Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

Dr. Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

Chairperson of the Department of Computer Science and Information Technology | Jubail Industrial College (JIC) | Saudi Arabia

Dr. Faisal Alamri is an accomplished artificial intelligence researcher specializing in computer vision, machine learning, object detection, classification, segmentation, similarity search, adversarial perturbation, and zero-shot learning. He holds a Ph.D. in Computer Science with a focus on computer vision and machine learning from the University of Exeter, and completed his undergraduate and master’s degrees in computer systems engineering and networking. He currently serves as the Computer Science Department Chairperson at Jubail Industrial College, where he oversees academic and administrative activities and leads departmental initiatives. Previously, he worked as a machine learning engineer developing practical AI solutions, a postdoctoral research fellow, and a teaching assistant, and has also contributed as an online tutor and teaching volunteer. His research interests include developing innovative approaches for object detection, image analysis, and real-world AI applications. Dr. Alamri has been recognized for his achievements through multiple certifications and active participation in international conferences, workshops, and professional communities such as IEEE, Kaggle, NVIDIA, and MATLAB. He possesses strong technical skills in Python, MATLAB, C#, SPSS, AWS, Google Cloud ML Engine, and other platforms, and has completed various professional courses in deep learning, AI, cybersecurity, and digital analytics. His dedication to research, education, and community engagement reflects his commitment to advancing both science and society. He has a total of 49 citations, 7 documents, and an h-index of 5.

Profiles: Google Scholar | Scopus | ORCID | LinkedIn

Featured Publications

  1. Alamri, F., & Dutta, A. (2021). Multi-head self-attention via vision transformer for zero-shot learning. arXiv preprint arXiv:2108.00045.

  2. Alamri, F., & Pugeault, N. (2020). Improving object detection performance using scene contextual constraints. IEEE Transactions on Cognitive and Developmental Systems, 14(4), 1320–1330.

  3. Alamri, F., & Dutta, A. (2021). Implicit and explicit attention for zero-shot learning. In DAGM German Conference on Pattern Recognition (pp. 467–483).

  4. Alamri, F., & Dutta, A. (2023). Implicit and explicit attention mechanisms for zero-shot learning. Neurocomputing, 534, 55–66.

  5. Alamri, F., Kalkan, S., & Pugeault, N. (2021). Transformer-encoder detector module: Using context to improve robustness to adversarial attacks on object detection. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 9577–9584). IEEE.

Zhe-Wang-System Security-Best Researcher Award

Prof. Zhe-Wang-System Security-Best Researcher Award 

Institute of Computing Technology, Chinese Academy of Sciences-China

Author Profile

Early Academic Pursuits

Prof. Zhe Wang's academic journey began with a strong foundation in software engineering. He completed his Bachelor of Science degree in Software Engineering at the Beijing University of Technology in 2012. His undergraduate education laid the groundwork for his future pursuits, providing him with essential knowledge and skills in software development and computer systems. Following his passion for computer architecture, Wang pursued a Ph.D. at the University of Chinese Academy of Sciences (UCAS), focusing on computer architecture, which he completed in 2018. His doctoral studies at UCAS were marked by rigorous research and a deep dive into complex computing concepts, setting the stage for his future contributions to the field.

Professional Endeavors

After obtaining his Ph.D., Prof. Zhe Wang joined the Institute of Computing Technology (ICT) of the Chinese Academy of Sciences (CAS) as an associate professor. At ICT, he became a key figure in the State Key Laboratory of Processor, where he engaged in advanced research and development in computing technologies. His role at ICT involves not only conducting cutting-edge research but also mentoring students and collaborating with other leading researchers in the field. Wang’s professional endeavors are characterized by his focus on dynamic binary translation and optimization, cross-ISA system-level virtualization, hardware-assisted virtualization, multi-threaded program record and replay, memory corruption attacks and defenses, and cache-based side-channel attacks and defenses.

Contributions and Research Focus

Prof. Zhe Wang's research interests span several critical areas in computer science, particularly in enhancing the efficiency and security of computing systems. His work in dynamic binary translation and optimization involves developing techniques to translate and optimize binary code dynamically, improving the performance of software across different hardware platforms. In cross-ISA system-level virtualization, Wang explores methods to enable virtualization across different instruction set architectures, facilitating the execution of software designed for one architecture on another.

His research in hardware-assisted virtualization aims to leverage hardware features to enhance virtualization performance and security. Wang's contributions to multi-threaded program record and replay focus on techniques to record the execution of multi-threaded programs and replay them accurately, which is crucial for debugging and security analysis. He has also addressed critical issues in memory corruption attacks and defenses, devising strategies to protect against vulnerabilities that could compromise system security. Additionally, Wang has investigated cache-based side-channel attacks and defenses, working to understand and mitigate attacks that exploit cache memory to extract sensitive information.

Accolades and Recognition

Prof. Zhe Wang's exceptional work has earned him numerous awards and scholarships. He was the recipient of the prestigious CAS Presidential Scholarship in 2018, awarded to the top 1% of students. Additionally, he received the ICT Presidential Scholarship (Special Prize) in 2017, recognizing him among the top 0.2% of students. Wang was also honored with the National Scholarship for Ph.D. Candidates twice, in 2015 and 2017, for being in the top 2.9% of candidates. His early academic excellence was recognized with the Sugon Scholarship for Ph.D. Candidates in 2014 and the Pacemaker to Merit Student Award at UCAS, both placing him in the top 8% of students.

Impact and Influence

Prof. Zhe Wang's research has had a significant impact on the field of computer science, particularly in the areas of virtualization, security, and optimization. His work has been published in prestigious conferences and journals, such as the ACM Conference on Computer and Communications Security (CCS) and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). His paper on "PANIC: PAN-assisted Intra-process Memory Isolation on ARM," presented at CCS 2023, was awarded the Distinguished Paper Award, highlighting the importance and quality of his research. Another notable publication, "SpecWands: An Efficient Priority-based Scheduler Against Speculation Contention Attacks," published in TCAD 2023, showcases his contributions to improving security against speculative execution attacks.

The System Security Award is a prestigious recognition honoring exceptional contributions to safeguarding digital systems and data from cyber threats.

Legacy and Future Contributions

As an associate professor and researcher at the forefront of computer science, Prof. Zhe Wang continues to push the boundaries of what is possible in computing technology. His work not only advances the state of the art but also provides a foundation for future innovations in virtualization, security, and optimization. Wang's legacy is marked by his commitment to excellence in research and his contributions to the scientific community. Looking ahead, he is poised to continue making significant strides in his areas of expertise, influencing both academia and industry with his groundbreaking work.

Citations

  • Citations     186
  • h-index           8
  • i10-index        7

Notable Publication