Ms. Jingyi Gao | University of Virginia | United States

Ms. Jingyi Gao | University of Virginia | United States

Jingyi Gao is a Ph.D. candidate in Systems and Information Engineering at the University of Virginia with a 3.75 GPA, focusing on time series prediction, Bayesian probabilistic modeling, and federated learning. She holds an M.S. in Applied Mathematics and Statistics from the Johns Hopkins University (GPA 3.9) and dual bachelor’s degrees in Mathematics–Computer Science and Economics from the University of California, San Diego. Jingyi has extensive teaching experience, serving as a teaching assistant at UVA where she has instructed over 1,000 students across multiple courses in statistical modeling, data mining, AI, and big data systems, and previously supported courses at Johns Hopkins and UC San Diego. She has mentored underrepresented students through the Data Justice Academy and completed research internships at the University of Pittsburgh and Tencent, developing machine learning models for stress detection, healthcare data analysis, and cloud resource forecasting. Jingyi has authored several publications, including work accepted by Pattern Recognition and under review at AAAI and IISE Transactions. Her recent projects involve designing deep latent variable models for ergonomic risk assessment, developing real-time adaptive prediction frameworks for occupational health monitoring, creating federated learning approaches for multi-output Gaussian processes, and modeling behavioral regularity and predictability from multidimensional sensing signals. Combining expertise in machine learning, statistical modeling, and data-driven decision systems, Jingyi aims to advance human-centered intelligent systems through interpretable and privacy-preserving predictive modeling.

Profile: Scopus | Google Scholar

Featured Publications

Gao, J., Rahman, A., Lim, S., & Chung, S. TimeSets: A real-time adaptive prediction framework for multivariate time series (Manuscript under review at the Association for the Advancement of Artificial Intelligence).

Gao, J., Lim, S., & Chung, S. Gait-based hand load estimation via deep latent variable models with auxiliary information (Manuscript under review at IISE Transactions).

Gao, J., & Chung, S. Federated automatic latent variable selection in multi-output Gaussian processes (Accepted for publication in Pattern Recognition)*.

Gao, J., Yan, R., & Doryab, A. Modeling regularity and predictability in human behavior from multidimensional sensing signals and personal characteristics. Proceedings of the International Conference on Machine Learning and Applications (ICMLA). Institute of Electrical and Electronics Engineers.

Chen, T., Chen, Y., Gao, J., Gao, P., Moon, J. H., Ren, J., … & Woolf, T. B. Machine learning to summarize and provide context for sleep and eating schedules. bioRxiv.

Prof. Zeng Meng | Engineering | Best Researcher Award

Professor, at Hefei university of technology, China.

Professor Meng Zeng is a leading academic at Hefei University of Technology, specializing in the optimization of uncertain structures, aerospace and civil structural design, and structural topology optimization. With a sharp focus on engineering innovation, Prof. Zeng has guided over 20 funded projects, including prestigious grants from the National Natural Science Foundation of China. Recognized as an Outstanding Youth of Anhui Province, he is celebrated for his dedication to scientific progress. His excellence is marked by receiving two first-class Science & Technology Progress Awards from the Anhui Society of Mechanics. From 2021 to 2024, he has been consistently listed among the world’s top 2% scientists and is a highly cited author in the journal Computers & Structures. 📚 He has authored 90+ SCI papers, including 50+ as the first or corresponding author. His work has received over 4,200 citations, with 8 ESI highly cited papers and 3 hot papers. 🌟

Professional Profile

Scopus

ORCID

🎓 Education

Professor Meng Zeng earned his academic credentials with distinction in civil and structural engineering. Though detailed records of his academic institutions are not publicly specified, his educational background reflects a strong foundation in mechanics and structural design, which paved the way for his current leadership in aerospace and civil engineering innovation. Throughout his education, Prof. Zeng focused on uncertainty modeling, computational mechanics, and optimization techniques, equipping him with the analytical expertise required for cutting-edge structural analysis. His academic training has fostered a mindset geared toward solving real-world engineering problems using theoretical rigor and computational sophistication. 🎓 As an educator and mentor, he now imparts this rich knowledge base to his students and research collaborators at Hefei University of Technology. His journey from a dedicated student to a globally recognized professor exemplifies the impact of solid academic preparation in shaping research excellence. 💼

