Diogo Santiago | Computer Science | Best Researcher Award

Published on by Book of Award

Mr. Diogo Santiago | Computer Science | Best Researcher Award

Oracle | Brazil

Mr. Diogo Santiago is a highly accomplished technology professional with extensive experience spanning software engineering, big data, and artificial intelligence. Beginning his career in 2009 as a software engineer developing major e-commerce platforms in Brazil, he transitioned into data engineering and science, mastering technologies like Hadoop, Spark, Hive, and Sqoop for large-scale data processing and migration. Since 2018, he has specialized in data science and AI, contributing to diverse projects in computer vision, anomaly detection, logistics optimization, and generative AI, including GAN and diffusion model applications for virtual try-on systems. As an AI Architect at Oracle for LATAM, he designs advanced AI architectures, supports clients with resource planning, and enhances model deployment efficiency through GPU optimization and large language model serving using vLLM and SGLang. His prior roles at Lambda3, Tivit, and Qintess involved developing ML models, data pipelines, and automation systems using cloud technologies such as GCP, AWS, and OCI. With multiple postgraduate qualifications in Big Data and Machine Learning for Finance, along with a Master’s in Medical Texture Imaging, he exemplifies innovation and leadership in merging AI research with scalable enterprise solutions.

Profile : Orcid

Featured Publication

Adorno, P. L. V., Jasenovski, I. M., Santiago, D. F. D. M., & Bergamasco, L. (2023, May 29). Automatic detection of people with reduced mobility using YOLOv5 and data reduction strategy. Conference paper.

 

Luis Cavique | Computer Science | Best Research Award

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Prof. Luis Cavique | Computer Science | Best Research Award

Universidade Aberta | Portugal

Author Profile

Scopus

Orcid

Early Academic Pursuits 🎓

Prof. Luís Cavique began his academic journey with a strong foundation in Computer Science, earning a degree in Computer Science Engineering in 1988 from the Faculty of Science and Technology at the New University of Lisbon. His pursuit of advanced knowledge continued with a Master’s in Operational Research and Systems Engineering in 1994 from the Instituto Superior Técnico, Technical University of Lisbon, where he focused on complex problems like crew scheduling. His academic commitment culminated in 2002 with a PhD in Engineering Systems, where he explored meta-heuristics for the Maximum Clique Problem, emphasizing applications in market basket analysis. This robust educational background set the stage for a career marked by analytical depth and academic rigor.

Professional Endeavors 👨‍🏫

Prof. Cavique’s teaching career spans several decades, beginning in 1991 in the Polytechnic Education System in Portugal, where he held adjunct positions at Setúbal and Lisbon Polytechnic Institutes until 2008. Since then, he has served as an Assistant Professor with tenure at the Universidade Aberta, focusing on computer science within the Department of Sciences and Technology. His dedication to fostering knowledge extends to graduate and doctoral levels, where he teaches courses such as Data Mining, Social Network Analysis, and Optimization. Beyond academia, Prof. Cavique also gained hands-on experience in the banking sector as a Systems Engineer at Banco Pinto & Sotto Mayor and through internships at prominent institutions like Banco Espírito Santo and the National Laboratory of Civil Engineering.

Contributions and Research Focus 🔍

With a strong interdisciplinary approach, Prof. Cavique’s research bridges Computer Science and Engineering Systems, focusing primarily on heuristic optimization and data mining. His work has addressed three core data mining challenges: classification, association, and segmentation. Notable publications include groundbreaking algorithms and tools, such as the LAID algorithm for classification, Ramex for association in financial product analysis, and ComDetection for community detection in social networks. These contributions have positioned Prof. Cavique at the forefront of data-driven research, and his methods are applied widely in sectors requiring complex data analysis.

Accolades and Recognition 🏆

Prof. Cavique's scholarly work has been recognized internationally, with several of his papers published in prestigious journals. His 1999 paper on crew scheduling received the IFORS-Lisbon Prize in 2000 from the Association of Operational Research in Portugal (APDIO). Many of his publications are highly cited, with articles featured in Q1-ranked journals, illustrating the high impact and quality of his research. His dedication to advancing data mining and optimization has earned him both peer recognition and a strong citation record, showcasing his influence in these fields.

Impact and Influence 🌍

Throughout his career, Prof. Cavique has made a lasting impact on the fields of data mining and heuristic optimization. His research has influenced approaches in financial analytics, community detection in social networks, and data reduction techniques, providing foundational tools and algorithms that are utilized in academia and industry alike. His methodologies have empowered researchers and practitioners in various domains to make informed, data-driven decisions, underscoring his role as a pioneer in computational research.

