Yunwen Xu | Engineering | Best Researcher Award

Published on 23/10/2025 by Young Scientists Awards

Dr. Yunwen Xu l Engineering
| Best Researcher Award

Shanghai Jiao Tong University | China

Dr. Yunwen Xu’s research focuses on advancing intelligent transportation systems, autonomous driving control, and predictive control for complex and embedded systems. Her innovative work integrates graph-based spatial-temporal modeling, data-driven control algorithms, and real-time optimization to enhance vehicle trajectory prediction, traffic signal management, and collaborative control in large-scale dynamic environments. Through over 50 high-impact publications, including 15 in top-tier journals and several ESI highly cited papers, Dr. Xu has significantly contributed to the theoretical and practical foundations of predictive control and intelligent mobility. Her research achievements include developing FPGA-based predictive controllers, robust model predictive frameworks, and reinforcement learning-based control systems for V2X-enabled autonomous vehicles. By leading national and provincial research projects and collaborating internationally with institutions like Purdue University and industrial partners such as Shanghai Electric Wind Power Group, she bridges the gap between academic innovation and industrial application. Her patents and successful technology transfers in microgrid energy management and advanced temperature control demonstrate the translational strength of her research. Recognized with prestigious honors, including the Best Paper Award at the Chinese Process Control Conference and championship at the Autonomous Driving Algorithm Challenge, Dr. Xu continues to pioneer next-generation control and automation technologies that drive the evolution of intelligent, efficient, and sustainable transportation ecosystems.

Profile: Google Scholar

Featured Publications

Xu, Y., Sheng, Z., Xue, S., & Li, D. (2022). Graph-based spatial-temporal convolutional network for vehicle trajectory prediction in autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 23(10), 17654–17665. https://doi.org/10.1109/TITS.2022.XXXXX (Add DOI if available)

He, S., Chen, W., Li, D., Xi, Y., Xu, Y., & Zheng, P. (2021). Iterative learning control with data-driven-based compensation. IEEE Transactions on Cybernetics, 52(8), 7492–7503. https://doi.org/10.1109/TCYB.2021.XXXXX

Xu, Y., Li, D., Xi, Y., Lan, J., & Jiang, T. (2018). An improved predictive controller on the FPGA by hardware matrix inversion. IEEE Transactions on Industrial Electronics, 65(9), 7395–7405. https://doi.org/10.1109/TIE.2018.XXXXX

Yang, L., Lu, J., Xu, Y., Li, D., & Xi, Y. (2020). Constrained robust model predictive control embedded with a new data-driven technique. IET Control Theory & Applications, 14(16), 2395–2405. https://doi.org/10.1049/iet-cta.2020.XXXXX

Xu, Y., Xi, Y., Li, D., & Zhou, Z. (2016). Traffic signal control based on Markov decision process. IFAC-PapersOnLine, 49(3), 67–72. https://doi.org/10.1016/j.ifacol.2016.07.011 (example DOI)

Haneen Alamirah | Engineering | Best Researcher Award

Published on 14/10/2025 by Young Scientists Awards

Ms. Haneen Alamirah l Engineering
| Best Researcher Award

United Arab Emirates University | United Arab Emirates

Ms. Haneen Alamirah is an accomplished Architectural Engineer and researcher from the United Arab Emirates, specializing in occupant comfort in the built environment and sustainable building design. She holds a Bachelor’s degree in Architectural Engineering from the UAE University, a Master’s degree in Sustainable Critical Infrastructure from Khalifa University, and is currently pursuing her Ph.D. in Architectural Engineering at the UAE University . Her professional experience includes serving as a Graduate Teaching and Research Assistant at both Khalifa University and UAE University, where she has been involved in teaching, mentoring, and conducting advanced research in sustainability and human–environment interaction. Ms. Alamirah’s research contributions focus on the integration of immersive virtual environments for evaluating occupant comfort, adaptive behavior, and personal comfort models in shared spaces. Her scholarly work has been featured in high-impact journals such as Building and Environment and presented at international conferences including the Building Simulation Conference (2023, Shanghai; 2025, Brisbane) and the UAE Graduate Students Research Conference. With 68 citations and an h-index of 1 (Scopus ID: 57288505500), she continues to advance knowledge at the intersection of architecture, sustainability, and digital simulation tools, contributing to more resilient and human-centered design practices.

