Marcelo Vasconcelos | Artificial Intelligence | Best Researcher Award

Mr. Marcelo Vasconcelos | Artificial Intelligence | Best Researcher Award

IT Auditor at Court of Auditors of the Federal District, Brazil

Marcelo Oliveira Vasconcelos is a seasoned professional and researcher from Brasรญlia, Brazil, with over two decades of experience across public administration, financial auditing, and technology-based risk management. Currently pursuing a Ph.D. in Web Science and Technology, Marceloโ€™s expertise spans various roles, including Financial and External Control Analyst at the Tribunal de Contas do Distrito Federal (TCDF). He holds multiple certifications, such as Certified Information Systems Auditor (CISA) and Risk Management Professional (ISO 31000:2018). His research focuses on enhancing corruption risk assessments in public administration using advanced data science methods, making him a prominent figure in the application of technology for public sector improvements. Proficient in Portuguese, English, and Spanish, Marcelo brings a global perspective to his work, bolstered by leadership training from ร‰cole Nationale dโ€™Administration (ENA) in France. His contributions, such as his recent publications on artificial intelligence applications in public administration, underscore his commitment to advancing effective governance practices through data-driven insights and innovative methodologies.

Professional Profile

Education

Marcelo Vasconcelos has a comprehensive academic background that blends technology, law, and public administration. He is currently a Ph.D. candidate in Web Science and Technology at the University of Trรกs-os-Montes e Alto Douro (UTAD), Portugal, which builds on his Masterโ€™s degree in Computer Science from the University of Brasรญlia, completed in 2020. His formal education is supplemented by a range of specialized qualifications: an MBA in Public Law from Instituto Processus and another in Constitutional Law from Instituto de Direito Pรบblico, Brasรญlia. Marcelo also holds a Bachelorโ€™s degree in Public Administration from the State University of Goiรกs and an undergraduate degree in Science from UniCEUB Brasรญlia. His academic trajectory is further complemented by international training in leadership and public management from ร‰cole Nationale dโ€™Administration (ENA) in France, which has enriched his expertise in governmental processes and administration. Marceloโ€™s educational journey reflects a balanced combination of technical expertise, public policy, and governance, aligning with his goal to leverage data science for practical solutions in public administration.

Professional Experience

Marcelo Vasconcelos has accumulated diverse professional experience, with a primary focus on public sector auditing and analysis. Since August 2004, he has served as a Financial and External Control Analyst at the Tribunal de Contas do Distrito Federal (TCDF), where he applies his expertise in data auditing, fraud detection, and risk management to enhance public accountability. Previously, he held various roles, including Social Security Tax Auditor at the National Social Security Institute (INSS) from 2003 to 2004, and Foreign Trade Analyst at the Secretariat of Foreign Trade, where he honed his skills in regulatory compliance and policy analysis. His early career also includes work as a Federal Revenue Analyst for the Secretariat of Federal Revenue and as a Teacher of Science and Mathematics in the Federal Districtโ€™s Secretariat of Education. Marceloโ€™s professional journey reflects a commitment to strengthening governance and public sector efficiency, leveraging both his analytical and technological skills to contribute to Brazilโ€™s federal and financial control sectors.

Research Interest

Marceloโ€™s primary research interest lies in the intersection of data science, public administration, and ethics, particularly in using technology to tackle corruption and enhance governance transparency. His research explores the application of artificial intelligence and machine learning to identify and mitigate risks associated with public administration processes. Notably, Marcelo has focused on creating models that assess corruption risk in public administration, emphasizing the development of imbalanced learning techniques to improve accuracy in risk detection. His work, such as his study on mitigating false negatives in imbalanced datasets, aligns with his commitment to data-driven governance reforms. In addition, Marceloโ€™s interest extends to Web Science and the application of large datasets for public decision-making. By advancing methodologies that blend computer science with public policy, he seeks to bridge gaps in data application and ethical governance, positioning his research within the broader movement of responsible AI in public services.

