Guillermina Avila Garcia | Information Technology | Innovative Research Award

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Innovative Research Award

Guillermina Ávila García
National Polytechnic Institute (Instituto Politécnico Nacional), Mexico

Guillermina Ávila García
Affiliation National Polytechnic Institute
Country Mexico
Google Scholar uVTVBPwAAAAJ
Documents 20
Citations 99
h-index 5
Subject Area Artificial Intelligence
Event Top Teachers Awards
Scopus ID 57781072000
ORCID 0000-0001-5229-3384

The Innovative Research Award recognition highlights the scholarly achievements of Guillermina Ávila García, a Mexican researcher, educator, and science education specialist affiliated with the National Polytechnic Institute. Her academic work integrates physics education, educational technology, artificial intelligence applications in learning environments, mathematical modeling, and STEM innovation. Through research leadership, postgraduate supervision, international collaboration, and dissemination activities, she has contributed to advancing educational practice and evidence-based teaching methodologies within Mexico and abroad.[1][2]

Abstract

Guillermina Ávila García is a researcher and educator whose work focuses on physics education, mathematics education, educational innovation, technology-enhanced learning, and artificial intelligence in academic contexts. She earned a doctorate in sciences with specialization in physics education from CICATA-IPN, receiving honorable mention and the institutional award for the best postgraduate thesis in 2023. Her scholarly activities encompass research, teaching, curriculum development, international collaboration, conference participation, scientific dissemination, and postgraduate supervision. The combination of peer-reviewed publications, indexed research output, educational leadership, and recognition within the National System of Researchers demonstrates a sustained commitment to advancing science education and innovative pedagogical practices.[1][3]

Keywords

Artificial Intelligence; Physics Education; STEM Education; Educational Innovation; Mathematical Modeling; Educational Technology; Science Communication; Higher Education; Research Leadership; Learning Analytics.

Introduction

Guillermina Ávila García serves as a full-time professor at the Center for Scientific and Technological Studies No. 11 of the National Polytechnic Institute and collaborates with CICATA and CIECAS. Her academic trajectory spans undergraduate, master’s, and doctoral education in physics, mathematics, scientific teaching, and physics education. She actively participates in national and international research networks dedicated to science education, mathematics education, and educational innovation. Her scholarly profile reflects continuous engagement in teacher training, curriculum improvement, scientific dissemination, and interdisciplinary educational research.[1][5]

Research Profile

Her research profile combines educational sciences, pedagogy, physics education, mathematics education, and technology-supported learning. She has completed more than forty specialized courses, seminars, workshops, and thirteen diplomas related to educational innovation, digital learning environments, curriculum development, statistical thinking, science communication, and artificial intelligence. Her participation in international conferences, including ICME-14, GIREP, CIAIQ, RELME, and AAPT-MX, demonstrates consistent scholarly engagement with contemporary educational challenges. She is also an active member of professional organizations and research networks dedicated to educational transformation and scientific literacy.[1][4]

Research Contributions

The principal contributions of Guillermina Ávila García involve the design and evaluation of innovative teaching methodologies in physics and mathematics, the integration of digital technologies into learning environments, and the promotion of critical thinking through modeling and simulation approaches. Her work explores blended learning, Moodle-based instruction, problem-based learning, educational technologies, and artificial intelligence applications in education. Research supervision activities include master’s and doctoral theses focused on educational innovation, STEM learning, environmental education, and AI-supported pedagogy. These contributions support the modernization of science education while emphasizing evidence-based instructional practices.[4][5]

  • Physics and mathematics education research.
  • Educational innovation using technology-enhanced learning.
  • Artificial intelligence applications in educational contexts.
  • STEM-oriented instructional design and assessment.
  • Postgraduate supervision and research mentoring.

