Mona Ebadi Jalal | Computer Science | Best Researcher Award

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Ms. Mona Ebadi Jalal | Computer Science | Best Researcher Award

University of Louisville | United States

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Early Academic Pursuits 🎓

Ms. Mona Ebadi Jalal's academic journey is marked by excellence and dedication. She is currently pursuing a PhD in Computer Science at the University of Louisville, where she maintains a perfect GPA of 4.00. Her research focuses on the cutting-edge fields of Machine Learning and Deep Learning, under the guidance of Professor Adel Elmaghraby. Prior to this, she earned a Master’s Degree in Information Technology Engineering from K. N. Toosi University of Technology (KNTU) in Tehran, Iran, where she graduated with an impressive GPA of 17.75/20. Her master’s thesis involved developing a novel deep learning model using recurrent neural networks to forecast incoming call volumes in call centers, a project that earned a perfect grade of 20/20. She also holds a Bachelor’s Degree in Computer Engineering - Software from Payame Noor University in Hamedan, Iran, where she developed a patient information management system for a hospital as part of her thesis.

Professional Endeavors 💼

Ms. Ebadi Jalal’s professional career is equally distinguished. She is currently a PhD Fellow and Research Assistant at the University of Louisville, where she conducts in-depth research in customer behavior analysis, medical image analysis, and diagnostics prediction, utilizing advanced Machine Learning and Deep Learning methods. Before pursuing her PhD, she worked as an IT Consultant specializing in SAP ABAP and Business Data Analysis at Naghshe Aval Keyfiat (NAK) and Faraz Andishan Hesab Companies in Tehran, Iran. During this period, she designed and implemented custom solutions within the SAP framework, conducted thorough analyses of business processes, and managed end-to-end project lifecycles. She has also served as a Software Developer, developing and maintaining web applications and managing relational databases.

Contributions and Research Focus 🔬

Ms. Ebadi Jalal’s contributions to the field of computer science are significant and diverse. Her research primarily focuses on the application of Machine Learning and Deep Learning to customer behavior analysis and medical diagnostics. She has developed predictive models for call center operations and contributed to the advancement of personalized marketing through counterfactual analysis. Her recent work includes a deep learning framework for abnormality detection in nailfold capillary images, which has the potential to revolutionize diagnostics in medical imaging.

Accolades and Recognition 🏅

Ms. Ebadi Jalal’s academic and professional achievements have been recognized with numerous awards and honors. She was awarded a prestigious fellowship for her PhD studies at the University of Louisville in 2022. During her time at K. N. Toosi University of Technology, she was nominated for the Superior Student Researcher honor in 2014. Additionally, she ranked in the top 1% in Iran’s nationwide graduate-level entrance exam in Information Technology Engineering in 2012 and received a national graduate-level full scholarship.

Impact and Influence 🌍

Ms. Ebadi Jalal’s work has had a profound impact on both academia and industry. Her research has led to new insights in customer behavior analysis and medical image diagnostics, influencing the development of more effective marketing strategies and diagnostic tools. As a peer reviewer for several prestigious journals, including IEEE Access and Scientific Reports, she contributes to the advancement of knowledge in her field by ensuring the quality and rigor of published research.

Legacy and Future Contributions 🌟

Ms. Ebadi Jalal is poised to leave a lasting legacy in the field of computer science. Her ongoing research in machine learning and deep learning holds the potential to drive significant advancements in both customer behavior analysis and medical diagnostics. With her strong academic background, extensive professional experience, and numerous accolades, she is well-positioned to continue making groundbreaking contributions to the field in the years to come. Her future work will likely influence the next generation of researchers and practitioners, further solidifying her impact on the world of technology.

Publications

📝 Artificial Intelligence Algorithms in Nailfold Capillaroscopy Image Analysis: A Systematic Review

Journal: MedRxiv
Year: 2024
Authors: Emam, Omar S.; Jalal, Mona Ebadi; Garcia-Zapirain, Begonya; Elmaghraby, Adel S.

📝 Analyzing the Dynamics of Customer Behavior: A New Perspective on Personalized Marketing through Counterfactual Analysis

Journal: Journal of Theoretical and Applied Electronic Commerce Research
Year: June 2024
Authors: Mona Ebadi Jalal; Adel Elmaghraby

📝 Forecasting Incoming Call Volumes in Call Centers with Recurrent Neural Networks

Journal: Journal of Business Research
Year: November 2016
Authors: Mona Ebadi Jalal; Monireh Hosseini; Stefan Karlsson

📝 Analysis of Customer Behavior in Purchasing and Sending Online Group SMS Using Data Mining Based on the RFM Model

Journal: Sharif Journal of Industrial Engineering & Management
Year: February 20, 2016
Authors: Mona Ebadi Jalal; Somayeh Alizadeh




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

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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