Dr. José Carlos López Figueroa | Digital Leadership | Best Researcher Award

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Dr. José Carlos López Figueroa | Digital Leadership | Best Researcher Award

Teacher, Sonora Institute of Technology, Mexico

Dr. José Carlos López Figueroa is a distinguished academic and researcher in the field of management and organizational studies. Currently serving as a Professor and Coordinator of the Master’s Program in e-Business at the Sonora Institute of Technology, he has made significant contributions to digital leadership, governance, and institutional research. With a Ph.D. in Organizational Studies from the Universidad Autónoma Metropolitana (UAM), he has been recognized with the prestigious third place in the National Award for Doctoral Theses. As an active researcher, he is a Level Candidate Member of the National System of Researchers (SNII-México) and has published extensively in high-quality indexed journals. His collaborations with national and international networks, including REMINEO and Huika Mexihco, further solidify his impact on the academic and business communities.

Publication Profile

🎓 Academic Background

Dr. López Figueroa earned his Ph.D. in Organizational Studies from the Universidad Autónoma Metropolitana (UAM), demonstrating his strong foundation in research and management sciences. His doctoral work was highly acclaimed, securing third place in the National Award for Doctoral Theses. His research expertise spans across leadership, governance, institutions, and digital transformation in SMEs. He has authored numerous research articles and book chapters, contributing significantly to the understanding of managerial and organizational dynamics in various sectors.

💼 Professional Experience

With a profound academic career, Dr. López Figueroa has actively engaged in both teaching and research. As the coordinator of the Master’s Program in e-Business at the Sonora Institute of Technology, he has played a crucial role in shaping future professionals in digital business strategies. His expertise in managerial skills, entrepreneurship, public policy, and marketing strategies has been instrumental in guiding various research projects. He has successfully coordinated studies like “Managerial Management Practices as a Key Factor for the Development of Competitive Advantage in SMEs in Southern Sonora” and “Digital Leadership Skills in SMEs,” producing impactful research outputs, including indexed articles and theses. Additionally, he has contributed as a principal investigator and collaborator in 12 externally funded research projects.

🏆 Awards and Honors

Dr. López Figueroa has been recognized for his scholarly excellence with multiple accolades. His Ph.D. dissertation received third place in the National Award for Doctoral Theses, showcasing the significance of his research. He is a Level Candidate Member of the National System of Researchers (SNII-México), reflecting his growing influence in academic research. Beyond awards, he holds key editorial positions, including serving as General Coordinator and Member of the Academic Committee for the National Congress on Organizational Studies. His contributions to the field have also been acknowledged through invitations as a volunteer reviewer for prestigious indexed journals.

🔬 Research Focus

Dr. López Figueroa’s research interests center around leadership, governance, territory, and institutional studies, with a particular emphasis on digital transformation in SMEs. His work explores how managerial and organizational practices influence competitive advantage in businesses. Through his extensive involvement in research projects, he has analyzed key areas such as digital leadership, organizational culture, entrepreneurship, and institutional logics applied to various economic sectors. His research integrates methodologies like survey-based analysis, statistical modeling, and systematic reviews, ensuring a robust and data-driven approach. His findings have been widely disseminated through journal publications, book chapters, conferences, and podcasts, reinforcing his thought leadership in organizational studies.

🔍 Conclusion

Dr. José Carlos López Figueroa is a pioneering researcher and educator dedicated to advancing knowledge in digital leadership and organizational studies. His impactful research, collaborative projects, and commitment to student mentorship have positioned him as an influential figure in academia. His ability to bridge theoretical research with practical business applications has made significant contributions to SMEs and institutional governance. Through his collaborations with REMINEO, Huika Mexihco, and international institutions, he continues to shape the future of management and digital transformation. His scholarly excellence, extensive publications, and research-driven initiatives make him a strong candidate for the Best Researcher Award.

