Dr. Yongho Jeong | Computer vision | Best Researcher Award

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Dr. Yongho Jeong | Computer vision | Best Researcher Award 

Dr. Yongho Jeong, Konkuk University, South Korea

Dr. Yongho Jeong is a physicist specializing in experimental particle physics, high-energy physics, and AI-driven data analysis. He earned his Ph.D. in Physics from Sungkyunkwan University, Korea, in 2022 under the supervision of Prof. YongIl Choi, focusing on the search for R-parity violating supersymmetry in proton-proton collisions at √s = 13 TeV using the CMS detector. He completed his B.S. in Physics from Soonchunhyang University in 2013. Dr. Jeong has held multiple postdoctoral positions, including at the University of Seoul, where he worked on Gas Electron Multiplier (GEM) detector aging tests, and at the Korea Astronomy and Space Science Institute (KASI), where he contributed to quantum noise reduction for future gravitational wave detectors. He has also been involved in AI software development at Mustree Company and is set to join Konkuk University as a postdoctoral researcher in 2024, focusing on 3D point cloud data analysis. His extensive research experience includes collaborations with CERN on the GEM Detector Upgrade Project for the CMS experiment, where he worked on quality control, chamber assembly, and detector performance studies. His expertise spans detector development, high-energy physics simulations, data analysis for supersymmetry searches, and advanced AI applications in physics.

Professional Profile:

ORCID

Summary of Suitability for Community Impact Award 

Dr. Yongho Jeong is a highly deserving candidate for the Community Impact Award, given his significant contributions to experimental particle physics, AI-driven technological advancements, and quantum noise reduction for future scientific applications. His extensive research collaborations, contributions to international projects, and involvement in technology-driven community advancements make him a strong nominee for this award.

📚 Education

  • Ph.D. in Physics (2014.09 – 2022.02) – Sungkyunkwan University, Suwon, Korea
    • Supervisor: YongIl Choi
    • Thesis: Search for R-parity violating supersymmetry in pp collisions at √s = 13 TeV in the CMS detector
  • B.S. in Physics (2007.03 – 2013.02) – Soonchunhyang University, Asan, Korea
    • Thesis: The Age of the Universe

💼 Work Experience

  • Postdoctoral Researcher – KonKuk University (KU), Seoul, Korea (2024.09 – Present)

    • Integrated Analytical Models and Advanced Strategies for 3D Point Cloud Data Analysis

  • Postdoctoral Researcher – University of Seoul (UOS), Seoul, Korea (2023.11 – 2024.08)

    • Gas Electron Multiplier (GEM) Detector Aging Test
    • Production of GEM Detector Foil for the Rare Isotope Accelerator Complex (RAON) Laboratory

  • Technology Team – AI Software Development – Mustree Company, Seoul, Korea (2023.07 – 2023.10)

    • Developed AI Software for Size Measurement

  • Postdoctoral Researcher – Korea Astronomy and Space Science Institute (KASI), Daejeon, Korea (2022.05 – 2023.07)

    • Quantum Noise Reduction Technology for Future Gravitational Wave Detectors
    • Development of a 2 µm Laser Squeeze System

  • Ph.D. Researcher – High Energy Physics – Korea University, Seoul, Korea (2020.01 – 2022.04)

    • Data Analysis for Supersymmetric Particles
    • Search for R-parity Violating Supersymmetry in Proton-Proton Collisions at √s = 13 TeV

  • Ph.D. Researcher – GEM Detector Upgrade Project (Phase II) – CERN, Geneva, Switzerland (2018.01 – 2019.12)

    • Quality Control (QC) of GEM Chambers in the CMS Experiment
    • QC2: Leakage Current Test
    • QC3: Gas Leak Test
    • QC4: High Voltage Test
    • GEM Chamber Assembly, Aging Tests & Discharge Probability Studies

  • Ph.D. Researcher – Experimental Particle Physics – Sungkyunkwan University, Suwon, Korea (2017.06 – 2018.02)

    • Data Analysis for tt̄ Inclusive Decay
    • Measurement of the Inclusive Top Quark Cross Section in Di-lepton Channels at √s = 13 TeV

  • Ph.D. Researcher – Experimental Particle Physics – University of Seoul, Seoul, Korea (2016.01 – 2017.06)

    • CMS Detector Simulation for Phase II Upgrade
    • Muon Isolation Optimization Simulation

🏆 Achievements & Contributions

✅ Authored numerous research papers in High-Energy Physics & Detector Technology
✅ Significant contributions to CMS Detector R&D at CERN
✅ Advanced AI-based measurement software for industry applications
✅ Developed quantum noise reduction techniques for future gravitational wave detectors

