Diogo Santiago | Computer Science | Best Researcher Award

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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.

 

Swathi Priyadarshini Tigulla | Computer Science | Best Researcher Award

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Dr. Swathi Priyadarshini Tigulla | Computer Science | Best Researcher Award

Osmania University | India

Author Profile

Scopus

Early Academic Pursuits

Dr. Swathi Priyadarshini Tigulla laid the foundation of her academic journey with a degree in Information Technology, followed by a master’s program in Information Technology with a specialization in network security. Her pursuit of advanced knowledge culminated in a doctoral degree in Computer Science and Engineering from Osmania University. From the beginning, she demonstrated a strong inclination toward solving computational problems and a keen interest in the emerging domains of artificial intelligence, machine learning, and network security.

Professional Endeavors

Her professional career reflects an extensive teaching and mentoring journey across reputed institutions. She began her career as an Assistant Professor in engineering colleges where she taught computer science, network security, and software engineering, and guided student projects. Over the years, she progressed to significant academic roles, including serving as Head of the Department, coordinating extracurricular activities, and contributing to student training and placement. Presently, she continues her academic engagement as an Assistant Professor specializing in artificial intelligence and machine learning, while also actively mentoring projects and participating in innovative academic initiatives such as GEN-AI teams and project schools.

Contributions and Research Focus

Dr. Tigulla’s research is strongly anchored in artificial intelligence, machine learning, and soft computing, with a particular focus on healthcare applications such as heart stroke prediction models. Her publications have proposed innovative approaches that integrate clustering, classification, and deep learning techniques to enhance medical predictions, combining accuracy with practical applicability. Beyond healthcare, her work also explores security strategies in cloud computing and data-driven approaches to protect systems from vulnerabilities. This blend of healthcare informatics and cyber security positions her research at the intersection of technology and community impact.

Accolades and Recognition

Her expertise has been recognized through publications in reputed international journals such as Measurement: Sensors and Journal of Positive School Psychology, along with contributions to international conferences under IEEE. She has served as a reviewer for scholarly journals and academic book chapters, demonstrating her standing as a trusted evaluator in her field. Her involvement as an organizer of technical workshops, hackathons, and project expos reflects her commitment to academic innovation and student skill development, further reinforcing her recognition as a versatile academic leader.

Impact and Influence

The impact of Dr. Tigulla’s work is evident in both her research outcomes and her teaching contributions. Her models for heart stroke prediction contribute significantly to community health by combining artificial intelligence with real-world medical applications. As an educator, she has influenced generations of students by equipping them with knowledge in machine learning, artificial intelligence, and advanced computational concepts. Her leadership in academic events has fostered a culture of innovation, creativity, and hands-on learning among students, thereby extending her influence beyond traditional teaching.

Legacy and Future Contributions

Dr. Tigulla’s legacy is one of blending research excellence with community benefit. By focusing on both healthcare prediction models and system security, she has addressed two domains of immense social importance—public health and digital trust. Looking forward, her future contributions are expected to further deepen the integration of artificial intelligence into real-world applications, enhance her role as a reviewer and academic guide, and continue her efforts to shape students into innovative researchers and industry-ready professionals.

Publications

Article: Developing Heart Stroke Prediction Model using Deep Learning with Combination of Fixed Row Initial Centroid Method with Naïve Bayes, Decision Tree, and Artificial Neural Network
Authors: T. Swathi Priyadarshini, Vuppala Sukanya, Mohd Abdul Hameed
Journal: Measurement: Sensors
Year: 2024

Article: Collaboration of Clustering and Classification Techniques for Better Prediction of Severity of Heart Stroke using Deep Learning
Authors: T. Swathi Priyadarshini, Vuppala Sukanya, Mohd Abdul Hameed
Journal: Measurement: Sensors
Year: 2025

Article: Deep Learning Prediction Model for Predicting Heart Stroke using the Combination Sequential Row Method Integrated with Artificial Neural Network
Authors: T. Swathi Priyadarshini, Mohd Abdul Hameed, Balagadde Ssali Robert
Journal: Journal of Positive School Psychology
Year: 2022

Article: Methods of Hidden Pattern Usage in Cloud Computing Security Strategies with K-means Clustering
Authors: T. Swathi Priyadarshini, Dr. S. Ramachandram
Journal: AIJREAS
Year: 2021

Article: A Review on Security Issue Solving Methods in Public and Private Cloud Computing
Authors: T. Swathi Priyadarshini, S. Ramachandram
Journal: IJMTST
Year: 2020

Conclusion

Dr. Swathi Priyadarshini Tigulla embodies the qualities of an academician and researcher who successfully bridges the gap between theoretical advancements and community impact. Her journey, marked by academic rigor, extensive teaching experience, and impactful research, showcases her dedication to advancing artificial intelligence and machine learning for practical applications. Recognized as both a researcher and a mentor, she continues to inspire through her contributions in education, healthcare, and cyber security. In conclusion, her career highlights a sustained commitment to knowledge, innovation, and community-oriented research, establishing her as a distinguished academic voice in the field of computer science and engineering.

