Lirong Wang | Artifical Intelligence | Best Researcher Award

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Ms. Lirong Wang | Artifical Intelligence | Best Researcher Award

professor at Suzhou University, China

Professor Lirong Wang is a distinguished researcher at Soochow University, specializing in intelligent wearable devices and information processing. She earned her B.S. and Ph.D. from Jilin University and has been serving as a professor since 2014. Her research integrates microelectronics, machine learning, and biomedical engineering, with a strong focus on signal acquisition and analysis. Professor Wang leads several interdisciplinary projects and supervises graduate students, fostering innovation and academic growth. As the Principal Investigator of a National Key R&D Program, she demonstrates outstanding leadership in advancing cutting-edge technologies. She has authored over 40 peer-reviewed publications in prestigious journals such as IEEE Transactions on Biomedical Engineering and holds more than 20 invention patents, highlighting her contributions to both academic research and practical innovation. In addition to her research work, she actively participates in the global scientific community as a journal reviewer and organizer of international conference sessions in wearable technology and computer science.

Publication Profile

Education🎓

Professor Lirong Wang received her formal education at Jilin University, one of China’s premier institutions, where she earned both her Bachelor of Science (B.S.) and Doctor of Philosophy (Ph.D.) degrees. Her academic training focused on electronic engineering and information processing, laying a strong foundation for her specialization in intelligent wearable devices. Throughout her educational journey, she developed expertise in signal acquisition technologies, microelectronics, and data analysis, which later became the core pillars of her research. During her Ph.D. studies, Professor Wang engaged in interdisciplinary work that bridged engineering, computer science, and biomedical applications, positioning her at the forefront of next-generation health monitoring technologies. Her rigorous academic background and commitment to research excellence have equipped her with the analytical skills and innovative mindset needed to lead complex scientific projects. This strong educational grounding has played a pivotal role in shaping her successful academic and research career at Soochow University.

Professional Experience 💼

Professor Lirong Wang has built a robust professional career centered on interdisciplinary research and academic leadership. Since 2014, she has served as a professor at Soochow University, where she specializes in intelligent wearable devices, signal acquisition, and biomedical information processing. Her professional experience spans leading national-level R&D programs and supervising numerous graduate students, fostering innovation in both academia and applied technology. As the Principal Investigator of a National Key Research and Development Program, she has demonstrated exceptional capability in managing large-scale, collaborative research projects. Professor Wang has authored over 40 peer-reviewed publications and holds more than 20 invention patents, reflecting a strong commitment to both theoretical advancement and technological innovation. Beyond her university role, she contributes to the global research community as a reviewer for prestigious journals and an organizer of international conference sessions, particularly in wearable technology and computer science. Her experience reflects a deep integration of research, mentorship, and scientific engagement.

Research Interest 🔬

Professor Lirong Wang has a diverse and forward-thinking research portfolio centered on the development and application of intelligent wearable devices and biomedical information processing. Her primary interests lie in signal acquisition technology, physiological data analysis, and the integration of machine learning with microelectronic systems for real-time health monitoring and diagnostics. She is particularly focused on designing wearable platforms capable of accurately capturing and interpreting complex biological signals, such as ECG and EMG, to support early disease detection and personalized healthcare. Her interdisciplinary approach merges principles from biomedical engineering, computer science, and electrical engineering, creating practical solutions for next-generation health technologies. Additionally, she explores low-power sensor systems, data fusion algorithms, and human-computer interaction interfaces within wearable technologies. Professor Wang’s research aims to bridge the gap between theoretical modeling and real-world applications, ultimately enhancing the reliability and usability of wearable systems in clinical, athletic, and daily life settings.

Research Skill🔎

Professor Lirong Wang possesses a comprehensive set of research skills that reflect her expertise in intelligent wearable technology, biomedical engineering, and data-driven signal processing. She is highly skilled in designing and developing advanced wearable systems, with a strong command of microelectronic circuit design, sensor integration, and embedded system programming. Her proficiency in signal acquisition and processing allows her to extract meaningful insights from complex physiological data such as ECG, EMG, and PPG. She is also adept at applying machine learning algorithms for pattern recognition, anomaly detection, and predictive modeling in healthcare applications. In addition, she demonstrates expertise in managing interdisciplinary research teams, coordinating large-scale projects, and supervising graduate-level research. Professor Wang is experienced in securing research funding, particularly as a Principal Investigator on national R&D initiatives. Her ability to bridge theoretical knowledge with practical innovation highlights her strong analytical, experimental, and collaborative research capabilities across multiple scientific domains.