Profile: Scopus

Featured Publication

Alamirah, H. (2023, September). A bibliometric analysis of immersive virtual environment applications for occupant comfort and behavior research. In Proceedings of the Building Simulation Conference 2023 (p. 1397). Shanghai, China. https://doi.org/10.26868/25222708.2023.1397

Yogesh Thakare | Engineering | Best Researcher Award

Published on 04/02/202504/02/2025 by Young Scientists Awards

Yogesh Thakare | Engineering | Best Researcher Award

Dr Yogesh Thakare, Ramdeobaba University, Nagpur, India

Dr. Yogesh Thakare 🎓 is an accomplished researcher and educator in Electronics and Communication Engineering. He earned his Ph.D. (2020) from SGB Amravati University, specializing in DRAM design using submicron technology 💾. Currently an Assistant Professor at Shri Ramdeobaba College of Engineering & Management, Nagpur 👨‍🏫, he has published in SCIE and Scopus-indexed journals 📑. His research spans FPGA architectures, AI, IoT, and biomedical systems 🤖. A GATE qualifier (94.92%), he has led government-funded projects 💰 and organized AI & IoT workshops 🏗️. Passionate about innovation, he contributes to cutting-edge electronics and computing technologies ⚡.

Publication Profile

Google Scholar

Academic Excellence

Dr. Yogesh Thakare earned his Ph.D. in Electronics Engineering from SGB Amravati University in 2020, focusing on Dynamic Random Access Memory (DRAM) design using submicron technology ⚡🔬. His academic journey reflects excellence, having completed his M.Tech with Distinction (85.10%) 🎓🏆 and his B.E. with First-Class (72.62%) 📚✨. With a strong foundation in electronics and a passion for advanced semiconductor technologies, Dr. Thakare has made significant contributions to memory design and innovation. His expertise in microelectronics and circuit design continues to drive advancements in the field, shaping the future of high-performance computing and digital storage solutions 💡🔍.

Funded Research & Grants

Dr. Yogesh Thakare has demonstrated exceptional research leadership by securing ₹24.6 Lakhs from CSIR for developing an automated water distribution system 💧🔬. His innovative approach aims to enhance water management efficiency through automation, contributing to sustainable resource utilization 🌱💡. This significant funding underscores his expertise in engineering solutions that address real-world challenges 🏗️⚙️. With a strong commitment to technological advancement, Dr. Thakare continues to drive impactful research that promotes water conservation and smart distribution systems 🌍📊. His work not only fosters scientific progress but also supports community welfare by ensuring efficient and equitable water access 🚰✅.

Experience

Dr. Yogesh Thakare is an experienced educator with over 14 years of teaching in top engineering institutes 🏫, including Shri Ramdeobaba College of Engineering and Management, Nagpur. As an Assistant Professor, he has played a key role in shaping technical education 📚. His passion for emerging technologies has led him to organize numerous workshops on Artificial Intelligence 🤖, the Internet of Things 🌐, and Machine Learning 📊, empowering students with cutting-edge knowledge. Through his dedication to academic excellence and innovation, Dr. Thakare continues to inspire the next generation of engineers and researchers 🚀.

Research Focus

Dr. Yogesh Thakare’s research spans electronics, artificial intelligence, IoT, and machine learning 🤖📡. His work includes DRAM memory design 🏗️💾, FPGA-based cryptography 🔐, and deepfake detection using neural networks 🕵️‍♂️🎭. He has contributed to environmental intelligence systems 🌱📊, weather prediction for agriculture 🌦️🚜, and smart monitoring technologies 📡🏠. Additionally, he has explored cortisol detection for stress monitoring 🧪⚕️ and crime reporting frameworks 🚔📜. His interdisciplinary research integrates hardware and AI-driven solutions, making impactful advancements in computing, security, and human well-being 🔬💡. His innovative approach bridges technology and real-world applications, enhancing automation, safety, and intelligence. 🚀