Publications

Guillermina Ávila García has developed a publication record spanning peer-reviewed journal articles, conference proceedings, book chapters, and science dissemination works. Her publications address socio-emotional competencies, technology integration in physics education, problem-based learning, educational innovation, teacher training, mathematical modeling, and digital learning environments. Indexed research outputs and scholarly visibility through Google Scholar and Scopus demonstrate measurable academic influence. Notable works include studies on socio-emotional competencies in higher education, hybrid learning methodologies, and teacher preparation for technology-rich educational settings.[2][3][4][5]

  • The socio-emotional competencies of high school and college students in the National Polytechnic Institute (2022).
  • Tools for the implementation of PBL and DIPCING in engineering in a hybrid modality (2022).
  • Teacher training at the IPN high school level facing ICT challenges in physics teaching (2020).
  • Digital Natives or Zombies? (2021).
  • Multiple book chapters on educational innovation, technology integration, and science education.

Research Impact

Research impact indicators report approximately 99 Google Scholar citations across 20 indexed documents and an h-index of 5. Her scholarly influence extends beyond citation metrics through curriculum innovation, teacher professional development, thesis supervision, conference presentations, scientific outreach, and international collaboration. The researcher has participated in academic stays, including CERN in Switzerland, where experiences in particle physics dissemination informed educational initiatives implemented within Mexican institutions. Recognition through national awards and membership in Mexico’s National System of Researchers further supports the significance of her academic contributions.[1][2]

Award Suitability

The designation of Guillermina Ávila García for an Innovative Research Award is supported by a combination of academic excellence, educational leadership, interdisciplinary research, and demonstrated societal impact. Her record includes the Best Postgraduate Thesis Award, Cum Laude doctoral distinction, SNI Level 1 membership, international conference participation, postgraduate supervision, educational innovation projects, and contributions to science communication. These achievements collectively indicate a sustained commitment to advancing research-informed educational practice and fostering innovation within science and mathematics education.[1][5]

Conclusion

Guillermina Ávila García represents a research profile characterized by academic rigor, innovation in teaching and learning, interdisciplinary collaboration, and sustained engagement with educational transformation. Her contributions to physics education, artificial intelligence applications in learning, teacher development, and STEM pedagogy have generated scholarly outputs, educational resources, and professional recognition. The evidence presented through publications, citations, awards, supervision activities, and institutional leadership supports the relevance of her nomination within the context of academic and research excellence.[1][2]

References

  1. Elsevier. (n.d.). Scopus author details: Guillermina Ávila García, Author ID 57781072000. Scopus. https://www.scopus.com/authid/detail.uri?authorId=57781072000
  2. Google Scholar. (n.d.). Guillermina Ávila García citation profile and indexed publications. https://scholar.google.com/citations?user=uVTVBPwAAAAJ&hl=en&oi=sra
  3. Huerta Cuervo, R., Téllez, L. S., Luna Acevedo, V. H., Ramírez Solís, M. E., et al. (2022). The socio-emotional competencies of high school and college students in the National Polytechnic Institute (Mexico). Social Sciences, 11(7), 278. DOI: https://doi.org/10.3390/socsci11070278
  4. Escobar Moreno, F., Ávila García, G., & Suárez Téllez, L. (2022). Tools for the implementation of PBL and DIPCING in engineering in a hybrid modality. Sinéctica. https://sinectica.iteso.mx/index.php/SINECTICA/en/article/view/1343
  5. García, G. Á., & Ramírez, M. L. H. (2020). Teacher training at the IPN high school level, facing the challenges of using ICT in physics teaching. Multidisciplinary Journal of Research Advances, 6(2), 14–22. https://www.remai.ipn.mx/index.php/REMAI/article/view/73

Mercedes Premalatha Ramesh | Computer Science and Artificial Intelligence | Young Scientist Award

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Young Scientist Award

Mercedes Premalatha Ramesh — Senior Research Engineer, Air Traffic Management Research Institute (ATMRI), and Part-time PhD Researcher in Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore.

