Qingling Zhao | Computer Science | Research Excellence Award

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Prof. Qingling Zhao | Computer Science | Research Excellence Award

Nanjing University of Science and Technology | China

Prof. Qingling Zhao is a leading researcher in embedded systems, real-time systems, mixed-criticality scheduling, and intelligent computing, with significant contributions spanning system architecture, cyber–physical systems, and AI-driven embedded intelligence. With an h-index of 12, 26 scholarly documents, and 456 citations across 345 citing publications, the research output demonstrates sustained academic impact. The work covers core areas such as mixed-criticality scheduling theory, resource synchronization, stack memory optimization, AUTOSAR model optimization, and schedulability analysis, alongside recent advances in deep learning, reinforcement learning optimization, network-on-chip systems, intrusion detection, and remote sensing object detection. Publications appear in high-impact venues including ACM Transactions on Embedded Computing Systems, IEEE Transactions, Journal of Systems Architecture, IEEE Access, and major international conferences. Recent research extends classical real-time system theory toward AI-enabled embedded and cyber-secure systems, reflecting a strong integration of theoretical rigor and practical applicability across safety-critical and intelligent computing platforms.

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Featured Publications

Patruni Rajshekhar Rao | Computer Science | Best Researcher Award

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Mr. Patruni Rajshekhar Rao | Computer Science | Best Researcher Award

FTD Infocom Pvt Ltd | India

Mr. Patruni Rajshekhar Rao is an avionics research professional whose work integrates test and verification engineering, data analysis, and safety-critical system evaluation across aerospace platforms. His contributions span functional RTL verification, aerospace data analysis, and reliability assessment of embedded systems. His early work involved functional verification of ARINC818 protocol IP cores, where he designed assertion-based test benches using VHDL and file-driven debugging to enhance precision in timing-sensitive validation. He later expanded into flight data analysis for advanced aircraft systems such as the SARAS platform, performing hardware–software integration testing, developing low-level test cases, and analyzing stall-warning system performance. His research also includes pioneering efforts in software health management, where he explored self-healing software systems using AI-driven methods to automate fault detection and recovery in avionics architectures. He has contributed to safety-critical processes aligned with DO-178B and DO-254 standards, including MCDC-level testing for auto-generated code in A-FADEC systems and performing dynamic and static analysis to identify and mitigate software defects. Across conferences and journals, he has published studies on verification methodologies, safety criteria, IP-core validation procedures, and AI-based static analysis, reinforcing his role in advancing dependable avionics engineering.

Profile : Scopus

Featured Publications

Nanda, M., & Rao, P. R. (2018, May 17). Implementation and verification of an asynchronous FIFO under boundary conditions (Paper ID: NCESC18-181). National Conference on Electronics, Signals and Communication (NCESC-2018), GSSS Institute of Engineering & Technology for Women, Mysore.

Nanda, M., Jayanthi, J., & Rao, P. R. (2018, May 18–19). Aerospace compliant test bench to verify critical aerospace functionalities (Paper ID: CRP18-1007). 3rd International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT-2018), Department of Electronics and Communication Engineering, SVCE, Bangalore.

Nanda, M., & Rao, P. R. (2018). An approach for generating self-checking test bench. International Journal for Research in Applied Science and Engineering Technology, 6(6). (Paper ID: IJRASET17914).

Nanda, M., & Rao, P. R. (2018). Aerospace data bus safety criteria as per DO-254. International Journal of Research and Innovation in Applied Science, 3(6).

Nanda, M., & Rao, P. R. (2018). A procedure to verify and validate an FPGA level testing as per DO-254. International Journal of Research and Innovation in Applied Science, 3(6).

Nanda, M., & Rao, P. R. (2018). Verification cases and procedure for IP-core development. International Journal of Engineering Research and Advanced Technology. (ISSN 2454-6135).

