Yanli Shi | Deep Learning for Computer Vision | Best Innovation Award

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Dr. Yanli Shi | Deep Learning for Computer Vision | Best Innovation Award

Jilin University of Chemical Technology | China

Dr. Yanli Shi is a researcher at the Jilin Institute of Chemical Technology, Jilin, China, with recognized contributions in image processing, computer vision, and intelligent information technologies. As a first author, Dr. Shi has published nearly 20 high-quality SCI and EI-indexed journal articles, including three papers in JCR Zone 1 journals, reflecting strong research impact and international visibility. According to Scopus, Dr. Shi’s work has received 160 citations, with an h-index of 7, demonstrating consistent scholarly influence.Dr. Shi has led and successfully completed several competitive research projects, including one project funded by the Jilin Provincial Natural Science Foundation, one project under the “13th Five-Year Plan” Science and Technology Program of the Jilin Provincial Department of Education, and one vertical project supported by the Jilin Municipal Science and Technology Bureau, which also included the Outstanding Young Talent Cultivation Program. These projects have advanced both fundamental research and applied technological development.With a strong emphasis on technology transfer and practical innovation, Dr. Shi holds one national invention patent and has actively translated research outcomes into industrial solutions. Through extensive collaboration, Dr. Shi has participated in over 100 horizontal projects with Inner Mongolia University and local enterprises, generating more than 1.6 million yuan in research funding. These collaborations have addressed real-world technical challenges and promoted regional industrial and technological development.Dr. Shi’s recent publications in leading journals such as Pattern Recognition and Scientific Reports further highlight expertise in fine-grained visual classification, deep learning, and image super-resolution. Overall, Dr. Shi’s work demonstrates a balanced integration of academic excellence, cross-sector collaboration, and measurable societal and economic impact.

Profile: Scopus 

Featured Publications

1.Shi, Y., et al. (2025). Multi-scale adversarial diffusion network for image super-resolution. Scientific Reports.  Cited By: 1

2.Shi, Y., et al. (2025). LDH-ViT: Fine-grained visual classification through local concealment and feature selection. Pattern Recognition. Cited By : 1

Dr. Yanli Shi research advances state-of-the-art computer vision and image intelligence technologies, bridging fundamental algorithms with real-world industrial applications. Through high-impact publications, patented innovations, and extensive university–industry collaborations, the work delivers scalable solutions to practical technical challenges. This integration of scientific excellence and technology transfer contributes meaningfully to societal development and global innovation.

Abu Hanzala | Deep Learning for Computer Vision | Research Excellence Award

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Mr. Abu Hanzala | Deep Learning for Computer Vision | Research Excellence Award

Research Assistant | Daffodil International University | Bangladesh

Mr. Abu Hanzala Daffodil International University, Dhaka, BangladeshHanzala, Abu is an emerging researcher specializing in artificial intelligence–driven medical image analysis, deep learning, and explainable healthcare systems. The researcher’s scholarly work focuses on developing robust hybrid and ensemble learning frameworks that integrate convolutional neural networks (CNNs), vision transformers (ViTs), graph neural networks (GNNs), transfer learning, self-supervised learning, and attention mechanisms for disease detection and classification.A key research achievement includes the publication of a peer-reviewed article in Array (2025) titled “A Hybrid Approach for Cervical Cancer Detection: Combining D-CNN, Transfer Learning, and Ensemble Models”, which demonstrates improved diagnostic accuracy using advanced ensemble strategies. In addition, the researcher has several manuscripts under peer review in high-impact international journals including Scientific Reports Neuroscience, IEEE Transactions on Medical Imaging, ACM Transactions on Computing for Healthcare, Discover Applied Science and Computers & Education: Artificial Intelligence. These studies address a wide range of clinically significant problems such as cervical, lung, and colorectal cancer, Alzheimer’s disease pneumonia neuromuscular disorders peripheral nerve disease and cerebral cortex pathology.The researcher has authored 5 scholarly documents receiving 5 citations, and currently holds an h-index of 2, reflecting a growing academic impact within the medical AI research community. International visibility is further strengthened through a peer-reviewed IEEE conference paper and an invited oral presentation at the 15th International Conference on Computing Communication and Networking Technologies (ICCCNT 2024).Research collaborations span multidisciplinary teams involving computer scientists biomedical engineers and healthcare researchers. The societal impact of this work lies in advancing early disease detection reliable clinical decision support and explainable AI models contributing to scalable trustworthy and globally relevant healthcare technologies.

