Licheng Liu | Computer Science | Best Researcher Award

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Assoc. Prof. Dr. Licheng Liu | Computer Science | Best Researcher Award 

Associated professor, at Hunan University, China.

Dr. Licheng Liu (刘立成) is an Associate Professor at the School of Electrical and Information Engineering, Hunan University, China. He earned his Ph.D. from the University of Macau under the mentorship of Prof. C.L. Philip Chen, a Fellow of the IEEE and a Member of the European Academy of Sciences. Dr. Liu is recognized as a Yue Lu Scholar and serves as a Ph.D. advisor. He is a Senior Member of IEEE and has received the Hunan Provincial Outstanding Young Scientist Fund. His research interests encompass deep learning, broad learning systems, and sparse manifold learning. He has authored nearly 50 papers in top-tier journals and conferences, including IEEE Transactions on Cybernetics, Neural Networks and Learning Systems, and Circuits and Systems for Video Technology. His work has garnered over 1,265 citations and an h-index of 17.

Professional Profile

Scopus

🎓 Education 

Dr. Liu’s academic journey began with a Bachelor’s degree in Mathematics and Physics from China University of Geosciences (Wuhan) in 2010. He then pursued a Master’s degree in Mathematics at Hunan University, graduating in 2012. His doctoral studies were completed at the University of Macau in 2016, where he worked under the supervision of Prof. C.L. Philip Chen. Throughout his education, Dr. Liu focused on areas such as sparse representation, image processing, and machine learning, laying a strong foundation for his subsequent research endeavors.

💼 Experience

Dr. Liu commenced his professional career as an Assistant Professor at Hunan University’s School of Electrical and Information Engineering in 2016. By 2019, he was promoted to Associate Professor and was honored as a Yue Lu Scholar. In his academic role, Dr. Liu has supervised numerous graduate students and has been actively involved in various research projects, particularly those funded by the National Natural Science Foundation of China. His research contributions have significantly advanced the fields of image restoration, face hallucination, and noise reduction in visual data.

🔬 Research Interests 

Dr. Liu’s research interests are centered on deep learning, broad learning systems, and sparse manifold learning. He is particularly focused on developing novel algorithms and models to enhance image restoration, low-light object detection, and low-quality image recognition. His work aims to address challenges in visual data processing, such as noise reduction and image enhancement, by leveraging advanced machine learning techniques. Dr. Liu’s innovative approaches have led to the development of robust models capable of improving the quality and accuracy of visual data interpretation in various applications.

🏆 Awards 

Dr. Liu has received several prestigious awards throughout his career. In 2016, he was honored with the Macao SAR Graduate Student Science and Technology Research Award by the Macao Science and Technology Development Fund. In 2018, he was recognized as a Yue Lu Scholar by Hunan University. His excellence in teaching was acknowledged in 2021 when he received the First-Class Teaching Achievement Award from Hunan University. The following year, he was awarded the Special Prize for Higher Education Teaching Achievement by the Hunan Provincial Department of Education. In 2023, Dr. Liu received the National Teaching Achievement Award (Second Class), and in 2024, he was named an Outstanding Master’s Thesis Advisor in Hunan Province. Additionally, he was honored with the Third Prize in Natural Science by the Chinese Association of Automation in 2024.

📚Top Noted  Publications 

Dr. Liu has authored nearly 50 research papers, with 25 published in IEEE/ACM journals. Notable publications include:

1. Weighted Joint Sparse Representation for Removing Mixed Noise in Image (2017)

  • Journal: IEEE Transactions on Cybernetics, 47(3), 600–611.

  • Summary: This paper introduces a method for removing mixed noise in images using a weighted joint sparse representation. The approach aims to effectively address challenges posed by mixed noise types in image processing.

2. Robust Face Hallucination via Locality-Constrained Bi-Layer Representation (2018)

  • Journal: IEEE Transactions on Cybernetics, 48(4), 1189–1201.

  • Summary: The authors propose a robust face hallucination method that utilizes a locality-constrained bi-layer representation. This technique enhances face image resolution while maintaining robustness against noise and outliers. europepmc.org

3. Mixed Noise Removal via Robust Constrained Sparse Representation (2018)

  • Journal: IEEE Transactions on Circuits and Systems for Video Technology, 28(9), 2177–2189.

  • Summary: This paper presents a robust constrained sparse representation method for removing mixed noise from images. The approach adapts to different noise types and effectively restores image quality. figshare.com

4. Discriminative Face Hallucination via Locality-Constrained and Category Embedding Representation (2021)

  • Journal: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 7314–7325.

  • Summary: The authors introduce a discriminative face hallucination method that combines locality-constrained representation with category embedding. This approach improves the quality of face image super-resolution by considering category-specific information.

5. Modal-Regression-Based Broad Learning System for Robust Regression and Classification (2023)

  • Journal: IEEE Transactions on Neural Networks and Learning Systems, 35(9), 12344–12357.

  • Summary: This paper proposes a modal-regression-based broad learning system to enhance robustness in regression and classification tasks. The method addresses challenges posed by noisy and outlier-prone data, improving model performance. pubmed.ncbi.nlm.nih.gov

Conclusion

Dr. Licheng Liu demonstrates exceptional strength as a mid-career researcher with an outstanding publication record, robust funding history, and recognized academic leadership in AI and image processing. His ability to balance theoretical innovation with practical application is evident in his funded projects and impactful publications.

Uday Garg | Artificial Intelligence | Best Researcher Award

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Mr. Uday Garg | Artificial Intelligence | Best Researcher Award 

Mr. Uday Garg, at Chandigarh University, India.

