Zobeir Raisi | Vision Sensing | Best Researcher Award

Posted on by Global Sensor Awards

Best Researcher Award

Zobeir Raisi
Affiliation Chabahar Maritime University
Country Canada
Scopus ID 54897975500
Documents 11
Citations 115
h-index 4
Subject Area Vision Sensing
Event Global Sensor Awards
ORCID 0000-0002-1591-4492

Zobeir Raisi
Chabahar Maritime University

The Best Researcher Award recognizes researchers whose scholarly activities demonstrate sustained contributions to their academic disciplines through publications, scientific collaboration, innovation, and research impact. Zobeir Raisi, affiliated with Chabahar Maritime University, is associated with research activities in the field of Vision Sensing. His scholarly profile, indexed in Scopus and identified through ORCID, reflects participation in internationally recognized research dissemination and academic communication.[1][2]

Abstract

This article presents a concise academic overview of Zobeir Raisi and highlights the research profile associated with the field of Vision Sensing. The profile emphasizes scholarly visibility through internationally recognized researcher identifiers, publication indexing, and participation in scientific research. The article adopts a neutral encyclopedic style suitable for academic recognition while encouraging readers to consult the original researcher profiles for continuously updated bibliometric information.[1]

Keywords

  • Vision Sensing
  • Sensor Research
  • Image Processing
  • Computer Vision
  • Research Evaluation
  • Academic Publications
  • Scopus Author Profile
  • ORCID

Introduction

Vision sensing represents an interdisciplinary research area integrating sensing technologies, imaging systems, artificial intelligence, and computer vision for observation, monitoring, automation, and decision support. Researchers working within this field contribute to developments across industrial automation, transportation, robotics, healthcare, and environmental monitoring. Academic recognition programs acknowledge contributions that support scientific advancement while maintaining research integrity and scholarly dissemination.[3]

Research Profile

Zobeir Raisi is affiliated with Chabahar Maritime University and maintains internationally recognized researcher identifiers through Scopus and ORCID. These scholarly platforms improve research discoverability, facilitate author identification, and support accurate attribution of publications, citations, and academic collaborations. Such persistent identifiers are increasingly important within the global research ecosystem.[1][2]

Research Contributions

Research associated with Vision Sensing commonly includes imaging technologies, machine vision algorithms, intelligent sensing systems, feature extraction, pattern recognition, sensor integration, and automated decision-making. Such investigations contribute to technological innovation by improving accuracy, efficiency, and reliability across diverse engineering and scientific applications. Published studies in this domain frequently encourage interdisciplinary collaboration between engineering, computer science, and applied sciences.[3][4]

Publications

The researcher’s indexed publication record is maintained through the Scopus Author Profile, where bibliographic information is periodically updated as additional scholarly works are indexed. Readers are encouraged to consult the official profile for the latest publication list, citation metrics, collaboration networks, and subject classifications. Digital Object Identifiers (DOIs) associated with individual publications provide permanent access to published research.[1][4]

Research Impact

Research impact is commonly evaluated using publication quality, citation performance, collaboration, innovation, and contributions to scientific knowledge. Bibliographic databases such as Scopus provide standardized metrics that assist institutions, funding agencies, and award committees in assessing scholarly productivity while recognizing that quantitative indicators should be interpreted together with qualitative research achievements.[1]

Award Suitability

The Best Researcher Award recognizes sustained scholarly engagement, responsible research practices, publication quality, and contributions to advancing scientific knowledge. Based on publicly available academic identifiers and institutional affiliation, Zobeir Raisi demonstrates participation within an internationally indexed research environment relevant to Vision Sensing and sensor technologies. Final award decisions are typically determined according to the official evaluation criteria established by the organizing committee.[5]

Conclusion

The academic profile presented here summarizes publicly identifiable scholarly information relating to Zobeir Raisi, his institutional affiliation, and research interests in Vision Sensing. Continued publication, collaboration, and participation in international research activities contribute to scientific progress and strengthen the global research community. Official researcher profiles remain the primary source for updated bibliographic records and citation information.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Zobeir Raisi, Author ID 54897975500. Scopus.https://www.scopus.com/authid/detail.uri?authorId=54897975500
  2. ORCID. (n.d.). ORCID record for Zobeir Raisi.https://orcid.org/0000-0002-1591-4492
  3. International literature on computer vision and intelligent sensing systems.https://doi.org/10.1109/5.726791
  4. Crossref. (n.d.). Digital Object Identifier (DOI) System.https://doi.org/10.1038/s41586-019-1666-5
  5. Global Sensor Awards. (n.d.). Award information and evaluation framework.

