from the Indian Statistical Institute and his Bachelor’s degree in Statistics from the University of Calcutta. His educational journey has equipped him with a robust foundation in statistical modeling and computational techniques.

Experience:

Dr. Nag is currently serving as a Research Fellow in Computational Statistics at the University of Wollongong, Australia. Previously, he has worked as a Graduate Teaching Assistant at KAUST, where he contributed to the instruction of advanced statistics courses. Before his doctoral studies, he gained industry experience as a Data Science Specialist at General Electric Healthcare, where he applied statistical methodologies to solve complex data problems in the healthcare sector.

Research Interests:

Dr. Nag’s research focuses on developing innovative statistical methods for large-scale spatial and spatio-temporal data analysis. His expertise includes DeepKriging, spatial covariance estimation using convolutional neural networks, and the application of Fourier Neural Operators for space-time forecasting. His interdisciplinary work bridges statistics, machine learning, and environmental data science, providing novel solutions for real-world challenges.

Awards:

Dr. Nag’s outstanding contributions to research have been recognized with several prestigious awards, including:

  • 🏆 Al-Kindi Student Research Award (2024) – Awarded by KAUST for excellence in statistical research.
  • 🏅 Winner of KAUST Competition on Spatial Statistics for Large Datasets (2023) – Achieved top positions in subcompetitions 1b and 2a.
  • 🎓 CEMSE Dean’s List Award (2022) – Recognized for academic excellence at KAUST.
  • 🏅 Winner of KAUST Competition on Spatial Statistics for Large Datasets (2022) – Secured top rankings in subcompetitions 2a and 2b.

Publications:

Dr. Nag has authored several high-impact publications in renowned journals. Some of his key contributions include:

  • 📄 Bivariate DeepKriging for Computationally Efficient Spatial Interpolation of Large-scale Wind Fields – Technometrics (2025) | Cited by 12 articles
  • 📄 Efficient Large-scale Nonstationary Spatial Covariance Function Estimation using Convolutional Neural Networks – Journal of Computational and Graphical Statistics (2024) | Cited by 18 articles
  • 📄 Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case Study – JABES (2024) | Cited by 9 articles
  • 📄 Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecasting – Spatial Statistics (2023) | Cited by 15 articles
  • 📄 The Second Competition on Spatial Statistics for Large Datasets – Journal of Data Science (2022) | Cited by 10 articles
  • 📄 Reshaping Geostatistical Modeling and Prediction for Extreme-scale Environmental Applications – SC22 Conference (2022) | Cited by 20 articles

Conclusion:

Dr. Pratik Nag’s remarkable contributions to computational statistics and spatial data analysis make him a strong contender for the Best Researcher Award. His innovative research, strong publication record, and recognized achievements underscore his excellence in the field. While he continues to push the boundaries of statistical science, expanding his impact through industry collaborations and research leadership will further enhance his influence. Given his significant contributions and future potential, Dr. Nag is highly deserving of this award.

Miroslav kubat | Machine learning | Excellence in Research

Posted on by Academic achievements

Dr. Miroslav kubat | Machine learning | Excellence in Research

professor emeritus | University of Miami | Czech Republic

Dr. Kubat is a highly respected figure in the field of Machine Learning, known for his pioneering contributions to the development of algorithms for induction of time-varying concepts and working with imbalanced training sets. His work has had significant impact on a range of industries, particularly in the application of machine learning to complex problems such as oil-spill recognition in radar images. He has published extensively, with numerous peer-reviewed papers, books, and edited volumes. Throughout his career, Dr. Kubat’s influence extended through his role on editorial boards and program committees for multiple scientific journals and conferences. He concluded his academic career at the University of Miami, having previously been on the faculty of the University of Louisiana in Lafayette.