🎖️ Awards & Honors

🏅 Recognized Researcher in Particle Physics for contributions to Supersymmetry & CMS Experiments
🏅 Recipient of CERN Research Fellowships for GEM Detector Upgrade & Testing
🏅 Awarded Postdoctoral Research Positions in Multiple Leading Korean Institutions
🏅 Contributor to the CMS Collaboration at the European Organization for Nuclear Research (CERN)

Publication Top Notes:

A Mobile LiDAR-Based Deep Learning Approach for Real-Time 3D Body Measurement

A Multi-View Integrated Ensemble for the Background Discrimination of Semi-Supervised Semantic Segmentation

Mr. Adrian Barglazan | Computer Vision | Best Researcher Award

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Mr. Adrian Barglazan | Computer Vision | Best Researcher Award

Mr. Adrian Barglazan, University “Lucian Blaga” Sibiu, Romania

Adrian Barglazan is a Senior Software Engineer at Cognizant Softvision, based in Sibiu, Romania, with a strong focus on continuous learning and growth in software development. He holds a Bachelor’s and Master’s degree in Computer Science from Lucian Blaga University of Sibiu, where he is also pursuing a Ph.D. with a research focus on media forensics. With over 15 years of professional experience, Adrian has worked in various roles, including software development, team leadership, and teaching. His expertise spans Microsoft-related technologies, agile development, clean code principles, and design patterns. Throughout his career, he has contributed to projects in cloud ERP systems, pharmaceutical software, and ERP applications, working with technologies such as C#, ASP.NET, JavaScript, React, and Azure. In addition to his industry work, Adrian has been a teaching assistant at Lucian Blaga University of Sibiu since 2011, specializing in data compression and DirectX. His interests extend to computer vision and machine learning, reflecting his passion for innovative and high-quality software solutions

Professional Profile:

ORCID

Suitability for Best Researcher Award – Adrian Barglazan

Adrian Barglazan demonstrates strong expertise in software development, computer vision, and media forensics, with a balance of industry experience and academic involvement. His Ph.D. research in media forensics, combined with over a decade of teaching experience in data compression and image processing, positions him as a knowledgeable professional in his field. However, for a Best Researcher Award, factors such as high-impact publications, patents, funded research projects, and citations play a crucial role. While Adrian has valuable technical contributions, his eligibility for this award would be strengthened by more peer-reviewed research publications and recognized contributions to the scientific community. Therefore, he is a strong candidate for an innovation or industry-academic impact award but may need further academic credentials to be fully competitive for a Best Researcher Award.

🎓 Education:

  • PhD in Computer Science (2019 – Present) 📖🔍
    Lucian Blaga University of Sibiu – Focus on Media Forensics
  • Master’s Degree in Computer Science (2009 – 2011) 🎓
    Lucian Blaga University of Sibiu
  • Bachelor’s Degree in Computer Science (2005 – 2009) 🎓
    University “Lucian Blaga”, Faculty of Engineering “Hermann Oberth”, Sibiu

💼 Work Experience:

🔹 Senior Software Engineer – Cognizant Softvision (Sept 2020 – Present)
📍 Sibiu, Romania

  • Focus on Microsoft-related technologies, agile development, and clean code
  • Expertise in software architecture, development, testing, and mentoring

🔹 PhD Student & Teaching Assistant – Lucian Blaga University of Sibiu (Sept 2011 – Present)
📍 Sibiu County, Romania

  • Research in Media Forensics 🔍
  • Teaching Data Compression & DirectX to 4th-year students 🎓
  • Covers key algorithms like Shannon, Huffman, LZ77, JPEG, MPEG

🔹 Software Developer – Visma (Apr 2017 – Sept 2020)
📍 Sibiu County, Romania

  • Senior developer in cloud ERP Single Page Application (SPA) development ☁️💻
  • Technologies: C#, ASP.NET MVC, Azure SQL, React, TypeScript
  • Worked with Kanban methodology, CI/CD, and cross-country teams

🔹 Developer – iQuest Technologies (Sept 2011 – Apr 2017)
📍 Sibiu County, Romania

  • Lead developer in Pharma sector projects 💊
  • Software architecture, risk management, and recruitment 📋

🏆 Achievements, Awards & Honors:

🌟 PhD Researcher in Media Forensics 📸🔬
🌟 Senior Software Engineer with over 17 years of experience in the software industry 💻
🌟 Specializes in Microsoft technologies, Agile development, and Clean Code principles ⚡
🌟 Mentor & Teacher – educating future developers on Data Compression & DirectX 🎓
🌟 Experienced in cloud-based ERP systems, software architecture, and machine learning ☁️🤖
🌟 Contributor to recruitment & technical interviews in multiple companies 🏅

Publication Top Notes:

Wavelet Based Inpainting Detection

Enhanced Wavelet Scattering Network for Image Inpainting Detection

Lung Sounds Anomaly Detection with Respiratory Cycle Segmentation

Image Inpainting Forgery Detection: A Review

Image Inpainting Forgery Detection: A Review

Ms. Saleha Kamal | Pattern Recognition Awards | Best Researcher Award

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Ms. Saleha Kamal | Pattern Recognition Awards | Best Researcher Award 

Ms. Saleha Kamal, Air University, Pakistan

Saleha Kamal is an accomplished AI and Computer Vision professional based in Rawalpindi, Pakistan, with expertise in image processing, silhouette detection, segmentation, and feature classification. She is currently pursuing an MS in Computer Science at Air University, Islamabad, Pakistan (2023-2025). Saleha’s research focuses on human interaction analysis and the development of advanced algorithms for computer vision tasks. Her work has been published in esteemed international conferences, including IEEE ICECT 2024 and IEEE ICET 2024, showcasing her innovative contributions to multi-feature descriptors and composite feature-based classifiers for human interaction recognition.

Professional Profile:

GOOGLE SCHOLAR

Suitability of Saleha Kamal for the Best Researcher Award

Saleha Kamal demonstrates exceptional potential and achievements in AI, machine learning, and computer vision research, making her a compelling candidate for the Best Researcher Award. Her dedication to advancing knowledge in human interaction recognition, along with her technical and academic accomplishments, positions her as a rising star in the research community.

Education 🎓

  • MS in Computer Science (2023 – 2025)
    Air University, Islamabad, Pakistan

Work and Research Experience 💼

  • Research Experience
    • Co-authored research papers published in international conferences:
      • “Multi-Feature Descriptors for Human Interaction Recognition in Outdoor Environments” – IEEE ICECT, 2024.
      • “A Novel Human Interaction Recognition via Composite Features and Max Entropy Classifier” – IEEE ICET, 2024.

Achievements and Certifications 🏆

  • Published research in prestigious IEEE conferences.
  • Certifications:
    • Advanced Computer Vision with TensorFlow – Coursera, 2023.
    • Machine Learning Specialization – Coursera, 2023.

Publication Top Notes:

A Novel Human Interaction Recognition via Composite Features and Max Entropy Classifier

CITED:8

CITED:6

Ms. Hyunseo Kim | Computer Vision Awards | Best Researcher Award

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Ms. Hyunseo Kim | Computer Vision Awards | Best Researcher Award 

Ms. Hyunseo Kim, Konkuk University, South Korea

Hyunseo Kim is an ambitious student pursuing dual degrees in Biomedical Science and Engineering and Computer Science and Engineering at Konkuk University in Seoul, South Korea. With a strong focus on applying artificial intelligence techniques to the medical domain, he is currently engaged in research at the AI & CV Lab, where he works on projects involving computer vision and audio data for medical applications, including MRI data analysis and hearing loss classification. Hyunseo’s passion for healthcare technology led him to participate in various competitions, winning first place in a medical hackathon for developing a drug side effect management program and third place in a software convergence competition for creating a DNA editing application. Fluent in Korean and proficient in English and Chinese, he is well-equipped for interdisciplinary collaboration. As he approaches graduation, Hyunseo is eager to leverage his skills in AI and programming to contribute to advancements in healthcare and improve the quality of life for individuals.

Professional Profile:

ORCID

Summary of Suitability for the Best Researcher Award: Hyunseo Kim

Hyunseo Kim demonstrates a strong foundation in interdisciplinary research and technological innovation, making her a promising candidate for the Best Researcher Award. Below is an evaluation of her qualifications and accomplishments

Education 🎓

  • Bachelor of Science in Biomedical Science and Engineering
    Konkuk University, Seoul, Republic of Korea
    Expected Graduation: August 2024
  • Bachelor of Science in Computer Science and Engineering
    Konkuk University, Seoul, Republic of Korea
    Double Major Acceptance: March 2022

Work Experience 💻

  • AI & CV Lab (June 2022 – Present)
    • Conducting projects on MRI data, including tasks for lacunar detection, Enlarged Perivascular Spaces (EPVS) detection, and microbleed detection.
    • Engaged in audio data projects, including a study on hearing loss classification and participating in the ADRESSM Challenge, where the team won first place in Alzheimer’s Disease classification tasks.