📚 Top Publications 

La organización digital: comprensión de un campo y tendencias de estudio (2024)

Territorio y organizaciones: un análisis bibliométrico en Web of Science (2024)

Digital Leadership Skills for SMEs in Northwest Mexico (2023)

Governance and Institutions in Organizational Studies (2023)

Managerial Management Practices and Competitive Advantage in SMEs (2022)

Obsa Gilo Wakuma | Computer Science | Best Researcher Award

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Dr. Obsa Gilo Wakuma | Computer Science | Best Researcher Award

Ass. Prof, Wallaga University, Ethiopia

Dr. Obsa Gilo Wakuma is a dedicated computer scientist specializing in deep learning and domain adaptation. With extensive experience in academia, he has contributed significantly to the field through his research and teaching. Dr. Obsa has held various positions at Wallaga University, Ethiopia, and currently serves as a research scholar at the Indian Institute of Technology Patna, India. His expertise spans multiple programming languages and database management systems, making him a versatile and valuable contributor to the field of computer science.

Profile

Strengths for the Award:

  1. Extensive Research Background: Dr. Obsa Gilo has a robust academic background, culminating in a Ph.D. in Computer Science and Engineering with a focus on deep learning approaches for efficient domain adaptation. His research in domain adaptation, particularly in sensor data and image classification, showcases his innovative contributions to the field.
  2. Publications: Dr. Gilo has a significant number of publications in reputed journals and conference proceedings. Notable among them are:
    • “Kernel bures metric for domain adaptation in sensor data” in Expert System with Applications (2024)
    • “Subdomain adaptation via correlation alignment with entropy minimization for unsupervised domain adaptation” in Pattern Analysis and Applications (2024)
    • “Rdaot: Robust unsupervised deep sub-domain adaptation through optimal transport for image classification” in IEEE Access (2023)
  3. Teaching and Mentoring: His employment history includes roles such as Lecturer and Graduate Assistant at Wallaga University, demonstrating his commitment to education and mentoring the next generation of scholars.
  4. Skills and Competencies: Dr. Gilo possesses strong technical skills in various programming languages, databases, and web development technologies, along with proficiency in English, Afaan Oromoo, and Amharic. This multilingual ability enhances his capacity to engage with diverse communities.
  5. Community Service: His experience includes academic research, teaching, training, consultation, and community service, reflecting a well-rounded professional dedicated to both academic and societal contributions.

Areas for Improvement:

  1. Practical Community Impact: While Dr. Gilo has an impressive academic and research portfolio, there could be more emphasis on the practical application of his research directly benefiting local communities. Highlighting specific projects or initiatives where his work has directly impacted community development would strengthen his case.
  2. Collaboration with Local Institutions: Greater collaboration with local institutions and involvement in projects addressing community-specific issues could further enhance his profile. Establishing partnerships with local universities, NGOs, or governmental bodies to implement his research findings in real-world settings would be beneficial.
  3. Visibility and Outreach: Increasing the visibility of his work through public lectures, community workshops, or outreach programs can help in demonstrating the broader societal impact of his research. Engaging with the community through these platforms can showcase the practical benefits of his research.

 

Education: 🎓

Dr. Obsa Gilo Wakuma holds a Ph.D. in Computer Science and Engineering from the Indian Institute of Technology Patna, with a thesis on “Deep Learning Approaches for Efficient Domain Adaptation.” He earned his M.Sc. in Computer Science from Wallaga University, where he developed an Information Extraction Model for Afaan Oromo news texts. Dr. Obsa also holds a B.Sc. in Computer Science from Wallaga University and has a strong foundation from Sibu Sire Preparatory and High School.

Experience: 💼

Dr. Obsa’s professional journey includes roles such as Recorder and Laboratory Technician at Wallaga University. He progressed to become a Graduate Assistant, Lecturer, and finally a Research Scholar at IIT Patna. His career reflects a blend of administrative, technical, and academic responsibilities, showcasing his diverse skill set and commitment to the field.

Research Interests: 🔍

Dr. Obsa’s research interests lie in deep learning, domain adaptation, and unsupervised learning. He has focused on developing efficient methods for domain adaptation in sensor data and image classification, contributing to several high-impact publications. His work aims to enhance the applicability and robustness of machine learning models in diverse environments.

Awards: 🏆

Dr. Obsa has been recognized for his academic excellence and research contributions throughout his career. He has received accolades for his innovative work in domain adaptation and deep learning, highlighting his role as a prominent researcher in the field of computer science.