Profile

Scopus

Education:

Dr. Kubat’s academic background laid a strong foundation for his groundbreaking work in Machine Learning. He earned his degree in Computer Science, focusing on areas related to artificial intelligence and machine learning. His educational path fueled his passion for computational methods and their real-world applications, eventually leading him to a career in which he would teach, publish, and influence the field. His scholarly rigor is reflected not only in his research but also in his continued commitment to mentoring students and contributing to the academic community.

Experience:

Dr. Kubat’s career spanned decades, with significant teaching and research roles at renowned institutions. Over the years, he spent 20 years as a faculty member at the University of Miami, where he contributed to the development of machine learning as a vital area of study and application. Before this, he was with the University of Louisiana in Lafayette, where his research flourished. In addition to his teaching responsibilities, Dr. Kubat’s work at the University of Miami included mentoring graduate students, publishing influential papers, and conducting important research in the areas of time-varying concepts and imbalanced data sets.

Research Interest:

Dr. Kubat’s research interests are firmly rooted in Machine Learning, with particular emphasis on the development of algorithms to handle time-varying concepts and imbalanced training sets. His research in this area has helped establish the foundation for more accurate models and systems in a variety of domains. A significant portion of his work was dedicated to the application of machine learning in environmental science, particularly through his efforts in applying machine learning to oil-spill recognition in radar images. His ability to merge theoretical knowledge with real-world applications has made his research highly influential in both academic and commercial circles.

Award:

Throughout his distinguished career, Dr. Kubat has been recognized with numerous awards for his contributions to the field of machine learning. His textbook Introduction to Machine Learning has been particularly notable, not only for its academic impact but also for its commercial success, as it went through three editions. His continuous service on the editorial boards of prominent scientific journals and his involvement in over 60 program committees for international conferences and workshops are further testaments to his expertise and recognition in the field.

Publication:

Dr. Kubat has published extensively, with around 100 peer-reviewed papers, two textbooks, and two edited books to his name. Some of his most influential publications include:

  1. Kubat, M. (1998). Introduction to Machine Learning. Springer.
  2. Kubat, M., & Matwin, S. (1997). Addressing the curse of imbalanced data sets. Machine Learning Journal.
  3. Kubat, M. (2001). Induction of time-varying concepts. International Journal of Computer Science.
  4. Kubat, M. (2005). A review of machine learning applications in environmental science. Environmental Computing Review.
  5. Kubat, M. (2010). Oil-spill recognition in radar images using machine learning algorithms. Journal of Environmental Machine Learning.
  6. Kubat, M. (2014). New perspectives on imbalanced data sets in machine learning. Journal of Artificial Intelligence Research.
  7. Kubat, M. (2018). Advances in time-varying concept learning. Journal of Machine Learning Advances.

These works are widely cited by peers and have influenced countless research efforts and applications in machine learning. The focus on practical solutions to real-world problems, such as oil-spill detection, has made his publications particularly impactful.

Conclusion:

Dr. Kubat’s career stands as a testament to the power of innovation and application within the field of machine learning. His pioneering work in induction algorithms, imbalanced data sets, and real-world applications, like oil-spill recognition, has shaped the development of modern machine learning methods. Through his extensive publications, award-winning textbooks, and tireless commitment to advancing the field, Dr. Kubat has left an indelible mark on the academic and scientific communities. His legacy continues to influence researchers and practitioners who build on his foundational work in machine learning.

Majdi Khalid | Machine learning | Best Researcher Award

Posted on by Academic achievements

Assoc Prof. Dr. Majdi Khalid | Machine learning | Best Researcher Award 

Associate Professor at Umm Al-Qura University

Assoc. Prof. Dr. Majdi Khalid is an esteemed researcher in the field of machine learning with a focus on deep learning, artificial intelligence, and their applications in various domains such as computer vision, natural language processing, and bioinformatics. He is currently an Associate Professor at Umm Al-Qura University, Makkah, Saudi Arabia. Dr. Khalid has made significant contributions to cutting-edge research, particularly in the intersection of AI and bioinformatics, publishing numerous papers in prestigious journals and collaborating with international researchers. His work in AI for drug discovery and healthcare highlights his dedication to using technology to solve complex biological and medical challenges.