Award and Honor🏆

Professor Lirong Wang has received several prestigious awards and honors in recognition of her outstanding contributions to research and innovation in the fields of intelligent wearable devices and biomedical engineering. As the Principal Investigator of a National Key R&D Program, she has been recognized at the national level for her leadership and scientific excellence. Her pioneering work has earned accolades from academic institutions and government agencies, including awards for Technological Innovation and Excellence in Research. She has also been honored for her contributions to patent development, with over 20 invention patents credited to her name, many of which have led to real-world applications. Professor Wang’s high-impact publications in leading journals such as IEEE Transactions on Biomedical Engineering have further contributed to her reputation as a top researcher. Additionally, she has received invitations to serve as a reviewer and session chair at international conferences, reflecting her respected status in the global scientific community.

Conclusion📝

Professor Lirong Wang is highly suitable for the Best Researcher Award. His sustained contributions to interdisciplinary research, innovation through patents, and leadership in national research programs mark him as a leading figure in the field of intelligent wearable devices and biomedical engineering. With some enhancement in international collaboration and outreach, his profile stands as exemplary in both academic and practical domains.

Publications Top Noted📚

  • End-to-End ECG Signal Compression Based on Temporal Information and Residual Compensation

    • Year: 2025

    • Journal: Circuits, Systems, and Signal Processing

  • QRS Wave Detection Algorithm of Dynamic ECG Signal Based on Improved U-Net Network

    • Year: 2025

    • Journal: ICIC Express Letters, Part B: Applications

  • TrCL-AGS: A Universal Sequential Triple-Stage Contrastive Learning Framework for Bacterial Detection With Across-Growth-Stage Information

    • Year: 2025

    • Journal: IEEE Internet of Things Journal

  • Multi-label Few-Shot Classification of Abnormal ECG Signals Using Metric Learning

    • Year: 2025

    • Journal: Circuits, Systems, and Signal Processing

  • Automated Deep Learning Model for Sperm Head Segmentation, Pose Correction, and Classification (Open Access)

    • Year: 2024

    • Journal: Applied Sciences (Switzerland)

  • Instance Segmentation of Mouse Brain Scanning Electron Microscopy Images Based on Fine-Tuning Nature Image Model

    • Year: 2024

    • Journal: Guangxue Jingmi Gongcheng / Optics and Precision Engineering

    • Citations: 1

  • Multi-label Classification of Arrhythmia Using Dynamic Graph Convolutional Network Based on Encoder-Decoder Framework

    • Year: 2024

    • Journal: Biomedical Signal Processing and Control

    • Citations: 4

  • Two-Stage Error Detection to Improve Electron Microscopy Image Mosaicking

    • Year: 2024

    • Journal: Computers in Biology and Medicine

    • Citations: 2

Seyed Abolfazl Aghili | Deep Learning | Best Review Paper Award

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Dr. Seyed Abolfazl Aghili | Deep Learning | Best Review Paper Award

lecturer, iran university of science and technology, Iran

Seyed Abolfazl Aghili is a passionate civil engineer with a strong focus on construction engineering and management. With a Ph.D. in Civil Engineering from the prestigious Iran University of Science and Technology (IUST), he specializes in using artificial intelligence for enhancing the resilience of HVAC systems in hospitals. His research integrates cutting-edge technologies such as machine learning and deep learning to optimize building systems and improve decision-making in construction projects. Seyed’s dedication to his field has earned him a reputation as a driven academic and professional in the civil engineering community. 🏗️🤖

Publication Profile

ORCID

Education Background

Seyed Abolfazl Aghili completed his Ph.D. in Civil Engineering with a specialization in Construction Engineering and Management from Iran University of Science and Technology (IUST) between 2019 and 2024. His doctoral thesis focused on developing a framework to assess the long-term resilience of hospital air conditioning systems using artificial intelligence. Prior to that, he earned his M.Sc. in Civil Engineering with a focus on Construction Engineering and Management at IUST, where he investigated employee selection methods in construction firms. He also holds a B.Sc. in Civil Engineering from Isfahan University of Technology (IUT). 🎓📚

Professional Experience

Seyed Abolfazl Aghili has extensive experience in both academic research and practical applications of civil engineering, particularly in construction management. He has worked on various projects involving energy management, risk management, and resilience within the construction industry. His academic journey has seen him contribute significantly to the research community, particularly in the areas of AI in construction systems and HVAC performance. Furthermore, he has been an integral part of various conferences and publications, sharing his insights on improving construction management processes through technology. 💼🏢