Achievements 🏆

  • 1st Place in Medical Hackathon (March 2022 – September 2022)
    • Developed a drug side effect management program in collaboration with school seniors, focusing on patient medication tracking and side effect reporting.
  • 3rd Place in SW Convergence Competition (August 2022 – November 2022)
    • Created a DNA editing program in partnership with a senior, enhancing the efficiency of DNA sequence editing compared to existing programs.
  • 2nd Place in Big Data Analysis Competition (November 2022)
    • Participated in a competition organized by CJ Enterprises, focusing on exploratory data analysis (EDA) of corporate financial statements.

Awards and Honors 🎖️

  • ICASSP 2023 Workshop Participation
    • Gained valuable experience and recognition through the team’s first-place win in the ADRESSM Challenge, leading to participation in the ICASSP 2023 workshop.

Publication Top Notes:

EEG-RegNet: Regressive Emotion Recognition in Continuous VAD Space Using EEG Signals

Dr. Jinxin Cao | Computer Vision Award | Best Researcher Award

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Dr. Jinxin Cao | Computer Vision Award | Best Researcher Award 

Dr. Jinxin Cao, China University of Petroluem, Beijing, China

Jinxin Cao is a Doctor of Engineering and a PhD student at the China University of Petroleum, Beijing. Since joining the institution in August 2018, he has focused on the integration of artificial intelligence with energy and mining, specializing in computer vision in microfluidics, signal processing, and time series analysis. His research covers a broad spectrum, including tight oil development, microfluidics, interfacial mechanisms, and numerical simulation. Cao has led over 15 major projects, including special projects, joint fund integrations, and comprehensive scientific research initiatives. He has achieved significant breakthroughs in microfluidic image processing, elucidating interface evolution laws and mechanical mechanisms, which are pivotal for advancing “Lab on a Chip” technologies. Additionally, he has applied signal processing techniques to petroleum engineering, utilizing empirical mode decomposition and Hilbert-Huang transforms to analyze and predict oil well production. His contributions include 11 published papers (8 indexed by SCI/EI), 5 granted patents, and 6 accepted articles. Cao has also earned 20 awards in science, technology, and competitions, highlighting his impact in his field

Professional Profile:

 

Summary of Suitability for Best Researcher Award:

Jinxin Cao is currently pursuing a PhD at China University of Petroleum, Beijing (CUPB) and has been a part of the institution since August 2018. His research focuses on artificial intelligence applications in petroleum engineering, including computer vision in microfluidics, signal processing, and time series analysis. With a total experience of 6 years at CUPB, he has made significant contributions to various interdisciplinary fields.

Education:

  • Doctor of Engineering
    Institution: China University of Petroleum, Beijing
    Specialization: Energy and Mining
    Research Focus: Computer Vision in Microfluidics

Work Experience:

  • Position: Doctor of Engineering
    Department: College of Petroleum Engineering
    Institution: China University of Petroleum, Beijing
    Duration: August 2018 – Present
    Experience: Jinxin Cao has been engaged in artificial intelligence with a focus on computer vision in microchips, signal processing, time series processing, tight oil development, microfluidics, and interfacial mechanisms. He has been involved in over 15 major projects, including special projects, joint fund integration projects, and comprehensive scientific research endeavors. His work has led to significant breakthroughs in microfluidic image processing, uncovering interface evolution laws and mechanical mechanisms in microfluidic processes using computer vision methods. Additionally, Cao has applied signal processing techniques to petroleum engineering, utilizing empirical mode decomposition and Hilbert-Huang transform to analyze oil well production and predict future production using artificial intelligence methods.

Academic Achievements:

  • Publications: 11 academic papers, 8 indexed by SCI/EI
  • Patents: 5 invention patents
  • Accepted Articles: 6
  • Awards: 20 science and technology or competition awards at various levels

Publication top Notes:

 

Microscopic experiment on efficient construction of underground gas storages converted from water-invaded gas reservoirs

Identification of Polymer Flooding Flow Channels and Characterization of Oil Recovery Factor Based On U-Net

Experimental investigation on the effect of interfacial properties of chemical flooding for enhanced heavy oil recovery

Study on reservoir damage characteristics of tight oil oxygen reduction air huff and puff development

Adaptability and enhanced oil recovery performance of surfactant-polymer flooding in inverted seven-spot well pattern

Research on the Adaptability of SP Flooding in Sand-Gravel Mixture Reservoir Based on the Inverted Seven-Spot Well Pattern