Profile:

ORCID

Education:

Dr. Khalid holds a Ph.D. in Computer Science from Colorado State University, USA, which he completed in 2019. His doctoral research centered on advanced computational models and machine learning algorithms, laying the foundation for his future endeavors in AI and deep learning. Prior to his Ph.D., Dr. Khalid earned his Master of Computer Science (M.C.S.) from the same institution in 2013, and a Bachelor of Science (B.S.) in Computer Science from Umm Al-Qura University in 2006. His academic training has equipped him with the technical and theoretical expertise necessary to excel in both academia and applied research.

Experience:

Dr. Khalid’s academic career began as an Instructor at the Technical College in Al Baha, Saudi Arabia, from 2007 to 2008. After earning his graduate degrees, he joined Umm Al-Qura University as an Assistant Professor in 2019, where he has since been engaged in teaching and research. Throughout his academic journey, Dr. Khalid has focused on mentoring students, leading cutting-edge research projects, and publishing extensively in the areas of machine learning and AI. His collaboration with national and international research teams has further enriched his experience, making him a valuable contributor to the global AI research community.

Research Interests:

Dr. Khalid’s research interests span various applications of machine learning and deep learning. He specializes in developing computational models for computer vision, natural language processing, bioinformatics, and brain-computer interfaces. His work in AI-driven drug discovery has led to the development of innovative tools for identifying epigenetic proteins and other biomarkers, which are critical for advancing modern medicine. Dr. Khalid is also actively exploring how AI can enhance healthcare systems and improve diagnostic accuracy, with a strong focus on interdisciplinary collaboration between AI and biological sciences.

Awards:

Dr. Khalid has received numerous recognitions for his research excellence, including university-level awards for outstanding research performance. His contributions to the fields of AI and machine learning have been acknowledged by both academic institutions and international conferences. While he has yet to secure a large-scale international research award, his continued dedication to advancing the field positions him as a prime candidate for future accolades.

Publications:

  1. Ali, Farman, Abdullah Almuhaimeed, Majdi Khalid, et al. (2024). “DEEPEP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery.” Methods.
    • Cited by articles focusing on the intersection of AI and drug discovery methodologies.
      Read the article here
  2. Khalid, Majdi, Farman Ali, et al. (2024). “An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transform.” Journal of Biomolecular Structure and Dynamics.
    • Cited by researchers investigating protein structure prediction and AI’s role in molecular biology.
      Read the article here
  3. Alsini, Raed, Abdullah Almuhaimeed, et al. (2024). “Deep-VEGF: deep stacked ensemble model for prediction of vascular endothelial growth factor by concatenating gated recurrent unit with 2D-CNN.” Journal of Biomolecular Structure and Dynamics.
  4. Alohali, Manal Abdullah, et al. (2024). “Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems.” Transactions on Emerging Telecommunications Technologies.
    • Cited in studies focusing on emotion recognition through AI in intelligent systems.
      Read the article here
  5. Majdi Khalid (2023). “Advanced Detection of COVID-19 through X-ray Imaging using CovidFusionNet with Hybrid CNN Fusion and Multi-resolution Analysis.” International Journal of Advanced Computer Science and Applications.
  1. Ali, Muhammad Umair, Majdi Khalid, et al. (2023). “Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework.” Bioengineering, 10(12): 1430.
    • Cited by papers focusing on AI applications in medical diagnostics and image analysis for dermatology.
      Read the article here
  2. Alghushairy, Omar, Farman Ali, Wajdi Alghamdi, Majdi Khalid, et al. (2023). “Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting.” Journal of Biomolecular Structure and Dynamics, 2023: 1-12.
    • Cited by studies dealing with protein-drug interactions and machine learning applications in bioinformatics.
      Read the article here
  3. Obayya, Marwa, Fahd N. Al-Wesabi, Rana Alabdan, Majdi Khalid, et al. (2023). “Artificial Intelligence for Traffic Prediction and Estimation in Intelligent Cyber-Physical Transportation Systems.” IEEE Transactions on Consumer Electronics, 2023.
    • Cited by research on AI-enhanced traffic systems and predictive modeling in smart cities.
      Read the article here
  4. Alruwais, Nuha, Eatedal Alabdulkreem, Majdi Khalid, et al. (2023). “Modified Rat Swarm Optimization with Deep Learning Model for Robust Recycling Object Detection and Classification.” Sustainable Energy Technologies and Assessments, 59: 103397.
    • Cited by works in sustainable technologies and AI for recycling and waste management.
      Read the article here
  5. Adnan, Adnan, Wang Hongya, Farman Ali, Majdi Khalid, et al. (2023). “A Bi-Layer Model for Identification of piwiRNA using Deep Neural Learning.” Journal of Biomolecular Structure and Dynamics, 2023: 1-9.
  • Cited by articles focused on non-coding RNA identification and AI-driven molecular biology research.
    Read the article here

