Dr. Didier Torres Guzmán | Machine Learning | Best Researcher Award

Professor | National Autonomous University of Mexico | Mexico

Dr. Didier Torres Guzmán is a distinguished researcher whose work focuses on biomedical signal processing, neuroimaging, and the application of machine learning to clinical diagnostics. His research contributions have advanced the understanding and analysis of neurological and physiological conditions, particularly through the development of innovative computational biomarkers and signal processing techniques. Notably, he has explored tortuosity and discrete compactness biomarkers for machine learning-based classification of mild cognitive impairment, providing new tools for early and accurate detection of cognitive decline. In addition, his studies on discrete neuroimaging metrics have enabled the identification of structural brain alterations associated with COVID-19, highlighting the relevance of his work to pressing global health challenges. Dr. Torres Guzmán has also contributed to non-invasive physiological monitoring, including methods for estimating heart and respiratory rates through face video processing and novel approaches for ECG signal morphology analysis using tortuosity estimation. His work consistently demonstrates a combination of methodological rigor, interdisciplinary application, and translational potential, bridging computational techniques with practical healthcare solutions. The originality and impact of his research are reflected in his publications in high-quality peer-reviewed journals and book chapters, where he collaborates with international researchers across biomedical engineering, signal processing, and clinical disciplines. Through these contributions, Dr. Torres Guzmán has established himself as a leading figure in his field, whose work not only advances scientific knowledge but also has tangible implications for improving patient care, diagnostic accuracy, and the integration of artificial intelligence in biomedical research, making him a highly deserving candidate for recognition with the Best Researcher Award.

Featured Publications

Torres Guzmán, D., Pinzón Vivas, J. D., & Barbará Morales, E. (2026). Tortuosity and discrete compactness biomarkers for machine learning-based classification of mild cognitive impairment. Biomedical Signal Processing and Control.

Delgado-Castillo, D., Barbará-Morales, E., Hevia-Montiel, N., Arámbula-Cosío, F., & Torres Guzmán, D. (2025). Discrete neuroimaging metrics for identifying structural alterations in COVID-19-related brain atrophy. International Journal of Online and Biomedical Engineering (iJOE).

Ruíz-Espinosa, G., Jimenez-Angeles, L., Torres Guzmán, D., Rojas-Arce, J. L., & Marmolejo-Saucedo, J. A. (2024). A comparison of algorithms to estimate heart and respiratory rate from face video processing. In Book chapter.

Pacheco González, L. E., Torres Guzmán, D., & Barbará-Morales, E. (2024). A novel method for ECG signal morphology analysis using tortuosity estimation. Biomedical Signal Processing and Control.

Dr. Tee Connie | Machine Learning Awards | Best Researcher Award

Dr. Tee Connie , Multimedia University , Malaysia

Dr. Tee Connie is a distinguished academic and researcher in the field of Information Technology, specializing in machine learning, pattern recognition, computer vision, and biometrics. She is currently a Professor at the Faculty of Information Science and Technology, Multimedia University, Malaysia, where she also serves as Dean of the Institute for Postgraduate Studies. Dr. Tee holds a Ph.D. and Master’s in Information Technology from Multimedia University, and a Bachelor’s degree in Information Technology with First Class Honours from the same institution. Her research is widely recognized, evidenced by numerous funded projects and publications, including notable grants for innovative applications in gait analysis, vehicle traffic analysis, and computer vision solutions. She has also contributed to the field with a patent for a hand geometry and palm print verification system. Her extensive experience and leadership in both research and academic administration underscore her significant impact in advancing information technology.

Professional Profile:

Scopus

Orcid

Summary of Suitability for the Research for Best Researcher Award: Tee Connie

Introduction: Dr. Tee Connie, a Professor at Multimedia University, is a distinguished candidate for the Research for Best Researcher Award. Her extensive background in machine learning, computer vision, and biometrics, coupled with her leadership roles and significant research contributions, positions her as a highly suitable nominee.

🎓Education:

Dr. Tee Connie completed her Doctor of Philosophy in Information Technology at Multimedia University, Malaysia, in 2015. Prior to this, she earned a Master of Science in Information Technology from the same institution in 2005. She also holds a Bachelor of Information Technology, with a major in Information System Engineering, graduating with First Class Honours and a CGPA of 3.92/4.00 in 2003.

🏢Work Experience:

Dr. Tee Connie has held several academic and administrative positions at Multimedia University, Malaysia. She has been a Professor at the Faculty of Information Science and Technology since 2023 and currently serves as the Dean of the Institute for Postgraduate Studies, a role she has held since April 2022. Prior to this, she was the Deputy Dean of the Institute for Postgraduate Studies from April 2021 to April 2022. Dr. Tee’s career at the university began as a Lecturer in 2005, and she was promoted to Senior Lecturer in 2008, a position she held until 2021. She has also worked as an Associate Professor at the Faculty of Information Science and Technology since 2021 and served as a Tutor from 2003 to 2005.

🏆Awards and Grants:

Dr. Tee Connie has been awarded several significant research grants. She is leading the Malaysia-Jordan Matching Grant project on “A Non-Invasive Gait Analysis for Parkinson’s Disease Screening Using Computer Vision and Machine Learning Techniques,” which runs from September 2024 to August 2026, with a funding amount of RM 23,000. She is also a project member for the TM R&D Fund’s “Smart-VeTRAN: Smart Vehicle Traffic Impact Analysis Using 4G/5G Network” (RM 678,453) and the “Machine Learning Based Distributed Acoustic Sensing (DAS) for Fiber Break Prevention” projects (Sub-project 1: RM 638,731; Sub-project 2: RM 599,061), both running from August 2022 to July 2024. Other notable grants include the Fundamental Research Grant Scheme’s “Confined Parking Spaces and Congestion Prediction using Deep Q-Learning Strategy” (RM 89,093) and the “Few-shot Learning Approach for Human Activity Recognition and Anomaly Detection” (RM 113,850), both spanning from September 2022 to April 2024. Additionally, she has secured funding for projects such as the “Cryptographically Secure Cloud-Based Infrastructure (CryptCloud)” (RM 917,504), the IR Fund’s “Gender and Age Estimation using Human Gait for Smart Cities Surveillance” (RM 24,000), and the Multimedia University-Telkom University Joint Research Grant for “Gait Analysis for Neurodegenerative Disorders using Computer Vision and Deep Learning Approaches” (RM 20,000). Her past projects include contributions to the International Collaboration Fund’s “Design and Development of A Drone Based Hyperspectral Imaging System for Precision Agriculture” (RM 264,660) and several other notable grants in fields related to computer vision, biometrics, and security surveillance.

Publication Top Notes:

Visual-based vehicle detection with adaptive oversampling
A Robust License Plate Detection System Using Smart Device
Review on Digital Signal Processing (DSP) Algorithm for Distributed Acoustic Sensing (DAS) for Ground Disturbance Detection
A Review of AI Techniques in Fruit Detection and Classification: Analyzing Data, Features and AI Models Used in Agricultural Industry
Boosting Vehicle Classification with Augmentation Techniques across Multiple YOLO Versions

Didier Torres Guzmán | Machine Learning | Best Researcher Award

Dr. Didier Torres Guzmán | Machine Learning | Best Researcher Award

Profile: ORCID | Scopus

Featured Publications