Mercedes Premalatha Ramesh
Affiliation Nanyang Technological University (NTU)
Country Singapore
Google Scholar _TNjmuMAAAAJ
Documents 19
Citations 5
h-index 1
Subject Area Multi Agents and Reinforcement Learning
Event Top Teachers Awards
Scopus ID 59234522100
ORCID 0009-0007-7242-0898

Mercedes Premalatha Ramesh is a Senior Research Engineer at NTU’s Air Traffic Management Research Institute and a part-time PhD researcher in Mechanical and Aerospace Engineering. Her academic and professional experience spans aviation, robotics, intelligent manufacturing and semiconductor systems, with research focused on human-AI collaboration, multi-agent reinforcement learning and autonomous systems for safety-critical applications.[1]

Abstract

Mercedes Premalatha Ramesh’s research is situated at the intersection of artificial intelligence, autonomous systems, human factors and safety-critical aviation. Her work addresses multi-agent reinforcement learning, dynamic airspace sectorization, adaptive air traffic control automation, workload-aware decision support and collision-constrained robot swarms. Her research portfolio also includes low-resource language AI, illustrating an interdisciplinary approach to intelligent systems that combines computational methods with operational and human-centred requirements.[1]

Keywords

Computer Science; Artificial Intelligence; Multi-Agent Systems; Reinforcement Learning; Robot Perception; Swarm Robotics; Air Traffic Optimisation; Human-AI Collaboration; Autonomous Systems; Large Language Models.

Introduction

Mercedes Premalatha Ramesh’s academic profile combines engineering education with applied research in aviation and intelligent autonomous systems. She holds a Bachelor of Engineering in Electronics and Communication Engineering and a Master of Technology in Intelligent Systems from the National University of Singapore. Her professional R&D experience includes work associated with A*STAR ARTC, ST Engineering, Micron, Hindustan Aeronautics Limited and ROS-Industrial, providing an interdisciplinary foundation for research involving robotics, industrial systems and artificial intelligence.

At NTU ATMRI, her current research addresses the interaction between human operators and increasingly autonomous decision-support systems. This focus is particularly relevant to air traffic management, where AI-based optimisation must operate within safety, workload, interpretability and human-oversight constraints. Her research therefore considers both algorithmic performance and the conditions under which intelligent systems can be responsibly integrated into operational environments.

Research Profile

The research profile comprises six principal streams: AIR-LION and multi-sector planning for air traffic management simulation; multi-agent reinforcement learning for dynamic airspace sectorisation; adaptive automation and AI-assisted air traffic control coordination; workload-aware human-AI decision support; collision-constrained and shared-control robot swarms; and low-resource Tamil-Malay machine translation. These streams connect reinforcement learning, autonomous control, human factors and language technologies across different safety- and resource-constrained settings.

Her consultancy and industry-oriented R&D experience further connects academic research with practical engineering requirements. Work across aviation, robotics, intelligent manufacturing and semiconductor environments has contributed to a research orientation centred on deployability, system constraints and interdisciplinary collaboration.[1]

Research Contributions

Mercedes Premalatha Ramesh develops research approaches for trustworthy human-AI systems in safety-critical environments. Her contributions combine multi-agent reinforcement learning, human-factors evidence and autonomous-system control to address coordination and decision-support challenges while retaining human oversight. Areas of contribution include dynamic airspace sectorisation, adaptive air traffic control automation, workload-aware decision support and collision-constrained swarm control.[1]

Her interdisciplinary work additionally extends to low-resource language AI, including computational analysis and translation-support systems for Tamil and related language contexts. The resulting research portfolio links simulation and algorithm development with operational constraints and human-centred evaluation, providing a foundation for further investigation of explainable and responsible autonomy.

Publications

Available publication records indicate more than nine peer-reviewed indexed outputs during 2024–2026, covering multi-agent reinforcement learning, air traffic control automation, human-AI decision support, swarm robotics, robot navigation and low-resource language AI. The publication record includes work presented through IEEE venues and Springer-associated research, together with a 2026 article in Education Sciences. Bibliographic records and author profiles provide the principal basis for assessing the documented publication portfolio. [1] [3] [4] [5]

Representative research includes a 2024 IEEE ISIE contribution on quantitative analysis of robot navigation under rain conditions, a 2026 Education Sciences study examining stream-aware vocabulary demands in Singapore secondary Tamil textbooks, and a 2026 Human-Computer Interaction conference contribution addressing linguistic ambiguity in low-resource translation-support systems. [3] [4] [5]

Research Impact

The documented research profile demonstrates an interdisciplinary trajectory spanning artificial intelligence, aviation, robotics and language technologies. Its potential research impact lies particularly in connecting algorithmic approaches such as reinforcement learning with human-centred and safety-oriented requirements. Applications in dynamic airspace management, autonomous coordination and decision support are relevant to the development of intelligent systems that must operate under uncertainty while maintaining human supervision.

