Faten AlQaifi | Artificial Intelligence | Best Researcher Award

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Dr. Faten AlQaifi | Artificial Intelligence | Best Researcher Award

Atilim University, Turkey.

Dr. Faten Musaed Alqaifi is a multidisciplinary researcher currently pursuing her Master’s in Healthcare Management at Atılım University, Turkey. She brings a unique blend of expertise in dental surgery, healthcare management, and artificial intelligence, having earned her MBA in Healthcare Management from UTM, Malaysia. With academic excellence reflected in her top CGPAs, she has contributed to both clinical and psychosocial research domains. Her recent studies explore AI’s role in improving oral healthcare outcomes and the psychological dimensions of international student life. She has worked as a general dentist, educator, and volunteer in diverse cultural settings, underscoring her global adaptability and social commitment. Fluent in Arabic, English, and intermediate Turkish, Dr. Alqaifi exemplifies the qualities of a globally engaged researcher.

🌐Author Profiles

Strengths for the Award

  1. Interdisciplinary Research in Healthcare and AI
    Faten Alqaifi has shown a strong interdisciplinary approach, particularly in integrating artificial intelligence into healthcare systems. Her 2024 publication titled “Artificial intelligence’s impact on oral healthcare in terms of clinical outcomes: a bibliometric analysis” reflects a forward-thinking research agenda. By analyzing AI’s role in improving clinical outcomes, she contributes meaningfully to a high-impact and emerging area in health sciences and management.

  2. Empirical Work in Mental Health and Social Integration
    Her 2025 paper, “International students’ adaptation in Ankara: The mediating roles of anxiety and self-esteem”, published in the International Journal of Intercultural Relations, indicates her engagement with global psychological and sociocultural issues. This work not only highlights her concern for vulnerable populations but also showcases the use of rigorous methodologies in behavioral and intercultural research.

  3. Diverse Experience and Multilingual Skills
    Faten has a well-rounded professional background as a dentist, academic tutor, volunteer, and general manager. She speaks Arabic natively and is proficient in English and Turkish, enabling her to conduct research and collaboration across regions. Her use of advanced research tools (e.g., SPSS, SmartPLS, bibliometric software) further enhances her research capacity.

🔹 Education 

Dr. Alqaifi holds a Bachelor’s in Dental Surgery from the Yemeni University of Science and Technology, where she ranked fifth in her class. She earned her first Master’s degree—an MBA in Healthcare Management—from Universiti Teknologi Malaysia (UTM) with a stellar GPA of 3.94/4. Building on her academic and clinical background, she is now completing a second Master’s in Healthcare Management at Atılım University, Turkey, where she maintains a GPA of 3.93/4. Her ongoing thesis investigates the adoption of artificial intelligence in dentistry, combining technology, healthcare policy, and clinical practice. This educational trajectory highlights her commitment to interdisciplinary learning and research excellence.

🔹 Research Focus on Artificial Intelligence

Dr. Faten Alqaifi’s research lies at the intersection of healthcare innovation, artificial intelligence, and behavioral science. Her ongoing thesis on AI integration in dentistry demonstrates a forward-looking approach to digital transformation in healthcare. Her research agenda emphasizes both technological effectiveness and human-centered outcomes, as evidenced by her bibliometric analysis on AI’s clinical impact in oral healthcare. Simultaneously, she investigates psychological dimensions of social adaptation, particularly anxiety and self-esteem among international students—adding depth to her interdisciplinary reach. She is proficient in tools like SPSS, SmartPLS, and bibliometric analysis platforms, enabling her to conduct statistically robust, data-driven research. Faten’s aim is to bridge the digital and human aspects of healthcare policy and practice.

📚 Publication Top Notes

1. International Students’ Adaptation in Ankara: The Mediating Roles of Anxiety and Self-Esteem

Author: Faten Alqaifi
Journal: International Journal of Intercultural Relations, Vol. 108, Article 102249, 2025
Summary:
This study explores the psychological adjustment process among international students in Ankara, focusing on the mediating impact of anxiety and self-esteem on their adaptation levels. Using structural equation modeling, Alqaifi identifies critical pathways by which mental health factors influence sociocultural integration. The research provides insights for universities and policymakers aiming to improve the student experience in multicultural settings.

