Prof Dr. Gulnihal Ozbay | Machine Learning Award | Best Researcher Award

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Prof Dr. Gulnihal Ozbay | Machine Learning Award | Best Researcher Award 

Prof Dr. Gulnihal Ozbay, Delaware State University, United States

Dr. Gulnihal Ozbay is a distinguished Professor and Extension Specialist in Natural Resources at Delaware State University, where she also serves as Director of the Environmental Health & Seafood Safety Lab and the Integrative Ph.D. Program in Agriculture, Food, and Environmental Sciences. Her career is marked by significant achievements in diverse fields, including aquaculture, fisheries, water chemistry, and aquatic ecology. Dr. Ozbay is highly regarded for her expertise in program development, grant writing, and student mentorship. She has built and managed several research labs, including the Mariculture Lab and GIS Lab, and has a strong record of collaboration with various institutions and agencies. Dr. Ozbay holds multiple degrees in relevant fields, including a Ph.D. in Fisheries & Allied Aquacultures from Auburn University and an M.Sc. in Food Science & Biotechnology from Delaware State University. Her leadership extends beyond teaching and research to include roles such as Vice President of DSU AAUP and Chair of the DSU Faculty Research Committee. Her commitment to environmental science is evident in her active participation in programs addressing sustainability, climate change, and seafood safety.

Professional Profile:

Suitability for the Best Researcher Award

Dr. Gulnihal Ozbay’s extensive career demonstrates exceptional proficiency in various fields related to natural resources, including aquaculture, fisheries, water chemistry, aquatic ecology, climate science, seafood chemistry, and microbiology. His role as a Professor and Extension Specialist, combined with his leadership positions, showcases his strong research background and administrative capabilities.

🎓 Professional Preparation

  • Ph.D., Fisheries & Allied Aquacultures (Water Quality)
    Auburn University, 2002
  • Ph.D. Credits, Food Science & Technology
    Dalhousie University, 1999
  • M.Sc., Bio-Resource Engineering (Marine Bio-Resources)
    University of Maine, 1996
  • M.Sc., Food Science & Biotechnology
    Delaware State University, 2016
  • B.Sc., Fisheries & Aquaculture Engineering
    University of Ondokuzmayis, 1991

🏆 Professional Appointments

  • Professor & Extension Specialist, Natural Resources
    Delaware State University, 2012 – Present
  • Adjunct Faculty, Food Science & Biotechnology Graduate Program
    DSU, 2008 – Present
  • Adjunct Faculty, Applied Chemistry Graduate Program
    DSU, 2018 – Present
  • Director, Environmental Health & Seafood Safety Lab
    DSU, 2009 – Present
  • Director, Integrative Ph.D. Program in Agriculture, Food and Environmental Sciences (IAFES)
    DSU, 2021 – Present
  • Vice President, DSU AAUP
    2021 – Present

📚 Teaching Experience

  • Environmental Toxicology
    DSU, 2020-Present
  • Climatology
    DSU, 2012-Present
  • Introduction to Environmental Science
    DSU, 2011-Present
  • Special Problems (Sustainability & Climate Change)
    DSU, 2004-Present
  • Graduate Seminar
    DSU, 2010

Publication top Notes:

CITED: 78
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CITED: 51

Assoc Prof Dr. Izabela Rojek | Artificial Intelligence | Best Researcher Award

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Assoc Prof Dr. Izabela Rojek | Artificial Intelligence | Best Researcher Award 

Assoc Prof Dr. Izabela Rojek, Kazimierz Wielki University, Poland

Dr. Izabela Rojek is a prominent academic and researcher serving as the Head of the Department of Data Processing Methods and Tools and the Dean of the Faculty of Computer Science at Kazimierz Wielki University in Bydgoszcz, Poland. She holds the qualifications of Ph.D., D.Sc.Eng., and Associate Professor. Dr. Rojek’s research is centered on engineering sciences, specifically in Technical Informatics, Telecommunications, and Mechanical Engineering. Her extensive scientific output includes five books, 190 articles and chapters in monographs, and over 6000 points in the Ministry of Science and Higher Education (MNiSW) ranking, with a Hirsch index of 18 (Web of Science and Scopus) and 20 (Google Scholar). She has been recognized with 15 national and international awards, including four UKW Rector’s Awards and three foreign medals for outstanding inventions. Dr. Rojek’s contributions extend to 20 grants and innovation projects, and she actively participates in the Manufacturing Engineering Committee of the Polish Academy of Sciences, where she chairs the Manufacturing Digitisation Section.

Professional Profile:

 

Suitability for Best Researcher Award:

Izabela Rojek is an exemplary candidate for the Best Researcher Award due to her outstanding contributions to the field of engineering sciences, particularly in Technical Informatics and Telecommunications. Her extensive publication record, high citation metrics, and significant involvement in national and international research projects highlight her impact on the field. Her leadership roles and innovative research further demonstrate her exceptional qualifications for this award.