💼 Experience

Professor Meng Zeng currently serves as a professor at Hefei University of Technology, where he leads research in structural optimization and engineering mechanics. With over 20 funded projects, his contributions span across the National Natural Science Foundation of China (NSFC), including two general and two youth projects. 🏗️ He has applied his research expertise to both aerospace and civil structure applications, combining theory with practical innovations. His role encompasses research leadership, postgraduate supervision, and national-level project management. His accolades from Anhui Society of Mechanics, including two first prizes in scientific and technological progress, affirm his high impact on the engineering community. 🏆 Prof. Zeng also represents China in international research through his involvement in peer-reviewed journals and collaborations. A consistent presence in the top 2% of global scientists (2021–2024), his work shapes modern methodologies in topology optimization and structural resilience under uncertain conditions. 🧠

🔬 Research Interest

Prof. Meng Zeng’s research interests lie at the intersection of engineering mechanics and computational optimization. His primary focus is on the optimization of uncertain structures, where he develops methods to enhance structural performance despite variations in material properties or loading conditions. 🚀 He is also deeply involved in aerospace and civil structure analysis, contributing to safer and more efficient designs. Prof. Zeng is renowned for his work in structural topology optimization, an area that determines the optimal material layout within a given design space, a key element in lightweight and high-performance structural engineering. 🔧 His research integrates probabilistic methods, finite element analysis, and machine learning algorithms to solve complex, real-world problems. As a thought leader, Prof. Zeng not only advances theoretical mechanics but also offers transformative insights for engineering design under uncertainty, positioning him at the forefront of innovation in applied structural optimization. 📈

🏅 Awards

Professor Meng Zeng’s academic excellence and scientific innovation have earned him numerous accolades. Notably, he was selected as an Outstanding Youth of Anhui Province, a recognition of his early-career contributions to engineering science. 🏆 He has received two First-Class Science and Technology Progress Awards from the Anhui Society of Mechanics, underscoring the high societal and technological value of his work. Between 2021 and 2024, Prof. Zeng was consecutively listed among the world’s top 2% scientists, a global benchmark of research excellence. Additionally, he is a highly cited author in the prestigious journal Computers & Structures, highlighting the global reach and influence of his research. 🌟 These awards are a testament to his impact on both fundamental research and practical engineering applications, positioning him as a top-tier scientist and thought leader in the field of structural mechanics and optimization. 🎖️

📚 Top Noted Publications

Professor Meng Zeng has published over 90 peer-reviewed SCI papers, with more than 50 as first or corresponding author. His work is widely cited, having accumulated over 4,200 Google Scholar citations. He has contributed 8 ESI Highly Cited Papers and 3 Hot Papers, affirming his global academic influence. 🔍 His research often appears in top journals such as:

Reliability-Based Topology Optimization (RBTO):
- Articles:
  - Data-driven RBTO using extended multiscale FEM and neural networks
  - RBTO for continuum structures with nonlinear dynamics
  - Stress-constrained RBTO with fidelity transformation method
- Highlights: Use of machine learning (e.g., neural networks), multiscale finite element modeling, and probabilistic analysis to optimize structural performance under uncertainty.
Dynamic and Transient Response in Optimization:
- Articles:
  - Transient dynamic topology optimization using equivalent static loads
  - Uncertainty-oriented topology optimization of dynamic structures
- Highlights: Focus on efficient dynamic response prediction and hybrid uncertainties (probabilistic + spatial/random field modeling).
Metaheuristic and Bio-Inspired Algorithms:
- Article:
  - Starfish Optimization Algorithm (SFOA) – Compared with 100 optimizers.
- Highlights: Development of novel optimization algorithms inspired by biological behaviors; applied to structural or global optimization problems.
Concurrent Topology and Material Design:
- Article:
  - Concurrent optimization of topology and fiber orientation under stress constraints
- Highlights: Combines material orientation (like composite fibers) with shape optimization for optimal mechanical performance.
Uncertainty Quantification Techniques:
- Article:
  - Weight index-based uniform partitioning of multi-dimensional probability space
- Highlights: Proposes a novel method to efficiently sample and compute in high-dimensional uncertainty spaces.
Materials & Structural Systems Innovation:
- Articles:
  - Porous functionally graded composite plates with graphene reinforcements
  - Non-uniform rectangular honeycomb sandwich panel
- Highlights: Focus on lightweight, high-strength materials and sandwich structures for performance and efficiency.

Conclusion

Professor Meng Zeng is a highly suitable candidate for the Best Researcher Award. His extensive research output, impactful publications, strong citation record, and recognition at national and international levels highlight a career marked by innovation, consistency, and academic leadership. With minor improvements in global engagement and interdisciplinary expansion, he stands out as a role model for excellence in engineering research.

Jingyi Gao | Engineering | Best Researcher Award