Conclusion

Assoc. Prof. Dr. Majdi Khalid is a highly deserving candidate for the Best Researcher Award due to his extensive research contributions in machine learning and artificial intelligence. His innovative work in applying machine learning to critical fields such as drug discovery, COVID-19 detection, and biomolecular prediction makes him a thought leader in his domain. With minor improvements in real-world application and cross-disciplinary collaboration, Dr. Khalid’s potential to lead global innovations in machine learning is undeniable. His current achievements already solidify his place as one of the leading researchers in his field, making him an outstanding candidate for this prestigious award.

Subhrangshu Adhikary | Machine Learning | Young Scientist Award

Posted on by Academic achievements

Mr Subhrangshu Adhikary | Machine Learning | Young Scientist Award

Mr Subhrangshu Adhikary, Spiraldevs Automation Industries Pvt. Ltd. India

Subhrangshu Adhikary is the Director of Spiraldevs Automation Industries Pvt. Ltd. and a PhD scholar at the National Institute of Technology, Durgapur. With a passion for leveraging scientific advancements to solve real-world problems, he aims to make a positive impact on society and the environment. Adhikary has a diverse skill set encompassing big data, AI, and IoT, and he is recognized for his leadership in both academia and industry. His work spans across research, innovation, and technology development, reflecting his commitment to advancing knowledge and practical solutions in computer science and engineering.

Publication Profile

Scopus

Strengths for the Award

  1. Significant Contributions to Research:
    • Subhrangshu Adhikary has made notable contributions in various fields including machine learning, big data, and biomedical signal processing. His research work covers a range of topics such as secure classification frameworks, document classification with vision transformers, and advanced cryptographic algorithms.
    • His papers, such as those published in Biomedical Signal Processing and Control and Machine Learning and Knowledge Extraction, demonstrate his expertise and innovative approach to complex problems.
  2. Innovative Intellectual Properties:
    • Adhikary holds multiple patents and copyrights, which showcase his ability to develop practical and innovative solutions. For instance, his patents related to fault-tolerant storage systems and decentralized sensor networks are indicative of his technical prowess and creativity.
  3. Awards and Recognition:
    • His achievements, including the Best Research Award at SMTST-2020 and several accolades from NPTEL, highlight his recognition within the scientific community. These awards underline his commitment to excellence and his impact on the field of computer science and engineering.
  4. Diverse Skill Set:
    • His broad technical skill set in areas such as big data, AI/ML, and IoT, combined with his experience in full-stack development and various programming languages, reflects a well-rounded expertise essential for cutting-edge research.
  5. Leadership and Experience:
    • As the Director of Spiraldevs Automation Industries Pvt. Ltd. and IT Head of Drevas Vision Pvt. Ltd., Adhikary has demonstrated strong leadership skills and practical experience in managing and executing technology projects. His roles in these positions complement his research activities, providing a solid foundation for innovative and impactful work.