Education:

  • Ph.D. in Engineering Sciences from Kazimierz Wielki University
  • D.Sc.Eng. (Doctor of Science in Engineering)
  • Associate Professor (Assoc. Prof.)

Work Experience:

  • Kazimierz Wielki University, Bydgoszcz
    • Head of the Department of Data Processing Methods and Tools
    • Dean of the Faculty of Computer Science

Additional Roles and Experience:

  • Member of the Manufacturing Engineering Committee of the Polish Academy of Sciences
  • Chair of the Manufacturing Digitisation Section of this Committee
  • Participation in the implementation of the IFS Applications IT system, including solution design, data migration, and training material preparation

Research and Contributions:

  • Authored 5 books and over 190 articles and chapters in monographs
  • Achieved over 6000 points in MNiSW (Polish Ministry of Science and Higher Education) evaluation
  • Total Impact Factor (IF) above 120
  • Hirsch index: h=18 (573 citations, Web of Science), h=18 (717 citations, Scopus), h=20 (1097 citations, Google Scholar)
  • Involved in 20 grants and innovation projects and 10 research topics
  • Recipient of 15 national and international awards, including 4 UKW Rector’s Awards and 3 foreign medals for outstanding inventions

Publication top Notes:

 

Enhancing 3D Printing with Procedural Generation and STL Formatting Using Python

Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review

Use of Machine Learning to Improve Additive Manufacturing Processes

Review of the 6G-Based Supply Chain Management within Industry 4.0/5.0 Paradigm

Utilizing Selected Machine Learning Methods for Conicity Prediction in the Process of Producing Radial Tires for Passenger Cars

Mr. Omer Tariq | Artificial Intelligence Award | Best Researcher Award

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Mr. Omer Tariq | Artificial Intelligence Award | Best Researcher Award 

Mr. Omer Tariq, Korea Advanced Institute of Science and Technology, KAIST, South Korea

Omer Tariq is a Ph.D. candidate at the Korea Advanced Institute of Science and Technology (KAIST), specializing in efficient and privacy-preserving deep learning for AIoT and autonomous systems. With a strong foundation in digital ASIC design, embedded systems, and hardware design, Omer has over seven years of experience in developing and deploying innovative machine learning solutions using TensorFlow, TensorRT, and PyTorch. His research includes advanced robotics software systems, autonomous navigation, and state-of-the-art motion planning algorithms. He has led teams in high-performance SoC/RTL design and verification at the National Electronics Complex, Pakistan, and contributed to satellite imaging systems at SUPARCO. Omer holds a BSc in Electrical Engineering from the University of Engineering and Technology, Taxila, and has published several papers in prominent journals. His technical skills are complemented by a range of certifications in machine learning, data science, and digital signal processing.

Professional Profile:

Summary of Suitability for Best Researcher Award

Omer Tariq is a Ph.D. candidate specializing in efficient and privacy-preserving deep learning for AIoT and Autonomous Systems. His work is highly relevant to current technological advancements and addresses significant challenges in machine learning, robotics, and autonomous systems. His research includes:

Education

Korea Advanced Institute of Science and Technology (KAIST)
Doctor of Philosophy (Ph.D.) in Computer Science
May 2021 – July 2025

  • Majors: Machine Learning & AI
  • CGPA: 3.74/4.3
  • Coursework: Programming for AI, Introduction to Artificial Intelligence, Design and Analysis of Algorithms, Intelligent Robotics, Human-Computer Interaction, Artificial Intelligence and Machine Learning, Technical Writing for Computer Science, Advanced Machine Learning, IoT Datascience

University of Engineering and Technology (UET), Taxila
Bachelor of Science in Electrical Engineering
Nov 2010 – July 2014

  • CGPA: 3.25/4.0
  • Thesis: Computer Vision-Assisted Object Detection and Control Framework for 3-DoF Robotic Arm
  • Area: Microelectronics, Control Systems, and Advanced Computer Architecture

Work Experience

Department of Industrial & Systems Engineering (ISysE), KAIST
Research Assistant
Nov. 2023 – March 2024

  • Designed and developed the electronics and power management module for the DAIM-Autonomous Mobile Robot, enhancing operational efficiency and reliability.
  • Engineered advanced robotics software systems for autonomous navigation and task execution.
  • Implemented state-of-the-art robot motion planning, mapping, and localization (SLAM) algorithms to improve real-time navigation accuracy.

National Electronics Complex, Pakistan (NECOP)
Engineering Manager & Team Lead
Apr. 2019 – Sep. 2022

  • Led verification and validation of high-performance SoC/RTL designs, ensuring system performance and reliability.
  • Spearheaded RTL development and optimization for high-performance IC designs, including logic synthesis, DFT, scan chain insertion, formal verification, and static timing analysis.
  • Managed the use of Synopsys and Cadence EDA tools for front-end and back-end digital IC design processes.