Awards and Honors

Seyed Abolfazl Aghili has earned several prestigious awards throughout his academic journey. He was ranked 5th among 2200 participants in the Nationwide University Entrance Exam for the Ph.D. program in Iran in 2019. Additionally, he ranked 2nd among all construction management students at Iran University of Science and Technology during his M.Sc. studies. He was also ranked in the top 1% (220th out of 32,663) in the Nationwide University Entrance Exam for the M.Sc. program in Iran in 2013. 🏆🥇

Research Focus

Seyed’s primary research interests lie in the application of machine learning and deep learning techniques in construction engineering. His work focuses on enhancing the resilience of building systems, especially HVAC systems in healthcare settings. He also explores risk management, sustainability, lean construction, and decision-making systems for project managers. His interdisciplinary research combines civil engineering with advanced AI methodologies, driving innovations in construction management and systems optimization. 🔍💡

Conclusion

Seyed Abolfazl Aghili’s academic and professional journey reflects his commitment to advancing civil engineering through innovative solutions. His focus on integrating artificial intelligence into construction systems is helping to create smarter, more sustainable, and resilient built environments. Through his work, he continues to contribute valuable insights to both the academic world and the practical sector of construction engineering. 🌍🔧

Publications Top Notes

Artificial Intelligence Approaches to Energy Management in HVAC Systems: A Systematic Review. Journal of Buildings, 15.7 (2025).

Data-driven approach to fault detection for hospital HVAC system. Journals of Smart and Sustainable Built Environment, ahead-of-print (2024).

Feasibility Study of Using BIM in Construction Site Decision Making in Iran. International Conference on Civil Engineering, Architecture and Urban Infrastructure, July 2015.

Review of digital imaging technology in safety management in the construction industry. 1st National Conference on Development of Civil Engineering, Architecture, Electricity and Mechanical in Iran (December, 2014).

The role of insurance companies in managing the crisis after earthquake. 1st National Congress of Engineering, Construction, and Evaluation of Development Projects, May 2013.

The need for a new approach to pre-crisis and post-crisis management of earthquake. 1st National Conference on Seismology and Earthquake, February 2013.

Carolina Magalhães | Machine Learning | Best Researcher Award

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Dr. Carolina Magalhães | Machine Learning | Best Researcher Award

Investigadora, INEGI – Instituto de Ciência e Inovação em Engenharia Mecânica e Industrial, Portugal

👩‍🔬 Carolina Magalhães is a dedicated biomedical engineer and PhD candidate with expertise in applying AI and imaging technologies to healthcare challenges. Based in Porto, Portugal, she combines her passion for modern technology with a problem-solving mindset to develop innovative solutions in skin cancer diagnostics. Carolina has worked collaboratively with clinical experts to bridge research and practical applications, contributing significantly to advancing imaging-based decision support systems.

Publication Profile

ORCID

Education

🎓 Carolina holds a PhD in Biomedical Engineering from the Faculdade de Engenharia da Universidade do Porto (2020–2024). She also completed her MSc in Biomedical Engineering at the same institution (2016–2018) and earned her Bachelor’s in Bioengineering – Biomedical Engineering from Universidade Católica Portuguesa (2013–2016).

Experience

💼 Carolina has a rich research background, currently serving as a Graduate Research Fellow at INEGI, focusing on skin lesion diagnosis using multispectral imaging. Her work spans from leveraging machine learning models for skin cancer classification to thermal and UV imaging techniques. Previously, she contributed to projects on hyperhidrosis diagnosis, prosthetic device design, and thermal image analysis for musculoskeletal disorders, collaborating with leading hospitals and research centers in Portugal.

Research Interests

🔬 Carolina is passionate about exploring artificial intelligence, machine learning, and advanced imaging technologies for healthcare applications. Her interests include developing multispectral imaging systems, improving diagnostic tools for skin cancer, and advancing infrared thermography for clinical support systems.

Awards

🏆 Carolina’s innovative work has been recognized with prestigious research grants from the Foundation for Science and Technology (SFRH/BD/144906/2019) and other funding organizations. These awards have supported her impactful contributions to biomedical engineering and healthcare innovation.

