Jingyi Gao | Probabilistic Modeling | Best Researcher Award

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Ms. Jingyi Gao | Probabilistic Modeling | Best Researcher Award

University of Virginia | United States

Author Profile

Google Scholar

Academic and Research Profile of Jingyi Gao

Early Academic Pursuits

Jingyi Gao’s academic foundation is marked by a strong interdisciplinary focus, combining mathematics, computer science, and economics during her undergraduate studies at the University of California, San Diego. She pursued dual degrees-a Bachelor of Science in Mathematics-Computer Science and a Bachelor of Arts in Economics-demonstrating both technical and analytical versatility. Building on this, she earned a Master of Science in Applied Mathematics and Statistics from Johns Hopkins University, where she graduated with a GPA of 3.9/4.0. Currently, she is pursuing a Ph.D. in Systems and Information Engineering at the University of Virginia, with a research concentration in time series prediction, Bayesian probabilistic modeling, and federated learning.

Professional Endeavors

Gao has gained extensive teaching and mentoring experience across prestigious institutions. At the University of Virginia, she has served as a Teaching Assistant for multiple graduate and undergraduate courses, guiding more than a thousand students in areas such as data mining, AI, and big data systems. She has also contributed as a peer mentor for the Data Justice Academy, fostering diversity in data science research. Beyond academia, her professional journey includes research internships at the University of Pittsburgh and Tencent, where she applied machine learning techniques to healthcare stress detection and cloud infrastructure optimization. Her roles highlight both academic excellence and industry-relevant impact.

Contributions and Research Focus

Jingyi Gao’s research contributions lie at the intersection of machine learning, statistical modeling, and human-centered applications. She has worked on federated learning frameworks to enhance privacy in distributed systems, developed adaptive time series models for real-time prediction, and applied deep latent variable models in ergonomics and healthcare monitoring. Her publications span high-impact venues, including work accepted in Pattern Recognition and presented at IEEE conferences. Her efforts in behavioral modeling, stress detection, and multimodal sensor data analysis underscore her commitment to advancing computational methods for practical societal challenges.

Impact and Influence

Through her teaching, mentorship, and publications, Gao has influenced both academic communities and applied research domains. By mentoring underrepresented groups in data science, she has contributed to inclusive research culture. Her innovative approaches in federated learning and human behavior modeling provide scalable solutions for industries like healthcare, occupational health, and cloud services. Her conference presentations at IEEE CASE, ICMLA, and INFORMS further reflect her growing influence in the global research community.

Academic Citations

Although early in her career, Gao’s scholarly work has begun to attract attention, with multiple preprints available on arXiv and accepted publications in well-recognized journals and conferences. As her ongoing Ph.D. research matures and more of her contributions are published, her academic citation count and impact are expected to expand significantly.

Legacy and Future Contributions

Jingyi Gao’s trajectory suggests a promising future as a leader in data science and applied machine learning. With a foundation that bridges theory and practice, she is well-positioned to make lasting contributions in federated learning, real-time predictive modeling, and socially responsible AI applications. Her future work is likely to leave a meaningful legacy in shaping privacy-preserving, adaptive, and human-centered machine learning systems that address pressing global challenges.

Conclusion

In summary, Jingyi Gao exemplifies the qualities of a rising researcher who blends academic rigor, teaching excellence, and innovative research applications. Her interdisciplinary training, impactful publications, and commitment to mentorship signal a strong potential to become a thought leader in her field. With her ongoing contributions and dedication, Gao is poised to significantly advance both the academic and practical dimensions of data-driven science.

Notable Publications

“Gait-Based Hand Load Estimation via Deep Latent Variable Models with Auxiliary Information" 

  • Author: J Gao, S Lim, S Chung
  • Journal: arXiv preprint arXiv
  • Year: 2025

"Federated automatic latent variable selection in multi-output gaussian processes" 

  • Author: J Gao, S Chung‏
  • Journal: arXiv preprint arXiv
  • Year: 2025

"Modeling Regularity and Predictability in Human Behavior from Multidimensional Sensing Signals and Personal Characteristics" 

  • Author: J Gao, R Yan, A Doryab
  • Journal: International Conference on Machine Learning and Applications
  • Year: 2023

"Machine learning to summarize and provide context for sleep and eating schedules" 

  • Author: T Chen, Y Chen, J Gao, P Gao, JH Moon, J Ren, R Zhu, S Song, JM Clark
  • Journal: bioRxiv
  • Year: 2021

 

Faisal Alshami | Machine Learning | Best Researcher Award

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Faisal Alshami | Machine Learning | Best Researcher Award