National Space Agency, Pakistan (SUPARCO)
Assistant Manager
Oct. 2014 – Apr. 2019

  • Designed and developed satellite imaging payload systems for national satellite missions.
  • Engineered high-speed, multi-layer PCB designs and conducted signal/power integrity simulations for satellite systems.
  • Developed embedded systems for the Satellite Ku-Band Positioning Unit, enhancing communication and positioning capabilities.

Publication top Notes:

2D Particle Filter Accelerator for Mobile Robot Indoor Localization and Pose Estimation

TabCLR: Contrastive Learning Representation of Tabular Data Classification for Indoor-Outdoor Detection

Compact Walsh–Hadamard Transform-Driven S-Box Design for ASIC Implementations

DeepIOD: Towards A Context-Aware Indoor–Outdoor Detection Framework Using Smartphone Sensors

 

 

Mr. Lianfa Li | Artificial Intelligence Award | Top Researcher Award

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Mr. Lianfa Li | Artificial Intelligence Award | Top Researcher Award 

Mr. Lianfa Li, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, China 

Dr. Lianfa Li is a distinguished Senior Research Associate and Lead Data Scientist at the University of Southern California’s Department of Population and Public Health Sciences. Since August 2017, he has been at the forefront of innovations in data science and machine learning, with a particular focus on remote sensing and air pollution modeling to study exposure and health effects. Dr. Li’s academic journey began with a Bachelor of Science in Resources, Planning, and Management from Nanjing University in 1998, followed by a Ph.D. in Geographical Information Science from the Institute of Geographical Sciences and Natural Resources Research at the Chinese Academy of Sciences in 2005. His career includes significant roles such as Associate Professor at the Chinese Academy of Sciences, Postdoctoral Scholar and Associate Specialist at the University of California, Irvine, and Research Associate at USC’s Department of Preventive Medicine.

Professional Profile:

 

ORCID

 

Summary of Suitability for the Top Researcher Award

Lianfa Li, PhD, currently a Senior Research Associate and Lead Data Scientist at the University of Southern California’s Department of Population and Public Health Sciences, is an exemplary candidate for the Top Researcher Award. His extensive background in data science and machine learning, particularly in the realm of remote sensing and air pollution exposure, positions him as a leader in his field. Below are the reasons why Dr. Li is suitable for this prestigious award:

EDUCATION 🎓📚

  • PhD in Geographical Information Science (June 2005)
    Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
    Advisor: Prof. Jinfeng Wang
  • Bachelor of Science in Resources, Planning and Management (Aug 1998)
    Nanjing University, Nanjing, Jiangsu Province, China
    Advisor: Prof. Yunliang Shi

ACADEMIC EMPLOYMENT 🏛️💼

  • Senior Research Associate, Lead Data Scientist (Aug 2017-Present)
    Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA
    Leading innovations in data science and machine learning, and the modeling efforts in remote sensing and air pollution (exposure and health effects)
  • Research Associate (Aug 2017-July 2014)
    Department of Preventive Medicine, University of Southern California, Los Angeles, CA
  • Associate Specialist (June 2013-June 2014)
    Program in Public Health, University of California, Irvine, CA

HONORS AND AWARDS 🏆🎖️

  1. 2010.6
    The paper about Bayesian risk modeling (Risk Analysis, 30(7), 1157-1175) selected for a media outreach campaign in 2010 by Society for Risk Analysis
  2. 2007.5
    Chinese Academy of Sciences KC Wong Work Incentive Fund
  3. 2004.3
    The Excellent Presidential Scholarship of Chinese Academy of Sciences, 2004

WORKSHOP AND PRESENTATION 🎤📅

  1. Biweekly workshop: “Air pollution and exposure modeling” (2015-present, University of Southern California, California, USA)
  2. Invited presentation: “GCN-assisted U-Net for segmentation of OCT images” (Bay area data science workshop, Mar. 27, 2021)
  3. Invited presentation: “Enhancing semantic segmentation with contextual information” (Bay area data science workshop, Dec. 07, 2019)

Publication top Notes:

Geocomplexity Statistical Indicator to Enhance Multiclass Semantic Segmentation of Remotely Sensed Data with Less Sampling Bias

Multiscale Entropy-Based Surface Complexity Analysis for Land Cover Image Semantic Segmentation

Generating Fine-Scale Aerosol Data through Downscaling with an Artificial Neural Network Enhanced with Transfer Learning

Encoder–Decoder Full Residual Deep Networks for Robust Regression and Spatiotemporal Estimation

