University of Virginia | United States
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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