Dr. Fahman Saeed | Signal Distortion Awards | Best Researcher Award

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Dr. Fahman Saeed | Signal Distortion Awards | Best Researcher Award 

Dr. Fahman Saeed, Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia

Dr. Fahman Saeed is an Assistant Professor in the College of Computer and Information Sciences at Imam Mohammad Ibn Saud Islamic University (IMSIU) in Riyadh, Saudi Arabia. With a Ph.D. in Computer Science from King Saud University, his research focuses on deep learning models, particularly for automatic diabetic retinopathy screening. He has contributed significantly to various research projects, including the development of fingerprint interoperability solutions and privacy-protected breast cancer screening systems, earning multiple ISI papers, patents, and conference presentations. Dr. Saeed also has extensive experience in machine learning, specializing in PyTorch, TensorFlow, and large language models. In addition to his academic achievements, he actively participates in professional activities, such as curriculum development and leading workshops on AI, NLP, and generative AI. His dedication to education and research, coupled with his expertise in artificial intelligence, continues to influence both his academic institution and the broader scientific community.

Professional Profile:

ORCID

Suitability for Best Researcher Award: Fahman Saeed

Fahman Saeed is exceptionally suited for the Best Researcher Award due to his outstanding contributions to the field of computer science, particularly in the areas of deep learning, machine learning, and artificial intelligence. With a robust academic background and extensive experience in both research and teaching, Dr. Saeed has demonstrated leadership in advancing the application of machine learning technologies in critical areas like medical diagnostics and data security.

Education 🎓

  • Ph.D. in Computer Science
    • Institution: King Saud University, Saudi Arabia 🎓
    • Graduation: November 2021 📅
    • Dissertation: Developing an auto deep learning model with less complexity and high performance for automatic diabetic retinopathy screening 🧠💻
  • M.Sc. in Computer Science
    • Institution: King Saud University, Saudi Arabia 🎓
    • Graduation: May 2014 📅
  • B.Sc. in Computer Science
    • Institution: King Saud University, Saudi Arabia 🎓
    • Graduation: February 2007 📅

Academic Experience 📚

  • Assistant Professor
    • Institution: College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia 🏫
    • Duration: 2022 to Present ⏳
    • Responsibilities: Teaching courses in Artificial Intelligence 🤖, Natural Language Processing 💬, Algorithm Design and Analysis 💻, Image Processing 🖼️, and Computer Vision 👀
  • Lecturer (Part-time)
    • Institution: King Saud University, Riyadh, Saudi Arabia 🎓
    • Duration: 2017 to 2021 ⏳
  • Researcher
    • Institution: King Saud University, Riyadh, Saudi Arabia 🧪
    • Duration: March 2015 to 2021 ⏳
    • Projects:
      • Automatic Diabetic Retinopathy Screening 🩺👁️
        • Achievements: Two ISI papers 📄
      • Identification of Fingerprint Interoperability 🧑‍⚖️
        • Achievements: One patent, one ISI paper, two conference papers 📑
      • Cloud-Based Privacy-Protected Computer-Aided Diagnosis System for Breast Cancer Screening 🩻
        • Achievements: One ISI paper 📄

Publication Top Notes

Adaptive Renewable Energy Forecasting Utilizing a Data-Driven PCA-Transformer Architecture

Blockchain-Based Quality Assurance System for Academic Programs
Optimal Sizing and Placement of Distributed Generation under N-1 Contingency Using Hybrid Crow Search–Particle Swarm Algorithm
A Data-Driven Convolutional Neural Network Approach for Power Quality Disturbance Signal Classification (DeepPQDS-FKTNet)

Designing the Architecture of a Convolutional Neural Network Automatically for Diabetic Retinopathy Diagnosis

 

Dr. Zhigang Zhu | Signal Processing Award | Best Researcher Award

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Dr. Zhigang Zhu | Signal Processing Award | Best Researcher Award

Dr. Zhigang Zhu, Xidian University, China

Zhigang Zhu, born on October 27, 1989, is a distinguished postdoctoral researcher in the School of Electronic Engineering at Xidian University. With a robust educational foundation, Zhigang holds a Ph.D. in Control Science and Engineering from Xidian University. His academic journey began at Qingdao University of Technology, where he earned his undergraduate degree in Telecommunication Engineering in 2009.Zhigang’s expertise lies in deep learning and signal processing, with a keen focus on signal representation and recognition. His research achievements are substantial, having published over 20 SCI-indexed papers in prestigious journals such as Remote Sensing, IEEE TAES, IEEE TIM, and IEEE SPL. He is a recognized member of both the Chinese Institute of Electronics (CIE) and the Institute of Electrical and Electronics Engineers (IEEE).