 

Hongcheng Xue | Computer Science | Best Academic Researcher Award

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Dr. Hongcheng Xue | Computer Science | Best Academic Researcher Award

College of Information and Electrical Engineering, China Agricultural University | China

Author Profile

Scopus

Orcid

🎓 Early Academic Pursuits

Dr. Hongcheng Xue began his academic journey with a Bachelor's degree in Information and Computational Science from Hunan University of Science and Technology (2014–2018), where he demonstrated leadership as class monitor and held key student roles in the Cultural and Security Departments. His studies emphasized mathematical rigor with courses in analysis, algebra, geometry, and numerical methods. He advanced his education with a Master’s degree in Software Engineering from Inner Mongolia University of Technology (2018–2021), specializing in Data Science Applications. His focus areas included Deep Learning and Computer Vision. During his studies, he actively led his class, served as Vice Chair of the Student Union, and won multiple academic and innovation awards, including:

  • 🥈 Second-class and third-class academic scholarships

  • 🏆 First prize in the university-level Internet+ Innovation and Entrepreneurship Competitions (2018 & 2019)

💼 Professional Endeavors

Dr. Xue served as an Algorithm Engineer at Inner Mongolia Smart Animal Husbandry Co. Ltd. (March–November 2019), where he played a critical role in the development of a sheep delivery early warning detection system using deep learning. His contributions involved:

  •   ➤ Collecting and augmenting training datasets

  •   ➤ Building and fine-tuning neural network models for real-time birthing scene recognition

  •   ➤ Collaborating with frontend and backend teams to deploy the system successfully

  •   ➤ Monitoring system performance and continuously optimizing model behavior

This role showcased his ability to blend theoretical knowledge with real-world applications, especially in agricultural tech solutions.

🧠 Contributions and Research Focus

Dr. Xue’s core research interests lie in deep learning, object detection, and computer vision. His key contributions include:

📄 Published Paper:
“Sheep Delivery Scene Detection Based on Faster-RCNN” – presented at IVPAI 2019

📝 Submitted Research:
“Small Target Modified Car Parts Detection Based On Improved Faster-RCNN” – (Under review)

🔬 Patented Innovation:
Granted a utility model patent for an intelligent trough capable of collecting sheep identification data – Patent No. 202020674737.2

💻 Software Copyright:
Developed and registered a HOG-based Video Pedestrian Detection System V1.0 – Registration No. 2019SR0757039

🏅 Accolades and Recognition

Dr. Xue’s academic journey is marked with consistent excellence and recognition:

  •   ➤ Multiple scholarships during postgraduate studies

  •   ➤ Repeated champion in innovation competitions at university level

  •   ➤ Leadership roles acknowledged both academically and administratively

  •   ➤ Recognized contributor to interdisciplinary applications of AI in agriculture

🌍 Impact and Influence

Dr. Xue’s work reflects a rare synergy between technological innovation and agricultural transformation, especially in remote and rural contexts. His efforts in intelligent livestock management have the potential to significantly enhance productivity, monitoring, and sustainability in smart farming.

He serves as a model for researchers applying AI and deep learning in niche but impactful sectors, bridging gaps between modern tech and traditional industries.

🌟 Legacy and Future Contributions

As a young and dynamic researcher, Dr. Xue’s career is on a promising trajectory. His unique blend of academic rigor, applied research, and patented innovations positions him well for future leadership in AI-driven agricultural systems, smart sensing technologies, and computer vision applications.

He is expected to continue making contributions that transform rural technology landscapes, influence policy through innovation, and inspire future researchers in emerging interdisciplinary fields.

