Dr. Yang Zhao | Risk Management | Best Researcher Award

Staff Software Engineer, Xmotors.ai, United States

Yang Zhao is a distinguished researcher and software engineer specializing in operations research, machine learning, and simulation-based evaluation of autonomous systems. With a deep passion for data-driven insights, he has made significant contributions to the fields of causal inference, risk modeling, and volatility analysis. Currently based in Mountain View, California, he brings a wealth of expertise in statistics, econometrics, and artificial intelligence to his work in autonomous vehicle assessment and optimization. 🚀

Publication Profile

ORCID

🎓 Education

Yang Zhao holds a Ph.D. in Operations Research from North Carolina State University (2018), where he maintained a perfect 4.0 GPA and pursued a minor in Statistics. His research focused on simulation, risk management, and volatility modeling. Prior to this, he earned a B.S. in Financial Management from Huazhong University of Science and Technology in China (2013). His academic journey has equipped him with a strong foundation in mathematical modeling, optimization, and data science. 📚

💼 Experience

With a career spanning across top technology and research-driven companies, Yang Zhao has held key roles in the autonomous vehicle and data science sectors. He is currently a Staff Software Engineer at Xmotors.ai (2025–Present), where he develops simulation-based evaluation workflows for autonomous driving systems. Previously, as a Senior Data Scientist at Cruise LLC (2022–2025), he contributed to scene detection, safety proxy modeling, and reliability performance prediction. His prior experience includes roles at DiDi Labs (2021–2022), where he built causal inference models for pricing strategies, and SAS Institute (2018–2021), where he developed deep learning-based causal inference products and Monte Carlo simulation tools. His expertise in data science, machine learning, and simulation has been instrumental in advancing AI-driven decision-making. 🏆

🏅 Awards and Honors

Yang Zhao has been recognized for his groundbreaking contributions in statistics, econometrics, and AI-driven solutions. His innovative work in deep learning for causal inference and policy optimization has led to multiple patents. His contributions to risk estimation and volatility modeling have been published in top-tier journals, further solidifying his reputation in the field. His cutting-edge software products at SAS Institute have been widely utilized in financial modeling and simulation. 🏅

🔬 Research Focus

Yang Zhao’s research interests lie at the intersection of machine learning, statistical modeling, and AI-driven decision-making. He has extensively worked on causal inference, deep learning, survival analysis, time-series forecasting, and risk assessment. His expertise in simulation-based modeling for autonomous systems, safety evaluation, and reliability estimation has contributed to advancements in self-driving technology. Additionally, his work on Bayesian analysis, stochastic processes, and Monte Carlo methods has played a crucial role in optimizing financial and operational risk models. 📊

🎯 Conclusion

Yang Zhao is a visionary researcher and software engineer whose expertise in simulation, AI, and statistical modeling has had a profound impact on autonomous systems, risk assessment, and financial modeling. His deep understanding of machine learning and optimization, combined with hands-on experience in AI-driven evaluation, makes him a leading figure in his field. With multiple patents, high-impact publications, and contributions to top AI companies, he continues to drive innovation in data science and AI applications. 🚀

📚 Publications

A Simple and Robust Approach for Expected Shortfall Estimation (2021) – Journal of Computational Finance, 25(1). [[Read More](website W)] 📖

On GARCH and Autoregressive Stochastic Volatility Approaches for Market Calibration and Option Pricing (2023) – Risks, 13(2):31. [[Read More](website W)] 📖

Prof. Dr. Peng Yue | Mathematics | Best Researcher Award

Ocean University of China, China

Prof. Dr. Peng Yue is a distinguished Professor at the School of Physics and Optoelectronic Engineering, College of Information Science and Engineering, Ocean University of China 🌊. He is an active member of the Chinese Mathematical Society, the Chinese Society for Industrial and Applied Mathematics, and the Chinese Society of Mechanics. Before his tenure at Ocean University of China, he worked at the University of Electronic Science and Technology of China and served as a Professor at the Institute of Space Technology at the China Aerodynamics Research and Development Center 🏫. With a robust academic background, he earned his Bachelor’s (2011), Master’s (2013), and Ph.D. (2017) degrees in mechanics from the National Aerospace University “Kharkov Aviation Institute” 🎓. His work explores the development of mathematical and mechanical theories, focusing on nonlinear and complex systems 🔍.

Education

Prof. Dr. Peng Yue’s educational journey began at the National Aerospace University “Kharkov Aviation Institute,” where he completed his Bachelor’s degree in Theoretical and Applied Mechanics in 2011. He continued his studies, earning his Master’s degree in Mechanics in 2013 and a Ph.D. in Mechanics in 2017, all of which provided him with a strong foundation in the principles of mathematics, mechanics, and physics 🎓.

Experience

Prof. Yue has held prestigious academic and research positions over the years. From 2019 to 2023, he was part of the University of Electronic Science and Technology of China, contributing to the advancement of his field. Prior to that, he worked at the China Aerodynamics Research and Development Center, where he served as a Professor at the Institute of Space Technology from May 2020 to May 2023 🏢. His expertise extends to both theoretical and applied areas, demonstrating leadership and innovation across several roles in academia and research.

Research Focus

Prof. Yue’s research focuses on the development of mathematical and mechanical theories, algorithms, and models applied to nonlinear and complex systems. His expertise spans a variety of fields, including mathematics, mechanics, physics, and engineering technologies. He is particularly involved in advancing theoretical and practical knowledge through his research in complex system modeling and fluid dynamics 🔬. His innovative work includes developing algorithms for the analysis and computation of multi-field coupling mechanisms and fluid dynamics.