2. Artificial Intelligence’s Impact on Oral Healthcare in Terms of Clinical Outcomes: A Bibliometric Analysis

Authors: Faten Alqaifi, D. Tengilimoglu, I. Arslan Aras
Journal: Journal of Health Organization and Management, 2024
Summary:
This bibliometric study investigates global research trends on AI applications in oral healthcare. Analyzing data from Scopus and Web of Science, the authors assess publication patterns, key contributors, emerging keywords, and citation landscapes. The study concludes that AI is increasingly driving diagnostic precision, treatment planning, and clinical outcome optimization in dentistry. It positions AI as a transformative force and sets the foundation for future strategic investments in digital dentistry.

Conclusion

Dr. Faten Musaed Alqaifi shows strong potential for recognition as an emerging interdisciplinary researcher in healthcare, AI, and social sciences. Her academic excellence, early contributions in high-relevance areas, and diverse experience make her a promising candidate for the Best Researcher Award. However, to fully meet the expectations of this award at a global competitive level, she should aim to expand her publication portfolio, lead independent research projects, and pursue collaborative or funded initiatives. With her current trajectory and dedication, she is on a strong path to achieving these milestones.

 

Sirmayanti | AI and Digital Communication | Women Researcher Award

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Assoc. Prof. Dr. Sirmayanti | AI and Digital Communication | Women Researcher Award

Associate Professor at Politeknik Negeri Ujung Pandang, Indonesia.

Assoc. Prof. Dr. Sirmayanti, Ph.D., is a prominent Indonesian scholar in telecommunication engineering, currently serving at the State Polytechnic of Ujung Pandang (PNUP). With a career spanning over two decades, she combines academic excellence, cutting-edge research, and impactful community engagement. She earned her Ph.D. in Electrical and Electronic Telecommunication Engineering from Victoria University, Australia, with Cum Laude distinction. Her research explores a wide spectrum—from 5G and RF engineering to artificial intelligence, IoT, and green communication technologies. As Head of the Telecommunication Network Engineering Technology program and the Centre for Applied Telecommunications Technology Research at PNUP, she has mentored numerous scholars and led various national and international innovation projects. Her prolific output includes over 60 publications, 15 technical books, and numerous patented inventions. A passionate advocate for education and digital equity, she’s been widely recognized for her contributions, including multiple presidential awards and the Australia Awards Alumni honor.

🌐Author Profile

🎓 Education 

Sirmayanti’s educational journey reflects a trajectory of excellence across multiple continents. She earned her Ph.D. in Electrical and Electronic Telecommunication Engineering from Victoria University, Australia (2015), where she graduated cum laude for her innovative research in digital RF architectures. Her Master of Engineering from the same university (2008) was awarded with First-Class Honors, reinforcing her expertise in signal processing and software radio systems. Her Bachelor of Engineering in Telecommunications, from Hasanuddin University, Indonesia (2001), was completed with High Distinction. She began her technical training at SMK Telkom Sandhy Putra 2 (1997), where she laid the foundation for her career in communications technology. In 2024, she was formally recognized as an Insinyur Profesional Utama (IPU), ASEAN Engineer, and APEC Engineer, further validating her technical leadership at the global level. Her continuous professional development includes numerous international certifications in Open RAN, IoT, AI, and RF engineering, demonstrating her commitment to lifelong learning.

Strengths for the Award

  1. Academic Excellence & Global Exposure:

    • Holds a Ph.D. with Cum Laude honors in Electrical and Electronic Telecommunication Engineering from Victoria University, Australia.

    • Her academic track includes top honors at every level, showcasing consistent high performance.

  2. Extensive Multidisciplinary Research:

    • Research covers Wireless Communications, IoT, AI, RF Engineering, Satellite Communications, and 5G.

    • Led and contributed to over 35+ funded research projects from national and international agencies (e.g., BRIN, KEMENRISTEKDIKBUD, DIKTI, AFS, Australia Direct Aid).

  3. High Research Output:

    • Over 60 peer-reviewed publications, including Scopus-indexed journal articles (Q1–Q3) and IEEE Award proceedings.

    • Significant work on metaheuristic algorithms for diabetes prediction, 5G networks, and digital RF systems.

  4. Innovation and Patents:

    • Holds multiple patents and copyrights (2020–2025) on innovations such as:

      • Wireless Bridge Mi-Fi 4G

      • Techniques for RF Spectrum Image Cancellation

      • Tools for lithium battery drop testing

    • Demonstrates intellectual property development at a national level.

  5. Leadership and Capacity Building:

    • Head of the Telecommunication Network Engineering Program and Center for Applied Telecommunications Technology Research (PNUP).