Publications

📄HCTD: A CNN-transformer hybrid for precise object detection in UAV aerial imagery

Authors: Hongcheng Xue, Zhan Tang, Yuantian Xia, Longhe Wang, Lin Li
Journal: Computer Vision and Image Understanding
Year: 2025 (September)

📄 Aggressive behavior recognition and welfare monitoring in yellow-feathered broilers using FCTR and wearable identity tags

Authors: Hongcheng Xue, Jie Ma, Yakun Yang, Hao Qu, Longhe Wang, Lin Li
Journal: Computers and Electronics in Agriculture
Year: 2025

📄 Enhanced YOLOv8 for Small Object Detection in UAV Aerial Photography: YOLO-UAV

Authors: Hongcheng Xue, Xia Wang, Yuantian Xia, Lin Li, Longhe Wang, Zhan Tang
Conference: Proceedings of the International Joint Conference on Neural Networks (IJCNN)
Year: 2024

📄 Open Set Sheep Face Recognition Based on Euclidean Space Metric

Authors: Hongcheng Xue, Junping Qin, Chao Quan, Wei Ren, Tong Gao, Jingjing Zhao, Pier Luigi Mazzeo
Journal: Mathematical Problems in Engineering
Year: 2021

Elif Keskin Bilgiç | Engineering | Best Researcher Award

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Mrs. Elif Keskin Bilgiç | Engineering | Best Researcher Award

Istanbul University -Cerrahpaşa | Turkey

Author Profile

Orcid

Early Academic Pursuits 🎓

Mrs. Elif Keskin Bilgiç's academic journey began with a strong foundation in biology, earning her B.Sc. in Biology from Abant İzzet Baysal University in 2010. She further pursued her passion for biomedical engineering, completing an M.Sc. at Istanbul University in 2016. Her master's thesis focused on investigating the therapeutic effects of innovative biomaterials, such as L-Dopa and Lawsone, in wound healing. This early academic focus laid the groundwork for her expertise in biomedical engineering and clinical applications. In 2024, she completed her Ph.D. in Biomedical Engineering from Istanbul University-Cerrahpaşa, specializing in non-invasive clinical decision support systems for diagnosing gastrointestinal diseases using advanced machine learning methods. 📚

Professional Endeavors 💻

With over a decade of professional experience, Mrs. Bilgiç has made significant contributions as both a researcher and educator. She has taught Cambridge Biology at the A-Level from 2016 to 2024 at the International Gokkusagi School in Istanbul, equipping students with critical knowledge to excel in international examinations. As a researcher at Istanbul University-Cerrahpaşa since 2016, she has pioneered research using transfer learning techniques to detect celiac disease, developing predictive machine learning models to aid in diagnostics. Her work in wound healing and biomaterials during her early career helped shape her innovative approaches in biomedical engineering.

Contributions and Research Focus 🔬

Mrs. Bilgiç's research centers on developing non-invasive clinical decision support systems for diagnosing autoimmune diseases, with a particular focus on celiac disease. Her groundbreaking research involves the use of machine learning models, including transfer learning and deep learning, to diagnose the disease by analyzing facial images and predicting Marsh levels from patient data. This innovative approach merges cutting-edge AI technology with clinical diagnostics, advancing the field of medical science. In addition, her research on the therapeutic effects of biomaterials in wound healing has expanded the knowledge base in biomedical engineering.

Accolades and Recognition 🏆

Mrs. Bilgiç has published multiple scientific papers, including an original article on using transfer learning for celiac disease identification, which has garnered attention within the scientific community. Her work has been presented at numerous conferences and symposia, including international venues such as the Bilge Kagan 2nd International Science Congress in Barcelona, Spain, and the 11th Nanoscience and Nanotechnology Conference in Ankara, Turkey. Her innovative approaches to clinical diagnostics and contributions to autoimmune disease research have earned her recognition as a thought leader in the field.

Impact and Influence 🌍

Through her research, Mrs. Bilgiç is reshaping how clinical diagnostics are performed, particularly for gastrointestinal and autoimmune diseases. Her development of non-invasive diagnostic systems could revolutionize patient care, offering faster and more accurate diagnosis options. Her educational impact extends beyond the research lab, as she has inspired countless students through her teaching, blending her academic and professional expertise into practical applications that shape future scientists and researchers.

Legacy and Future Contributions ✨

Mrs. Bilgiç's work in machine learning, biomedical engineering, and education has laid a strong foundation for future advancements in healthcare technology. Her legacy will likely be marked by her innovations in non-invasive diagnostic tools and her contribution to the understanding of biomaterials in medical treatment. As her research evolves, she is poised to continue making significant contributions that will benefit patients and healthcare providers alike, influencing the future of clinical decision support systems and biomedical engineering for years to come.

