Patricia Gomes Rêgo de Almeida | Digital Transformation | Best Paper Award- 3985

Mrs. Patricia Gomes Rêgo de Almeida | Digital Transformation | Best Paper Award

Mrs. Patricia Gomes Rêgo de Almeida, Chamber of Deputies of Brazil.

Dr. Patricia Gomes Rêgo de Almeida is a distinguished leader in digital governance and artificial intelligence policy. With a PhD in Public Administration from the University of Brasília, her work explores the intersection of AI and institutional governance. She currently serves as the Head of Innovation, IT Governance, and Digital Strategy at the Brazilian Chamber of Deputies, leading the national legislature’s digital transformation efforts. Dr. Almeida is also a prominent figure in the global parliamentary tech space, coordinating the Parliamentary Data Science Hub at the Inter-Parliamentary Union (IPU). Her pioneering role in drafting AI Guidelines for Parliaments reflects her deep commitment to ethical, transparent, and innovative public sector modernization. Her leadership combines technical expertise with policy foresight, making her a pivotal voice in AI governance worldwide.

👨‍🎓 Profile

Scopus

🎓 Education

Dr. Patricia Almeida holds a PhD in Public Administration from the University of Brasília (UnB), with a focused research on Artificial Intelligence Governance—an area that explores regulatory, ethical, and strategic frameworks for the implementation of AI in public institutions. Prior to her doctoral studies, she earned a Master’s degree in Electrical Engineering from the Federal University of Rio Grande do Norte (UFRN), where she gained foundational knowledge in computing systems and data-driven technologies. Her unique blend of engineering and public administration education allows her to approach technology policy with both analytical precision and institutional insight. Her academic background reflects a rare interdisciplinary expertise, positioning her at the forefront of emerging debates around the governance of emerging technologies in democratic institutions.

💼 Professional Experience

Currently, Patricia serves as the Head of Innovation, IT Governance, and Digital Strategy at the Brazilian Chamber of Deputies, where she designs and executes the legislature’s Digital Transformation Strategy. She is responsible for steering AI project portfolios, defining institutional AI policy, and leading Data Fluency initiatives across the chamber. Her work ensures that Brazil’s legislative processes integrate ethical, inclusive, and forward-looking AI tools. Additionally, she is the Coordinator of the Parliamentary Data Science Hub at the Inter-Parliamentary Union (IPU)’s Centre for Innovation in Parliaments. In this role, she led the development of the AI Guidelines for Parliaments, establishing a global benchmark for responsible AI use in legislative contexts. Patricia’s dual leadership roles reflect her ability to navigate complex institutional environments while driving innovation.

🔬 Research Interests

Patricia’s research focuses on Artificial Intelligence Governance, digital transformation in the public sector, data ethics, and institutional transparency. Her work explores how parliaments and legislative institutions can harness AI in a trustworthy, accountable, and human-centered manner. Her academic and professional inquiry covers critical areas such as algorithmic decision-making, AI policy frameworks, ethical data management, and public trust in AI systems. She is especially interested in designing institutional safeguards that ensure responsible AI deployment while promoting citizen engagement and transparency. Patricia’s research is deeply policy-oriented and grounded in real-world applications, making a tangible impact on how public institutions think about and implement AI systems.

🏆 Awards & Honors

Patricia Almeida has received wide recognition for her contributions to AI policy and innovation in government. While specific named awards have not been publicly listed, her appointment as Coordinator of the Parliamentary Data Science Hub at IPU and her leadership in crafting the AI Guidelines for Parliaments signify international acclaim and trust in her expertise. Her work has been featured in global forums that bring together policymakers, technologists, and scholars, positioning her as a key voice in the development of digital governance models. Her achievements reflect not just personal excellence, but also her broader commitment to ethical leadership in the age of artificial intelligence.

📚 Publications

Cited by multiple institutions for responsible legislative AI implementation practices.

Gebeyehu B Gebremeskel | Innovation | Excellence in Research

Assoc Prof. Dr. Gebeyehu B Gebremeskel | Innovation | Excellence in Research

Assoc Prof Dr. Gebeyehu B Gebremeskel, Bahir Dar University, Ethiopia

Dr. Gebeyehu Belay Gebremeskel is an esteemed Associate Professor at Bahir Dar University, Ethiopia, specializing in Artificial Intelligence and Data Science. With over three decades of academic and research experience, he has significantly contributed to the fields of Machine Learning, Big Data Analytics, and Intelligent Systems. His academic journey includes a Ph.D. and Postdoctoral fellowship from Chongqing University, China, and an M.Sc. from London South Bank University, UK. Dr. Gebremeskel has published extensively, with over 35 academic papers, and has been instrumental in curriculum development and international conference organization.

Profile 👤 

Scopus

Education 🎓

Dr. Gebremeskel’s academic journey began with a B.Sc. from Alemaya University, Ethiopia. He then earned an M.Sc. in Advanced Information Technology from London South Bank University, UK, focusing on system dynamics modeling and decision science. Pursuing further specialization, he obtained a Ph.D. in Engineering from Chongqing University, China, where his dissertation centered on integrating data mining algorithms and multi-agent systems with business intelligence. He also completed a postdoctoral fellowship at Chongqing University, emphasizing machine learning and intelligent system control.

Experience 💼

Dr. Gebremeskel has held various academic and administrative roles, including Associate Professor at Bahir Dar University. He has been instrumental in curriculum development, program accreditation, and organizing international conferences. His teaching portfolio spans undergraduate to postgraduate courses, covering topics like artificial intelligence, machine learning, data mining, and big data analytics. He has also supervised numerous master’s and Ph.D. students, contributing to the growth of research in his field

Research Interests 🔬

His research interests encompass artificial intelligence, machine learning, big data analytics, data mining, intelligent systems, and business intelligence. He focuses on developing algorithms and models that enhance decision-making processes, optimize system performance, and address real-world challenges in various domains, including healthcare, agriculture, and transportation.

Awards 🏆

Dr. Gebremeskel’s contributions have been recognized through various awards and honors. Notably, he has been acknowledged for his work in developing intelligent systems and enhancing data analytics methodologies. His research has had a significant impact on both academic and practical applications, earning him a reputable standing in the scientific community.

Publications 📚

Dr. Gebremeskel has an extensive list of publications in reputable journals and conferences. Some of his notable works include:

“Leveraging big data analytics for intelligent transportation systems: optimize the internet of vehicles data structure and modeling,” published in the International Journal of Data Science and Analytics, 2023.

“Architecture and optimization of data mining modeling for visualization of knowledge extraction: Patient safety care,” published in the Journal of King Saud University – Computer and Information Sciences, 2022.

“Augmenting machine learning for Amharic speech recognition: a paradigm of patient’s lips motion detection,” published in Multimedia Tools and Applications, 2022.

“A critical analysis of the multi-focus image fusion using discrete wavelet transform and computer vision,” published in Soft Computing, 2022.

“Data mining misnomer nomenclature: myth or myopic based on its evolutional and trend analysis,” published in the International Journal of Knowledge Engineering and Data Mining, 2019.

“Combined data mining techniques based patient data outlier detection for healthcare safety,” published in the International Journal of Intelligent Computing and Cybernetics, 2016.

“Critical analysis of smart environment sensor data behavior pattern based on sequential data mining techniques,” published in Industrial Management & Data Systems, 2015.