Profiles: Scopus | ResearchGate

Featured Publication

1. Hanzala, A., Akter, T., & Rahman, M. S. (2025). A hybrid approach for cervical cancer detection: Combining D-CNN, transfer learning, and ensemble models. Cited By : 3

Mr. Abu Hanzala research advances global healthcare innovation by integrating reliable, explainable artificial intelligence with medical imaging to enable early disease detection and data-driven clinical decision support. This work bridges scientific rigor and real-world applicability, contributing to scalable, trustworthy AI solutions with meaningful societal and clinical impact.

Mohsen Edalat | Machine Learning for Computer Vision | Editorial Board Member

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Assoc. Prof. Dr. Mohsen Edalat | Machine Learning for Computer Vision | Editorial Board Member

Associate Professor | Shiraz University | Iran

Dr. Mohsen Edalat an accomplished researcher from Shiraz University, Iran, has made notable contributions to the fields of machine learning geospatial modeling and smart agriculture. With an impressive research record comprising 39 scientific publications and over 614 citations Dr. Edalat has demonstrated sustained academic productivity and influence in computational and environmental sciences. His research emphasizes the integration of advanced data-driven algorithms with ecological and agricultural systems to enhance sustainability and decision-making processes.Among his recent works Dr. Edalat has explored diverse applications of machine learning for ecological and agricultural optimization. His 2025 publications include studies on predicting nepetalactone accumulation in Nepeta persica through machine learning and geospatial analysis modeling ecological preferences of Kentucky bluegrass under varying water conditions (Water Switzerland)  and mapping early-season dominant weeds using UAV-based imagery to support precision farming. These investigations reflect his innovative approach to merging remote sensing artificial intelligence and environmental modeling to address complex agroecological challenges.With an h-index of 11 and collaborations with more than 60 co-authors  Dr. Edalat’s work highlights strong interdisciplinary engagement and a commitment to advancing data-driven sustainability. His studies contribute not only to the scientific community but also to practical agricultural applications that promote resource efficiency and ecological resilience. Through his ongoing research Dr. Edalat continues to shape the evolving landscape of smart agriculture and environmental informatics demonstrating the global relevance and societal value of computational intelligence in natural systems.

Profiles:  Scopus | ORCID

Featured Publications

1. Edalat, M., et al. (2025). Predicting nepetalactone accumulation in Nepeta persica using machine learning algorithms and geospatial analysis. Scientific Reports.

2. Edalat, M., et al. (2025). Modeling the ecological preferences and adaptive capacities of Kentucky bluegrass based on water availability using various machine learning algorithms. Water (Switzerland).

3. Edalat, M., et al. (2025). Early season dominant weed mapping in maize field using unmanned aerial vehicle (UAV) imagery: Towards developing prescription map. Smart Agricultural Technology.

Dr. Mohsen Edalat’s research integrates machine learning, geospatial analytics, and agricultural science to enhance crop management and environmental sustainability. His innovative work advances precision agriculture, supporting data-driven decisions that improve resource efficiency, boost food security, and promote sustainable development at a global scale.

Osman Yildirim | Deep Learning | Best Researcher Award

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Prof. Osman Yildirim | Deep Learning | Best Researcher Award

Head of the Department | Istanbul Aydın University | Turkey 

Prof. Osman Yildirim is a distinguished academic and researcher recognized for his contributions at the intersection of engineering, business, sustainability, and biomedical applications. He holds dual doctoral degrees in Engineering and Business Administration, a unique combination that has enabled him to approach research challenges with a strong interdisciplinary perspective. Over the course of his career, he has taken on significant academic leadership roles, including serving as Head of Department at Istanbul Aydin University, while also guiding doctoral students and fostering collaborative research projects. His professional experience spans teaching across engineering and business disciplines, coordinating research initiatives, and contributing to institutional development through mentorship and administrative leadership. His primary research interests focus on green transformation, sustainable supply chains, carbon policy impacts, energy management systems in universities, and AI-based medical imaging applications for improved diagnostics. These areas reflect his commitment to aligning research with both technological advancements and societal needs, particularly in the context of sustainable development and healthcare innovation. He has published widely in reputed Q1 and Q2 indexed journals such as Scopus and SCI, showcasing the impact of his work in both technical and applied fields. His achievements have been recognized through awards and honors that acknowledge his contributions to advancing interdisciplinary research and education. In addition, he has built valuable collaborations with international teams, integrating expertise from engineering, business, and medicine to deliver impactful solutions with global relevance. His research skills include expertise in machine learning, AI-driven image analysis, sustainable system design, and computational modeling for optimization under carbon constraints. These technical strengths, combined with his leadership and mentorship, position him as a leading scholar dedicated to advancing academic excellence and addressing global challenges through innovative and socially relevant research.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Ozturk, A. I., Yıldırım, O., İdman, E., & İdman, E. (2025). A comparative study of hybrid decision tree–deep learning models in the detection of intracranial arachnoid cysts. Neuroscience Informatics, 100234.