Research Skills

Marcelo Vasconcelos brings a robust skill set to his research, particularly in data analytics, risk assessment, and machine learning applications in public administration. He is proficient in using artificial intelligence techniques, specifically imbalanced learning methods, to enhance the reliability of corruption risk models. His technical skills extend to using Control Objectives for Information and Related Technologies (COBIT 5) and ISO 31000:2018 standards for risk management. Marcelo is certified as a Certified Information Systems Auditor (CISA), which bolsters his skills in cybersecurity and information systems auditing. His analytical expertise is complemented by his experience in developing ensemble approaches to minimize errors in data models. Marcelo also brings practical knowledge in data governance and policy application, supported by his academic research, which is published in journals like Expert Systems with Applications and Data in Brief. These skills position him as a research-driven professional with advanced capabilities in designing, implementing, and evaluating technology-based solutions for complex public sector challenges.

Awards and Honors

While Marceloโ€™s curriculum does not explicitly mention awards, his achievements reflect recognition through certifications and high-impact publications. His certifications, including CISA and ISO 31000:2018 for risk management, demonstrate his commitment to maintaining industry standards and developing expertise in information systems and public sector accountability. Marceloโ€™s acceptance of his work in respected journals, such as Data in Brief and Expert Systems with Applications, further highlights his research contributions. His participation in leadership training at the prestigious ร‰cole Nationale dโ€™Administration (ENA) also underscores his standing as a thought leader in the public sector. By achieving a high level of proficiency in his certifications and continuing professional development, Marcelo has positioned himself as a well-regarded expert in his field, aligning with the standards expected for research awards in public administration and technology applications.

Conclusion

Marcelo Vasconcelos demonstrates a robust profile for the Best Researcher Award, combining practical public sector expertise with advanced research in technology and data analytics. His work in assessing corruption risk through imbalanced learning models addresses critical issues, showcasing his contribution to public administration and AI fields. Strengthening his academic engagement and expanding his research scope could enhance his candidacy further, positioning him as a well-rounded researcher with substantial contributions to his field.

Publication Top Notes

  • Title: Mitigating False Negatives in Imbalanced Datasets: An Ensemble Approach
    • Publication: Expert Systems with Applications
    • Year: 2025
    • DOI: 10.1016/j.eswa.2024.125674
    • Authors: Marcelo Vasconcelos, Luรญs Cavique
  • Title: Dataset for Corruption Risk Assessment in a Public Administration
    • Publication: Data in Brief
    • Year: 2022
    • DOI: 10.1016/j.dib.2021.107768
    • Authors: Marcelo Oliveira Vasconcelos, Luรญs Cavique
  • Title: Imbalanced Learning in Assessing the Risk of Corruption in Public Administration
    • Publication: Book Chapter in Imbalanced Learning in Assessing the Risk of Corruption in Public Administration
    • Year: 2021
    • DOI: 10.1007/978-3-030-86230-5_40
    • Authors: Marcelo Oliveira Vasconcelos, Ricardo Matos Chaim, Luรญs Cavique

 

Ali Ghandi | Artificial intelligence | Best Researcher Award

Ali Ghandi | Artificial intelligence | Best Researcher Award

PhD, Sharif University of Technology, Iran.

Ali Ghandi is an innovative researcher and educator specializing in Artificial Intelligence, particularly in reinforcement learning and generative AI. Currently pursuing his Ph.D. at Sharif University of Technology, he is known for his groundbreaking work that enhances reinforcement learning processes by leveraging side-channel data. Ali’s academic journey began with a B.Sc. in Digital System Design, followed by an M.Sc. in Machine Learning, where he excelled as one of the top students. He has taught courses in Neural Networks and Deep Generative Models, effectively sharing his knowledge with students. His research has been recognized through publications in reputable journals and presentations at significant conferences, such as the Iran Workshop on Communication and Information Theory. Ali’s accomplishments include a top rank in a national entrance exam and membership in Iranโ€™s National Elites Foundation, underscoring his exceptional capabilities and contributions to the field of AI and his commitment to advancing technology for practical applications.