Pedro-Antonio Regidor | Vision Sensing | Excellence in Research Award

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Excellence in Research Award

Pedro-Antonio Regidor
Affiliation Exeltis Europe & Global Projects; Women’s Health Specialist
Country Germany
Scopus ID 57212377610
Documents 70
Citations 1,126
h-index 21
Subject Area Gynecology, Obstetrics, Reproductive Medicine, Women’s Health, Vision Sensing
Event Global Sensor Awards
ORCID 0000-0002-9551-2847

Pedro-Antonio Regidor is a physician-scientist and specialist in gynecology, obstetrics, gynecological endocrinology, reproductive medicine, and gynecological oncology. His professional career spans clinical practice, academic research, surgical innovation, and pharmaceutical medical leadership. Through extensive contributions to women’s health research, reproductive endocrinology, endometriosis management, contraceptive technologies, and obstetric medicine, he has established a notable scholarly profile within international medical communities.[1]

Abstract

This article summarizes the academic and professional achievements of Pedro-Antonio Regidor, a specialist in women’s health sciences. His work integrates clinical gynecology, reproductive medicine, gynecological oncology, endocrine disorders, contraceptive technologies, and translational research. His scholarly output includes peer-reviewed publications addressing endometriosis, uterine fibroids, hormonal therapies, pregnancy outcomes, inflammatory pathways, and reproductive endocrinology.[2]

Keywords

Gynecology, Obstetrics, Reproductive Medicine, Endometriosis, Women’s Health, Hormonal Therapy, Gynecological Oncology, Contraception, Pregnancy Outcomes, Endocrinology, Clinical Research.

Introduction

The advancement of women’s healthcare requires interdisciplinary expertise combining clinical practice, surgery, endocrinology, and evidence-based medicine. Pedro-Antonio Regidor has contributed to these domains through decades of clinical leadership, academic engagement, and scientific publication. His research interests focus on improving reproductive health outcomes and expanding understanding of gynecological diseases through translational and clinical investigation.[1]

Research Profile

Dr. Regidor earned his medical degree and later completed a doctoral specialization in gynecology and obstetrics. Throughout his career, he has held appointments including assistant medical director, department chief, consultant physician, and medical director within academic hospitals and international healthcare organizations. His expertise spans gynecological oncology, reproductive endocrinology, obstetrics, perinatal medicine, breast health, and women’s hormonal therapies.[1]

Research Contributions

  • Investigation of endometriosis pathophysiology and treatment strategies.
  • Research on hormonal contraception and reproductive endocrinology.
  • Clinical studies involving dienogest-based therapeutic interventions.
  • Analysis of inflammatory mechanisms in uterine fibroids and reproductive disorders.
  • Evaluation of maternal health outcomes and pregnancy-related inflammatory conditions.
  • Contributions to gynecological oncology and surgical women’s healthcare.

Publications

Recent scholarly publications include investigations into probiotic supplementation in endometriosis, contraceptive safety evaluation, polycystic ovary syndrome-associated hirsutism, chronic inflammation in uterine myoma, omega-3 fatty acids in pregnancy outcomes, and coagulation parameters associated with hormonal contraceptive therapies. These works have appeared in internationally recognized journals such as European Journal of Contraception & Reproductive Health Care, BMC Women’s Health, EClinicalMedicine, Biomedicines, and Gynecological Endocrinology.[2]

Research Impact

The impact of Dr. Regidor’s work is reflected through sustained publication activity, international collaborations, translational clinical studies, and continuing engagement in women’s health innovation. His research supports evidence-based approaches for reproductive medicine and contributes to discussions regarding contraceptive safety, endocrine management, inflammatory gynecological disorders, and maternal health outcomes.[2]

Award Suitability

Based on his extensive clinical experience, leadership positions, multidisciplinary specialization, academic qualifications, surgical expertise, and scientific publications, Pedro-Antonio Regidor demonstrates strong alignment with international academic recognition programs focused on excellence in gynecology, reproductive medicine, women’s health research, and translational clinical science. His professional contributions illustrate sustained commitment to advancing healthcare knowledge and practice.[1]

Conclusion

Pedro-Antonio Regidor represents a distinguished profile within women’s health sciences through a combination of clinical excellence, academic scholarship, surgical expertise, and international research engagement. His body of work contributes to ongoing advances in reproductive medicine, gynecology, endocrinology, and maternal healthcare while supporting evidence-based medical practice across diverse healthcare settings.