Ozturk, A. I., Yildirim, O., Kaygusuz, K., Idman, E., & Idman, E. (2025). Brain cyst detection using deep learning models. International Journal of Innovative Research and Scientific Studies, 8(5), 8974.

Borhan Elmi, M. M., & Yıldırım, O. (2025). Improve MPPT in organic photovoltaics with chaos-based nonlinear MPC. Balkan Journal of Electrical and Computer Engineering, 13(1), 1418574.

Ozturk, A. I., Yıldırım, O., & Deryahanoglu, O. (2025). A comprehensive strategy for the identification of arachnoid cysts in the brain utilizing image processing segmentation methods. International Journal of Innovative Technology and Exploring Engineering, 14(2), 1031.

Borhan Elmi, M. M., & Yıldırım, O. (2024). Improve LVRT capability of organic solar arrays by using chaos-based NMPC. International Journal of Energy Studies, 4(3), 1449558.

Yildirim, O., Khaustova, V. Y., & Ilyash, O. I. (2023). Reliability and validity adaptation of the hospital safety climate scale. The Problems of Economy, 4(1), 207–216.

Yildirim, O. (2023). Multidimensional and strategic outlook in digital business transformation: Human resource and management recommendations for performance improvement. In Book chapter.

Yildirim, O. (2023). Health professionals’ perspective in the context of social media, paranoia, and working autonomy during the COVID-19 pandemic period. Archives of Health Science Research, 10(1), 30–37.

Yildirim, O. (2023). The personified model for supply chain management. In Multidimensional and strategic outlook in digital business transformation: Human resource and management recommendations for performance improvement.

Yildirim, O., Ilyash, O. I., Khaustova, V. Y., & Celiksular, A. (2022). The effect of emotional intelligence and work-related strain on the employee’s organizational behavior factors. The Problems of Economy, 2(1), 124–131.

Yildirim, O. (2022). Investigation of the electrical conductivity of pernigranilin with carbon monoxide and nitrogen monoxide doping. Mathematical Statistician and Engineering Applications, 9(4).

Yildirim, O. (2022). Cyst segmentation using filtering technique in computed tomography abdominal kidney images. Mathematical Statistician and Engineering Applications, 9(4).

Yildirim, O. (2022). Design of flyback converter by obtaining the characteristics of polymer based R2R organic PV panels. International Journal of Renewable Energy Research, 12(4).

Avdullahi, A., & Yildirim, O. (2021). The mediating role of emotional stability between regulation of emotion and overwork. In Book chapter.

Tunç, P., Yıldırım, O., Göktepe, E. A., & Çapuk, S. (2021). Investigation of the relationship between personality, organizational identification and turnover in competitive flight model. TroyAcademy, 6(1), 894141.

Tunç, P., Yıldırım, O., Göktepe, E. A., & Çapuk, S. (2021). Investigation of the relationship between personality, organizational identification and turnover in competitive flight model. Çanakkale Onsekiz Mart Üniversitesi Uluslararası Sosyal Bilimler Dergisi, 4(1), 804959.

Dr. Zhou Zhang | Computer Vision | Best Researcher Award

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Dr. Zhou Zhang | Computer Vision | Best Researcher Award

Doctorate at SUNY Farmingdale State College, United States

👨‍🎓 Profiles

Scopus

Orcid

Google Scholar

🎓 Early Academic Pursuits

Dr. Zhou (Joe) Zhang embarked on his academic journey with a strong foundation in mechanical and electrical engineering. His early education culminated in a Ph.D. in Mechanical Engineering from Stevens Institute of Technology, where he was awarded the prestigious James Harry Potter Award in 2018 for outstanding doctoral performance. During his doctoral studies, Dr. Zhang explored virtual reality applications in engineering education, including camera pose tracking, data fusion, and the development of virtual laboratories—an area that would become a cornerstone of his future research.