Professional Profile

🎓 Education & Academic Achievements:

I hold a Ph.D. in Control Science and Engineering from Xidian University, completed in 2015. I began my academic journey with a Bachelor’s degree in Telecommunication Engineering from Qingdao University of Technology in 2009. Currently, I am a postdoctoral researcher in the School of Electronic Engineering at Xidian University. My specialization lies in deep learning and signal processing, particularly in signal representation and signal recognition.

📚 Experience & Professional Engagements:

Since 2015, I have been deeply involved in research and academia. I have led numerous projects, including a significant initiative by the National Natural Science Foundation of China focused on deep learning. My work in electronics science and technology has earned me accolades such as the Shaanxi Higher Education Institutions Scientific Research Outstanding Achievement Award. Additionally, I have made substantial contributions to the field by publishing over 20 SCI-indexed papers in renowned journals like IEEE TAES and IEEE TIM.

🌐 Research & Contributions:

My research interests include computer vision, signal processing, and deep learning. I have been recognized with multiple national and provincial awards for my innovative research and entrepreneurial efforts. As a member of both the Chinese Institute of Electronics (CIE) and the Institute of Electrical and Electronics Engineers (IEEE), I actively contribute to the scientific community. I have also guided a student team to win prestigious awards in competitions such as the Shaanxi Provincial Internet+ Innovation and Entrepreneurship Competition.

🏆 Recognition & Impact:

My dedication to advancing technology and fostering innovation has been recognized through various awards, including the Excellence Award at the National Post-Doctoral Innovation and Entrepreneurship Competition. I strive to inspire the next generation of researchers and apply my work for the benefit of society.

 

.Publications Notes:📄

 

 

 

Dr. Sangyeop Lee | Signal Processing | Best Researcher Award

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Dr. Sangyeop Lee | Signal Processing | Best Researcher Award

Dr. Sangyeop Lee, LG Electronics, South Korea

Sangyeop Lee, Ph.D., is a seasoned Senior Researcher and Data Scientist at LG Electronics, currently based at the Life Data Fusion Laboratory within the B2B Advanced Technology Center in Seoul, Republic of Korea. With a robust academic background, including a Ph.D. in Computer Science from Yonsei University, Sangyeop has been actively involved in both research and academia. His research interests span various domains, notably including LLM fine-tuning, artificial neural networks for biomedical signal processing, and context-awareness in the clinical domain using machine learning techniques. Throughout his career, he has contributed significantly to cutting-edge projects such as Smartcare in Kindergarten and neptuNE, addressing critical issues like child behavior monitoring and home healthcare. Sangyeop’s expertise extends to teaching and mentoring, evident from his engagements as a lecturer and teaching assistant at Yonsei University. His dedication to advancing technology and solving real-world problems underscores his commitment to innovation in the fields of data science and healthcare.

Professional Profile

Orcid

 

Affiliation:

Sangyeop is currently affiliated with the LEAD technology task at the Life Data Fusion Laboratory within the B2B Advanced Technology Center at LG Electronics, located in Seocho R&D Campus, Seoul, Republic of Korea.

Research Interests:

His research interests include LLM fine-tuning, artificial neural networks for biomedical signal processing, and context-awareness using machine learning techniques in clinical settings.

Teaching Experience:

Sangyeop has contributed to education as a lecturer and teaching assistant at Yonsei University, covering subjects like AI for Medical Problems and Engineering Information Processing, where he taught Python practice.

Projects:

  1. Smartcare in Kindergarten: Collaborated with DNX Kidsnote and Severance Hospital to utilize AI technology in studying children’s behavior and location in kindergartens using wearables/radars.
  2. neptuNE: Developed sensors and mobile devices for home monitoring, addressing nocturnal enuresis in children, in collaboration with Samsung Electronics and Severance Hospital.
  3. Ready-Made Implant: Conducted a confidential study on mass production with pre-made implants and recommending customized implant models through dental data analysis, in collaboration with Ostem Implant and Yonsei University.

Publications:

Sangyeop has several publications in prestigious conferences and journals, including IEEE Radar Conference and Sensors, focusing on topics like artificial intelligence, biomedical engineering, and healthcare.

Application:

Sangyeop has contributed to the development of in-home monitoring with wearables and NE Diary Application, enhancing healthcare solutions through technology.

Sangyeop’s dedication to advancing data-driven solutions in healthcare underscores his commitment to innovation and improving patient outcomes. 🌟

Publications Notes:📄

Wearable-Based Integrated System for In-Home Monitoring and Analysis of Nocturnal Enuresis

Continuous body impedance measurement to detect bladder volume changes during urodynamic study: A prospective study in pediatric patients