The publication record also demonstrates cross-domain collaboration, with research extending from autonomous robot navigation to educational language technologies. The available scholarly metrics report 19 documents, 5 citations and an h-index of 1 in the supplied profile data, while individual publications provide additional evidence of research activity and interdisciplinary engagement. [1] [2]

Award Suitability

For consideration under the Young Scientist Award category, Mercedes Premalatha Ramesh’s profile presents a combination of early-career research activity, multidisciplinary engineering experience and work in emerging AI applications. Particularly relevant elements include research on multi-agent reinforcement learning, human-AI collaboration, autonomous systems and safety-critical air traffic management, together with peer-reviewed scholarly outputs and industry-linked R&D experience.

The suitability assessment should be based on independently verifiable evidence, including indexed publications, documented research projects, institutional affiliation, scholarly profiles and supporting certificates. Professional memberships should be listed only where current membership can be documented. NTU identification and relevant academic or professional certificates may be supplied as supporting evidence during the award-submission process.[1] [2]

Conclusion

Mercedes Premalatha Ramesh’s research profile reflects an interdisciplinary focus on artificial intelligence, multi-agent learning, human-AI collaboration and autonomous systems. Her work connects aviation and robotics research with human-centred considerations and extends into low-resource language AI. The combination of applied R&D experience, peer-reviewed publications and research activity in safety-critical intelligent systems provides a documented basis for consideration within an early-career scientific recognition framework.[1]

References

  1. Google Scholar. (n.d.). Mercedes Premalatha Ramesh — Google Scholar author profile. https://scholar.google.com/citations?user=_TNjmuMAAAAJ&hl=en
  2. Elsevier. (n.d.). Scopus author details: Mercedes Premalatha Ramesh, Author ID 59234522100. Scopus. https://www.scopus.com/pages/authors/59234522100
  3. Ramu, U., Ramesh, M. P., Paranthaman, K., Khan, S. G. S., & Fern, T. C. (2024). Generalized framework for quantitative analysis of robot navigation under rain conditions. 2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE), 1–7. https://doi.org/10.1109/ISIE54533.2024.10595805
  4. Pal Thamburaj, K., & Ramesh, M. P. (2026). Stream-aware vocabulary demands in Singapore secondary Tamil textbooks: A morphology-and multiword-unit-sensitive corpus analysis, with a textbook-faithful GenAI item benchmark. Education Sciences, 16(8), 1270. https://doi.org/10.3390/educsci16081270
  5. Pal Thamburaj, K., & Ramesh, M. P. (2026). Deciphering divergence: Visualizing linguistic ambiguity in low-resource translation support systems. International Conference on Human-Computer Interaction, 581–591. https://link.springer.com/chapter/10.1007/978-3-032-30826-9_60

Sema Servi | Computer Science | Best Research Article Award

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Assist. Prof. Dr. Sema Servi | Computer Science | Best Research Article Award

Selçuk University | Turkey

Asst. Prof. Dr. Sema Servi is a researcher in computer engineering with a strong foundation in applied mathematics, specializing in machine learning, artificial intelligence, and numerical methods for complex problem solving. Her work focuses on data-driven approaches, including clustering algorithms, optimization techniques, and computer vision applications in healthcare and engineering. She has contributed to interdisciplinary research spanning digital competence analysis, bioinformatics, and intelligent systems. Asst. Prof. Dr. Sema Servi actively supervises postgraduate research and advises innovative, technology-driven projects supported by national programs. She has a solid research impact with 62 Scopus citations, 15 indexed documents, and an h-index of 5.

