Prof. Zhang Wenli | Computer Vision | Excellence in Research Award

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Prof. Zhang Wenli | Computer Vision | Excellence in Research Award 

Prof. Zhang Wenli | Computer Vision | Beijing University of Technology | China

Dr. Wenli Zhang is a distinguished scholar and innovative technology leader currently serving as a Professor in the Faculty of Information Technology at Beijing University of Technology, recognized for impactful contributions in signal and information processing, artificial intelligence, computer vision, 3D point cloud processing, unmanned aerial vehicle inspection technology, and brain-computer interfaces, positioning Dr. Wenli Zhang as a key figure advancing intelligent sensing and human-machine interaction research in China and globally. Building a strong academic foundation through advanced studies in computer science and informatics in both China and Japan, Dr. Wenli Zhang earned a Ph.D. in Engineering from the University of Tokyo, where a passion for applied research and innovation in intelligent systems was further strengthened. Prior to joining academia in China, Dr. Wenli Zhang developed extensive industrial innovation experience as Chief Researcher at Panasonic Corporation’s Tokyo Research Institute, driving real-world AI and vision-based solutions for next-generation automated applications. In her current role, Dr. Wenli Zhang leads interdisciplinary research that spans multiple sectors including smart agriculture, UAV-based intelligent inspection, and medical rehabilitation, effectively bridging fundamental theories with emerging societal needs and technological transformation. With strong collaboration networks and a commitment to promoting scientific excellence, Dr. Wenli Zhang serves actively in influential professional roles, including council member of the Beijing Interdisciplinary Science Society and committee member of the Innovation Engineering Branch of China Creative Studies Institute, contributing leadership within China’s innovation and engineering communities. Skilled in advanced algorithm development, intelligent visual perception, sensor network data fusion, and neural signal decoding, Dr. Wenli Zhang empowers her research team to develop practical systems that enhance automation, sustainability, and accessibility across industries. Her exceptional commitment to teaching and mentorship has earned her the prestigious “Distinguished Teacher” recognition at Beijing University of Technology, reflecting her dual dedication to academic excellence and student success.

Professional Profiles: ORCID  

Selected Publications:

  • Jiang, K., Guo, W., & Zhang, W. (2025). Amodal Segmentation and Trait Extraction of On-Branch Soybean Pods with a Synthetic Dual-Mask Dataset. Sensors.

  • Zhang, W., Peng, X., Bai, T., Wang, H., Takata, D., & Guo, W. (2024). A UAV-Based Single-Lens Stereoscopic Photography Method for Phenotyping the Architecture Traits of Orchard Trees. Remote Sensing.

  • Zhang, W., Peng, X., Cui, G., Wang, H., Takata, D., & Guo, W. (2023). Tree Branch Skeleton Extraction from Drone-Based Photogrammetric Point Cloud. Drones.

  • Li, Y., Liu, B., & Zhang, W. (2024). Driving-Related Cognitive Abilities Prediction Based on Transformer’s Multimodal Fusion Framework. Sensors.

  • Pang, G., Liu, B., & Zhang, W. (2025). Cloud Rehabilitation System Based on Automatic sEMG Signal Processing. Book Chapter.

  • Zhai, R., Gao, Y., Li, G., Ding, Q., Zhang, Y., & Zhang, W. (2025). Control System for Rehabilitation Bionic Hand Based on Precise Control Algorithms.

  • Wang, Y., Pang, G., Liu, B., Li, Y., & Zhang, W. (2025). Gesture Recognition Method Based on Hybrid Classifier Under Non-ideal Conditions.