    • National assessor, ISO lead auditor, and active in curriculum reform and national education initiatives.

💼 Professional Experience 

Since 2001, Sirmayanti has served as a Lecturer and Researcher at the State Polytechnic of Ujung Pandang (PNUP), where she currently leads both the Telecommunication Network Engineering Technology Program and the Centre for Applied Telecommunications Technology Research (CATTAR). Her experience spans academia, national policy, quality assurance, and technology innovation. She is a certified lead auditor for ISO 9001 and an assessor for national educational programs like BKD, BIP, and Praktisi Mengajar under Indonesia’s Ministry of Research and Education. Her leadership in internal quality systems and curriculum design has transformed vocational education frameworks. She has led over 40 funded research and community service projects across 5G, AI, IoT, and rural connectivity systems. Internationally, she collaborates with UiTM Malaysia and is a certified practitioner in Open RAN, AI, and RF planning. Her blend of administrative leadership, hands-on technical innovation, and commitment to community service uniquely positions her in advancing AI and digital communication technologies.

🏅 Awards and Honors

Assoc. Prof. Sirmayanti has been decorated with some of Indonesia’s highest honors for civil service, including the Satyalancana Karya Satya 20 Tahun and 10 Tahun awards from the President of the Republic of Indonesia in 2024 and 2018. She is also among the “70 Outstanding Alumni” of Australian institutions recognized by Australia Awards Indonesia. She received the PIN LEMHANNAS award in 2024 and the prestigious INSINAS Research Grant Award from BRIN for her research on distortion mitigation in 5G systems. Her academic journey was supported by multiple competitive scholarships, such as the Australian Development Scholarship (ADS) and DIKTI Fellowships for postgraduate research. Beyond institutional honors, her contributions to community service and STEM education have garnered attention from various national and international programs. Her consistent record of excellence in education, research, innovation, and social impact establishes her as a leading figure in digital communication and AI technology in Southeast Asia.

🔬 Research Focus on AI and Digital Communication

Sirmayanti’s research sits at the intersection of artificial intelligence, wireless communication, and digital signal processing. Her key focus areas include 5G/6G networks, RF engineering, IoT-based infrastructure, AI-powered healthcare diagnostics, and green communication technologies. She applies metaheuristic algorithms and machine learning models to enhance wireless performance, such as through feature optimization for disease prediction or spectral efficiency in OFDM systems. Notably, she has developed low-power RF transmitters, digital baseband tuning systems, and hardware innovations like dual-band filters for 5G. She also works on rural connectivity solutions using mini BTS and community Wi-Fi networks. Her projects are not only technically advanced but socially driven—such as her work on digital inclusion in remote Indonesian islands and health monitoring systems. Her patents and intellectual property showcase strong emphasis on applied research, while her academic publications reflect rigorous peer-reviewed contributions to the fields of AI, digital modulation, and next-generation communication protocols.

📚 Publication Top Notes

1. E-Government dan Digitalisasi Layanan Publik

Authors: M. Mirfan, M. Mariana, S. Abdullah, A. Nurfadly, S. Suhada, S. Sirmayanti
Publisher: Yayasan Kita Menulis (2025)
Type: Book/Handbook
Summary:
This publication explores the digital transformation of public services through e-government frameworks. It addresses how Indonesian government institutions adopt ICT-based platforms for public administration, service efficiency, and transparency. Sirmayanti’s contribution lies in her analysis of telecommunication infrastructure enabling smart governance and public service digitization. The book combines theoretical frameworks with empirical insights and practical models for policymakers and technocrats.

2. A Systematic Literature Review of Diabetes Prediction Using Metaheuristic Algorithm-Based Feature Selection: Algorithms and Challenges Method

Authors: FR Sirmayanti, Pulung Hendro Prastyo, Mahyati
Journal: Applied Computer Science, 21(1), pp. 126–142, 2025 (Scopus Q3)
DOI: Applied Computer Science
Summary:
This systematic review focuses on AI-based diabetes prediction models utilizing metaheuristic feature selection algorithms. The authors classify and analyze recent algorithms (GWO, PSO, ACO, etc.), evaluating their effectiveness in reducing feature dimensionality while maintaining high diagnostic accuracy. Challenges such as computational cost, algorithm convergence, and overfitting are critically discussed. The work contributes a taxonomy of algorithmic strategies and highlights future research gaps for clinical decision support systems in e-health.