🏫 Professional Endeavors

Dr. Zhang’s academic career is marked by progressive teaching and research roles. He currently serves as an Assistant Professor at SUNY Farmingdale State College, where he teaches Tool Design and Electronics Packaging. Previously, he held key positions including Associate Professor at Middle Tennessee State University and Assistant Professor at CUNY’s New York City College of Technology, where he played a central role in launching and coordinating the Robotics Concentration. His professional journey also includes roles as a Visiting Research Scholar at NYU, a Research Associate at Southeast University, and an Electrical Engineer at CRRC Nanjing Puzhen Co., Ltd, as well as a Mechanical Engineer at CETC’s 14th Research Institute.

🔬 Contributions and Research Focus

Dr. Zhang’s research bridges academic theory and practical implementation. His major contributions span virtual reality (VR) and augmented reality (AR) for engineering education, AI and machine learning applications in robotics, force-feedback robotics, and bio-inspired virtual assembly systems. His work has been funded by institutions such as CUNY GRTI and CUNY Research Awards, including notable projects like the AI and Machine Learning in Co-Robotics and the Virtual Assembly Platform for Engineering Education. Earlier in his career, he was also involved in state-funded research in China, including a $5 million smart controller project backed by the State Grid Corporation of China.

🌍 Impact and Influence

Dr. Zhang has made a tangible impact on student development, workforce readiness, and interdisciplinary education. His initiatives include establishing co-op and internship collaborations with industry, mentoring undergraduate research, and leading programs like the Virtual Reality and Artificial Intelligence Club. He also contributed to maintaining ABET accreditation, aligning curriculum development with institutional and industry standards. His mentorship has supported student participation in key events such as the Brooklyn Navy Yard Competition, Maker Faire, and the CUNY Black Male Initiative Conference.

📚 Academic Citations & Publications

Dr. Zhang’s scholarly work is extensively cited in the domains of VR-based education, 3D reconstruction, force-feedback robotics, and embedded systems. His contributions have not only advanced academic research but also enriched applied engineering education. As one of the main investigators in several NSF-funded projects, his research continues to influence both academic curricula and practical engineering tools.

💻 Technical Skills

Dr. Zhang is proficient in a variety of engineering and programming tools, including virtual reality system design, computer-aided engineering, middleware integration, finite element methods (FEM), and AI/machine learning applications in robotics. His skills encompass real-time 3D reconstruction, electromagnetic field simulation, and embedded systems design, with applications extending to DSP, ARM-based controls, and semiconductor converters.

🧑‍🏫 Teaching Experience

With over two decades of teaching experience, Dr. Zhang has taught a wide array of courses across institutions like SUNY Farmingdale, CUNY, NJIT, and Middle Tennessee State University. His teaching portfolio includes Mechanical Measurement, Stress Analysis, Rapid Prototyping, Programmable Logic Controllers, and AI-integrated robotics courses. He has served in diverse capacities—course designer, club advisor, curriculum developer, and research mentor—demonstrating his commitment to academic excellence and student engagement.

🏆 Awards and Honors

Dr. Zhang has received multiple accolades for his dedication to academic and research excellence. In addition to the James Harry Potter Award, he earned graduate travel grants from Stevens Institute of Technology, recognizing his contributions to engineering research and academic dissemination.

🚀 Legacy and Future Contributions

Dr. Zhang’s legacy lies in his ability to blend innovative research with effective teaching, transforming traditional mechanical engineering education through technology. His future goals include advancing interdisciplinary robotics education, expanding virtual learning platforms, and fostering global academic-industry collaborations. With a career devoted to bridging theoretical knowledge and real-world applications, Dr. Zhang continues to inspire students and colleagues alike, shaping the future of engineering education and technological innovation.

 

Publications

The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection

  • Authors: Momina Liaqat Ali, Zhou Zhang
    Journal: Computers
    Year: 2024

Project-Based Courses for B.Tech. Program of Robotics in Mechanical Engineering Technology

  • Authors: Zhang Z., Zhang A.S., Zhang M., Esche S.
    Journal:     Computers in Education Journal
    Year:     2020

A Virtual laboratory system with biometric authentication and remote proctoring based on facial recognition

  • Authors: Zhang, Z.; Zhang, M.; Chang, Y.; Esche, S.K.; Chassapis, C.
    Journal: Computers in Education
    Year: 2016

Real-time 3D reconstruction for facilitating the development of Game-based virtual laboratories

  • Authors: Zhang, Z.; Zhang, M.; Chang, Y.; Esche, S.K.; Chassapis, C.
    Journal:     Computers in Education
    Year:     2016

