Dr. Adina Aniculaesei | Technology | Best Researcher Award

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Dr. Adina Aniculaesei | Technology | Best Researcher Award

Dr. Adina Aniculaesei , Postdoctoral Researcher, Department of Computer Science and Engineering, University of Gothenburg and Chalmers University of Technology, Sweden.

Adina Aniculăesei is a passionate researcher and expert in automated safety‑critical systems, currently based in Gothenburg, Sweden. Born in Iași, Romania, she has dedicated her career to making autonomous vehicles and mobile robots safer, focusing on verification, formal methods, and runtime validation. Through years of multidisciplinary research and teaching, she has shaped the future of software engineering for intelligent transportation and collaborative robotics. Her deep knowledge of formal verification and system modeling has positioned her as a leading voice in the realm of dependable and trustworthy autonomous platforms, making significant impacts in both academia and industry.

Publication Profile

Google Scholar

🎓 Education Background

Adina earned her Doctorate (Dr. rer. nat.) in Computer Science from the Clausthal University of Technology, Germany, in 2024, graduating magna cum laude. She holds an M.Sc. in Computer Science from the Technical University of Braunschweig (2011) and a B.Sc. in Computer Science from Alexandru Ioan Cuza University, Romania (2007). An Erasmus–Socrates scholar, she enriched her studies with a year at the Technical University of Braunschweig. Her rigorous training combined formal methods, software engineering, and automated test case generation, making her adept at tackling complex, safety‑critical domains.

💼 Professional Experience

Adina Aniculăesei has worked as a Postdoctoral Researcher at the University of Gothenburg and Chalmers University of Technology (since October 2024), focusing on translating formal behavioral specifications into ROS2 nodes for collaborative robot applications. Previously, she served as a Doctoral Researcher and Research Assistant at TU Clausthal, leading industry collaborations, teaching, and mentoring students. Her experience includes roles across software and systems engineering, with a strong focus on safety, formal verification, and automated test generation for automotive and robotics domains, making her a sought‑after expert and educator in the field.

🏅 Awards and Honors

Throughout her academic journey, Adina Aniculăesei has been recognized for excellence and dedication. She received the Siemens Master Program Scholarship (2007–2009) and the Erasmus–Socrates Scholarship (2005–2006). Her doctoral studies earned her the magna cum laude distinction upon defending her Ph.D. thesis at Clausthal University of Technology in 2024. Additionally, she holds technical certifications including ISAQB Certified Professional for Software Architecture and ISTQB Certified Tester Foundation Level, highlighting her commitment to mastering both theoretical and practical elements of her field.

🔍 Research Focus

Adina Aniculăesei’s research centers on formal verification, automated test generation, and runtime monitoring for automated safety‑critical and collaborative multi‑agent systems. She explores methods for specifying, verifying, and validating complex operational design domains (ODDs) for autonomous vehicles and mobile robots. Her expertise includes formal methods (SPIN, NuSMV, PRISM), test case generation, model checking, and AI‑based environment perception, making her work pivotal in shaping next‑generation transportation and robotics technologies.

✅ Conclusion

With a profound background in formal methods, automated test generation, and verification of safety‑critical systems, Adina Aniculăesei has established herself as an influential expert in both academia and industry. Her dedication to mentoring students, publishing impactful research, and collaborating with international institutions has positioned her as a thought leader in software engineering for dependable, trustworthy, and safe autonomous technologies.

📚 Publication Top Notes

  • Towards a holistic software systems engineering approach for dependable autonomous systems – Proceedings of the 1st International Workshop on Software Engineering for AI (2018). Cited by 70
  • Towards the verification of safety‑critical autonomous systems in dynamic environments – arXiv preprint (2016). Cited by 42
  • Automated generation of requirements‑based test cases for an adaptive cruise control system – IEEE Workshop on Validation, Analysis and Evolution of Software Tests (2018). Cited by 24
  • UML‑based analysis of power consumption for real‑time embedded systems – IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications (2011). Cited by 24
  • Graceful degradation of decision and control responsibility for autonomous systems based on dependability cages – 5th International Symposium on Future Active Safety Technology Toward Zero Accidents (2019). Cited by 14

 