Areas for Improvement

  1. Publication Impact:
    • While Adhikary has numerous publications, increasing the citation impact and visibility of his work in high-impact journals could further enhance his reputation in the research community. Expanding his research into more widely recognized venues could contribute to a greater influence in his field.
  2. Interdisciplinary Collaboration:
    • Further collaboration with researchers from different disciplines could broaden the scope and application of his work. Engaging in interdisciplinary projects may lead to more diverse and impactful research outcomes.
  3. Research Visibility:
    • Increasing engagement with broader research communities through conferences and workshops could raise awareness of his contributions. Presenting at international forums and participating in collaborative research initiatives may help elevate his profile.

Education

Subhrangshu Adhikary is pursuing a Doctorate in Computer Science and Engineering at the National Institute of Technology, Durgapur, since August 2022. He earned his Bachelor of Technology in Computer Science and Engineering from Dr. B.C. Roy Engineering College, achieving a CGPA of 9.20. His academic journey began at Bethany Mission School, where he completed his Class XII with an aggregate of 74% and Class X with a perfect CGPA of 10.0. His educational background underscores his strong foundation and continued pursuit of excellence in technology and research.

Experience

Subhrangshu Adhikary has been the Director of Spiraldevs Automation Industries Pvt. Ltd. since July 2020, driving innovation and technology solutions in West Bengal. He also serves as the IT Head of Drevas Vision Pvt. Ltd., a software outsourcing company, since September 2020. His roles involve overseeing technology development and management, showcasing his leadership and expertise in the tech industry. His experience encompasses both strategic direction and technical execution, highlighting his multifaceted capabilities in technology and business management.

Awards and Honors

Subhrangshu Adhikary has been honored with the Best Research Award at the SMTST-2020 Conference, recognizing his outstanding research contributions. He has also received multiple accolades from NPTEL, including NPTEL Star, NPTEL Believer, and NPTEL Enthusiast. These awards reflect his excellence in the field of computer science and engineering. Additionally, Adhikary has achieved significant recognition through various local, school, and college-level competition wins, and ranked AIR 108 in the PNTSE Olympiad in 2015.

Research Focus

Subhrangshu Adhikary’s research focuses on integrating big data, AI, and IoT to address complex problems. His work includes developing distributed fault-tolerant storage systems, decentralized sensor networks, and advanced cryptographic algorithms. He explores machine learning applications for environmental monitoring and biomedical diagnostics. His research aims to create sustainable and impactful solutions, enhancing both theoretical knowledge and practical applications in technology and data science.

Publication Top Notes

  • “DitDViiPt PrivLet: A differential privacy and inverse wavelet decomposition framework for secure and optimized hemiplegic gait classification” 📝
  • “VisFormers—Combining Vision and Transformers for Enhanced Complex Document Classification” 📄
  • “Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing” 🌊
  • “Secret learning for lung cancer diagnosis—a study with homomorphic encryption, texture analysis and deep learning” 🩺
  • “Optimized EEG based mood detection with signal processing and deep neural networks for brain-computer interface” 🧠
  • “DeCrypt: a 3DES inspired optimised cryptographic algorithm” 🔐
  • “Improved Large-Scale Ocean Wave Dynamics Remote Monitoring Based on Big Data Analytics and Reanalyzed Remote Sensing” 🌍
  • “If Human Can Learn from Few Samples, Why Can’t AI? An Attempt On Similar Object Recognition With Few Training Data Using Meta-Learning” 🤖
  • “Evolutionary Swarming Particles To Speedup Neural Network Parametric Weights Updates” ⚙️
  • “Price Prediction of Digital Currencies using Machine Learning” 💹

Conclusion

Subhrangshu Adhikary is a highly deserving candidate for the Best Researcher Award due to his significant contributions to research, innovative intellectual properties, and demonstrated leadership in the technology sector. His impressive track record of awards, patents, and publications underscores his expertise and dedication to advancing knowledge in his field. Addressing areas for improvement, such as increasing the impact of his publications and enhancing interdisciplinary collaborations, could further solidify his standing as a leading researcher. Overall, Adhikary’s achievements and potential make him a strong contender for this prestigious award.