Usability evaluation of a virtual educational laboratory platform

  • Authors: Chang, Y.; Aziz, E.-S.S.; Zhang, Z.; Zhang, M.; Esche, S.K.
    Journal: Computers in Education
    Year: 2016

Dr. Recai Yilmaz | Applications of Computer Vision | Best Researcher Award

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Dr. Recai Yilmaz | Applications of Computer Vision | Best Researcher Award

Doctorate at Children’s National Hospital, Washington, D.C, United States

👨‍🎓 Profiles

Scopus

Orcid

Google Scholar

🎓 Early Academic Pursuits

Dr. Recai Yilmaz’s academic journey began with a strong foundation in medicine, earning his M.D. from Istanbul Faculty of Medicine in 2017. His passion for medical innovation led him to pursue a Ph.D. in Experimental Surgery at McGill University, focusing on neurosurgical simulation and artificial intelligence. His early education at Private Anafen Gaye High School in Istanbul, where he was a full-scholarship student, demonstrated his academic excellence from a young age.

💼 Professional Endeavors

Dr. Yilmaz has amassed extensive experience at the intersection of medicine, artificial intelligence, and computer vision. As a Postdoctoral Research Fellow at Children’s National Medical Center, Washington, D.C., he applies computer vision and machine learning to intraoperative surgical video analysis, aiming to improve real-time surgical performance assessment. His tenure at MultiCIM Technologies Inc. (CareChain) further reflects his leadership in integrating AI into patient triage and clinical decision-making systems.

🔬 Contributions and Research Focus

Dr. Yilmaz’s research is centered on AI-driven surgical assessment, medical data organization, and neurosurgical simulation. At McGill University’s Neurosurgical Simulation and Artificial Intelligence Learning Centre, he developed virtual reality surgical simulation models, advanced AI-based assessment tools, and real-time feedback mechanisms for neurosurgical expertise evaluation. His research also includes cloud-based medical data management and optical flow analysis in surgical procedures.

🌍 Impact and Influence

His pioneering work has significantly influenced AI applications in surgery and clinical decision-making. By integrating computer vision and deep learning into medical practice, he has improved the efficiency and accuracy of surgical skill evaluation, patient triage, and clinical outcome prediction. His projects have not only enhanced surgical education but also contributed to safer and more effective surgical procedures worldwide.

📚 Academic Citations and Recognitions

Dr. Yilmaz has been recognized with numerous awards and grants, including the prestigious Innovator of the Year Award (2023) by the Congress of Neurological Surgeons and research funding from the Brain Tumour Foundation of Canada and the Royal College of Physicians and Surgeons of Canada. His work has been published in high-impact journals and conferences, advancing the field of AI in medicine.

💻 Technical Expertise

  • Artificial Intelligence & Machine Learning (Medical AI applications, Neural Networks)
  • Computer Vision & Image Processing (Surgical video analysis, Optical flow)
  • Programming Languages (Python, MATLAB, C++, IBM SPSS)
  • Statistical Analysis & Data Science (AI-driven performance assessment, Data modeling)

🎓 Teaching and Mentorship

Dr. Yilmaz has actively mentored graduate students, medical researchers, and undergraduate students in AI, neurosurgical simulation, and data analysis. His mentorship spans institutions such as McGill University and Marianopolis College, where he has guided students in machine learning applications, research methodologies, and clinical AI integration.

🌟 Legacy and Future Contributions

Dr. Yilmaz’s legacy lies in his commitment to bridging AI and medicine. His contributions to surgical performance evaluation, AI-driven triage systems, and neurosurgical education continue to shape the future of AI-assisted medical practice. Moving forward, he aims to expand AI integration in real-time surgical decision-making, enhance global accessibility to AI-driven surgical training, and pioneer intelligent healthcare solutions.

 

Publications

AI in surgical curriculum design and unintended outcomes for technical competencies in simulation training

  • Authors:Ali M Fazlollahi, Recai Yilmaz, Alexander Winkler-Schwartz, Nykan Mirchi, Nicole Ledwos, Mohamad Bakhaidar, Ahmad Alsayegh, Rolando F Del Maestro
  • Journal: JAMA network open
  • Year: 2023

Utilizing artificial intelligence and electroencephalography to assess expertise on a simulated neurosurgical task

  • Authors: Sharif Natheir, Sommer Christie, Recai Yilmaz, Alexander Winkler-Schwartz, Khalid Bajunaid, Abdulrahman J Sabbagh, Penny Werthner, Jawad Fares, Hamed Azarnoush, Rolando Del Maestro
  • Journal: Computers in Biology and Medicine
  • Year: 2023