Prof. Dr. Len Gelman | Monitoring | Best Researcher Award

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Prof. Dr. Len Gelman | Monitoring | Best Researcher Award 

Prof. Dr. Len Gelman, The University of Huddersfield, United Kingdom

Professor Len Gelman is a distinguished academic and researcher in the fields of Signal Processing, Condition Monitoring, and Maintenance. He holds a PhD and Doctor of Science (Habilitation) degrees and is a Fellow of several prestigious institutions, including the British Institute of Non-Destructive Testing (BINDT), IAENG, IDE, and HEA. Since 2017, Professor Gelman has served as the Professor and Chair in Signal Processing and Condition Monitoring/Maintenance at the University of Huddersfield, where he is also the Director of the Maintenance Centre for Efficiency and Performance Engineering. Prior to this, he was a Professor at Cranfield University (2002-2017), where he established a leading research programme in vibro-acoustical condition monitoring. Professor Gelman has received numerous accolades, including the UK Rolls-Royce Innovation Award (2019), the COMADIT Prize (2017), and the Best Paper Award at the International Condition Monitoring/Maintenance Conference (2016 and 2013). With extensive experience in both academia and industry, he has developed pioneering technologies for damage detection in turbines and aircraft engines, with significant contributions to Rolls-Royce, Dresser-Rand, and Scottish Southern Energy. Professor Gelman has built strategic international partnerships with top universities and research centres across the globe, including institutions in China, Korea, the USA, and Europe. He has supervised numerous postdoctoral fellows and researchers and is renowned for his leadership in vibro-acoustical condition monitoring, a field in which he has secured over £7.3M in research grants.

Professional Profile:

SCOPUS

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award

Professor Len Gelman is an outstanding researcher whose extensive contributions to signal processing, condition monitoring, and maintenance engineering position him as a leading figure in his field, making him an ideal candidate for the Best Researcher Award. His innovative work has consistently benefited both industry and society, earning him significant recognition and awards.

Education 🎓

  • BSc (Hons), MSc (Hons) in Signal Processing and Condition Monitoring/Maintenance

  • PhD, Doctor of Science (Habilitation) in Vibro-Acoustical Monitoring/Maintenance

Work Experience 💼

  • 2017-present
    Professor and Chair in Signal Processing and Condition Monitoring/Maintenance
    Director of the Maintenance Centre for Efficiency and Performance Engineering
    University of Huddersfield, UK

  • 2002-2017
    Professor and Chair in Vibro-Acoustical Monitoring/Maintenance
    Cranfield University, UK

Achievements 🏆

  • Led research in condition monitoring and maintenance for complex systems.

  • Built the novel “Vibro-acoustical condition monitoring of complex mechanical systems” research program at Cranfield University.

  • Recruited over 90 MSc students from various international universities for MSc studies at Cranfield.

  • Successfully gained £7.3M in research grants for research projects involving leading companies like Rolls-Royce, Caterpillar, and Shell.

  • Established strategic international partnerships with world-class universities and research centres around the globe. Monitoring

Awards and Honors 🥇

  • UK Rolls-Royce Innovation Award (2019)

  • COMADIT Prize for significant contributions to condition monitoring/maintenance (2017)

  • Rolls-Royce Engineering Award for Innovation (2012)

  • EC Fellowship Award (2015) – European Social Fund-Human Capital Operational Programme

  • Oxford Academic Health Science Network Award (2014)

  • Best Paper Award at CM/MFPT 2016 and CM/MFPT 2013

  • William Sweet Smith Prize from the UK Institution of Mechanical Engineers (2010)

  • USA Navy Award for helicopter fault diagnosis methodologies (1998)

  • Acoustical Society of America Award (1998)

Professional Recognition 🌟

  • Chairman of several international committees, including:

    • International Institute of Acoustics and Vibration (USA) (2014-2016)

    • International Society for Condition Monitoring/Maintenance (2011-2017)

    • European Federation of NDT (2014-present)

  • Editorial Board Member for renowned journals:

    • “Insight” NDT and Condition Monitoring

    • “Electronics” (MDPI)

    • “Energies” (MDPI)

    • “Prognostics and Health Management”

    • IEEE Fellow (Recognized as a leading professional in the field)

Publication Top Notes:

Novel Investigation of Influence of Torsional Load on Unbalance Fault Indicators for Induction Motors

Vibration analysis of rotating porous functionally graded material beams using exact formulation

Novel instantaneous wavelet bicoherence for vibration fault detection in gear systems

Novel prediction of diagnosis effectiveness for adaptation of the spectral kurtosis technology to varying operating conditions

