Hao Zhang | Artificial Intelligence | Best Researcher Award

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

Zhou Yang | Artificial Intelligence | Best Researcher Award

Mr. Zhou Yang| Artificial Intelligence
| Best Researcher Award

 

PhD Candidate at Fuzhou University , China .

Zhou Yang is a PhD candidate in Computer Science and Technology at Fuzhou University, specializing in artificial intelligence. With a robust academic foundation from Chongqing University of Technology and Chongqing University of Posts and Telecommunications, he ranks in the top 10% of his class throughout. Zhou has gained significant industry experience as an algorithm engineer and research intern at top tech companies like Sohu, Baidu, and Qihoo 360, where he focused on deep learning, recommendation systems, and natural language processing. His research contributions include publications in top-tier venues such as ACL, EMNLP, and IPM. Zhou’s work in empathetic dialogue systems and personalized recommendation demonstrates strong interdisciplinary innovation, making him a promising young talent in AI-driven intelligent systems.

🌍 Professional Profile:

Scopus

🏆 Suitability for the Best Researcher Award

Zhou Yang exemplifies the qualities of a top emerging researcher. His academic excellence, cutting-edge research in AI, and impactful industry experience align with the values of the Best Researcher Award. Zhou has published in top-tier conferences and journals like ACL, EMNLP, and IPM, showcasing his thought leadership in natural language processing and recommendation systems. His work on empathetic models, deep learning architectures, and real-world applications reflects technical depth and societal relevance. Beyond academia, his contributions at Sohu, Qihoo 360, and Baidu show a consistent record of applied innovation. With a blend of scholarly rigor, innovation, and collaborative spirit, Zhou is highly suited for this award as a next-generation research leader in artificial intelligence.

🎓 Education 

Zhou Yang is pursuing a PhD in Computer Science and Technology at Fuzhou University, a “Double First-Class” institution, expected to graduate in 2025. He holds a Master’s degree in Computer System Structure from Chongqing University of Technology, completed in collaboration with the Chinese Academy of Sciences, where he was ranked in the top 10% of his cohort. During his undergraduate studies at Chongqing University of Posts and Telecommunications, he majored in Software Engineering and again stood in the top 10% academically. He has been recognized with prestigious honors such as the National Inspirational Scholarship and multiple awards for innovation, leadership, and academic excellence, setting a strong foundation for his advanced research in AI and computing systems.

🏢 Work Experience 

Zhou Yang brings a wealth of applied research and industry experience. At Sohu’s Smart Media R&D Center, he led deep learning initiatives for search and recommendation, implementing scalable big data architectures. While at the Chinese Academy of Sciences, he worked on the MatchZoo framework, enhancing state-of-the-art text matching models like DRMM and KNRM. At Qihoo 360, he applied deep learning models like BERT in recommendation systems, while at Baidu, he contributed to the Totem Project, working on image query and data analytics using distributed systems. His consistent focus on high-impact AI applications bridges academic research and practical deployment, marking him as an innovator in applied machine learning, recommendation systems, and intelligent information retrieval.

🏅 Awards and Honors 

Zhou Yang has received numerous accolades throughout his academic journey. As an undergraduate, he was awarded the National Inspirational Scholarship twice and earned recognition as an Outstanding Student Cadre and Three Good Student at Chongqing University of Posts and Telecommunications. His graduate work earned him a Best Paper Candidate award at CCIR for his contribution on deep relevance matching models. He has also achieved notable placements in national innovation and entrepreneurship competitions. Zhou’s research excellence continues with his publications in top-tier journals and conferences like ACL, EMNLP, and IPM. These honors highlight both his scholarly impact and leadership potential in artificial intelligence research and innovation.

🔬 Research Focus 

Zhou Yang’s research focuses on artificial intelligence, particularly natural language processing, recommendation systems, and deep learning architectures. His recent work includes empathetic dialogue generation, emotional semantic correlation modeling, and sequential recommendation enhanced with side information. He is also engaged in research on associative memory models for empathetic responses and preference-driven denoising methods. Zhou has worked extensively with text matching models (e.g., DRMM, KNRM) and frameworks like MatchZoo, contributing to substantial performance improvements on key datasets. His interdisciplinary approach integrates reinforcement learning, big data, and neural networks to solve real-world problems in smart search and personalized systems, paving the way for more human-centric AI applications.

📊 Publication Top Notes:

Citation:

Zhu, X., & Yang, Z. (2025). A Preference-driven Conjugate Denoising Method for Sequential Recommendation with Side Information. Information Processing & Management, 62(2), 103997.ACM Digital Library

Authors:

Publication Year:

2025​

Journal:

Information Processing & Management

Volume and Issue:

Volume 62, Issue 2

Article Number:

103997ACM Digital Library

Title: An Iterative Associative Memory Model for Empathetic Response Generation


Authors: Zhou Yang, Zhaochun Ren, Wang Yufeng, Haizhou Sun, Chao Chen, Xiaofei Zhu, Xiangwen Liao


Published in: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024)


Year: 2024


URL: https://aclanthology.org


Title : The second publication seems incomplete. However, based on the author list you shared:

Authors (Partial): Zhou Yang, Zhaochun Ren, Wang Yufeng, Xiaofei Zhu, Zhihao Chen, Tiecheng Cai, Wu Yunbing, Yisong Su, Sibo Ju, Xiangwen Liao
Please provide the title of the second paper to finalize its full citation.