O022 real-time artificial intelligence instructor vs expert instruction in teaching of expert level tumour resection skills–a randomized controlled trial

  • Authors: R Yilmaz, M Bakhaidar, A Alsayegh, R Del Maestro
  • Journal: British Journal of Surgery
  • Year: 2023

Effect of artificial intelligence tutoring vs expert instruction on learning simulated surgical skills among medical students: a randomized clinical trial

  • Authors: Ali M Fazlollahi, Mohamad Bakhaidar, Ahmad Alsayegh, Recai Yilmaz, Alexander Winkler-Schwartz, Nykan Mirchi, Ian Langleben, Nicole Ledwos, Abdulrahman J Sabbagh, Khalid Bajunaid, Jason M Harley, Rolando F Del Maestro
  • Journal: JAMA network open
  • Year: 2022

Assessment of learning curves on a simulated neurosurgical task using metrics selected by artificial intelligence

  • Authors: Nicole Ledwos, Nykan Mirchi, Recai Yilmaz, Alexander Winkler-Schwartz, Anika Sawni, Ali M Fazlollahi, Vincent Bissonnette, Khalid Bajunaid, Abdulrahman J Sabbagh, Rolando F Del Maestro
  • Journal: Journal of neurosurgery
  • Year: 2022

Ms. Joy Shen | Applications of Computer Vision | Best Researcher Award

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Ms. Joy Shen | Applications of Computer Vision | Best Researcher Award

Joy Shen at University of Maryland at College Park, United States

Profiles

Scopus

Orcid

📚 Summary

Ms. Joy Shen is a Ph.D. candidate in Reliability Engineering at the University of Maryland, with expertise in probabilistic risk assessments (PRA) and reliability analysis, particularly for nuclear power systems. She currently works as a Reliability Engineer at NIST, where she develops Bayesian networks and conducts risk-based analyses to improve safety and operational efficiency in nuclear reactors.

Education

  • Ph.D. in Reliability Engineering (Expected May 2025), University of Maryland
  • MSc. in Reliability Engineering (May 2023), University of Maryland
  • B.Sc. in Mechanical Engineering with Nuclear Engineering Minor (Aug 2018), University of Maryland

👩‍🏫 Work Experience

  • Reliability Engineer | NIST, Gaithersburg, MD (Feb 2024 – Present)
    Developed Bayesian network models for safety-related systems in NIST’s research reactor. Analyzed degradation and assisted with relicensing efforts for long-term operations.
  • Mechanical Engineer | NIST, Gaithersburg, MD (Aug 2019 – Aug 2023)
    Performed CFD analysis and developed CAD models for the design of a new neutron source.

🔬 Research Experience

  • Graduate Research Assistant | University of Maryland, College Park, MD
    Conducted pioneering research in external flood PRAs using Monte Carlo augmented Bayesian networks. Investigated nuclear power plant risks and contributed to the U.S. NRC’s efforts to assess system vulnerabilities during external floods.
  • Associate Researcher | NIST, Gaithersburg, MD
    Conducted neutron energy spectrum measurements, proving the viability of Cf-250 as a calibration source for radiation instrumentation.

🛠 Skills and Certifications

  • CAD: Solidworks, Autodesk Inventor
  • Programming: MATLAB, Python, IGOR
  • Accident Analysis: MCNP, RELAP5, TRACE, SNAP
  • Probabilistic Tools: SAPHIRE, GeNIE, Minitab
  • Certifications: 10 CFR 50.59 Training, Procedure Professionals Association (PPA)

🔍 Research Interests

Ms. Joy’s research interests focus on probabilistic risk assessments (PRA), Bayesian networks, nuclear reactor safety, and external flood risk assessments. She is passionate about enhancing the reliability and safety of critical infrastructure through advanced analytical models.

 

Publications

A Monte Carlo augmented Bayesian network approach for external flood PRAs

  • Authors: Shen, J., Bensi, M., Modarres, M.
  • Year: 2025

A Hybrid, Bayesian Network-Based PRA Methodology for External Flood Probabilistic Risk Assessments at Nuclear Power Plants

  • Authors: Shen, J., Frantzis, C., Marandi, S., Bensi, M., Modarres, M.
  • Year: 2023

Synthesis of Insights Regarding Current PRA Technologies for Risk-Informed Decision Making

  • Authors: Shen, J., Marandi, S., Bensi, M., Modarres, M.
  • Year: 2023