Dr. Xiaojuan Pang | Technologies | Best Researcher Award

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Dr. Xiaojuan Pang | Technologies | Best Researcher Award

lecturer, China University of Mining and Technology, China

Dr. Xiaojuan Pang is a dynamic Chinese computational chemist and academic serving as a Lecturer at the China University of Mining & Technology (CUMT) since 2019. With deep expertise in photochemistry, nonadiabatic dynamics, and photocatalytic hydrogen production, she bridges theoretical innovation and practical application. Her international research exposure includes a pivotal joint doctoral training at the Technical University of Munich under Prof. Wolfgang Domcke, positioning her as a global voice in computational reaction mechanism studies. 🌍

Publication Profile

ORCID

🎓 Education Background

Dr. Pang earned her Bachelor’s degree in Physics from Xinzhou Teachers University in 2013 🎓. She continued her academic journey with a Doctorate in Physics from Xi’an Jiaotong University (2013–2019), where she explored ultrafast photochemical mechanisms. Her international academic footprint includes a prestigious year (2017–2018) at the Technical University of Munich. She is currently undertaking a postdoctoral fellowship (since 2025) in a two-station program, co-hosted by CUMT and Zhejiang Changshan Textile Co., Ltd., further sharpening her cross-disciplinary skills in mining and material science. 📘🧪

👩‍🏫 Professional Experience

Dr. Pang began her academic career as a Lecturer in the Department of Physics at CUMT in 2019. She plays a vital role in teaching, curriculum reform, and scientific mentorship. Her involvement spans several cutting-edge research projects, including multiple national and provincial grants where she serves as Principal Investigator. She also collaborates with industrial partners to apply her research in real-world contexts, especially in energy materials and ultrafast dynamics. 🏫🧑‍🔬

🏅 Awards and Honors

Dr. Pang has garnered numerous accolades for her academic and teaching excellence. Highlights include the Outstanding Young Core Faculty Award (2024), Jiangsu “Double-Innovation Doctor” Talent Award (2020), and multiple teaching competition prizes. She has also been recognized as an Outstanding Communist Party Member, Outstanding Head Teacher, and earned three consecutive years of top annual performance ratings from 2020 to 2023. 🏆🎖️

🔍 Research Focus

Her core research explores the reaction mechanisms in photocatalytic water splitting, photoisomerization of molecular motors, and ultrafast nonadiabatic photochemical processes. Dr. Pang utilizes a powerful combination of computational tools—like Gaussian, Turbomole, and MNDO—to simulate and analyze excited-state dynamics. Her work significantly contributes to the development of efficient solar-to-hydrogen energy conversion technologies and light-driven molecular machines. 💡⚛️

🧩 Conclusion

With an impressive blend of academic rigor, international exposure, innovative research, and award-winning teaching, Dr. Xiaojuan Pang stands as a rising star in computational chemistry and photophysics. Her ongoing work at the intersection of theory and application is paving the way for advances in sustainable energy and smart molecular systems. 🚀

📚 Top Publications

Nonadiabatic Surface Hopping Dynamics of Photo-catalytic Water Splitting Process with Heptazine–(H2O)4 Chromophore
🔹Cited by: [Articles on MDPI and Google Scholar]

Study on the Photoinduced Isomerization Mechanism of Hydrazone Derivatives Molecular Switch
🔹Cited by: [Relevant studies in ACS database]

Effect of Load-Resisting Force on Photoisomerization Mechanism of a Single Second Generation Light-Driven Molecular Rotary Motor
🔹Cited by: [AIP citations and Scholar references]

Ultrafast Nonadiabatic Photoisomerization Dynamics Study of Molecular Motor Based on Indanylidene Frameworks
🔹Cited by: [CrossRef, ScienceDirect]

Photoinduced Electron-Driven Proton Transfer from Water to N-Heterocyclic Chromophore
🔹Cited by: 40+ citations (Google Scholar, Scopus)

Watching the Dark State in Ultrafast Nonadiabatic Photoisomerization of Light-Driven Motor
🔹Cited by: 70+ citations (ResearchGate, Google Scholar)

HaiTian Chen | Computer Science | Best Researcher Award

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Ms. HaiTian Chen | Computer Science | Best Researcher Award

College of Science, North China University of Science and Technology, China

Chen HaiTian is a dedicated researcher in the field of Cyberspace Security from China. Born in December 1998, Chen has made significant strides in federated learning, privacy preservation, and cybersecurity. His contributions span multiple peer-reviewed journals and patents, showcasing his commitment to advancing technology and safeguarding digital spaces.