Vibration health monitoring of rolling bearings under variable speed conditions by novel demodulation technique

Novel fault identification for electromechanical systems via spectral technique and electrical data processing

Novel method for vibration sensor-based instantaneous defect frequency estimation for rolling bearings under non-stationary conditions

Novel higher-order spectral cross-correlation technologies for vibration sensor-based diagnosis of gearboxes

Novel vibration structural health monitoring technology for deep foundation piles by non-stationary higher order frequency response function

 

Prof. Mehdi Behzad | Monitoring | Lifetime achievement Award

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Prof. Mehdi Behzad | Monitoring | Lifetime achievement Award 

Prof. Mehdi Behzad, Sharif University of Technology, Iran

Professor Mehdi Behzad is a distinguished academic and expert in mechanical engineering at the Sharif University of Technology, Tehran, Iran. He earned his Ph.D. from the University of New South Wales, Australia, in 1995, with a specialization in rotor dynamics and coupled vibrations. With over three decades of academic and industrial experience, Professor Behzad has led pioneering research in vibration analysis, condition monitoring, and fault diagnostics of rotating machinery. He has supervised more than 90 M.Sc. and 11 Ph.D. theses, contributed extensively to national industrial projects, and developed intelligent software solutions for signal processing and machinery health assessment. His professional service includes chairing major national conferences on condition monitoring and maintenance, as well as delivering keynote lectures at international forums such as the CM2024 in Oxford, UK. Professor Behzad’s contributions span academic teaching, applied research, and industrial consultancy, making him a leading figure in the field of vibration analysis and mechanical systems diagnostics.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for Lifetime Achievement Award

Prof. Mehdi Behzad is a distinguished academic and industry expert whose lifelong dedication to mechanical engineering, particularly in the field of vibration analysis and rotor dynamics, exemplifies the qualities honored by the Lifetime Achievement Award. His career spans over three decades of impactful teaching, groundbreaking research, industrial collaboration, and academic leadership.

👨‍🎓 Education

📍 Ph.D. in Mechanical Engineering
University of New South Wales, Sydney, Australia – May 1995

  • 🌀 Thesis: Transfer matrix analysis of rotor systems with coupled lateral and torsional vibrations

  • 🧮 Courses: Finite elements, vibration, frequency analysis, lubrication

  • 🧑‍💻 Developed vibration analysis software using Riccati transfer matrix

  • 📄 Published 3 papers on rotor dynamics

📍 M.Sc. in Mechanical Engineering
Sharif University of Technology, Tehran, Iran – May 1989

  • 📘 Thesis: Transfer Function and stability of electrohydraulic servo systems

  • 🧪 Repaired an electrohydraulic servo system for experiments

  • 📚 Advanced studies in control, dynamics, nonlinear vibration

📍 B.Sc. in Mechanical Engineering
Isfahan University of Technology, Iran – Feb 1986

  • 🔧 Broad mechanical engineering training including dynamics, turbomachinery, heat transfer

🧑‍🏫 Academic & Teaching Experience

📍 Professor – Sharif University of Technology (1994–2025)

  • 👨‍🔬 Supervised 90+ M.Sc. and 11 Ph.D. theses

  • 📘 Taught undergrad & grad courses in vibration, rotor dynamics, control, mathematics

  • 🛠 Developed curricula & practical labs

  • 🧑‍🏭 Founded training centers, oversaw solid mechanics lab & naval division

  • 📜 Organized nationwide Condition Monitoring & Fault Diagnosis conference (2007–2024)

🧪 Research & Industrial Experience

📍 University of New South Wales (1990–1995)

  • 📊 Built and used data acquisition systems

  • 🔁 Solved numerical issues in transfer matrix methods

  • 📝 Wrote reports for Sydney Electricity & Pacific Power

📍 Sazeh Consultant, Tehran (1988–1990)

  • 🛠 Vibration analysis for industrial structures

  • 🧾 Created guidelines for thermal stress, piping design, and actuator testing

📍 Industrial Consultant (1996–2024)

