Aljaz Hojski | Vision Sensing | Best Researcher

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Dr. Aljaz Hojski | Vision Sensing | Best Researcher

Dr. Aljaz Hojski | Vision Sensing | Cadre doctor at Universitätspital Basel | Switzerland

Dr. Aljaz Hojski is a highly respected thoracic surgeon and clinical researcher, currently affiliated with Universitätspital Basel. With a strong focus on surgical innovation and patient-centered care, his contributions in minimally invasive thoracic procedures and oncological surgery have gained widespread recognition across academic and clinical communities. His medical background is complemented by an extensive portfolio of scientific publications, collaborative research initiatives, and active peer-review responsibilities in high-impact journals. A committed academician and practicing consultant, Dr. Hojski is known for bridging the gap between clinical application and evidence-based research, especially in lung cancer management, thoracic trauma, and postoperative pain optimization.

Academic Profile:

ORCID

Scopus

Education:

Dr. Hojski obtained his foundational medical education at the University of Ljubljana, where he developed a keen interest in thoracic medicine and surgical procedures. His education included comprehensive training in general medicine, with progressive specialization in thoracic surgery during his clinical rotations and postgraduate residency programs. Throughout his academic journey, he emphasized scientific inquiry alongside clinical excellence, engaging in laboratory-based research and hospital-based surgical trials. This dual focus on science and surgery established a strong platform for his later contributions to applied clinical research and international collaborations in minimally invasive thoracic techniques.

Experience:

Dr. Hojski currently serves in a senior consultant role within the Department of Thoracic Surgery at Universitätspital Basel, a leading center for cardiothoracic care and research in Europe. He is actively involved in surgical planning, patient care, and mentoring junior clinicians. In addition to his clinical duties, he contributes to institutional and multicenter research protocols aimed at improving perioperative outcomes and refining surgical strategies. His professional experience spans diverse domains including advanced thoracoscopic resections, surgical pain management, and postoperative complication risk stratification. Dr. Hojski’s extensive collaborations with multidisciplinary teams, including radiologists, anesthesiologists, and oncologists, have enabled the successful translation of academic research into clinical best practices.

Research Interest:

Dr. Hojski’s primary research interests include thoracic oncology surgery, 3D imaging and surgical planning, postoperative pain control strategies, and risk prediction in lung resection patients. He has been an investigator and co-investigator on several funded research projects focused on optimizing pain therapy following minimally invasive lung operations, and the development of advanced imaging tools for segmental lung function assessment. His research also extends into clinical outcome analysis, where he contributes to developing predictive models for surgical complications and evaluating the effectiveness of new procedural technologies. His interdisciplinary approach enables him to align clinical insight with scientific rigor in solving real-world surgical challenges.

Awards:

Dr. Hojski has been nominated for several recognitions in the field of medical science and thoracic surgery, reflecting his continued impact on both clinical advancement and scientific contribution. His research output and leadership have earned him invitations to present at international symposia, while his peer-reviewed publications and service as a reviewer demonstrate his influence in academic publishing. He remains committed to excellence in both operative care and medical scholarship, making him a compelling nominee for awards that celebrate high-impact contributions to science and medicine.

Selected Publications:

  • Estimating Postoperative Lung Function Using Three-Dimensional Segmental HRCT-Reconstruction: A Retrospective Pilot Study on Right Upper Lobe Resections, 2025, 60 citations

  • Perioperative Intravenous Lidocaine in Thoracoscopic Surgery for Improved Postoperative Pain Control: A Randomized, Placebo-Controlled, Double-Blind, Superiority Trial, 2024, 85 citations

  • Planning Thoracoscopic Segmentectomies with 3-Dimensional Reconstruction Software Improves Outcomes, 2025, 45 citations

  • A Risk Score to Predict Postoperative Complications in Patients with Resectable Non-Small Cell Lung Cancer, 2025, 50 citations

Conclusion:

Dr. Aljaz Hojski represents the ideal candidate for prestigious international research recognition, owing to his consistent contributions to thoracic surgery, clinical research, and interdisciplinary innovation. Through a well-balanced integration of surgical expertise, scientific research, and professional leadership, he has advanced both patient care and academic knowledge in thoracic medicine. His published works continue to shape protocols and influence best practices within surgical communities globally. As a forward-looking clinician-scientist, Dr. Hojski is well-positioned to lead future developments in thoracic healthcare and surgical outcomes research, making him a deserving nominee for awards that honor excellence in clinical and academic medical sciences.

 

 

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

Ms. Congying Sun | Object Detection Awards | Best Researcher Award

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Ms. Congying Sun | Object Detection Awards | Best Researcher Award 

Ms. Congying Sun, Xi’an University of Technology, China

Congying Sun, a native of Xianyang City, Shaanxi Province, is an emerging researcher specializing in control science, engineering, and multi-modal remote sensing technologies. She earned her Bachelor’s degree in Printing Engineering from Xi’an University of Technology in 2022 and is currently pursuing a Master’s degree in Control Science and Engineering at the same institution, expected to graduate in 2025. Her professional experience includes a tenure at the Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, where she played a pivotal role in developing an infrared-visible aircraft image dataset and deploying state-of-the-art infrared small target detection models. Congying has demonstrated her ability to merge academic excellence with practical application through her contributions to national and engineering projects, including research on multi-source collaborative intelligent perception technology for aircraft and non-cooperative multi-target classification and cognition technology.

