Hao Zhang | Artificial Intelligence | Best Researcher Award

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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

Siyi Wang | Smart Manufacturing | Best Researcher Award

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Ms. Siyi Wang | Smart Manufacturing
| Best Researcher Award

Graduate student at Xi’an Technological University, China.

Siyi Wang is a graduate student at Xi’an Technological University, majoring in Industrial Engineering and Management Science. Under the mentorship of Professor Gao Xiaobing, she has focused her research on optimizing body-in-white (BIW) measurement station planning for automotive manufacturing. Her work addresses complex real-world constraints—such as environmental limitations, feature characteristics, equipment capability, and on-site operability—leading to significantly improved measurement efficiency in a major automobile factory. With a recent publication in Applied Sciences, she demonstrates strong research potential and the ability to apply academic insights to industrial practice. Her innovative approach reflects a rare blend of theoretical rigor and practical relevance, making her a promising candidate for recognition through the Best Researcher Award.

🌍 Professional Profile:

Google Scholar

🏆 Suitability for the Best Researcher Award :

Siyi Wang is highly suitable for the Best Researcher Award due to her outstanding application of engineering principles to solve real-world industrial challenges. Her research on body-in-white measurement station planning is not only academically rigorous but also has direct implications for enhancing manufacturing efficiency in the automotive sector. Despite being at the graduate level, she has authored a peer-reviewed paper in a reputable SCI-indexed journal, demonstrating her capability to contribute valuable knowledge to the field. Her ability to work under constraints and deliver measurable improvements in industrial settings reflects her innovation, problem-solving acumen, and technical insight—qualities befitting a future research leader. She exemplifies emerging excellence in engineering science and deserves recognition for her impactful contributions.

🎓 Education :

Siyi Wang is currently pursuing her graduate studies in Industrial Engineering and Management Science at Xi’an Technological University, China. She is under the academic supervision of Professor Gao Xiaobing, a recognized expert in measurement system optimization and intelligent manufacturing. Her education has been deeply focused on the practical aspects of industrial systems, measurement technologies, and operations research. Her curriculum includes advanced coursework in production system optimization, statistical modeling, and quality control systems. Through her graduate program, Siyi has developed a strong foundation in both theoretical and applied aspects of industrial engineering, with a particular interest in automotive manufacturing and laser radar systems. Her academic training equips her well to continue impactful research in smart manufacturing and systems optimization.

🏢 Work Experience :

Siyi Wang has accumulated significant research experience through her graduate work at Xi’an Technological University. Her primary project involves the planning of body-in-white (BIW) measurement stations, where she integrates theoretical modeling with industrial constraints to enhance manufacturing accuracy and efficiency. She has worked closely with real automotive production data, analyzing environmental limitations, measurement feature characteristics, equipment restrictions, and actual operating conditions. Her findings have led to a practical breakthrough—notably improving measurement efficiency in a collaborating automobile factory. Though early in her career, her experience reflects high-impact, real-world application of academic research. She is also the co-author of a published article in Applied Sciences, highlighting her ability to produce peer-reviewed work with industrial significance.

🏅 Awards and Honors :

As an emerging researcher, Siyi Wang has begun gaining recognition for her contributions to applied engineering science. Her notable achievement includes co-authoring an SCI-indexed paper in Applied Sciences titled “Research on Laser Radar Inspection Station Planning of Vehicle Body-In-White (BIW) with Complex Constraints” (2025). While she has not yet received formal awards, her selection for publication in a respected international journal as a graduate student demonstrates early-career research excellence. Her work has been acknowledged internally within her department for its relevance and innovation in solving industry-specific problems. Given her demonstrated potential and the measurable impact of her research, she is a strong candidate for future academic and professional honors, including the Best Researcher Award.

🔬 Research Focus :

Siyi Wang’s research centers on measurement station planning for body-in-white (BIW) systems in automotive manufacturing. She focuses on improving the efficiency and accuracy of vehicle inspection processes by considering a wide range of constraints, such as environmental conditions, geometry of features, sensor capabilities, and operational dynamics. Her work applies advanced methods in industrial engineering and systems optimization to model and solve these complex, multi-variable challenges. She is particularly interested in integrating laser radar technologies with planning algorithms to enhance the flexibility and precision of inspection stations. Her research is both practical and forward-looking, contributing to smart manufacturing, digital twin environments, and intelligent quality control systems. It has already shown real industrial value in a major automotive factory.

📊 Publication Top Notes:

📘 Solvent‐Annealed Crystalline Squaraine: PC70BM (1:6) Solar Cells
📅 Year: 2011 | 🔁 Cited by: 293 | 🧪 Topic: Organic Solar Cells

📘 Solution-Processed Squaraine Bulk Heterojunction Photovoltaic Cells
📅 Year: 2010 | 🔁 Cited by: 215 | ☀️ Topic: Photovoltaics, Squaraine

📘 Efficient, Ordered Bulk Heterojunction Nanocrystalline Solar Cells by Annealing of Ultrathin Squaraine Thin Films
📅 Year: 2010 | 🔁 Cited by: 189 | 🔬 Topic: Nanocrystalline Solar Cells

📘 High Efficiency Organic Photovoltaic Cells Based on a Vapor Deposited Squaraine Donor
📅 Year: 2009 | 🔁 Cited by: 153 | ⚡ Topic: Organic Photovoltaics