  • 🏭 Completed 50+ major vibration and condition monitoring projects

  • 🔍 Diagnosed faults in turbines, compressors, cement mills, pumps, and more

  • 🖥 Developed intelligent diagnostic software

  • 🌊 Assessed vibration in hydropower & petrochemical plants

  • 🚂 Involved in projects with railways, powerplants, and petrochemical complexes

🏆 Achievements, Awards & Honors

🎤 Keynote & Invited Speaker

  • 📍 20th International Conference on Condition Monitoring and Asset Management (CM2024), Oxford, UK

    • 🗣 “Challenges in Condition Monitoring”

    • 🎙 “Vibration Features for Machinery Condition Monitoring”

🏅 Leadership Roles

  • 🎖 Chairman of Iran Maintenance Association (2007–2012)

  • 🧩 Research Deputy, Sharif University – Mechanical Eng. Dept.

  • 🎓 Director, University Center for Training (since 2010)

📘 Curriculum Innovator & Educator

  • 🛠 Founded and led numerous industrial courses & workshops on:

    • Vibration Analysis Levels 1 & 2

    • Rotor Dynamics

    • API 687 Repair Technologies

    • Reliability Centered Maintenance

    • Shaft Alignment

Publication Top Notes:

CITED:219
CITED:118
CITED:103
CITED:73
CITED:66
CITED:65

Dr. Aamir Saghir | Surveillance | Best Researcher Award

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Dr. Aamir Saghir | Surveillance | Best Researcher Award

Dr. Aamir Saghir, Budapest University of Technology and Economics, Hungary

Dr. Aamir Saghir is an Associate Professor of Statistics in the Department of Statistics at Mirpur University of Science and Technology (MUST), Mirpur, Pakistan. He earned his Ph.D. in Statistics from Zhejiang University, Hangzhou, China, where his research focused on flexible and robust control charts for statistical process monitoring. Dr. Saghir has a strong academic background, having also completed his M.Phil. in Statistics from Quaid-e-Azam University, Islamabad, Pakistan, where he conducted research on Bayesian and classical approaches to monitoring process parameters. His professional career includes roles as a lecturer and assistant professor at various institutions, including MUST and the University of AJK. Dr. Saghir has contributed significantly to academia, having supervised numerous research projects, including a socio-economic survey and several research grants on tuberculosis treatment outcomes. His research interests primarily lie in statistical quality control, data analysis, probability models, and the application of machine learning methods in anomaly detection. He is a member of several professional boards and has participated in various training and professional development workshops. Dr. Saghir has also contributed to numerous professional events and conferences, further solidifying his reputation as an expert in his field.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Dr. Aamir Saghir, currently serving as an Associate Professor of Statistics at Mirpur University of Science and Technology (MUST), stands out as a seasoned academician and researcher with a distinguished career in statistical quality control, data analysis, and probabilistic modeling. His profile reflects a strong commitment to scholarly excellence, leadership, and applied research with real-world impact.

🎓 Education

  • Ph.D. in Statistics (2011–2014)
    🏛️ Zhejiang University, Hangzhou, China
    🎯 Thesis: Flexible and Robust Control Charts for Statistical Process Monitoring
    👨‍🏫 Supervisor: Prof. Zhengyan Lin

  • M.Phil. in Statistics (2006–2008)
    🏛️ Quaid-i-Azam University, Islamabad, Pakistan
    🎯 Thesis: Bayesian and Classical Approaches to Monitor Process Parameters
    👨‍🏫 Supervisor: Prof. Dr. Muhammad Aslam

  • M.Sc. in Statistics (2004–2006)
    🏛️ Quaid-i-Azam University, Islamabad, Pakistan
    🥇 First Position
    🎯 Thesis: Some New Approaches to Process Monitoring
    👨‍🏫 Supervisor: Mr. Muhammad Riaz

  • B.Sc. in Mathematics A & B / Statistics (2001–2003)
    🏛️ University of AJK, Muzaffarabad, Pakistan

💼 Work Experience

🧑‍🏫 Academic Positions

  • Associate Professor of Statistics
    📍 Mirpur University of Science and Technology (MUST), AJK, Pakistan
    ⏳ Since Aug 2017 – Present