3. An Enhanced Grey Wolf Optimizer with Opposition, Mutation, and Local Search Strategy for Feature Selection

Authors: M. Sirmayanti, Pulung Hendro Prastyo
Conference: 2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), pp. 96–101
Summary:
This Award paper proposes an improved version of the Grey Wolf Optimizer (GWO) for feature selection by incorporating opposition-based learning, mutation, and local search mechanisms. The hybridized GWO algorithm demonstrated enhanced exploration-exploitation balance and was tested on medical and telecom datasets. Results showed better performance compared to standard GWO and other algorithms, with significant implications for AI-driven feature selection in classification systems.

4. Design of Dual-Band Bandpass Filter (DBPF) for 5G Applications

Authors: M. Mimsyad, A. Bazergan, S. Sirmayanti
Journal: AIP Conference Proceedings, Vol. 3140, 040018 (2024)
DOI: 10.1063/5.0222871
Summary:
This study presents the design and simulation of a dual-band bandpass filter (DBPF) aimed at 5G wireless applications. Utilizing advanced substrate materials and electromagnetic modeling, the design supports frequency bands relevant for mid-band 5G use cases. Sirmayanti contributed to the filter miniaturization and optimization phase. The paper outlines how the DBPF improves signal selectivity and reduces interference, with potential application in small-cell architectures and 5G-enabled devices.

5. Rekayasa Mitigasi Kebocoran Gas LPG dengan Sistem Monitoring Telegram Bot Berbasis Internet of Things (IoT)

Authors: S. Sirmayanti, E. Dwi Melda
Conference: Seminar Nasional Teknik Elektro dan Informatika (SNTEI), Vol. 9(1), pp. 223–228, 2023
Summary:
This paper discusses the development of an IoT-based real-time monitoring system for LPG gas leak detection, integrated with a Telegram bot alert system. The authors designed and implemented an Arduino-based sensor framework to monitor gas concentration in households and notify users immediately through messaging. Sirmayanti led the system integration and sensor calibration. The project highlights practical IoT use cases in smart safety systems for domestic environments.

✅ Conclusion

Assoc. Prof. Sirmayanti is highly suitable for the Women Researcher Award. Her extensive academic, research, innovation, and community engagement portfolio, combined with her leadership in engineering education and commitment to women’s empowerment in STEM, exemplify the spirit of the award. Her work not only advances telecommunications and AI research but also bridges technology with sustainable development and inclusive education—hallmarks of a transformative woman researcher.

Fazilet Gokbudak | Sustainable Tech Solutions | Best Researcher Award

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Dr. Fazilet Gokbudak | Sustainable Tech Solutions | Best Researcher Award

ML Researcher, Apple, United Kingdom.

Fazilet Gokbudak is a machine learning researcher at Apple, specializing in generative models, computational photography, and inverse rendering. She received her PhD in Computer Science from the University of Cambridge in 2024, where she worked on neural rendering and efficient image manipulation techniques. Her academic path began at Bogazici University with a high honors degree in Electrical and Electronics Engineering, followed by an MSc with distinction from the University of Edinburgh. Her industrial journey includes pivotal contributions at Amazon as an Applied Scientist Intern. Fazilet actively promotes diversity in AI as a co-chair at Women@CL and reviewer for prestigious AI Awards. Her impactful work has been recognized through awards, publications in top-tier Awards like ECCV and SIGGRAPH, and cutting-edge research contributing to sustainable visual technologies.

Author’s Profile

Education 

Fazilet Gokbudak pursued her undergraduate studies in Electrical and Electronics Engineering at Bogazici University (2014–2018), graduating with High Honors and multiple scholarships. She then completed her MSc in Signal Processing and Communications at the University of Edinburgh in 2019, earning a distinction. Between 2020 and 2024, she conducted her PhD research in Computer Science at the University of Cambridge under full funding from the Computer Laboratory Studentship. Her doctoral work focused on generative neural techniques for photorealistic appearance manipulation and inverse rendering. Fazilet’s academic training spanned signal processing, computer vision, and machine learning. Throughout her education, she demonstrated a consistent commitment to innovation, excellence, and sustainability in computational research, reflected by her active participation in research projects and her growing publication record.

Experience

Fazilet currently serves as an ML Researcher at Apple (since October 2024), where she develops advanced camera algorithms to enhance mobile photography. Previously, she interned at Amazon (July–November 2022), working on high-fidelity conditional image generation using GANs with local histogram losses for skin tone realism. She also worked as a part-time Research Assistant at the University of Cambridge (Nov 2020–Jan 2022), where she led the Cambridge team on a joint blackgrass detection project using deep convolutional networks. Her work achieved 80% classification accuracy on novel agricultural datasets, showcasing practical impacts of AI in sustainable agriculture. Her career blends foundational machine learning research with real-world, production-level deployments.