Multi-Scale Residual Deep Network for Semantic Segmentation of Buildings with Regularizer of Shape Representation

Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation

Ms. Hind MEZIANE | Artificial Intelligence | Best Scholar Award

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Ms. Hind MEZIANE | Artificial Intelligence | Best Scholar Award 

Ms. Hind MEZIANE, ACSA Lab, Faculty of Sciences, University Mohammed First, Oujda, Morocco

Hind Meziane is a dedicated researcher and Ph.D. candidate in Computer Science at the ACSA Laboratory, Department of Mathematics, Faculty of Sciences, Mohammed Premier University, Oujda, Morocco. Her academic journey began with a Baccalaureate in Science (Science Mathematics Option B) from Mehdi Ben Berka High School in Oujda in 2012. She then pursued higher education at Mohammed Premier University, obtaining a DEUG in Mathematics and Computer Science (2012-2014), a LICENSE in Mathematics and Computer Science (2014-2016), and a Specialized Master’s in Computer Engineering with Honors (2017-2019).

Professional Profile:

Summary of Suitability for Best Scholar Award

Hind Meziane is a highly accomplished researcher whose work primarily focuses on the security of Internet of Things (IoT) systems. She is currently pursuing a Ph.D. in Computer Science at Mohammed Premier University and has an impressive academic background, including a specialized master’s degree in Computer Engineering and a bachelor’s degree in Mathematics and Computer Science. Her research contributions are well-documented through various publications in reputable international journals and conference proceedings.

🎓 Education:

  • 2019-Present: Doctorate (PhD) in Computer Science at Mohammed Premier University, Faculty of Sciences, Oujda.
  • 2017-2019: Specialized Master in Computer Engineering, with Honors, at Mohammed Premier University, Faculty of Sciences, Oujda.
  • 2014-2016: LICENSE in Mathematics and Computer Science from Mohammed Premier University, Faculty of Sciences, Oujda.
  • 2012-2014: DEUG in Mathematics and Computer Science from Mohammed Premier University, Faculty of Sciences, Oujda.
  • 2011-2012: Baccalaureate in Science, Mathematics Option B from Mehdi Ben Berka High School, Oujda.

Publication top Notes:

A survey on performance evaluation of artificial intelligence algorithms for improving IoT security systems

A Comparative Study for Modeling IoT Security Systems

Modeling IoT based Forest Fire Detection System with IoTsec

A Study of Modelling IoT Security Systems with Unified Modelling Language (UML)

Classifying security attacks in iot using ctm method

Internet of Things: Classification of attacks using CTM method

Prof. Changgyun Kim | Artificial Intelligence Award | Best Researcher Award

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Prof. Changgyun Kim | Artificial Intelligence Award | Best Researcher Award 

Prof. Changgyun Kim, Department of Artificial Intelligence & Software/Samcheok,South Korea

Changgyun Kim is an esteemed academic and researcher associated with Kangwon National University, Department of Artificial Intelligence & Software, and Dongguk University’s Industrial Engineering department in South Korea. His research expertise spans deep learning, healthcare, and data mining. He has made significant contributions to the field, including developing AI-based systems for detecting betting anomalies in sports, diagnosing tooth-related diseases using panoramic images, and creating models for obesity diagnosis using 3D body information. His work is published in renowned journals such as Scientific Reports, Annals of Applied Sport Science, JMIR Medical Informatics, Sensors, Sustainability, the International Journal of Distributed Sensor Networks, and Applied Sciences. Dr. Kim’s notable projects include establishing IoT-based smart factories for SMEs in Korea and developing web applications for obesity diagnosis using data mining methodologies. His extensive research portfolio underscores his commitment to advancing AI applications in various domains

Professional Profile:

ORCID

 

Education

No specific details about Changgyun Kim’s educational background are provided in the provided information. To give a more comprehensive overview, details such as degrees obtained, institutions attended, and fields of study would be needed.

Work Experience

  1. Dongguk University: Jung-gu, Seoul, KR
    • Department: Industrial Engineering
    • Position: Not specified in the provided information.
  2. Kangwon National University
    • Department: Artificial Intelligence & Software
    • Position: Not specified in the provided information.

Publication top Notes:

 

AI-based betting anomaly detection system to ensure fairness in sports and prevent illegal gambling

Detectability of Sports Betting Anomalies Using Deep Learning-based ResNet: Utilization of K-League Data in South Korea

Tooth-Related Disease Detection System Based on Panoramic Images and Optimization Through Automation: Development Study

Development of an Obesity Information Diagnosis Model Reflecting Body Type Information Using 3D Body Information Values

Development of a Web Application Based on Human Body Obesity Index and Self-Obesity Diagnosis Model Using the Data Mining Methodology

Establishment of an IoT-based smart factory and data analysis model for the quality management of SMEs die-casting companies in Korea