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

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🎓 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

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🎓 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

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📚 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

Prof. Hoonsoo Lee | Applications of Computer Vision | Best Researcher Award

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Hoonsoo Lee | Applications of Computer Vision | Best Researcher Award

Hoonsoo Lee at Chungbuk National University, South Korea

Profiles

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 Academic Background:

He is an Associate Professor in the Dept. of Biosystems Engineering at Chungbuk National University, located in Cheongju, Korea. The university is situated at 1 Chungdae-ro, BLDG# S21-24, RM# 202, Seowon-gu, Cheongju-si, Chungcheongbuk-do, 28644, Republic of Korea.

Education:

Prof. Lee earned his Ph.D. in Agricultural Machinery Engineering from Chungnam National University in August 2015, with a dissertation on the rapid detection of pathogenic infections in watermelon seeds using spectral image analysis. He completed his M.S. in the same field in August 2009, focusing on the development of an electronic nose system for evaluating meat freshness. He holds a B.S. in Bioindustrial Machinery Engineering, which he completed in August 2007.

 Employment History:

Prof. Lee has been an Associate Professor at Chungbuk National University since September 2018. Prior to this, he worked as a PostDoc Researcher at the United States Department of Agriculture (USDA) Agricultural Research Service (ARS) in Beltsville, MD, USA, from August 2015 to August 2018. His experience also includes serving as a Research Assistant at Chungnam National University from June 2008 to August 2015 and an internship at USDA, ARS from July 2010 to June 2011.

 Research Interests:

Prof. Lee’s research focuses on developing nondestructive sensing technology for agricultural and food products. He is also interested in data analysis using hyperspectral imaging in conjunction with machine learning and artificial intelligence techniques.

 Research Experience:

Prof. Lee specializes in non-destructive quality measurement of food and agricultural products using vibrational spectroscopic techniques. His work includes developing and commercializing a high-throughput online detection system utilizing optical techniques. He has created hyperspectral and multispectral imaging systems for pathogen-infected seeds and fecal contamination on leafy greens. Additionally, he has developed hyperspectral imaging systems to evaluate food quality, focusing on applications such as detecting physical damages in pears, identifying cracks in tomatoes, assessing color levels in pepper powder, and measuring moisture distribution in cooked meats, rice, and soybeans. Furthermore, he has created a multipurpose floating platform for hyperspectral imaging and monitoring E. coli concentrations in irrigation ponds in Maryland. His research also includes developing Vis/NIR hyperspectral models for assessing the effects of water and fertilizer on crops like cabbage, garlic, and soybeans, as well as laser speckle technology for diagnosing crop stress to enhance precision agriculture practices.

 Publications:

Current trends in the use of thermal imagery in assessing plant stresses: A review
  • Authors: Adhitama Putra Hernanda, R., Lee, H., Cho, J.-I., Cho, B.-K., Kim, M.S.
  • Journal: Computers and Electronics in Agriculture
  • Year: 2024
Chlorophyll Fluorescence Imaging for Environmental Stress Diagnosis in Crops
  • Authors: Park, B., Wi, S., Chung, H., Lee, H.
  • Journal: Sensors
  • Year: 2024
Construction of a sustainable model to predict the moisture content of porang powder (Amorphophallus oncophyllus) based on pointed-scan visible near-infrared spectroscopy
  • Authors: Amanah, H.Z., Rahayoe, S., Harmayani, E., Lee, H.
  • Journal: Open Agriculture
  • Year: 2024
Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits
  • Authors: Hernanda, R.A.P., Lee, J., Lee, H.
  • Journal: Applied Sciences (Switzerland)
  • Year: 2023
Snapshot-Based Multispectral Imaging for Heat Stress Detection in Southern-Type Garlic
  • Authors: Ryu, J., Wi, S., Lee, H.
  • Journal: Applied Sciences (Switzerland)
  • Year: 2023