Professional Profile:

ORCID

Suitability of Congying Sun for the Best Researcher Award

Congying Sun demonstrates exceptional qualifications and accomplishments, making her an outstanding candidate for the Research for Best Researcher Award. Below is a summary of her key achievements and strengths

🎓 Education

  • Master’s Degree in Control Science and Engineering (August 2022 – July 2025)
    🏫 Xi’an University of Technology
  • Bachelor’s Degree in Printing Engineering (September 2018 – July 2022)
    🏫 Xi’an University of Technology

🧑‍💻 Professional Experience

Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences (July 2023 – August 2024)

  • 📸 Independently created an infrared-visible aircraft image dataset, including camera selection, data collection, and image annotation.
  • 📝 Authored project proposals, research papers, and patents with great precision.
  • 🤖 Developed and deployed infrared small target detection models and multi-modal remote sensing image fusion detection models.

🚀 Research Projects

  • Multi-source Collaborative Intelligent Perception Technology for Aircraft (August 2023 – June 2024) ✈️
  • Non-cooperative Multi-target Classification and Cognition Technology (October 2023 – March 2024) 🎯

🏅 Achievements

  • Granted Invention Patents:
    • 📜 Infrared Small Target Detection Method (CN118762159A)
    • 📜 Multi-modal Feature Fusion Target Detection Model and Method (CN118865046A)

💡 Research Interests

🔍 Machine Learning | 🌍 Multi-modal Remote Sensing | 🎯 Target Detection

Congying Sun’s innovative approach, technical expertise, and impactful contributions to cutting-edge research make her a rising star in the field of control science and engineering. 🌟

Publication Top Notes:

Location-Guided Dense Nested Attention Network for Infrared Small Target Detection

MMYFnet: Multi-Modality YOLO Fusion Network for Object Detection in Remote Sensing Images

Ms. Maryam Moshrefizadeh | Computer Vision Awards | Best Researcher Award

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Ms. Maryam Moshrefizadeh | Computer Vision Awards | Best Researcher Award 

Ms. Maryam Moshrefizadeh, Siant Louis University, United States

Maryam Moshrefizadeh is a Ph.D. student in Computer Science at Saint Louis University, with previous experience as a Graduate Research Assistant at South Dakota State University. She holds a Master’s degree in Artificial Intelligence from Amirkabir University of Technology and a Bachelor’s degree in Computer Software Engineering from K. N. Toosi University of Technology, both in Tehran, Iran. Maryam’s research interests lie in computer vision, deep learning, and machine learning. Professionally, she has worked as an AI researcher and developer, including roles at DRNEXT.IR, Payesh24, and Cobenefit, where she contributed to the development of AI-driven platforms, machine learning models, and website functionality. She has a strong technical background in programming languages like Python, JavaScript, and C, as well as expertise in frameworks and tools like PyTorch, TensorFlow, Vue.js, and Docker. Maryam is fluent in English and Persian and is passionate about mountaineering, cycling, photography, and outdoor activities.

Professional Profile:

ORCID

Summary of Suitability for the Best Researcher Award

Maryam Moshrefizadeh is a promising and highly capable PhD student with extensive experience and research contributions in the field of Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision. Her academic background, practical work experience, and emerging research output position her as an excellent candidate for the Best Researcher Award.

Education

🎓 Saint Louis University
Ph.D. | Graduate Research Assistant | Computer Science
📅 Jan 2024 ‑ Dec 2028 | St. Louis, MO, USA

🎓 South Dakota State University
Ph.D. | Graduate Research Assistant | Computer Science
📅 Aug 2022 ‑ Dec 2023 | Brookings, SD, USA

🎓 Amirkabir University of Technology (Polytechnic)
M.S. in Artificial Intelligence
📅 Jan 2014 ‑ Sept 2017 | Tehran, Iran

🎓 K. N. Toosi University of Technology
B.S. in Computer Software Engineering
📅 Sept 2009 ‑ Aug 2013 | Tehran, Iran

Work Experience

💼 DrNext.ir | Developer and AI Researcher
📅 Nov 2020 – Present
• Developed prescription writing notepad allowing doctors to type or use a pen 🖊️
• Implemented features for appointment scheduling and clinic reception handling 🗓️
• Worked in an agile team with Kanban, Scrum, Jira, and Git 🔧

💼 Payesh24 | AI Engineer
📅 Nov 2017 – Jul 2020
• Researched and implemented various AI algorithms and machine learning models 🤖
• Worked with supervised and unsupervised learning algorithms such as SVM and KNN 📊

💼 BeFine | Developer
📅 Apr 2006 – Feb 2009
• Developed and maintained website for diabetic products and information 💻
• Shared health tips and updates on diabetes 🩺

💼 Cobenefit Developer | Remote
📅 Oct 2021 – Present
• Develop and maintain websites using Vue.js, ES6, HTML5, CSS3, and SASS 🌐

Research Interests

🔍 Computer Vision | 🤖 Deep Learning | 📚 Machine Learning

Publication Top Notes

EC-WAMI: Event Camera-Based Pose Optimization in Remote Sensing and Wide-Area Motion Imagery

Multimodal Fusion of Heterogeneous Representations for Anomaly Classification in Satellite Imagery