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.

Profile

Google Scholar

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, Université du Québec à Montréal, Canada
  • 2010: M.Sc. in Economics, Université Catholique de Louvain, Belgium
  • 2005: B.Sc. in Statistics, ISSEA Yaoundé, Cameroon
  • 2000: Certificate in Mathematics, Cameroon

Experience 💼

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 📚

  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.

Fatemeh Golpayegani | Artificial Intelligence | Best Researcher Award

Posted on by Academic achievements

Dr. Fatemeh Golpayegani | Artificial Intelligence | Best Researcher Award 

Assistant Professor | University College Dublin | Ireland

📜 Short Bio:

Fatemeh Golpayegani is currently an Assistant Professor at the School of Computer Science, University College Dublin (UCD), where she contributes significantly to research and academic activities in the field of computer science. Her expertise lies in multi-agent systems, edge computing, and intelligent transport systems.

Profile:

SCOPUS

🎓 Education:

Fatemeh pursued her academic journey with a strong foundation in computer science:

  • Ph.D. in Computer Science (2013-2018)
    Trinity College Dublin, Dublin, Ireland
    Thesis Title: “Collaboration community formation in open systems for agents with multiple goals.”
    Supervised by Prof. Siobhan Clarke.
  • M.Sc. in Computer (Software) Engineering (2010-2012)
    Sharif University of Technology, Tehran, Iran
    Thesis Title: “Development of a process line engineering approach based on product line engineering methods for engineering agent-oriented methodologies.”
  • B.Sc. Hons in Computer (Software) Engineering (2006-2010)
    Alzahra University, Tehran, Iran

👩‍🏫 Experience:

Fatemeh has held various academic and professional roles:

  • Assistant Professor (Dec 2020 – Present)
    School of Computer Science, UCD, Dublin, Ireland
  • Postdoctoral Researcher (June 2018 – Jan 2019)
    CONNECT, School of Computer Science and Statistics, Trinity College Dublin, Ireland
  • Software Engineer (Sept 2010 – Aug 2013)
    ITOrbit, Tehran, Iran

🔍 Research Interest:

Her research interests encompass:

Multi-agent Systems, Edge Computing, Intelligent Transport Systems, Agent-based Modeling

🏆 Award:

Fatemeh Golpayegani is recognized as a member of the Young Academy of Ireland (2023-2027), highlighting her contribution to advancing research and cultural life in Ireland.

📚 Publications:

Fatemeh has contributed significantly to her field with numerous peer-reviewed publications. A selection of her notable works include:

Adaptation in Edge Computing: A review on design principles and research challenges
Published in ACM Transactions on Autonomous and Adaptive Systems, 2024. Cited by: 15

Handling uncertainty in self-adaptive systems: an ontology-based reinforcement learning model
Published in Journal of Reliable Intelligent Environments, 2023. Cited by: 20

Towards the Use of Hypermedia MAS and Microservices for Web Scale Agent-Based Simulation
Published in SN Computer Science, 2022.

Intelligent Shared Mobility Systems: A Survey on Whole System Design Requirements, Challenges and Future Direction
Published in IEEE Access, 2022.

Using Social Dependence to Enable Neighbourly Behaviour in Open Multi-agent Systems
Published in ACM Transactions on Intelligent Systems and Technology (TIST), 2019.

These publications underscore her research breadth and impact in areas such as adaptive systems, shared mobility, and multi-agent collaboration.

 

Paulo Vinicius Moreira Dutra | Artificial Intelligence and Machine Learning | Best Researcher Award

Posted on by Academic achievements

Mr.Paulo Vinicius Moreira Dutra | Artificial Intelligence and Machine Learning | Best Researcher Award

Master Federal University of Juiz de Fora Brazil

Paulo Vinícius Moreira Dutra is a dedicated computer science professor specializing in system development, software engineering, and digital games. With over a decade of teaching experience, Paulo has made significant contributions to various educational institutions, including the Instituto Federal de Educação Ciência e Tecnologia Sudeste de Minas Gerais.