Publications

“Systematic Review of Deep Learning Techniques in Skin Cancer Detection”
BioMedInformatics, 11/2024
Read here

“Skin Cancer Image Classification with Artificial Intelligence Strategies: A Systematic Review”
Journal of Imaging, 10/2024
Read here

“Use of Infrared Thermography for Abdominoplasty Procedures in Patients with Extensive Subcostal Scars: A Preliminary Analysis”
Plast Reconstr Surg Glob Open, 06/2023
Read here

“Classic Versus Scarpa-Sparing Abdominoplasty: An Infrared Thermographic Comparative Analysis”
J Plast Reconstr Aesthet Surg, 06/2023
Read here

“Towards an Effective Imaging-Based Decision Support System for Skin Cancer”
Handbook of Research on Applied Intelligence for Health and Clinical Informatics, 10/2022
Read here

Sara Tehsin | Deep learning | Best Researcher Award

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Ms. Sara Tehsin | Deep learning | Best Researcher Award

PhD Student, National University of Sciences and Technology, Islamabad, Pakistan

Sara Tehsin is a motivated and results-driven professional with over ten years of experience in Image Processing and Machine Learning. As an Engineering Lecturer at HITEC University in Taxila, Pakistan, she excels in delivering high-quality educational experiences and has a proven track record of producing outstanding results through her strong work ethic, adaptability, and effective communication skills. She is passionate about academic development and seeks opportunities to contribute her expertise while furthering her professional growth. 📚💻

Publication Profile

Google Scholar

Education

Sara Tehsin is currently pursuing a PhD in Computer Engineering at the National University of Sciences and Technology (NUST), Islamabad, where she has achieved a remarkable GPA of 3.83/4.00. Her research focuses on Digital Forensics, Deep Learning, and Digital Image Processing. She holds a Master’s degree in Computer Engineering from NUST, where she graduated with a GPA of 3.7/4.0, and a Bachelor’s degree from The Islamia University of Bahawalpur, with a GPA of 3.36/4.00. 🎓🌟

Experience

Sara has extensive teaching experience, currently serving as an Engineering Lecturer at HITEC University since September 2019, where she develops engaging curriculum and delivers lectures aligned with international standards. Previously, she was a Computer Science Lecturer at Sharif College of Engineering and Technology, and she also served as a Teaching Assistant at NUST and a Lab Engineer at Foundation University. Her roles have encompassed curriculum development, practical instruction, and student support in various computer science subjects. 👩‍🏫🔧

Research Interests

Sara’s research interests encompass Digital Forensics, Deep Learning, Digital Image Processing, and Machine Learning. She focuses on developing innovative solutions for image recognition and forgery detection, contributing significantly to the fields of computer vision and machine learning. Her work aims to enhance the accuracy and efficiency of image processing systems. 🧠🔍

Publications

Self-organizing hierarchical particle swarm optimization of correlation filters for object recognition
S. Tehsin, S. Rehman, M.O.B. Saeed, F. Riaz, A. Hassan, M. Abbas, R. Young, …
IEEE Access, 5, 24495-24502 (2017)
Cited by: 21

Improved maximum average correlation height filter with adaptive log base selection for object recognition
S. Tehsin, S. Rehman, A.B. Awan, Q. Chaudry, M. Abbas, R. Young, A. Asif
Optical Pattern Recognition XXVII, 9845, 29-41 (2016)
Cited by: 18

Fully invariant wavelet enhanced minimum average correlation energy filter for object recognition in cluttered and occluded environments
S. Tehsin, S. Rehman, F. Riaz, O. Saeed, A. Hassan, M. Khan, M.S. Alam
Pattern Recognition and Tracking XXVIII, 10203, 28-39 (2017)
Cited by: 12

Comparative analysis of zero aliasing logarithmic mapped optimal trade-off correlation filter
S. Tehsin, S. Rehman, A. Bilal, Q. Chaudry, O. Saeed, M. Abbas, R. Young
Pattern Recognition and Tracking XXVIII, 10203, 22-37 (2017)
Cited by: N/A

Tesfay Gidey | Artificial Intelligence | Best Researcher Award

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Dr. Tesfay Gidey | Artificial Intelligence | Best Researcher Award

Lecturer, Addis Ababa Science and Technology University, Ethiopia

Tesfay Gidey Hailu is a highly skilled Information and Communication Engineer and data scientist with a passion for leveraging data to drive innovation and business insights. With expertise in computer science, software engineering, machine learning, and data analytics, he excels in problem-solving, leadership, and technology project management. Tesfay’s work focuses on indoor localization, signal processing, and health data applications, making him a forward-thinking leader in his field. His dedication to continuous learning and delivering actionable results underscores his impressive career in academia and industry. 💼🔧📊

