Dr. Jiaheng Peng | Data Science | Best Researcher Award

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Dr. Jiaheng Peng | Data Science | Best Researcher Award

PhD Candidate, East China Normal University, China

Jiaheng Peng is a dedicated Ph.D. candidate at East China Normal University, specializing in Open Source Ecosystem, Natural Language Processing, and Evaluation Science. With a strong academic record and a passion for research, he has contributed significantly to understanding Open Source dataset evaluation. His work bridges the gap between academic research and real-world Open Source applications, earning him recognition in the field.

Publication Profile

Google Scholar

🎓 Academic Background

Jiaheng Peng is pursuing his Ph.D. at East China Normal University, focusing on innovative methods to assess Open Source datasets. His research emphasizes citation network analysis, evaluating long-term dataset usage, and developing advanced Natural Language Processing (NLP) models. His academic journey is marked by high-impact publications in top-tier journals and international conferences, reflecting his expertise in computational analysis and data evaluation.

👨‍💼 Professional Experience

Although Jiaheng does not have industry consultancy or ongoing research projects, his scholarly contributions have made a substantial impact on Open Source ecosystem analysis. He actively publishes in high-impact scientific journals and conferences, ensuring that his findings help enhance dataset evaluation metrics. His commitment to advancing data-driven methodologies sets a solid foundation for future research in Open Source analysis.

🏆 Awards and Honors

Jiaheng Peng’s research excellence has been acknowledged with the Best Paper Award at the 1st Open Source Technology Academic Conference (2024). His publications in Q1-ranked journals further highlight his academic impact. His continuous contributions to the Open Source community demonstrate his dedication to advancing research and innovation in Open Source evaluation.

🔬 Research Focus

Jiaheng’s research primarily addresses the limitations of traditional Open Source data insight metrics. His work connects Open Source datasets with their corresponding academic papers, evaluating their significance through citation network mining. By bridging Open Source data with academic insights, he introduces novel evaluation methodologies that enhance dataset usability and long-term impact analysis. His research also extends into Aspect-Based Sentiment Classification, employing advanced Graph Attention Networks and NLP models to extract meaningful insights.

📌 Conclusion

Jiaheng Peng is a rising scholar in the Open Source and NLP domains, with a keen focus on dataset evaluation, citation network analysis, and sentiment classification. His academic contributions, recognized through prestigious awards and top-tier publications, establish him as a promising researcher dedicated to advancing Open Source dataset analytics. With a commitment to scientific excellence, his work continues to influence the global research community.

📚 Publication Top Notes

Evaluating long-term usage patterns of open source datasets: A citation network approach
BenchCouncil Transactions on Benchmarks, Standards and Evaluations (2025)
Cited by: Pending

DRGAT: Dual-relational graph attention networks for aspect-based sentiment classification
Information Sciences (2024)
Cited by: Pending

Data Driven Visualized Analysis: Visualizing Global Trends of GitHub Developers with Fine-Grained Geo-Details
International Conference on Database Systems for Advanced Applications (2024)
Cited by: Pending

ASK-RoBERTa: A pretraining model for aspect-based sentiment classification via sentiment knowledge mining”
Knowledge-Based Systems (2022)
Cited by: Multiple researchers in NLP and sentiment analysis

Ulas Bagci | Artificial Intelligence | Outstanding Scientist Award

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Assoc. Prof. Dr. Ulas Bagci | Artificial Intelligence | Outstanding Scientist Award

Assoc. Prof., Northwestern University, United States

Dr. Ulas Bagci is a distinguished researcher and tenured Associate Professor at Northwestern University, specializing in Radiology, Electrical and Computer Engineering, and Biomedical Engineering. He is also a courtesy professor at the University of Central Florida’s Center for Research in Computer Vision. As the Director of the Machine and Hybrid Intelligence Lab, Dr. Bagci focuses on the integration of artificial intelligence, deep learning, and medical imaging. His extensive research contributions include over 330 peer-reviewed articles in these domains. Previously, he was a staff scientist and lab co-manager at the National Institutes of Health (NIH), where he played a pivotal role in advancing AI-driven medical imaging applications. Dr. Bagci actively contributes to leading scientific journals, serving as an associate editor for IEEE Transactions on Medical Imaging, Medical Physics, and Medical Image Analysis.

Publication Profile

🎓 Education

Dr. Ulas Bagci holds a Ph.D. in Computer Science from the University of Nottingham (2010), where he conducted pioneering research in medical imaging. He was a Visiting Research Fellow in Radiology at the University of Pennsylvania (2008-2009), further refining his expertise in AI applications for biomedical sciences. He earned his M.Sc. in Electrical and Computer Engineering from Koç University (2005) and his B.Sc. in Electrical and Computer Engineering from Bilkent University (2003).

💼 Experience

Dr. Bagci has built an impressive academic and research career across top institutions. Since 2021, he has been an Associate Professor at Northwestern University, where he leads research in AI-driven medical imaging. Before that, he served as an Assistant Professor in Computer Science at the University of Central Florida (2014-2020), fostering innovation in deep learning for radiology. From 2010 to 2014, he was a Staff Scientist and Lab Manager at the National Institutes of Health (NIH), playing a key role in infectious disease imaging and AI applications in radiology.

🏅 Awards and Honors

Dr. Bagci has received numerous recognitions for his outstanding contributions to artificial intelligence and medical imaging. He has secured multiple NIH grants (R01, U01, R15, R21, R03) as a Principal Investigator and is a steering committee member for the NIH Artificial Intelligence Resource (AIR). Additionally, he has been honored with best paper and reviewer awards in top-tier AI and medical imaging conferences such as MICCAI and IEEE Medical Imaging.

🔬 Research Focus

Dr. Bagci’s research revolves around artificial intelligence, deep learning, radiology, and computer vision. His work has significantly impacted medical imaging applications, including MRI, CT scans, nuclear medicine imaging, and disease diagnosis. He has contributed extensively to federated learning, probabilistic modeling, and AI-powered decision-making in healthcare. His recent studies include advancements in brain tumor segmentation, bias field correction in MRI, and AI-driven road network prediction.

🔚 Conclusion

Dr. Ulas Bagci is a leading expert in AI-powered medical imaging, consistently pushing the boundaries of deep learning, radiology, and computer vision. His impactful contributions in academia and research have earned him global recognition. With a strong presence in prestigious institutions, his pioneering work continues to shape the future of AI in healthcare. 🚀

📚 Publications

Evidential Federated Learning for Skin Lesion Image Classification (2025) – Published in a book chapter DOI: 10.1007/978-3-031-78110-0_23 📖

Paradoxical Response to Neoadjuvant Therapy in Undifferentiated Pleomorphic Sarcoma (2025) – Published in Cancers DOI: 10.3390/cancers17050830 🏥

Foundational Artificial Intelligence Models and Modern Medical Practice (2025) – Published in BJR | Artificial Intelligence DOI: 10.1093/bjrai/ubae018 🧠

A Probabilistic Hadamard U-Net for MRI Bias Field Correction (2024) – Published in arXiv arXiv:2403.05024 🖥️

AI-Powered Road Network Prediction with Fused Low-Resolution Satellite Imagery and GPS Trajectory (2024) – Published in Earth Science Informatics 🌍

Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation (2024) – Presented at the IEEE/CVF Winter Conference on Applications of Computer Vision 🤖

Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation  (2024) – Published in arXiv arXiv:2405.18383 🏥