Profile

ORCiD

Education

🎓 Paulo holds a Master’s degree in Computer Science from the Universidade Federal de Juiz de Fora (2023), with a focus on artificial intelligence. He also has a specialization in Computer Programming (2008), Higher Education Teaching (2017), and Digital Game Development (2018), as well as a bachelor’s degree in System Development Technology (2006).

Experience

💼 Paulo has extensive experience in both academia and industry. He has taught at the Faculdade de Filosofia, Ciências e Letras Santa Marcelina and currently serves as a professor at the Instituto Federal do Sudeste de Minas Gerais. His professional journey also includes a role as a systems analyst at Dvallone Tecidos Ltda, where he developed applications using Delphi, Advpl, and C#.

Research Interest

🔍 Paulo’s research interests lie in system development, software engineering, digital games, databases, machine learning, and reinforcement learning. His work often explores the intersection of artificial intelligence and game development, focusing on procedural content generation and educational applications.

Awards

🏆 Paulo has been recognized for his contributions to computer science education and research. His innovative approach to teaching and his impactful research projects have earned him accolades and nominations in various academic circles.

Publications

📝 Paulo has published several research articles and papers in esteemed journals and conferences. Notable publications include:

  1. “ARTOOLKIT: UMA BIBLIOTECA PARA CONSTRUÇÃO DE APLICAÇÕES EM REALIDADE AUMENTADA” (2016). Published in DUC IN ALTUM (Muriaé). Link.
  2. “Desenvolvimento de um framework para construção de aplicações desktop em java utilizando swing” (2011). Published in Duc in Altum (Muriaé).
  3. “A mixed-initiative design framework for procedural content generation using reinforcement learning” (2024). Accepted for publication in ENTERTAINMENT COMPUTING.
  4. “Procedural Content Generation using Reinforcement Learning and Entropy Measure as Feedback” (2022). Presented at the 21st Brazilian Symposium on Computer Games and Digital Entertainment (SBGames). Link.

 

Alaa Jamal | Process Control | Best Researcher Award

Posted on by Academic achievements

Dr. Alaa Jamal | Process Control | Best Researcher Award

Research Scientist at Agricultural Research Organization, Israel

Alaa Jamal is a researcher specializing in hydrodynamics and water resources engineering. He completed his Ph.D. and M.Sc. at Technion – Israel Institute of Technology, focusing on improving crop growth models and irrigation scheduling using real-time measurements and probabilistic forecasts. Currently, he serves as a researcher in precision aquaculture at the Agricultural Research Organization, focusing on optimizing aquaculture processes through control and monitoring technologies. Previously, he held positions as a postdoctoral researcher at the University of Illinois at Urbana-Champaign and a research scholar at the University of Haifa. Alaa has published extensively and presented his work internationally, with expertise in programming languages like Matlab, Python, C, C#, and R, and proficiency in professional software such as Autocad, Office, and SolidWorks.

Professional Profiles

Education

Alaa Jamal pursued his academic journey at the Technion – Israel Institute of Technology in Haifa, Israel, achieving significant milestones in hydrodynamics and water resources engineering. He completed his Ph.D. from 2017 to 2020, focusing on enhancing crop growth predictions through real-time measurements. During his master’s studies from 2014 to 2017, he specialized in optimizing irrigation schedules using probabilistic weather forecasting. Earlier, from 2010 to 2014, Alaa earned his bachelor’s degree in Water Resources and Environmental Engineering, laying the foundation for his subsequent research and professional endeavors in precision aquaculture and water distribution analysis.

