Dr. Seyed Reza Nabavi | Nueral Network Awards | Best Researcher Award

Posted on by ScienceFather

Dr. Seyed Reza Nabavi | Nueral Network Awards | Best Researcher Award 

Dr. Seyed Reza Nabavi, University of Mazandaran, Iran

Dr. Seyed Reza Nabavi is an Associate Professor of Applied Chemistry in the Department of Applied Chemistry at the University of Mazandaran, Babolsar, Iran. he earned his Ph.D. in Applied Chemistry from the University of Tabriz in 2009, focusing on hybrid modeling and artificial intelligence applications for olefin process optimization. As a visiting scholar at the National University of Singapore in 2008, Dr. Nabavi further honed his expertise in chemical and biomolecular engineering. His teaching repertoire spans diverse topics, including transport phenomena, chemical reactor design, and chemical process modeling at both undergraduate and postgraduate levels. A prolific researcher, his interests lie in polymer nanotechnology, catalytic processes, machine learning in chemical process optimization, and pyrolysis. Notably, he has collaborated on significant projects, such as studying coke formation and inhibitors in naphtha thermal cracking at the bench scale, bridging academia and industry. Married and based in Iran, Dr. Nabavi has received recognition for his academic excellence, including being the top-ranked B.Sc. graduate.

Professional Profile:

ORCID

Suitability for Best Researcher Award: Seyed Reza Nabavi

Based on the provided curriculum vitae, Dr. Seyed Reza Nabavi demonstrates exceptional qualifications that make him a strong candidate for the Best Researcher Award. Below is a summary of his key accomplishments and attributes supporting his suitability for this recognition

🎓 Educational Background

  • Ph.D. in Applied Chemistry (2009), University of Tabriz 🧬
    Thesis: Application of Hybrid Modeling and Artificial Intelligence in Modeling and Optimization of Olefin Processes.

    • Visiting Scholar: National University of Singapore (Apr-Dec 2008).
  • M.Sc. in Applied Chemistry (2003), University of Tabriz 🧵
    Thesis: Preparation and Characterization of Conducting Polyaniline/Nylon-6 Composite Fibers.
  • B.Sc. in Applied Chemistry (2000), University of Sistan and Baluchestan 🏛️
    • 🎖️ First Rank among graduate students.

👨‍🏫 Teaching Experience

  • Expertise in teaching at M.Sc. and B.Sc. levels 📚, including advanced courses:
    • Transport Phenomena, Design of Experiments (DOE), Chemical Reactors, Process Control, and Petrochemical Processes.
    • Proficient in Modeling and Simulation and Unit Operation Laboratories.

🔬 Research Interests

  • Nanotechnology of Polymers: Electrospinning and Nanofiber Membranes 🧵.
  • Catalytic Processes: Ozonation, Photocatalysts, and Reaction Engineering ⚗️.
  • Modeling and Optimization: Applying Machine Learning and Evolutionary Algorithms 🤖.
  • Thermal Cracking & Pyrolysis: Exploring Coke Formation and Mitigation 🔥.

🏅 Academic Positions

  • Associate Professor: 2022 – Present, University of Mazandaran 🏫.
  • Assistant Professor: 2012 – 2022, University of Mazandaran.

🧪 Research Highlights

  • Lead researcher in projects like Coke Formation and Inhibitors in thermal cracking of naphtha (collaboration with Tabriz Petrochemical Company) 🛢️.
  • Published impactful research on polymers, reaction engineering, and optimization using cutting-edge AI techniques.

Publication top Notes:

Multi-Criteria Decision Making in Chemical and Process Engineering: Methods, Progress, and Potential

A liter scale synthesis of hierarchically mesoporous UiO-66 for removal of large antibiotics from wastewater

Data-Based Modeling, Multi-Objective Optimization and Multi-Criteria Decision Making of a Catalytic Ozonation Process for Degradation of a Colored Effluent

A bacterial cellulose-based LiSrVO4:Eu3+ nanosensor platform for smartphone sensing of levodopa and dopamine: point-of-care diagnosis of Parkinson’s disease

Parametric optimization of poly(ether sulfone) electrospun membrane for effective oil/water separation

Deep Learning Aided Multi-Objective Optimization and Multi-Criteria Decision Making in Thermal Cracking Process for Olefines Production

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

Posted on by ScienceFather

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
CITED: 74
CITED: 68
CITED:56
CITED: 53
CITED: 51

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

Posted on by ScienceFather

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

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

Posted on by ScienceFather

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