Awards and Honors 

Fazilet has earned multiple accolades recognizing her academic excellence and research impact. At the University of Cambridge, she received the prestigious Computer Laboratory Studentship (2020–2024), which fully funded her doctoral studies. She also earned a Graduate Student Travel Award from Queens’ College in 2023 to attend SIGGRAPH, a top-tier graphics Award. At Bogazici University, she graduated with High Honors and received the Dean’s High Honor Certificate in 2018. She was also a recipient of the TEKFEN Holding Private Scholarship (2009–2018) for her outstanding performance in national exams, ranking 185 out of over two million candidates. Her high school career concluded as Valedictorian, earning Summa Cum Laude. These honors reflect her long-standing dedication to academic excellence and societal impact.

Research Focus 

Fazilet’s research spans several cutting-edge areas in machine learning and computer vision, with a core focus on generative models, neural rendering, and appearance manipulation. Her PhD work centered on data-efficient methods for realistic image generation, including patch-based transformations and BRDF modeling. She explores inverse rendering techniques that enable physically consistent reconstructions of visual scenes, contributing to advancements in sustainable graphics systems. At Apple, her focus includes developing intelligent algorithms for mobile camera systems, aligning technological performance with energy efficiency and visual realism. Her research also supports sustainable tech by optimizing neural computations and minimizing training overhead, critical for eco-conscious AI applications. She brings a unique blend of academic depth and practical innovation.

Publication Top Notes

Multispectral Fine-Grained Classification of Blackgrass in Wheat and Barley Crops  (2025, Computers and Electronics in Agriculture)

Co-authored with M. Darbyshire et al. This study develops a deep learning model using multispectral image data to accurately identify blackgrass weed species in cereal crops. Fazilet was responsible for model training and evaluation, contributing to a high-accuracy, fine-grained classification system. The work supports sustainable agriculture by enabling targeted herbicide use and reducing environmental impact.

Spatial Receptor Allocation for a Multiple Access Hub in Nanonetworks (2019, IEEE Transactions on Molecular, Biological and Multi-Scale Communications)

Fazilet’s early research explored theoretical models for receptor allocation in nanoscale communication networks. The study introduces simulation-based methods to optimize signal clarity and reduce cross-interference in molecular communication systems, marking a foundational step in nano-IoT frameworks.

Hypernetworks for Generalizable BRDF Representation (2024, European Conference on Computer Vision)

This paper presents a novel hypernetwork design that enables generalization across various materials when estimating Bidirectional Reflectance Distribution Functions (BRDFs). Fazilet contributed to network architecture and experimentation, demonstrating the model’s ability to capture complex material appearances using fewer parameters, facilitating efficient rendering in graphics pipelines.

Data-efficient Neural Appearance Manipulations (2025, in preparation)

This upcoming work proposes neural models for photo editing that require less training data and computational power. Fazilet introduces modular architectures that allow intuitive and efficient image edits, especially suited for low-resource devices. The approach balances realism and sustainability by minimizing hardware dependency.

Physically Based Neural BRDF (2024, arXiv preprint)

Fazilet co-authored this paper which merges physical reflectance models with neural networks to improve accuracy in appearance modeling. The technique enhances inverse rendering applications and supports high-fidelity visual reconstruction by incorporating physical consistency into the learning process.

One-shot Detail Retouching with Patch Space Neural Transformation Blending (2023, ACM SIGGRAPH CVMP)

The paper introduces a one-shot learning-based approach for image retouching that uses patch-space neural blending. Fazilet’s contributions include model design and testing, enabling fast, high-quality transformations from a single reference image—a technique ideal for low-data environments.

Patch Space Neural Field based Transformation Blending (2022, CoRR)

A precursor to her 2023 SIGGRAPH CVMP paper, this work investigates neural field-based image transformation using patch-level information. It showcases the potential for detail-preserving edits with minimal training samples, advancing low-data generative editing.

Conclusion

Fazilet Gokbudak is an exceptionally strong candidate for a Best Researcher Award in the fields of Machine Learning, Computer Vision, and Generative AI. Her multidisciplinary background, strong publication record, and industry-academic synergy position her as a next-generation leader in AI research.