Legacy and Future Contributions 🌟

As a leading academic and researcher, Prof. Cavique’s legacy is defined by his contributions to both knowledge and education in computer science. His focus on heuristic optimization and data mining continues to inspire new research, particularly in emerging fields such as bioinformatics and social network analysis. His commitment to teaching and mentoring the next generation of scientists ensures that his impact will extend well into the future, enriching the scientific community and driving innovation in computational methods.

 

Publications

  • 📝 Mitigating false negatives in imbalanced datasets: An ensemble approach
    Authors: Marcelo Vasconcelos; Luís Cavique
    Journal: Expert Systems with Applications
    Year: 2024
  • 📝 Assessment in Collaborative Learning
    Authors: Luis Cavique; M. Rosário Ramos
    Journal: Revista de Educación a Distancia (RED)
    Year: 2024

 

Xinhai Wang | Computer Science | Best Researcher Award

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Mr. Xinhai Wang | Computer Science | Best Researcher Award

Northeastern University | China

Author Profile

Orcid

Early Academic Pursuits 🎓

Mr. Xinhai Wang's academic journey began with an undergraduate degree in Mathematics and Applied Mathematics from Northeastern University, where he achieved a GPA of 3.81/5. His academic excellence earned him several accolades, such as the "Outstanding Student Cadre" and "Three Good Students" awards, reflecting his dedication to both academics and extracurricular activities. Wang was actively involved in numerous projects during his undergraduate years, honing his skills in advanced algebra, data mining, and mathematical modeling, laying the groundwork for his future endeavors.

Professional Endeavors 🏆

In September 2022, Xinhai Wang assumed the role of monitor for Northeastern University's Master of Science Class 2201, demonstrating exemplary leadership and organizational skills. His work extended beyond the classroom, where he helped in the construction of class activities and assisted in Party branch operations. Wang was awarded the honorary title of Outstanding Graduate Student Cadre for his relentless efforts in promoting student engagement and fostering a collaborative environment. As a deputy director in the Project Development Department of the Social Practice Department, he organized impactful student initiatives such as charity sales, making significant contributions to the student community.

Contributions and Research Focus 🔬

Mr. Wang's contributions to academia and research are vast, with his work primarily centered on applying advanced algorithms in real-world scenarios. He has engaged in several high-level projects, including the application of genetic algorithms in mobile chess and using deep learning techniques like Deep Q Networks for stock market predictions. His research has tackled challenges in time series prediction, exploring fractional order random configuration networks (FSCN) to address the inherent non-stationarity in real-world data. These projects showcase his technical expertise in MATLAB and Python, alongside his growing knowledge of reinforcement learning and machine learning.

Accolades and Recognition 🏅

Xinhai Wang's academic brilliance has been recognized throughout his career, both during his undergraduate and graduate studies. His GPA of 3.40/4 ranked him 2nd in his class, further earning him prestigious honors such as the President Scholarship and First-Class Academic Scholarship. His leadership in class and organizational roles has led to multiple "Outstanding Class Cadre" awards. Wang's academic achievements extend beyond his GPA and awards, with his research work being submitted to conferences and awaiting SCI journal reviews, positioning him as a rising star in applied statistics and data science.

Impact and Influence 🌟

Through his roles in student governance and research, Wang has had a lasting impact on both his peers and the academic community. He has innovated branch activities, guided students in social practice initiatives, and created platforms for broader engagement in scientific and social matters. His research endeavors, such as the application of deep learning to stock prediction and time series analysis, contribute to the growing body of knowledge in the field of statistical modeling and artificial intelligence, influencing future technological advancements.

Legacy and Future Contributions 💡

Mr. Xinhai Wang's journey reflects a commitment to excellence in academic leadership, research, and innovation. As he continues to explore the boundaries of machine learning, algorithm design, and data modeling, his future contributions will likely have a profound effect on emerging fields like stock prediction and industrial data analysis. His ongoing projects in MATLAB and Python, combined with his growing expertise in reinforcement learning, position him for future success in both academic and professional arenas.

 

Publications

📄  Prediction of Ship-Unloading Time Using Neural Networks
Author: Zhen Gao, Danning Li, Danni Wang, Zengcai Yu, Witold Pedrycz, Xinhai Wang
Journal: Applied Sciences
Year: 2024-09

📄  Novel Admissibility Criteria and Multiple Simulations for Descriptor Fractional Order Systems with Minimal LMI Variables
Author: Xinhai Wang, Jin-Xi Zhang
Journal: Fractal and Fractional
Year: 2024-06