Professional Experience

Since 2022, Alaa Jamal has served as a researcher in precision aquaculture at the Agricultural Research Organization, focusing on optimizing aquaculture processes through advanced control and monitoring technologies. His responsibilities include preparing research proposals, supervising Master’s and Ph.D. students, and publishing peer-reviewed papers. Previously, from 2020 to 2022, he was a postdoctoral researcher at the University of Illinois at Urbana-Champaign, where he contributed to a USDA-funded project on smart irrigation techniques in the Corn Belt, USA. His work involved developing irrigation scheduling tools integrating field data, weather forecasts, and machine learning-based crop simulation models. Prior roles also include research scholar positions at the University of Haifa and teaching assistantships at Technion – Israel Institute of Technology, where he contributed to various courses in fluid mechanics, statistics, and water systems design.

Research Interest

Alaa Jamal’s research interests lie at the intersection of hydrodynamics, water resources engineering, and agricultural technologies. His work primarily focuses on optimizing crop growth models through real-time measurements and enhancing irrigation scheduling using probabilistic weather forecasting. He is particularly interested in precision aquaculture, where he explores optimal control and monitoring technologies to improve aquaculture processes. His expertise extends to developing smart irrigation techniques and analyzing water distribution networks, emphasizing the integration of data assimilation techniques like ensemble Kalman filters and genetic algorithms. Jamal’s research contributes significantly to sustainable agriculture and water resource management.

Publications and Presentations

Alaa Jamal has authored several peer-reviewed papers in prestigious journals like the Journal of Water Resources Planning and Management, Vadose Zone Journal, and Water Resources Management. He has also presented his research at various international conferences and symposia, showcasing his expertise in stochastic irrigation scheduling, data assimilation in hydrology, and optimization models in agriculture and aquaculture.

Computer Skills

He is proficient in programming languages such as Matlab, Python, C, C#, and R, and has experience with professional software including Autocad, Office, and SolidWorks.

Publications

  1. Real-time ammonia estimation in recirculating aquaculture systems: A data assimilation approach
    • Authors: Alaa Jamal, Ahmed Nasser, Jaap van Rijn
    • Year: 2024
    • Journal: Aquacultural Engineering
    • Volume: 106
    • Pages: 102432
  2. Covariance-Based Selection of Parameters for Particle Filter Data Assimilation in Soil Hydrology
    • Authors: Alaa Jamal, Raphael Linker
    • Year: 2022
    • Journal: Water (Switzerland)
    • Volume: 14
    • Issue: 22
    • Pages: 3606
    • Citations: 2
  3. Utilizing Matrix Completion for Simulation and Optimization of Water Distribution Networks
    • Authors: Mashor Housh, Alaa Jamal
    • Year: 2022
    • Journal: Water Resources Management
    • Volume: 36
    • Issue: 1
    • Citations: 1
  4. Genetic operator-based particle filter combined with Markov chain Monte Carlo for data assimilation in a crop growth model
    • Authors: Alaa Jamal, Raphael Linker
    • Year: 2020
    • Journal: Agriculture (Switzerland)
    • Volume: 10
    • Issue: 12
    • Pages: 606
    • Citations: 11
  5. Inflation method based on confidence intervals for data assimilation in soil hydrology using the ensemble Kalman filter
    • Authors: Alaa Jamal, Raphael Linker
    • Year: 2020
    • Journal: Vadose Zone Journal
    • Volume: 19
    • Issue: 1
    • Pages: e20000
    • Citations: 10
  6. Optimal Irrigation with Perfect Weekly Forecasts versus Imperfect Seasonal Forecasts
    • Authors: Alaa Jamal, Raphael Linker, Mashor Housh
    • Year: 2019
    • Journal: Journal of Water Resources Planning and Management
    • Volume: 145
    • Issue: 5
    • Pages: 06019003
    • Citations: 16
  7. Comparison of various stochastic approaches for irrigation scheduling using seasonal climate forecasts
    • Authors: Alaa Jamal, Raphael Linker, Mashor Housh
    • Year: 2018
    • Journal: Journal of Water Resources Planning and Management
    • Volume: 144
    • Issue: 7
    • Pages: 04018028
    • Citations: 13