Uday Garg is a passionate and dynamic Computer Science Engineer with a proven record in leadership and technology. A former Head Boy and current Class Representative, he balances academic rigor with hands-on experience in development and event management. Proficient in programming languages such as Python and C++, Uday has contributed to both web and Android development, delivering projects in collaboration with startups like Zonson Infotech. His involvement in IEEE at Chandigarh University, both as Treasurer and Executive Member, underscores his dedication to community engagement and tech growth. With a minor in AI from IIT Ropar and multiple NPTEL certifications, Uday exemplifies a commitment to continuous learning. His creative interests in graphic design and love for classical music add depth to his well-rounded personality. A tech innovator with a vision, Uday aspires to bridge technological solutions with real-world challenges.

Professional Profile

ORCID

Education 🎓

Uday Garg’s educational journey showcases both academic versatility and technical depth. Currently pursuing a Minor in Artificial Intelligence (2024–25) from IIT Ropar, he earlier completed his Bachelor of Engineering in Computer Science from Chandigarh University (2020–2024), securing a CGPA of 7.9. His foundation in school academics was laid at Chapra Central School, achieving 9.6 CGPA in Class X and 60.5% in Class XII (CBSE). Uday has also earned industry-recognized certifications, including Software Testing from NPTEL IIIT Bangalore, Data Mining from NPTEL IIT Kharagpur, and an A-grade in Space Technology Awareness Training by ISRO. This academic trajectory reflects a persistent curiosity and a commitment to expanding both theoretical and applied knowledge. With a strong command of programming, AI, and emerging technologies, Uday’s academic record complements his innovative drive and career ambitions.

Experience 💼 

Uday Garg’s professional experience bridges academia and industry with impactful contributions. In 2024, he interned as a Digital Marketing Intern at Sharkify Technology Pvt Ltd, gaining valuable exposure to SEO, analytics, and digital strategies. His practical expertise also extends to software development; between September 2022 and February 2023, he worked as a Freelance Developer at Zonson Infotech Private Limited, where he handled real-time development projects, especially Android and UI/UX tasks. Uday’s collaborative development of a Dormitory Management App using Flutter and Firebase highlights his technical execution skills. He also designed and deployed a Semester Syllabus Portal independently using HTML, CSS, and JavaScript within a tight 20-day timeline. From RPA-based invoice generators to full-stack food blogging platforms, Uday’s hands-on experience reflects adaptability and competence in solving diverse technical challenges. His leadership roles in IEEE also speak to his management skills and collaborative spirit.

Research Interest 🔬

Uday Garg’s research interests lie at the intersection of Artificial Intelligence, Cybersecurity, and Human-Centric Computing. He is particularly fascinated by the vulnerabilities within facial recognition systems and how advanced algorithms can enhance both security and privacy. His recent work titled “An Overview of Vulnerabilities and Mitigation Strategies in Facial Recognition Systems” presented at AIDE-2023, underscores his commitment to identifying real-world issues in biometric technologies and proposing viable solutions. With a minor in AI from IIT Ropar and foundational training in data mining, Uday is also keen on exploring Machine Learning-based automation, natural language processing, and ethical AI. His practical orientation is balanced by philosophical curiosity, often delving into ethics, privacy, and the societal implications of AI. Uday aspires to contribute to research that not only pushes technical boundaries but also remains grounded in human values, making technology inclusive, accountable, and forward-looking.

Awards 🏅 

Uday Garg’s academic and extracurricular achievements reflect a balanced profile of intellect and leadership. He was elected Treasurer and Executive Member of the IEEE Chandigarh University Student Branch (2021–2022), a role where he managed event planning, financial oversight, and community engagement. One of his most notable accolades was presenting his research paper at the International Conference on Artificial Intelligence and Data Engineering (AIDE-2023). His paper, focused on cybersecurity in facial recognition systems, was accepted for publication in the American Institute of Physics (AIP)—a significant milestone for a budding researcher. Uday’s appointment as Head Boy in school and his ongoing service as Class Representative in university further reinforce his leadership credentials. These honors and roles speak volumes about his credibility, dedication, and the trust placed in him by peers and mentors alike. With a strong track record of excellence, Uday is poised to make impactful contributions to the tech world.

Top Noted Publication 📚

Uday Garg’s publication record began with a compelling research contribution to the AI and cybersecurity landscape. He authored and presented the paper titled “An Overview of Vulnerabilities and Mitigation Strategies in Facial Recognition Systems” at the International Conference on Artificial Intelligence and Data Engineering (AIDE-2023). This paper was published by the American Institute of Physics (AIP) in 2023 and focuses on identifying systemic flaws in facial recognition systems and proposing mitigation techniques through advanced algorithms and encryption.

The paper titled “An Overview of Vulnerabilities and Mitigation Strategies in Facial Recognition Systems” by Uday Garg and Arti Rani was presented at a conference and published in the AIP Conference Proceedings on March 1, 2025. It is part of the series with the ISSN: 0094-243X.

Citation Information

  • Title: An Overview of Vulnerabilities and Mitigation Strategies in Facial Recognition Systems

  • Authors: Uday Garg, Arti Rani

  • Publication Date: March 1, 2025

  • Journal: AIP Conference Proceedings

  • ISSN: 0094-243X

  • DOI: 10.1063/5.0262020

Accessing the Paper

You can access the full paper through the DOI link provided above. If you encounter any access restrictions, you might consider checking if your institution provides access or contacting the authors directly for a copy.

Conclusion

Uday Garg shows promising research potential, especially in the intersection of AI, cybersecurity, and application development. His technical breadth, leadership experience, and a published research paper mark him as a strong early-career researcher. However, to fully align with the standards typically expected for a Best Researcher Award, continued focus on depth of research, academic performance, and publication output is advised.