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Xulei Cao | Computer Science | Research Excellence Award

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Mr. Xulei Cao | Computer Science | Research Excellence Award

University of Science and Technology of China | China

Mr. Xulei Cao research centers on advancing intelligent communication systems, large-scale machine learning, and adaptive networked environments, with a primary emphasis on vehicular ad hoc networks (VANETs), device–edge–cloud collaboration, and large language models. His work explores street-centric and microtopology-based routing strategies to address the challenges of dynamic mobility, frequent topology changes, and complex urban communication environments, proposing opportunistic routing protocols that leverage link correlation to enhance reliability, reduce packet loss, and optimize end-to-end performance. He has contributed to routing solutions grounded in urban road structure awareness, improving scalability and robustness in dense vehicular networks and supporting next-generation intelligent transportation systems. In parallel, his research extends into intelligent computing frameworks that integrate device, edge, and cloud layers to enable efficient distributed learning, resource-aware decision-making, and latency-sensitive AI applications. He also investigates algorithmic innovation within large language models, emphasizing scalability, deployment efficiency, and real-world applicability. Additionally, his work on biometric recognition, including palmprint feature extraction and direction coding, demonstrates expertise in pattern recognition and vision-based authentication systems. Supported by growing scholarly recognition, his work has been cited 212 times overall, including 101 citations since 2020, with an h-index of 3 and an i10-index of 2, underscoring the increasing impact and relevance of his contributions to networking, artificial intelligence, and intelligent mobility research.

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Abdalilah Alhalangy | Computer Science | Innovative Research Award

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Assoc. Prof. Dr. Abdalilah Alhalangy | Computer Science | Innovative Research Award

Qassim university | Saudi Arabia

Assoc. Prof. Dr. Abdalilah Alhalangy, Ph.D., is an Associate Professor in Computer Engineering at Qassim University, Kingdom of Saudi Arabia, specializing in advanced areas of artificial intelligence, machine learning, intelligent systems, and cybersecurity. His research spans deep learning, ensemble methods, neural networks, computer vision, wireless networks, cloud computing, big data analytics, robotics, augmented reality, mobile applications, image and video analysis, GIS, and e-learning systems. He has a particular focus on artificial neural networks, wavelet neural networks, fuzzy logic, evolutionary algorithms, and computational intelligence, applied to enhancing the security and functional performance of intelligent systems. Dr. Al-Halangy has published 6 documents cited by 59 Scopus-indexed papers, achieving a Scopus h-index of 3 and an i10-index of 2 on Google Scholar, with a total of 131 citations. His work has earned recognition in fields ranging from Arabic speech emotion recognition and fake account detection in mobile networks to generative AI-driven cybersecurity systems and the evaluation of e-learning effectiveness. Dr. Al-Halangy’s research is characterized by its innovative integration of AI techniques to solve complex real-world problems, positioning him as a leading contributor to modern computing challenges. He has received accolades including the Innovative Research Award for his contributions to the development of secure, intelligent, and efficient computational systems. His work continues to impact both academic research and practical applications, advancing the state of intelligent and adaptive technologies globally.

Publication Profile

Scopus | Orcid | Google Scholar

Featured Publications

  • Alhalangy, A., & AbdAlgane, M. (2023). Exploring the impact of AI on the EFL context: A case study of Saudi universities.

  • Alhalangy, A. (2024). Deep learning, ensemble and supervised machine learning for Arabic speech emotion recognition. Engineering, Technology & Applied Science Research, 14, 1-10.

  • Hassan, A., & Alhalangy, G. I. A. (2023). Fake accounts identification in mobile communication networks based on machine learning. SSRN.

  • Alhalangy, A., Elhadi, O. A. M., & Mohamed, E. H. G. (2025). E-learning effectiveness and efficiency in Kassala and Gedaref universities: An IS-impact evaluation. UtilitasMathematica, 122(2), 1301-1317.

  • Alhalangy, A. (2025). Generative AI-driven information system for behavioral detection of zero-day cyber attacks. UtilitasMathematica, 122(2), 1194-1210.