Profile

ORCID

 

Education

Chen HaiTian holds a major in Cyberspace Security, demonstrating his expertise and focus in this critical area of study. His academic background has equipped him with the skills and knowledge necessary to tackle complex cybersecurity challenges and contribute to innovative solutions in the field. 🎓

Research Interests

Chen HaiTian’s research interests focus on federated learning, privacy preservation, and cybersecurity. He is particularly interested in developing robust aggregation techniques to defend against poisoning attacks in federated learning and exploring personalized fair split learning for resource-constrained Internet of Things (IoT). 🔍

Awards

Chen HaiTian has received recognition for his contributions to software development, including the Huali Academy Backstage Management System V1.0 and the DC Early Warning System V1.0. His work has been registered with computer software registration numbers, showcasing his achievements in developing innovative solutions for network management and security. 🏆

Publications

Chen, H.; Chen, X.; Peng, L. (2023). FLRAM: Robust Aggregation Technique for Defense Against Byzantine Poisoning Attacks in Federated Learning. Electronics. Cited by Electronics.

Chen, H.; Chen, X.; Peng, L. (2024). Personalized Fair Split Learning for Resource-Constrained Internet of Things. Sensors, 24, 88. Cited by Sensors.

Chen, H., Chen, X., Ma R., et al. (2024). A federated learning privacy preserving approach for remote sensing data. Computer Applications. Cited by Computer Applications.

Chen, H., Chen, X. (2023). A Robust Aggregation Technique for Poisoning Attack Defense in Federated Learning. Cited by Journal.

Xu C., Zhang S., Chen H., et al. (2024). A federated learning approach based on adaptive differential privacy and customer selection optimization. Computer Applications. Cited by Computer Applications.

Peng L., Zhang S., Chen H., et al. (2023). Clustered federated learning based on improved CFSFDP algorithm. Journal of North China University of Science and Technology (Natural Science Edition). Cited by NCUST.

Qiang Li | Computer Science | Best Researcher Award

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Mr. Qiang Li | Computer Science | Best Researcher Award

Lecturer, Qingdao University, China

Dr. Li Qiang is an experienced lecturer in computer science with a PhD in Engineering. He specializes in high-performance computing and has a strong background in both teaching and research. Committed to fostering academic excellence and technological innovation, Dr. Li has been a dedicated educator and researcher at Qingdao University since 2015.

Profile

ORCID

 

Education 🎓

PhD in Engineering: University of the Chinese Academy of Sciences, Computer Network Information Center (2010-2014), Advisor: Lu Zhonghua. Master’s in Information Science and Engineering: Shandong University of Science and Technology (2007-2010), Advisor: Zhao Maoxian. Bachelor’s in Education: Qingdao University (2003-2007).

Experience 👨‍🏫

Lecturer at Qingdao University, School of Computer Science and Technology (January 2015-Present). Teaching undergraduate and graduate courses in computer science. Supervising student research projects and theses. Conducting research in high-performance computing. Published 12 research papers in journals and conferences. Granted 2 patents.

Research Interests 🔬

Dr. Li Qiang’s research interests lie in high-performance computing, particularly in the optimization and parallel implementation of numerical simulations and the development of new computational frameworks. His work focuses on enhancing computational efficiency and scalability in large-scale scientific computations.

Awards 🏆

Dr. Li Qiang has been recognized for his contributions to the field of high-performance computing through multiple publications and patents. His innovative work has led to advancements in computational methods and has garnered attention in the academic community.

Publications 📄

Optimization Research of Heterogeneous 2D-Parallel Lattice Boltzmann Method Based on Deep Computing Unit. Appl. Sci. 2024, 14, 6078.

Heterogeneous Parallel Implementation of Large-Scale Numerical Simulation of Saint-Venant Equations. Appl. Sci. 2022, 12, 5671. Cited by 6

The Study of Parallelization of SWAT Hydrology Cycle. The 32nd ACM International Conference on Supercomputing, Beijing, 2018. [Cited by 3]

A New Parallel Framework of Distributed SWAT Calibration. Journal of Arid Land, 2015, 7(1): 122-131. [Cited by 7]

Parallel Simulation of High-Dimensional American Option Pricing Based on CPU VS MIC. Concurrency and Computation: Practice and Experience, 2014, 27(5): 1110-1121. [Cited by 5]