  • Assistant Professor of Statistics
    📍 MUST, AJK
    ⏳ Jul 2014 – Aug 2017

  • Lecturer in Statistics
    📍 MUST, AJK
    ⏳ Nov 2010 – Jul 2014

  • Lecturer in Statistics
    📍 University of AJK, Muzaffarabad
    ⏳ Oct 2006 – Nov 2010

🧪 Research Positions

  • Research Associate
    🏛️ Budapest University of Technology and Economics, Hungary
    ⏳ Sep 2024 – Present

  • Postdoc Research Fellow
    🏛️ University of Pannonia, Veszprem, Hungary
    ⏳ Jan 2022 – Jan 2023

🏢 Administrative Roles

  • 📚 Chief Librarian, Ali Ahmad Shah Library (2023–2024)

  • 🎓 Founder Chairperson, Department of Statistics (2020–2022)

  • 💰 Treasurer/Chief Financial Officer (2019–2020)

  • 📝 Additional Controller (2017–2018)

  • 🗂 Assistant Director AS&RB (2015–2017)

🏆 Achievements & Awards

  • 🥇 First Position in M.Sc. Statistics

  • 🎓 China Scholarship Council (CSC) Award for Ph.D.

  • 📜 Distinguished Certificate in Ph.D. from Zhejiang University

  • ✅ HEC Approved Ph.D. Supervisor

  • 📊 Survey Supervisor: Socio-Economic Impact on Telecommunication (2008)

  • 👨‍🏫 Board Member:

    • Dept. of Statistics, University of Hazara

    • Dept. of Statistics, MUST

💰 Research Grants

  • 🧫 Treatment Outcomes in TB Patients (2015–16)
    🏛️ Funded by ORIC MUST – 💵 PKR 0.25 Million – ✅ Completed (2017)

  • 📈 Phase II Control Charts for Monitoring Dispersion Based on False Alarm Probability
    📌 Status not specified

Publication Top Notes:

EWMA control chart framework for efficient Maxwell quality characteristic monitoring: An application to the aerospace industry

Explainable Transformer-Based Anomaly Detection for Internet of Things Security

Pareto Distribution-Based Shewhart Control Chart for Early Detection of Process Mean Shifts

Adaptive EWMA control charts for the Rayleigh distribution

A probability distribution for precipitation data analysis

 

Prof. Dr. Weidong Jiao | Smart Detection | Best Researcher Award

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Prof. Dr. Weidong Jiao | Smart Detection | Best Researcher Award 

Prof. Dr. Weidong Jiao, Zhejiang Normal University, China

Dr. Weidong Jiao was born in Wafangdian, Liaoning, China, in 1970. He received his B.E. and M.E. degrees in Safety Engineering and Mechanical Engineering from Gansu University of Technology in 1992 and 2001, respectively, and earned his Ph.D. in Mechanical Engineering from Zhejiang University in 2004. From 2004 to 2009, he served as a Professor in the Mechanical Engineering Department at Jiaxing University. Since 2013, he has been a Professor at the School of Engineering, Zhejiang Normal University. Dr. Jiao has authored over 100 research articles and holds approximately 20 invention patents. His research focuses on smart testing and signal processing, mechanical dynamics, and condition monitoring and fault diagnosis of mechanical equipment. He also serves as an Editor for the Journal of Vibration, Measurement & Diagnosis.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award – Prof. Weidong Jiao

Prof. Weidong Jiao is a highly qualified candidate for the Best Researcher Award, based on his extensive contributions to mechanical engineering, fault diagnosis, and intelligent signal processing. His strong research background, innovative work, and leadership in academia make him a worthy contender for this prestigious recognition.