Elahe Karampour | Artificial Intelligence | Best Researcher Award

Ms. Elahe Karampour | Artificial Intelligence| Best Researcher Award

 

Ms. Elahe  Karampour  K.N.Toosi University Of Technology, Iran

Elahe Karampour is a dedicated researcher specializing in Geodesy and Geomatics Engineering with a focus on spatial-temporal data analysis and network science. She is currently pursuing her Master of Science at K.N. Toosi University of Technology, Tehran, Iran, with a thesis on analyzing location-based social networks using geometric curves. She has served as a research assistant, contributing to advancements in community detection and link prediction in spatial networks. Elahe is also an experienced educator, having taught GIS, spatial databases, and social network analysis. She has received national recognition for her academic excellence and was awarded a fully funded research visit to ScaDS.AI in Germany. Her expertise spans programming, spatial modeling, and AI-driven geospatial analytics.

🌍 Professional Profile:

Orcid

🏆 Suitability for the Best Researcher Award

 

Elahe Karampour is an outstanding researcher with significant contributions to geospatial data analysis, particularly in network science and location-based social networks. Her pioneering research integrates Ricci curvature and hyperbolic geometry for community detection and link prediction, leading to novel advancements in spatial-temporal modeling. She has published influential research in high-impact journals, demonstrating her expertise and innovation. Elahe’s research excellence is further recognized through a prestigious fully funded research visit to ScaDS.AI in Germany. With strong technical proficiency in R, Python, PostgreSQL, and GIS software, she bridges theory and application, developing intelligent recommender systems for urban planning. Her exceptional academic record, teaching excellence, and innovative research make her a prime candidate for the Best Researcher Award.

🎓 Education 

Elahe Karampour holds a Master of Science in Geodesy and Geomatics Engineering from K.N. Toosi University of Technology, Tehran, Iran (2021–2024). Her research focuses on spatial-temporal data analysis and social network modeling, with a thesis titled “Analysis of Location-Based Social Networks with Geometric Curves,” receiving a perfect grade of 20/20. She completed her Bachelor of Science in Geodesy and Geomatics Engineering at the University of Zanjan, Iran (2015–2020). Elahe ranked 23rd nationwide in the Iranian Master’s University Entrance Exam, showcasing her academic excellence. Her strong analytical and technical skills, coupled with expertise in GIS, AI, and network analysis, enable her to make significant contributions to the field of geospatial research and urban data science.

🏢 Work Experience 

Elahe Karampour has extensive research and teaching experience in geospatial data analysis and network science. As a Research Assistant (2022–2024) at K.N. Toosi University of Technology, she developed advanced models for community detection and link prediction in location-based social networks using geometric methods. She also worked as a Lecturer (2024) at Babol Noshirvani University of Technology, teaching spatial analysis and visualization to undergraduate students. Additionally, she served as a Teaching Assistant (2023–2024), guiding master’s and PhD students in GIS, social network analysis, and spatial databases. Her technical expertise in R, Python, PostgreSQL, and QGIS, combined with her ability to integrate AI with geospatial analysis, has positioned her as a leader in her research domain.

🏅 Awards and Honors 

Elahe Karampour has received multiple accolades for her academic and research excellence. She ranked 23rd nationwide in the Iranian Master’s University Entrance Exam (2021), demonstrating her strong academic foundation. She was awarded a fully funded research grant for a short-term visit to ScaDS.AI Center for Scalable Data Analytics and Artificial Intelligence in Germany (2023), recognizing her contributions to AI-driven geospatial analysis. Additionally, she was listed among the top-ranked teachers by her students for her exceptional teaching performance. Her work in network science and geospatial modeling has led to publications in high-impact journals, further cementing her status as a leading researcher. These achievements underscore her dedication and outstanding contributions to the field of geospatial and network science.

🔬 Research Focus 

Elahe Karampour’s research centers on spatial-temporal data analysis, network science, and AI-driven geospatial modeling. She specializes in analyzing location-based social networks using advanced mathematical frameworks such as Ricci curvature and hyperbolic geometry for community detection and link prediction. Her work integrates graph-based modeling with GIS technologies to enhance urban planning, mobility analysis, and personalized recommender systems. Elahe has developed innovative approaches to analyzing complex data structures, utilizing machine learning and AI techniques for geospatial applications. She is particularly interested in the intersection of mathematics, computer science, and geospatial technologies, aiming to create data-driven solutions for urban analytics and smart city development. Her research has been recognized internationally, reinforcing her expertise in geospatial data science.

📊 Publication Top Notes:

  • Karampour, E., Malek, M.R., & Eidi, M. (2025). Discrete Ricci flow: A powerful method for community detection in location-based social networks. Computers and Electrical Engineering, 123, 110302.