 

Publications

📖 Innovative Approaches to Clinical Diagnosis: Transfer Learning in Facial Image Classification for Celiac Disease Identification 
Author: Elif Keskin Bilgiç, Inci Zaim Gokbay, Yusuf Kayar
Journal: Applied Sciences
Year: 2024

 

Soopil Kim | Computer Science | Best Researcher Award

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Dr. Soopil Kim | Computer Science | Best Researcher Award

Daegu Gyeongbuk Institute of Science and Technology | South Korea

Author Profile

Scopus

Orcid

Early Academic Pursuits 🎓

Dr. Soopil Kim's academic journey began with a Bachelor of Engineering in Robotics and Mechatronics Engineering from Daegu Gyeongbuk Institute of Science & Technology (DGIST), where he graduated Cum Laude. He continued his studies at DGIST, pursuing a Master’s and Ph.D. in the same field, focusing on medical image segmentation. His research during these years emphasized label-efficient segmentation models and limited pixel-level annotation, laying a strong foundation for his future work in deep learning and computer vision.

Professional Endeavors 💼

Dr. Kim's career has seen significant milestones, including a role as a Visiting Student at Stanford University's CNSLAB under the supervision of Prof. Kilian M. Pohl and Ehsan Adeli. Currently, he is a Post-Doctoral Research Fellow at the Medical Image & Signal Processing Lab (MISPL) at DGIST, where he works under Prof. Sang Hyun Park. His professional trajectory reflects a commitment to advancing the field of computer vision through innovative research and collaboration.

Contributions and Research Focus 🔬

Dr. Kim’s research is at the forefront of deep learning and computer vision. His work addresses the challenges of image segmentation with partially labeled datasets by developing federated learning strategies and few-shot segmentation techniques. His notable contributions include the creation of a medical image segmentation model that integrates meta-learning and bi-directional recurrent neural networks, a semi-supervised segmentation model based on uncertainty estimation, and a transductive segmentation model for industrial imaging. These advancements aim to improve the efficiency and accuracy of image segmentation processes.

Accolades and Recognition 🏆

Dr. Kim has received several awards that highlight his exceptional contributions to the field. Notably, he was ranked 3rd among 40 teams in the SNUH Sleep AI Challenge in 2021 and was honored with the Outstanding Student Award from the Department of Robotics and Mechatronics Engineering at DGIST in 2022. In 2024, he was recognized at the KCCV Oral/Poster Presentation Doctoral Colloquium for his work on label-efficient segmentation models.

Impact and Influence 🌍

Dr. Kim's research has made a significant impact on the field of computer vision, particularly in the area of image segmentation. His innovative approaches to handling partially labeled datasets and federated learning have the potential to advance both academic research and practical applications in medical imaging and beyond. His work on few-shot learning and uncertainty-aware models addresses critical challenges in the field, contributing to more robust and adaptable segmentation solutions.

Legacy and Future Contributions 🚀

As Dr. Kim continues his research, his focus on improving segmentation models and developing new methodologies promises to shape the future of computer vision. His commitment to exploring federated learning and few-shot learning techniques will likely drive further innovations in the field, offering solutions to complex challenges and enhancing the accuracy of image analysis across various applications.

 

Publications 📘

📄Few-shot anomaly detection using positive unlabeled learning with cycle consistency and co-occurrence features
Authors: Sion An, Soopil Kim, Philip Chikontwe, Jiwook Jung, Hyejeong Jeon, Jaehong Kim, Sang Hyun Park
Journal: Expert Systems with Applications
Year: 2024

📄Federated learning with knowledge distillation for multi-organ segmentation with partially labeled datasets
Authors: Soopil Kim, Haejun Park, Myeongju Kang, Kilian M. Pohl, Sang Hyun Park
Journal: Medical Image Analysis
Year: 2024

📄FedNN: Federated learning on concept drift data using weight and adaptive group normalizations
Authors: Myeongju Kang, Soopil Kim, Kwang-Hyun Jin, Kilian M. Pohl, Sang Hyun Park
Journal: Pattern Recognition
Year: 2024

📄Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection
Authors: Soopil Kim, Sion An, Philip Chikontwe, Kilian M. Pohl, Sang Hyun Park
Conference: Proceedings of the AAAI Conference on Artificial Intelligence
Year: 2024

📄Uncertainty-aware semi-supervised few shot segmentation
Authors: Soopil Kim, Philip Chikontwe, Sion An, Sang Hyun Park
Journal: Pattern Recognition
Year: 2023