Hao Zhang | Artificial Intelligence | Best Researcher Award

Posted on by Engineering Scientist

Dr. Hao Zhang | Artificial Intelligence
| Best Researcher Award

Associate professor at Carnegie Mellon University, United States.

Hao Zhang is a Research Associate at Carnegie Mellon University (CMU), conducting postdoctoral research at the Safe AI Lab under Prof. Ding Zhao. He also serves as the Associate Director of the ETAIC Research Lab at the University of Texas at Arlington, led by Prof. Eric Tseng (NAE Member). He holds a Ph.D. from Tsinghua University, co-advised by Prof. Zhi Wang and Prof. Shengbo Eben Li. With over 35 SCI/EI publications and 17 patents, his research advances multi-agent reinforcement learning and closed-loop LLMs for real-world AI deployment in autonomous vehicles, robotics, and smart energy systems. He collaborates globally with academic and industrial leaders such as BYD, SAIC, Dongfeng Motor, and UCL, making impactful contributions to intelligent mobility.

🌍 Professional Profile:

ORCID

Google Scholar 

Scopus 

🏆 Suitability for the Best Researcher Award :

Dr. Hao Zhang is an exceptional candidate for the Best Researcher Award due to his groundbreaking work at the intersection of artificial intelligence and real-world applications. His achievements in scalable AI for micro-mobility and autonomous vehicles have led to industrial deployments across leading automotive manufacturers. With a strong publication record, global collaborations, and 17 patents, he exemplifies innovation, impact, and leadership. He bridges theory and practice, pushing the boundaries of safe and trustworthy AI agents. His dual appointments at CMU and UTA and contribution to both academia and industry reflect his versatile excellence. Dr. Zhang’s work not only enhances technological advancement but also fosters a responsible and intelligent future for mobility and energy systems.

🎓 Education :

Hao Zhang received his Ph.D. in Mechanical Engineering from Tsinghua University, one of China’s most prestigious institutions, where he was co-advised by renowned scholars Prof. Zhi Wang and Prof. Shengbo Eben Li. During his Ph.D., he focused on reinforcement learning and its applications to intelligent vehicle systems. Prior to that, he completed his undergraduate and master’s studies with distinction, developing a strong foundation in robotics, automation, and control systems. His education also included collaborative learning experiences with industry, which laid the groundwork for his multidisciplinary approach to research. Currently, he is expanding his expertise through postdoctoral research at Carnegie Mellon University, contributing to the development of safe AI systems under the mentorship of Prof. Ding Zhao.

🏢 Work Experience :

Dr. Zhang has a rich portfolio of academic and industrial experience. As a Research Associate at Carnegie Mellon University, he works at the forefront of AI safety, while simultaneously serving as Associate Director at the ETAIC Lab at UTA. He has led or participated in five major government-funded research projects and four OEM-sponsored industry projects. His efforts have directly supported intelligent system development for companies such as BYD Auto, SAIC Motor, and Dongfeng. His engineering solutions have real-world applications in autonomous driving, energy management, and mobile robotics. His academic roles, coupled with his industrial consultancy, enable him to effectively translate research into practice. Dr. Zhang’s interdisciplinary experience sets him apart as a leader in applied AI and automation.

🏅Awards and Honors

Hao Zhang’s research excellence has earned him notable recognition across academia and industry. He has published over 35 SCI/EI-indexed journal articles, authored a technical book (ISBN: 9780443329845), and holds 17 patents related to intelligent control and autonomous systems. His work has been cited nearly 500 times, demonstrating significant influence. His research contributions have been integrated into industrial platforms at BYD and Dongfeng, marking a rare crossover between lab and large-scale deployment. Although he is still early in his postdoctoral career, his consistent innovation and impact have made him a rising leader in AI-powered mobility. His contributions position him for prestigious honors such as the Best Researcher Award and similar recognitions for scientific leadership.

🔬 Research Focus :

Dr. Zhang’s research focuses on scalable and trustworthy AI for autonomous systems and smart energy applications. His core expertise includes multi-agent reinforcement learning, closed-loop large language models (LLMs), and intelligent motion control. He develops AI algorithms that can be safely deployed in micro-mobility devices (assistive and mobile robots), connected vehicles, and distributed energy platforms. His work contributes to both algorithmic innovation and real-world adoption, ensuring AI agents are reliable, interpretable, and responsive to dynamic environments. He is particularly interested in bridging theory with practice by collaborating with top-tier institutions and OEMs. Dr. Zhang’s interdisciplinary approach merges robotics, automotive systems, control engineering, and deep learning to create adaptive, secure, and energy-efficient intelligent agents.