Profile:

 

Education

Ali Ghandi has an impressive academic background in electrical and computer engineering, with a particular focus on Artificial Intelligence. He is currently pursuing a Ph.D. at Sharif University of Technology (SUT) in Tehran, where he is conducting innovative research aimed at improving reinforcement learning processes using side-channel data. Prior to his doctoral studies, Ali earned his Masterโ€™s degree in Machine Learning from SUT, where his thesis focused on analyzing IoT systems through location-based data, effectively modeling traffic based on dynamic maps and registered commutes. He completed his Bachelorโ€™s degree in Digital System Design at the same university, where he developed an online coordinate system for managing thermal loads in IoT applications. Throughout his educational journey, Ali has consistently demonstrated academic excellence, evidenced by his top rankings in national examinations and competitive academic events, establishing him as a leading figure among his peers in the field of electrical engineering and AI.

Professional Experiences

Ali Ghandi has an impressive academic background in electrical and computer engineering, with a particular focus on Artificial Intelligence. He is currently pursuing a Ph.D. at Sharif University of Technology (SUT) in Tehran, where he is conducting innovative research aimed at improving reinforcement learning processes using side-channel data. Prior to his doctoral studies, Ali earned his Masterโ€™s degree in Machine Learning from SUT, where his thesis focused on analyzing IoT systems through location-based data, effectively modeling traffic based on dynamic maps and registered commutes. He completed his Bachelorโ€™s degree in Digital System Design at the same university, where he developed an online coordinate system for managing thermal loads in IoT applications. Throughout his educational journey, Ali has consistently demonstrated academic excellence, evidenced by his top rankings in national examinations and competitive academic events, establishing him as a leading figure among his peers in the field of electrical engineering and AI.

 

Research skills

Ali Ghandi possesses a strong set of research skills that position him as a leading figure in the field of Artificial Intelligence. His primary focus is on reinforcement learning, where he has developed innovative approaches, such as utilizing side-channel data to enhance the learning process. Ali’s expertise extends to deep generative models, where he explores the potential of generative AI in various applications. Additionally, he is adept at massive data mining, allowing him to extract valuable insights from large datasets, which is crucial in todayโ€™s data-driven world. His research also includes analyzing IoT systems, particularly in modeling traffic using location-based data. This multifaceted skill set enables Ali to approach complex problems with a comprehensive perspective, combining theoretical knowledge with practical applications. His ability to publish in reputable journals and present at conferences demonstrates his commitment to advancing the field and contributing to the academic community.

 

Awards And Recoginition

Ali Ghandi has received numerous accolades that underscore his academic excellence and contributions to the field of Artificial Intelligence. He achieved a remarkable 68th rank in a highly competitive university entrance exam, placing him among the top candidates out of 250,000 participants. His outstanding performance in the International A-lympiad, where he ranked third, showcases his proficiency in applied mathematics within a global context. Additionally, Ali has been a member of Iranโ€™s National Elites Foundation since 2013, reflecting his recognition as a leading talent in his field. His academic journey at Sharif University of Technology has been marked by multiple distinctions, including first place among students in his Digital Systems minor and second place among all M.Sc. Electrical Engineering students. These honors highlight Ali’s commitment to excellence in research and education, positioning him as a promising contributor to the advancement of Artificial Intelligence.

Conclusion

In conclusion, Ali Ghandi possesses a solid foundation of academic excellence, innovative research, and early recognition in his field. His focus on advanced topics within AI positions him well for the Best Researcher Award. By addressing areas for improvement, such as increasing the practical impact of his work and expanding his collaborative efforts, Ali can further enhance his candidacy for this prestigious recognition. His commitment to advancing knowledge in AI and machine learning makes him a strong contender for the award.

Publication Top Notes

  • Title: Ex-RL: Experience-based Reinforcement Learning
    Authors: Ghandi, A., Shouraki, S.B., Gholampour, I., Kamranian, A., Riazati, M.
    Year: 2025
    Citation: Information Sciences, 689, 121479 ๐Ÿ“š๐Ÿค–
  • Title: Deep ExRL: Experience-Driven Deep Reinforcement Learning in Control Problems
    Authors: Ghandi, A., Shouraki, S.B., Riazati, M.
    Year: 2024
    Citation: 12th Iran Workshop on Communication and Information Theory (IWCIT 2024) ๐Ÿ“„๐Ÿ”