Publication Profile

ORCID

Strengths for the Award:

  1. Diverse Expertise: Tesfay’s expertise spans across critical areas such as signal processing, indoor localization, machine learning, data fusion, and health informatics, aligning well with cutting-edge research areas.
  2. Impressive Academic Qualifications: Holding a Ph.D. in Information and Communication Engineering, along with two MSc degrees, he possesses deep knowledge in interdisciplinary fields.
  3. Research Contributions: He has authored numerous peer-reviewed publications in high-impact journals such as Sensors, Intelligent Information Management, and Journal of Biostatistics. His work in Wi-Fi indoor positioning, predictive modeling, and health informatics shows a broad application of research across industries.
  4. Leadership in Academia: His roles as Associate Dean and Head of Department demonstrate his leadership in driving research, improving curriculum quality, and promoting technology transfer.
  5. Innovative Research Focus: His Ph.D. dissertation on transfer learning for fingerprint-based indoor positioning and various data fusion methods reflect his innovative contributions to solving real-world problems with advanced technologies.

Areas for Improvement:

  1. Broader Industry Impact: While his research is highly academic, incorporating more industry-driven collaborations or commercial applications could strengthen the practical impact of his work.
  2. Public Engagement: Increasing public outreach and collaboration with non-academic sectors or public talks could elevate his visibility and expand the impact of his research findings.
  3. Global Collaboration: Expanding his research collaborations beyond local and regional levels, particularly with international industries, could further showcase the global relevance of his work.

Education 🎓

Tesfay holds a Ph.D. in Information and Communication Engineering from the University of Electronic Science and Technology of China (2023), where his research centered on signal and information processing applied to indoor positioning using machine learning algorithms. He also earned an MSc in Software Engineering from HILCOE School of Computer Science and Information Technology (2018) and an MSc in Health Informatics and Biostatistics from Mekelle University (2013). Additionally, he completed his BSc in Statistics with a minor in Computer Science at Addis Ababa University (2006). 📚💻📈

Experience 💼

Tesfay has held several leadership positions, including Associate Dean at Addis Ababa Science and Technology University (AASTU), where he led research, technology transfer, student recruitment, and faculty training initiatives. He was also the Head of Department and Coordinator at Jimma University, contributing to curriculum enhancement and student retention programs. His experience spans research in manufacturing industries, project management, and academic administration. 🏫📊👨‍🏫

Research Focus 🔬

Tesfay’s research focuses on signal processing, indoor localization, machine learning, data mining, and information fusion. He specializes in developing advanced models for indoor positioning systems, predictive modeling, and statistical quality control, aiming to solve complex problems in health informatics, manufacturing industries, and public health. His work integrates cutting-edge technologies to advance both theoretical and applied fields. 📡📉🤖

Awards and Honors 🏆

Tesfay has been recognized for his contributions to the fields of information and communication engineering and data science. He has received multiple awards and honors for his research and leadership roles in academia, particularly in driving innovative projects that bridge the gap between technology and industry. 🌍🎖️

Publications Highlights 📚

Tesfay has published extensively in top-tier journals, with a focus on indoor positioning systems, data fusion, and health informatics. His research includes the development of novel machine learning models and statistical analysis tools. His works have been widely cited, showcasing his impact in the academic community. 📊✍️

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection (2023). Intelligent Information Management, 15, 391-425. Cited by 2 articles. Link

Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting (2022). Sensors, 22, 8720. Cited by 15 articles. Link

Heterogeneous Transfer Learning for Wi-Fi Indoor Positioning Based Hybrid Feature Selection (2022). Sensors, 22, 5840. Cited by 10 articles. Link

OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning (2022). Sensors, 22, 9044. Cited by 5 articles. Link

A Multilevel Modeling Analysis of the Determinants and Cross-Regional Variations of HIV Testing in Ethiopia (2016). J Biom Biostat, 7, 277. Cited by 8 articles. Link

Conclusion:

Tesfay Gidey Hailu’s robust academic background, extensive research portfolio, and leadership roles make him a strong candidate for the Best Research Award. His work in signal processing, machine learning, and data-driven innovation in health informatics and communication systems demonstrates a clear commitment to advancing technology and solving societal problems. While his impact could be enhanced by deeper industry collaborations and global outreach, his current achievements already reflect substantial contributions to the field, making him deserving of recognition.