📊 Publication Top Notes:

📘 Impact of ammonia addition on knock resistance and combustion performance in a gasoline engine with high compression ratio
📅 Year: 2023 | 📊 Cited by: 75 | 🛠️ Energy efficiency, combustion

📘 Hierarchical energy management strategy for plug-in hybrid electric powertrain integrated with dual-mode combustion engine
📅 Year: 2021 | 📊 Cited by: 42 | ⚡ Hybrid vehicles, control systems

📘 Data-driven predictive energy consumption minimization strategy for connected plug-in hybrid electric vehicles
📅 Year: 2023 | 📊 Cited by: 40 | 📡 Connected vehicles, optimization

📘 Experimental study on combustion and emission characteristics of ethanol-gasoline blends in a high compression ratio SI engine
📅 Year: 2023 | 📊 Cited by: 36 | 🔬 Fuel science, engine performance

📘 Learning-based supervisory control of dual mode engine-based hybrid electric vehicle with reliance on multivariate trip information
📅 Year: 2022 | 📊 Cited by: 34 | 🤖 AI control, mobility systems

📘 Integrated thermal and energy management of connected hybrid electric vehicles using deep reinforcement learning
📅 Year: 2023 | 📊 Cited by: 30 | 🧠 Deep learning, hybrid energy systems

Zhiying Mu| Neural Networks | Best Researcher Award

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Dr. Zhiying Mu| Neural Networks
|Best Researcher Award

Dr . Zhiying Mu  Northwestern Polytechnical University, China .

Zhiying Mu, a Ph.D. candidate in Cyberspace Security at Northwestern Polytechnical University, is a distinguished young scholar dedicated to AI safety and intelligent system security. With a rigorous academic foundation and cross-disciplinary insight from her mathematics background at the University of Connecticut and the University of Nebraska–Lincoln, she has contributed to multiple national-level research projects, including the “New Generation Artificial Intelligence” initiative. Her publications in top-tier journals like IEEE IoT Journal and Neural Processing Letters demonstrate high academic impact. She leads multiple research and data-driven modeling projects with outstanding results. Her innovative mindset, strong leadership, and publication record make her an exceptional candidate for the Best Researcher Award.



🌍 Professional Profile:


Scopus


🏆 Suitability for the Best Researcher Award


 




















Zhiying Mu, a Ph.D. candidate in Cyberspace Security at Northwestern Polytechnical University, is a distinguished young scholar dedicated to AI safety and intelligent system security. With a rigorous academic foundation and cross-disciplinary insight from her mathematics background at the University of Connecticut and the University of Nebraska–Lincoln, she has contributed to multiple national-level research projects, including the “New Generation Artificial Intelligence” initiative. Her publications in top-tier journals like IEEE IoT Journal and Neural Processing Letters demonstrate high academic impact. She leads multiple research and data-driven modeling projects with outstanding results. Her innovative mindset, strong leadership, and publication record make her an exceptional candidate for the Best Researcher Award.




















🎓 Education 


Zhiying Mu earned her Ph.D. in Cyberspace Security (2021–2025) from Northwestern Polytechnical University, under the supervision of Academician He Dequan. Her curriculum includes machine learning, optimization, complex networks, and academic ethics. She actively contributed to major national and industrial research projects related to AI safety and power systems. She previously earned a Master’s degree in Mathematics from the University of Connecticut (2017–2019), and a Bachelor’s degree in Mathematics from the University of Nebraska–Lincoln (2013–2017). Her academic performance has been consistently excellent, with a GPA of 3.8/4.0 during her Ph.D. Her multidisciplinary training bridges cybersecurity and data science, laying a robust foundation for her research excellence and interdisciplinary innovation.


🏢 Work Experience 


Zhiying Mu has led and participated in various high-impact research projects involving AI safety, network attack modeling, and climate risk forecasting. Notable projects include storm damage prediction using regression models, ACI index modeling via time-series analysis, and optimal strategy evaluation in Tic-Tac-Toe using generalized linear models. She has also organized institutional reading programs, promoting interdisciplinary knowledge sharing. Her responsibilities typically involve end-to-end project management: problem formulation, data collection and preprocessing, statistical modeling, visualization, and outcome documentation. She is proficient in R and Python and applies advanced analytics and machine learning techniques. Her blend of theoretical depth and practical implementation reflects a versatile and impactful research profile in both academic and applied contexts.


🏅 Awards and Honors 


Zhiying Mu has received significant recognition for her research and academic contributions. She was the top borrower of the year at her university library, reflecting her deep engagement with academic literature. She has been entrusted with leadership roles in national projects funded under China’s “New Generation Artificial Intelligence” program and major horizontal projects with the State Grid Corporation of China. Her peer-reviewed publications in top SCI-indexed journals such as IEEE IoT Journal, Neurocomputing, and Neural Processing Letters highlight her academic excellence. She has also served as a project lead in multiple interdisciplinary modeling initiatives. Her academic and extracurricular leadership underscores her status as an emerging thought leader in AI security and intelligent systems.


🔬 Research Focus 
































































Zhiying Mu’s research centers on artificial intelligence security, multi-task learning, risk modeling, and network attack analysis. She investigates adversarial learning techniques, identity-preserving dialogue generation, and neural machine translation enhancement using syntactic features. Her work integrates mathematical modeling, machine learning, and cybersecurity to address challenges in intelligent power systems, social network robustness, and data-driven decision-making. She has explored both black-box and white-box vulnerabilities in AI systems and proposed defense mechanisms with theoretical grounding. Her interdisciplinary focus also includes time-series forecasting for insurance risk and strategic modeling in game theory. She actively contributes to national AI safety platforms and is committed to advancing secure, interpretable, and reliable AI technologies for critical infrastructures.




























































































📊 Publication Top Notes:


Prompt-enhanced Neural Machine Translation with POS Tags


Authors:
Mu, Zhiying; Lin, Shengchuan; Guo, Sensen; Yu, Shanqing; Gao, Dehong


Journal:
Neurocomputing, 2025


Citations:
0 (as of now)