References

  1. Professional curriculum vitae and academic profile of Pedro-Antonio Regidor, including medical qualifications, appointments, memberships, and clinical expertise.
  2. Regidor, P.A., et al. Recent publications in gynecology, reproductive medicine, endometriosis, contraception, and women’s health.
    https://doi.org/10.1080/13625187.2026.2644896
  3. Elsevier. (n.d.). Scopus author details: Pedro-Antonio Regidor, Author ID 57212377610.
    https://www.scopus.com/authid/detail.uri?authorId=57212377610

Assist. Prof. Dr. Hye-Youn Lim | Computer Vision | Excellence in Research Award

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Assist. Prof. Dr. Hye-Youn Lim | Computer Vision | Excellence in Research Award

Assist. Prof. Dr. Hye-Youn Lim | Computer Vision | Dong-A University | South Korea

Assist. Prof. Dr Hye-Youn Lim is a distinguished researcher and academic in artificial intelligence, computer vision, and intelligent systems, serving in the Department of Electronics Engineering at Dong-A University, Republic of Korea. Hye-Youn Lim obtained her Ph.D. from a leading research university and has accumulated extensive professional experience, including leading national and international research projects and collaborating with multiple industry partners on AI-based technology applications. Her research interests focus on intelligent video analysis, visual recognition, and smart city applications, demonstrating her expertise in applying computational methods to real-world problems. Hye-Youn Lim possesses a diverse set of research skills, including deep learning model development, attention-driven network design, data preprocessing and augmentation strategies, and applied computer vision for automated systems. Her scholarly output includes more than 30 SCI- and Scopus-indexed journal articles, with verified metrics of 22 Scopus documents, over 100 citations, and a recorded h-index, reflecting both impact and consistency in high-quality research dissemination.

Citation Metrics (Scopus)

120

90

60

30

0

Citations
105

Documents
22

h-index
3

Citations
Documents
h-index

View Scopus Profile
 View ORCID Profile

Featured Publications

Mr. Suresha R | Computer Vision Awards | Excellence in Research Award

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Mr. Suresha R | Computer Vision Awards | Excellence in Research Award 

Mr. Suresha R | Computer Vision Awards | Amrita Vishwa Vidyapeetham | India

Mr. Suresha R. is a results-driven educator and technologist with over six years of combined experience in teaching computer science and academic leadership. He holds an M.Sc. in Computer Science and has qualified in UGC-NET and K-SET, while currently pursuing a Ph.D. Mr. Suresha R. has demonstrated expertise in curriculum design and research, particularly focusing on AI in autonomous solutions and computer vision applications. In his professional career, Mr. Suresha R. has served as an Assistant Professor at Amrita Vishwa Vidyapeetham, School of Computing, Mysuru Campus, and at SBRR Mahajana First Grade College, Mysuru, where he delivered advanced courses in Computer Vision, Digital Image Processing, Pattern Recognition, Computational Intelligence, Computer Graphics, Machine Learning, Exploratory Data Analysis, R Programming, Information Retrieval, Data Mining, Numerical Analysis, and Operations Research, consistently achieving high student satisfaction. His research interests encompass small traffic sign detection and recognition in challenging scenarios using computer vision and LiDAR-based techniques with ROS2 framework, deep learning-based vehicle detection and distance estimation for autonomous systems, motion blur image restoration, wild animal recognition through vocal analysis, and SVM-based medical image classification. Mr. Suresha . possesses strong research skills in Python, MATLAB, ROS2, machine learning, deep learning, image processing, and data analysis. He has successfully guided Bachelor’s and Master’s students in research projects, fostering innovation and academic growth. His academic contributions are recognized through multiple publications in prestigious journals and conferences, including IEEE Access, Procedia Computer Science, ICCCNT, CCEM, ICECAA, and INDIACom. Mr. Suresha . has a proven record of collaborating in interdisciplinary teams, effectively communicating complex technical concepts, and mentoring students to achieve excellence in research and practical applications. His dedication to lifelong learning and active engagement in both teaching and research demonstrates his commitment to advancing knowledge in computer science and autonomous systems. Throughout his career, Suresha  has received awards and recognitions for research excellence, contributing to the development of sustainable and intelligent solutions in the field of computer vision and AI. Overall, Mr. Suresha exemplifies a passionate and innovative professional, bridging theoretical foundations with applied research, and continues to make significant contributions to academia and technology

Professional Profiles: ORCID

Selected Publications 

  1. Suresha, R., Manohar, N., Ajay Kumar, G., & Singh, R. (2024). Recent advancement in small traffic sign detection: Approaches and dataset.

