Profiles 

Scopus

📍Current Position

He currently serves as a Software Engineer at A&C Technology, Inc. since May 2023. In this role, Jiayi has demonstrated exceptional skills in architecting hybrid consensus systems and implementing scalable AI solutions. The work primarily involves integrating slot-based Proof of Credit generation and Byzantine Fault Tolerance (BFT) finalization algorithms. This integration is fortified with homomorphic encryption for privacy and enabled by a Trustful Execution Environment for real-time transactions. Additionally, Jiayi has led the implementation of a multi-faceted AI for enhanced biometric feature extraction, streamlining operations with a token-based framework for registration, login, and recovery.

📝Publication Achievements 

He has made significant contributions to the field through numerous publications. Some notable works include: Luo, J., Huang, H., Ding, K., Qamhia, I. I., Tutumluer, E., Hart, J. M., … & Sussmann, T. R. (2023). “Toward Automated Field Ballast Condition Evaluation: Algorithm Development Using a Vision Transformer Framework.” Transportation Research Record. Luo, J., Ding, K., Huang, H., Hart, J. M., Qamhia, I. I., Tutumluer, E., … & Sussmann, T. R. (2023). “Toward Automated Field Ballast Condition Evaluation: Development of a Ballast Scanning Vehicle.” Transportation Research Record. Luo, J., Ding, K., Huang, H., Hart, J. M., Qamhia, I. I., Tutumluer, E., … & Sussmann, T. R. (2023). “A Deep Learning Approach for Automated Railroad Ballast Condition Evaluation.” Manuscript submitted for publication.

🔍Ongoing Research 

His ongoing research includes the development of advanced computer vision-based techniques for evaluating ballast conditions and degradation. This involves creating image segmentation frameworks using Mask R-CNN and Swin Transformer backbones and enhancing these frameworks through contrastive learning with unlabeled field ballast images. Another notable project is the design of a synthetic ballast data generator using Unreal Engine, aimed at enriching the ballast database with ground-truth labels and various environmental conditions.

🔬Research Interests 

His research interests are diverse and include: Transportation systems and infrastructure. Civil engineering with a focus on ballast condition and degradation evaluation. Advanced computer vision techniques. Deep learning and AI applications in civil engineering. Development of numerical analysis engines for flexible pavement evaluation.

🎓Academic Background 

He has an impressive academic record, having achieved high GPAs in multiple programs at the University of Illinois at Urbana-Champaign: Ph.D. in Transportation (3.92/4.00), 2018–2023. Master of Computer Science (4.00/4.00), 2016–2018. Master of Science in Transportation (4.00/4.00), 2016–2018. Additionally, Jiayi holds a Bachelor of Engineering in Civil Engineering with a Minor in Economic Management from Tsinghua University, Beijing, China (GPA: 88/100).

🏆Scholarships and Awards 

As a researcher, he has completed various significant tasks, including: Developing and implementing image segmentation frameworks. Designing synthetic data generators. Building finite element analysis engines for pavement evaluation. Creating generative models for 2D particle shape completion. Conducting experimental investigations on concrete behavior at ultra-low temperatures.

🚀Success Factors 

His success can be attributed to a combination of deep technical knowledge, innovative thinking, and a strong academic foundation. Their ability to integrate advanced technologies like AI and deep learning into practical engineering solutions has been a key factor in their professional achievements.

🧪Publications & Laboratory Experience

His extensive publication record and laboratory experience showcase their expertise and contributions to civil engineering and transportation research. The ability to translate complex research into practical applications has significantly advanced the field and set a high standard for future research.

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