📘 Independent Control of Bulk and Interfacial Morphologies of Small Molecular Weight Organic Heterojunction Solar Cells
📅 Year: 2012 | 🔁 Cited by: 146 | 🧫 Topic: Morphology Control, OPV

📘 N,N-Diarylanilinosquaraines and Their Application to Organic Photovoltaics
📅 Year: 2011 | 🔁 Cited by: 144 | 🧪 Topic: Squaraine Chemistry

📘 Functionalized Squaraine Donors for Nanocrystalline Organic Photovoltaics
📅 Year: 2012 | 🔁 Cited by: 133 | ⚙️ Topic: Donor Design, Solar Cells

R Lakshman Naik |Computer Science and Engineering | Best Paper Award

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Mr.R Lakshman Naik|Computer Science and Engineering| Best Paper Award

Research Scholar at Indian Institute of Information Technology Sonepat , Haryana, India

Mr. R. Lakshman Naik is a Research Scholar at the Indian Institute of Information Technology (IIIT) Sonepat, Haryana, India. His research focuses on advanced topics in computer science, artificial intelligence, data science, or related fields. As a dedicated scholar, he actively contributes to academic research, publications, and innovative technological developments. IIIT Sonepat, recognized as an Institute of National Importance, provides a dynamic environment for cutting-edge research and interdisciplinary collaboration.

Publication Profile

Scopus

Education :

Lakshman Naik Ramavathu holds a Master of Technology (M.Tech) degree in Digital Communication from Kakatiya University, Warangal (2014-16), and another M.Tech in Computer Science and Engineering from JNT University, Hyderabad (2009-11). He completed his Bachelor of Technology (B.Tech) in Electronics and Communication Engineering from JNT University, Hyderabad (2001-05). His diverse educational background provides a strong foundation in computer science, digital communication, and information technology.

Experience :

Currently, he serves as an Assistant Professor (C) in the Department of Information Technology at KU College of Engineering & Technology, Warangal, where he has been teaching since 2016. His teaching expertise includes Operating Systems, Computer Architecture and Organization, Data Communication and Networking, Machine Learning, Python Programming, and Mobile Cloud Computing.

Prior to his academic career, he worked as a Part-time Lecturer in the Department of Computer Science at Kakatiya University (2012-2016), where he taught subjects like System Software, Cloud Computing, Mobile Communication, and Open-Source Software.

Research Focus :

Lakshman Naik Ramavathu’s research interests include Machine Learning, Cloud Computing, Data Mining, and Computer Networking. His work revolves around optimizing computational frameworks, developing intelligent predictive models, and improving networking protocols for enhanced system performance.

Skills:

Sun Certified System Administrator for Sun Solaris 9 (Part-I & II)Microsoft Certified Professional in Windows 2003 Enterprise Server Expertise in Cloud Computing, Machine Learning, Computer Networks, and Data Mining

Awards:

Recognized for impactful research contributions in cloud computing and machine learning Multiple research papers published in high-impact international journals Significant contributions to academia and industry in system administration and computing

 

Publication :

  • Comparison of Data Mining Versus Traditional Analysis in Textile Business”

    • Publication: IFRSA International Journal of Data Warehousing & Mining
    • ISSN (Online): 2249–2186
    • ISSN (Print): 2249–7161
    • Volume: 1, Issue 1

  • “DFFS: Detecting Fraud in Finance Sector”

    • Authors: R. Lakshman Naik, Dr. Manjula Bairam
    • Publication: International Journal of Advanced Engineering Sciences and Technologies
    • ISSN: 2230-7818
    • Volume: 9, Issue 2

  • “Study of Trends in Higher Education”

    • Publication: International Journal of Computer Trends and Technology
    • ISSN: 2231-2803
    • Volume: 1, Issue 1

  • “Stock Prediction using Neural Network”

    • Publication: International Journal of Advanced Engineering Sciences and Technologies
    • ISSN: 2230-7818
    • Volume: 10, Issue 1

  • “Session Data Protection Using Tree-Based Dependency”

    • Publication: International Journal of Advances in Engineering & Technology
    • ISSN: 2231-1963
    • Volume: 2, No. 1

  • “Secure Authentication Scheme for Mobile Ad Hoc Networks”

    • Publication: International Journal of Mobile & Adhoc Network
    • ISSN (Online): 2231-6825
    • ISSN (Print): 2249-202X
    • Volume: 2, Issue 1

  • “Secure Scheme of Data Protection in Cloud Computing”

    • Publication: International Journal of Computer Science and Technology
    • ISSN: 0976-8491
    • ISSN: 2229-4333
    • Volume: 3, No. 1

  • “Cloud Computing: Research Issues and Implications”

    • Publication: International Journal of Cloud Computing and Services Science
    • ISSN: 2089-3337
    • Volume: 2, No. 2

  • “Prediction of BSE Stock Data using MapReduce K-Mean Cluster Algorithm”

    • Publication: International Journal of Current Engineering and Technology, INPRESSCO
    • E-ISSN: 2277–4106
    • P-ISSN: 2347–5161
    • Volume: 5, No. 3

  • “Current Apprises of Opinion Mining Methods”

    • Publication: International Journal of Engineering and Advanced Technology (IJEAT)
    • ISSN: 2249–8958
    • Volume: 9, Issue 2

 

 Conclusion

Based on his research achievements, Lakshman Naik Ramavathu is well-suited for a Best Paper Award, provided the submission is among his most impactful and high-quality research works. Enhancing recent publications, collaborations, and practical implementations will further solidify his standing in the academic and research community.