Dalian University of Technology | China

Author Profile

Google Scholar

Early Academic Pursuits

Faisal Alshami’s academic journey reflects a deep commitment to software engineering and technological innovation. He began his undergraduate studies at Sana’a University, Yemen, earning a BSc in Network Technology and Computer Security (2008–2012). His undergraduate thesis, “General Management System for Plant Protection,” showcased his early ability to integrate security and system management using ASP.NET, C#, and VPN with OSPF protocols, signaling his strong foundation in both networking and software development. Building on this groundwork, Faisal pursued a Master’s in Software Engineering at Northeastern University, China (2019–2022), where he specialized in advanced machine learning techniques. His master’s thesis, “Design and Implementation of Web API Recommendation System Based on Deep Learning,” utilized CNN, BLSTM, K-modes, and Word2Vec, demonstrating his growing expertise in AI-driven software solutions. Currently, Faisal is advancing his academic pursuits with a PhD in Software Engineering at Dalian University of Technology, China, focusing on federated learning, distributed systems, blockchain, edge computing, and graph neural networks (GNNs).

Professional Endeavors

Alongside his academic progression, Faisal has accumulated over 5 years of professional experience in the software and networking industry. His early career as a VoIP Engineer/Developer at Communication Services Company (2013–2015) allowed him to develop communication APIs and optimize large-scale systems. As a Network Manager and Systems Engineer at EliteTecs (2015–2016), he designed high-reliability networks using advanced protocols such as OSPF, EIGRP, and WiMAX, showcasing his expertise in secure and resilient infrastructures. His role as Full-Stack Developer and DevOps Lead at Almorisi Exchange Company (2016–2018) highlighted his ability to manage mission-critical systems with real-time performance and security. Here, Faisal excelled in building scalable architectures, simulation frameworks, and automated DevOps pipelines, which contributed to operational excellence.

Contributions and Research Focus

Faisal’s research is strategically positioned at the intersection of distributed systems, intelligent computing, and aerospace applications. His focus includes:

  • Federated learning and secure communication for multi-agent systems such as satellite constellations.

  • Edge computing and real-time distributed systems tailored for resource-constrained environments.

  • Robust machine learning frameworks for aerospace, automation, and high-reliability embedded systems.

  • Blockchain integration with AI to enhance security in data networks.

  • Simulation and testing methodologies to ensure fault tolerance in mission-critical software.
    This body of research reflects his ambition to address pressing challenges in space exploration, aerospace engineering, and advanced communication networks.

Impact and Influence

Faisal’s impact lies in bridging the gap between theory and applied innovation. His academic research is not confined to publications alone but extends into real-world applications in secure communications, high-availability systems, and intelligent software architectures. By combining his professional experience with cutting-edge research, Faisal has influenced the fields of network security, distributed computing, and AI-driven system optimization, making his contributions valuable to both academia and industry.

Academic Cites

His work has strong potential for academic citations due to its interdisciplinary nature—linking software engineering, AI, networking, and aerospace technologies. His focus on federated learning, blockchain, and edge computing positions his research at the forefront of emerging scholarly and industrial discussions, ensuring that his publications will attract citations in journals focusing on AI, distributed systems, cybersecurity, and aerospace software engineering.

Legacy and Future Contributions

Faisal Alshami is on a trajectory to build a lasting legacy in intelligent, secure, and scalable software engineering systems. His research is particularly impactful in aerospace applications and secure communications, areas that are becoming increasingly vital in a digital and space-driven era. As he progresses with his doctoral research, Faisal is expected to contribute significantly to the development of resilient federated learning frameworks, advanced distributed architectures, and mission-critical simulations. His blend of academic depth and industry experience ensures that his future work will leave a lasting influence on next-generation computing systems and aerospace engineering technologies.

Other Notable Highlights

  • Certifications: Faisal holds multiple certifications, including Neural Networks & Deep Learning (DeepLearning.AI), CCNP, CCNA, and advanced language certifications (Chinese HSK4, English YALI).

  • Training: He gained practical exposure at NEUSOFT Project Training, where he contributed to developing the Borrow-Seller System (BSS) using Java, Spring Boot, Vue.js, and Android Studio.

  • Core Competencies: His expertise spans software architecture, DevOps, distributed systems, full-stack development, secure networking, and agile collaboration.

Conclusion

In conclusion, Faisal Alshami is an emerging leader in the domain of software engineering, distributed systems, and intelligent computing. His academic journey, professional experiences, and research pursuits demonstrate a rare combination of technical mastery, innovation, and practical problem-solving skills. With his ongoing doctoral work and focus on future technologies such as federated learning, blockchain, and aerospace applications, Faisal is poised to make significant contributions that will influence both academia and industry for years to come.

Notable Publications

"A detailed analysis of benchmark datasets for network intrusion detection system" 

  • Author: M Ghurab, G Gaphari, F Alshami, R Alshamy, S Othman
  • Journal: Asian Journal of Research in Computer Science
  • Year: 2021

"Intrusion detection model for imbalanced dataset using SMOTE and random forest algorithm" 

  • Author: R Alshamy, M Ghurab, S Othman, F Alshami
  • Journal: International Conference on Advances in Cyber Security
  • Year: 2021