🎓 Education:

  • B.E. in Safety Engineering – Gansu University of Technology, Lanzhou (1992)
  • M.E. in Mechanical Engineering – Gansu University of Technology, Lanzhou (2001)
  • Ph.D. in Mechanical Engineering – Zhejiang University, Hangzhou (2004)

💼 Work Experience:

  • Professor, Mechanical Engineering Department, Jiaxing University (2004–2009)
  • Professor, School of Engineering, Zhejiang Normal University (Since 2013)

🏆 Achievements & Contributions:

  • 📚 Published over 100 research articles
  • 🔬 Invented approximately 20 innovations
  • 🛠️ Expertise in smart testing, signal processing, mechanical dynamics, condition monitoring, and fault diagnosis
  • 📝 Editor of Journal of Vibration, Measurement & Diagnosis

🏅 Awards & Honors:

  • 🎖️ Recognized for contributions in mechanical engineering and diagnostics
  • 🏅 Honored for advancements in fault diagnosis and condition monitoring
  • 🔍 Acknowledged for outstanding research and academic contributions in mechanical dynamics

Publication Top Notes:

Compact multiphysics coupling modeling and analysis of self-excited vibration in maglev trains

Deep learning in industrial machinery: A critical review of bearing fault classification methods

Recursive prototypical network with coordinate attention: A model for few-shot cross-condition bearing fault diagnosis

Double attention-guided tree-inspired grade decision network: A method for bearing fault diagnosis of unbalanced samples under strong noise conditions

Cross-Conditions Fault Diagnosis of Rolling Bearing Based on Transitional Domain Adversarial Network

Dr. Rebecca Robbins | Monitoring Awards | Best Researcher Award

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Dr. Rebecca Robbins | Monitoring Awards | Best Researcher Award

Dr. Rebecca Robbins, Harvard Medical School, Brigham and Women’s Hospital, United States

Dr. Rebecca Robbins is an esteemed Assistant Professor at Harvard Medical School and the T.H. Chan School of Public Health, with a notable focus on sleep, circadian disorders, and health communication. She earned her BS in Hotel Administration with a concentration in Business and Finance from Cornell University in 2009, followed by an MS and PhD in Health Communication and Health Marketing from the same institution. In 2022, she completed a second MSc in Medical Sciences from Harvard Medical School. Dr. Robbins has completed extensive postdoctoral training, including research in population health at NYU School of Medicine and sleep and respiratory neurobiology at Brigham and Women’s Hospital, Boston.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Dr. Rebecca Robbins is an excellent candidate for the Best Researcher Award at Harvard Medical School, given her remarkable career trajectory, expertise in sleep research, and significant contributions to public health.

Education:

  • 2009: BS in Hotel Administration, Business & Finance, Cornell University, Ithaca, NY
  • 2013: MS in Health Communication & Health Marketing, Cornell University
  • 2015: PhD in Health Communication & Health Marketing, Cornell University (advised by Drs. Jeff Niederdeppe, Brian Wansink, James Maas, and Sahara Byrne)
  • 2022: MSc in Medical Sciences (Clinical Investigation), Harvard Medical School, Boston, MA

Postdoctoral Training:

  • 05/2015 – 08/2019: Postdoctoral Fellow, Population Health & Health Disparities Research, NYU School of Medicine, New York, NY (mentored by Dr. Girardin Jean-Louis)
  • 08/2019 – 10/2020: Postdoctoral Research Fellow, Sleep, Circadian and Respiratory Neurobiology, Brigham and Women’s Hospital (BWH), Boston, MA & Harvard Medical School (HMS), Boston, MA (mentored by Dr. Charles Czeisler)

Faculty Academic Appointments:

  • 11/2020 – Present: Instructor of Medicine, Harvard Medical School
  • 03/2023 – Present: Assistant Professor of Medicine, Harvard Medical School
  • 01/2024 – Present: Assistant Professor at the T.H. Chan School of Public Health, Harvard University

Appointments at Hospitals:

  • 11/2020 – Present: Associate Scientist, Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital (BWH)

Publication top Notes:

Estimating Community Disruption from Nighttime Gunshots in 6 U.S. Cities, 2015 to 2021

Exploring sleep difficulties, alcohol, illicit drugs, and suicidal ideation among adolescents with a history of depression

Author Response: Sleep Apnea and Incident Stroke in a National Cohort of Black and White Adults

Accuracy of Three Commercial Wearable Devices for Sleep Tracking in Healthy Adults

Pioneering a multi-phase framework to harmonize self-reported sleep data across cohorts