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

 

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