Publications

  1. Unsupervised Sub-Domain Adaptation Using Optimal Transport. Journal of Visual Communication and Image Representation, 94, 103857. Cited by: 1 article.
  2. Kernel Bures Metric for Domain Adaptation in Sensor Data. Expert System with Applications, 255(Part C), 124725. Cited by: 1 article.
  3. Subdomain Adaptation via Correlation Alignment with Entropy Minimization for Unsupervised Domain Adaptation. Pattern Analysis and Applications, 27(1), 13. Cited by: 1 article.
  4. RDAOT: Robust Unsupervised Deep Sub-Domain Adaptation through Optimal Transport for Image Classification. IEEE Access. Cited by: 1 article.
  5. Integration of Discriminate Features and Similarity Preserving for Unsupervised Domain Adaptation. In 2022 IEEE 19th India Council International Conference (INDICON), pp. 1–6. Cited by: 1 article.

 

Ke Wu | Computer Science | Best Dissertation Award

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Prof. Ke Wu | Computer Science | Best Dissertation Award

professor, China University of Geosciences (Wuhan), China

Dr. Ke Wu is a distinguished professor at the China University of Geosciences, specializing in hyperspectral remote sensing and its applications in geosciences 🌏. Born on October 2, 1981, in Hubei, China, Dr. Wu has established himself as a leading expert in his field, contributing significantly to research and education 📚. Fluent in both Chinese and English, he excels in both written and spoken communication, making him a valuable asset to the academic community.

Profile

ORCID

 

Education

Dr. Ke Wu holds a Ph.D. in Photogrammetry and Remote Sensing from Wuhan University (2008) 🎓, where he also completed his B.S. in Information Engineering (2002) 🏫. His advanced education has provided a strong foundation for his research and teaching career in remote sensing and geophysics.

Experience

Since January 2020, Dr. Ke Wu has been a professor at the China University of Geosciences 👨‍🏫. Prior to this, he served as an associate professor from 2011 to 2019 and as a postdoctoral researcher in geophysics from 2009 to 2011. His extensive experience in academia has enabled him to mentor many students and contribute to numerous research projects.

Research Interests

Dr. Ke Wu’s research interests focus on hyperspectral remote sensed image processing and its applications in geosciences 🔬. He has led several significant research projects funded by the National Natural Science Foundation of China and other prestigious organizations. His work aims to advance the understanding and practical applications of remote sensing technologies.

Awards

In recognition of his contributions to the field, Dr. Ke Wu and his team have received numerous awards 🏆. Notably, in 2022, they won the third prize in the National Hyperspectral Satellite Remote Sensing Image Intelligent Processing and Industry Application Competition of the “Obit Cup”. His group also secured the third prize in the South Division of the “Yuan Chuang Cup” Innovation and Creativity Competition in 2019 and the first prize of the Surveying and Mapping Science and Technology Progress Award of the China Society of Surveying, Mapping, and Geographic Information in 2017.

Publications

Junfei Zhong, Ke Wu, Ying Xu* (2024). “Deep Spatial Feedback Refined Network with Multi-Level Feature Fusion for Hyperspectral Image Sub-pixel Mapping,” IEEE Transactions on Geoscience and Remote Sensing. DOI: 10.1109/TGRS.2024.3419157 – Cited by: 3 articles

Ke Wu, Fan Yang, Huize Liu, Ying Xu* (2024). “Detection of coral reef bleaching by multitemporal Sentinel-2 data using the PU-bagging algorithm: A feasibility study at Lizard Island,” Remote Sens. DOI: 10.3390/rs16132473 – Cited by: 5 articles

Ke Wu, Yanting Zhan, Ying An, Suyi Li* (2024). “Multiscale Feature Search-Based Graph Convolutional Network for Hyperspectral Image Classification,” Remote Sens. DOI: 10.3390/rs16132328 – Cited by: 4 articles

Wenjie Tang, Ke Wu, Yuxiang Zhang, Yanting Zhan* (2023). “A Siamese Network Based on Multiple Attention and Multilayer Transformer for Change Detection,” IEEE Transactions on Geoscience and Remote Sensing. DOI: 10.1109/TGRS.2023.3325220 – Cited by: 6 articles

Yanting Zhan, Ke Wu, Yanni Dong* (2022). “Enhanced Spectral–Spatial Residual Attention Network for Hyperspectral Image Classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. DOI: 10.1109/JSTARS.2022.3197934 – Cited by: 8 articles