  2. Suresha, R., Manohar, N., & Jipeng, T. (2024). Two-stage traffic sign classification system.

  3. Sudharshan Duth, P., Manohar, N., Suresha, R., Priyanka, M., & Jipeng, T. (2024). Wild animal recognition: A vocal analysis.

  4. Suresha, R., Jayanth, R., & Shriharikoushik, M. A. (2023). Computer vision approach for motion blur image restoration system.

  5. Srinivasa, C., Suresha, R., Manohar, N., Dharun, G. K., Sheela, T., & Jipeng, T. (2023). Deep learning-based techniques for precise vehicle detection and distance estimation in autonomous systems.

  6. Suresha, R., Devika, K. M., & Prabhu, A. (2022). Support vector machine classifier based lung cancer recognition: A fusion approach.

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.

 

 

Prof. Zuofeng Zhou | Optical Imaging | Best Researcher Award

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Prof. Zuofeng Zhou | Optical Imaging | Best Researcher Award 

Prof. Zuofeng Zhou, Xi’an Institute of Optics and Precision Mechanics of CAS, China

Dr. Zuofeng Zhou is a Professor at the Xi’an Institute of Optics and Precision Mechanics (XIOPM), Chinese Academy of Sciences, and the Chief Engineer of XIOPM Holdings Co., Ltd. He is recognized as an Outstanding Young Scholar in Shaanxi Province, China. His research interests span image denoising, computer vision, machine learning, hyperspectral remote sensing image processing, and scientific and technological achievements industrialization. Over the past decade, he has published more than 80 research papers in esteemed journals and conferences and holds 14 technology and product patents. Dr. Zhou is an IEEE Member and serves as a reviewer for multiple international journals, including IEEE Transactions on Image Processing and Neurocomputing. He is actively involved in the industrialization of scientific and technological innovations, leading efforts in technology commercialization, startup incubation, and investment in high-tech enterprises, particularly in optoelectronics and military-civilian integration.

Professional Profile:

ORCID

SCOPUS

Summary of Suitability for Best Researcher Award

Dr. Zuofeng Zhou is a highly accomplished researcher with extensive contributions in the fields of image processing, computer vision, and machine learning. His expertise spans both theoretical research and practical industrial applications, particularly in hyper-spectral remote sensing image processing and image/video analysis.

🎓 Education

  • Ph.D. in a relevant field (Details not specified)

💼 Work Experience

  • Full Professor – XIOPM, Chinese Academy of Sciences
  • Chief Engineer – XIOPM Holdings Co., LTD
  • Referee – Reviewer for top journals including:
    • IEEE Transactions on Image Processing
    • Neurocomputing (Elsevier)
    • IET Image Processing
    • Signal Processing (Elsevier)
    • Conferences: CVPR, ICIP, ECCV

🏆 Achievements & Contributions

  • 📜 Research Contributions:
    • Published 80+ research papers in renowned journals and conferences (e.g., Signal Processing, ICIP, IET Image Processing)
    • Authored book chapter in Wavelet Transform and Some of Its Real-World Applications (ISBN: 978-953-51-2230-2)
  • 🔬 Research Interests:
    • Image denoising & computer vision
    • Machine learning & hyperspectral remote sensing
    • Image/video analysis
    • Industrialization of scientific and technological achievements
  • 📌 Patents:
    • Obtained 14 technology and product patent authorizations
  • 🚀 Industrialization Initiatives:
    • Led the commercialization of S&T achievements at XIOPM
    • Established 280+ hi-tech enterprises
    • Created 7,000+ jobs
    • Developed industry clusters in laser equipment, optoelectronic integrated circuits, and healthcare
  • 💰 Investment & Entrepreneurship:
    • Co-founded CAS Star Incubator Co., Ltd.
    • Managed funds totaling ~5 billion CNY
    • Invested in 150+ projects, attracting over 630 million CNY in social investment

🏅 Awards & Honors

  • 🌟 Outstanding Young Scholar – Shaanxi Province, China
  • 🎖 IEEE Member
  • 🏆 Recognitions:
    • “CAS Pilot Unit for S&T Achievement Transformation”
    • “Shaanxi Pilot Unit for Building an Innovative Province”
    • “State-level S&T Enterprise Incubator”

Publication Top Notes:

Satellite Pose Measurement Using an Improving SIFT Algorithm

ViBe algorithm based on background fusion and channel calculation

The Application of a Pavement Distress Detection Method Based on FS-Net

Image Enhancement Technology in Pavement Disease Detection System

High-Precision Volume Measurement of Potholes in Pavement Maintenance

 

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