Ziang Liu | Engineering | Best Researcher Award

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Mr. Ziang Liu | Engineering | Best Researcher Award

Nanjing University | China

Author Profile

Scopus

Orcid

Early Academic Pursuits

Mr. Ziang Liu began his academic journey with distinction at Tianjin University, where he earned his Bachelor of Science in Electronic Engineering. His strong foundation in engineering and mathematics laid the groundwork for advanced research and innovation. Continuing his academic trajectory, he pursued a Master of Science in Electronic Engineering at the prestigious Nanjing University, where he was recognized as an Outstanding Student and awarded the First-class Academic Scholarship.

Professional Endeavors

Ziang has accumulated valuable industry experience through impactful internships. At Meituan Shanghai, he served as an LLMs Evaluation Algorithm Intern, where he designed evaluation schemes and analyzed instruction-following capabilities across large language models such as Qwen, Doubao, ChatGPT 3.5/4, and Llama2-70B.  In another significant role at Alibaba DingTalk in Hangzhou, he worked on the back-end development of Chatmemo, an enterprise AI assistant. There, he implemented knowledge graph subgraph displays and integrated Retrieval-Augmented Generation (RAG), significantly boosting response speed and system performance.

Contributions and Research Focus

Mr. Liu’s core interests revolve around LLMs (Large Language Models), RAG (Retrieval-Augmented Generation), and knowledge graph technologies. He has contributed to the design and optimization of backend systems for intelligent applications in healthcare and enterprise settings. His work on deploying frameworks like Graph RAG and utilizing tools like Redis, MySQL, and Spring Boot has shown practical outcomes in real-world systems, particularly in performance optimization, load balancing, and cache management. His participation in the Nanjing University Intelligent Hospital Project resulted in a custom online medication purchasing system, complete with AI-powered Q&A capabilities and scalable backend infrastructure.

Accolades and Recognition

Ziang Liu’s academic excellence is evident through a remarkable series of accolades earned during both his undergraduate and postgraduate studies. He was honored as the Outstanding Student of Nanjing University in 2023 and received the First-class Academic Scholarship in 2022, recognizing his superior academic performance. His analytical and technical skills were demonstrated through competition achievements, including the Third Prize in the 19th Chinese Graduate Mathematical Modeling Competition (2022) and the Second Prize in the 18th Chinese Electronic Design Competition (2023). Earlier in his academic journey, he was named a Meritorious Winner in the Mathematical Contest in Modeling (MCM) in 2021 and was recognized as an Outstanding Graduate of Tianjin University in 2022. These accomplishments reflect his consistent dedication, innovation, and leadership in engineering and applied mathematics.

Impact and Influence

Ziang Liu’s work has made a tangible impact in both academia and industry. His efforts in improving instruction-following performance in LLMs and optimizing backend systems for enterprise AI applications have proven valuable for real-world implementation. His innovations in intelligent hospital systems demonstrate a commitment to applying advanced AI technologies to enhance societal well-being and operational efficiency.

Legacy and Future Contributions

Poised at the intersection of AI, backend engineering, and applied innovation, Mr. Ziang Liu is emerging as a key contributor to the next generation of AI infrastructure. His hands-on experience with cutting-edge technologies like gRPC, GraphRAG, JWT, and multi-threaded optimization positions him to drive future advancements in AI systems, enterprise platforms, and digital healthcare. With a strong academic record and robust technical expertise, he is well on his way to becoming a leading voice in intelligent systems development.

 

 

Publications

Channel-Dependent Multilayer EEG Time-Frequency Representations Combined with Transfer Learning-Based Deep CNN Framework for Few-Channel MI EEG Classification

Authors: Ziang Liu, Kang Fan, Qin Gu, Yaduan Ruan
Journal: Bioengineering
Year: 2025

Studying Multi-Frequency Multilayer Brain Network via Deep Learning for EEG-Based Epilepsy Detection

Authors: Weidong Dang, Dongmei Lv, Linge Rui, Ziang Liu, Guanrong Chen, Zhongke Gao
Journal: IEEE Sensors Journal
Year: 2021