Prof. Khaled Shaban | Data Science | Best Researcher Award

Prof. Khaled Shaban | Data Science | Best Researcher Award

Prof. Khaled Shaban, Qatar University, Qatar

Prof. Khaled Shaban is a distinguished researcher and professor in Computer Science and Engineering at Qatar University. With expertise in Computational Intelligence, Machine Learning, and Data Science, he has significantly contributed to advancing pattern recognition, cloud computing, and cybersecurity. A senior member of IEEE and ACM, he has received multiple accolades for his groundbreaking research. He also holds an adjunct professorship at the University of Waterloo, reinforcing his global academic influence. His work focuses on AI-driven disease prediction, smart systems, and optimization techniques, making him a leader in intelligent computing innovations.

๐ŸŒย Professional Profile:

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๐Ÿ† Suitability for Best Researcher Award

Prof. Khaled Shabanโ€™s research excellence, innovative contributions, and global recognition make him an ideal candidate for the Best Researcher Award. His pioneering work in Machine Learning, AI, and Computational Intelligence has led to influential publications and prestigious awards, such as the Best Paper Award at IRICT 2021. His ability to merge theory and application in AI, cloud computing, and cybersecurity has significantly impacted academia and industry. His leadership in top-tier conferences and IEEE/ACM communities underscores his commitment to advancing knowledge, making him a highly deserving candidate for this distinguished recognition.

๐ŸŽ“ Education

Prof. Khaled Shaban holds a Ph.D. in Electrical and Computer Engineering from the University of Waterloo, Canada (2006), specializing in Pattern Recognition and Machine Intelligence. His academic journey began with an M.Sc. in Engineering Systems and Computing (2002) from the University of Guelph, Canada, where he developed a strong foundation in computational intelligence and optimization. His interdisciplinary education has enabled him to integrate machine learning, data science, and engineering systems into cutting-edge research. His expertise in algorithms and computing theory has positioned him as a global leader in AI and intelligent systems research.

๐Ÿ’ผ Experience

Prof. Khaled Shaban has an extensive academic career, currently serving as a Professor at Qatar Universityโ€™s College of Engineering (since April 2021). He previously held roles as Associate Professor (2016-2021) and Assistant Professor (2008-2016). Additionally, he is an Adjunct Professor at the University of Waterloo (2021-2027), collaborating on AI-driven computing innovations. His professional affiliations with IEEE, ACM, and international research communities enhance his impact on global technological advancements. Over the years, he has mentored numerous students and led transformative research in Artificial Intelligence, Data Science, and Optimization.

๐Ÿ… Awards & Honors

  • ๐Ÿ† Best Paper Award โ€“ IRICT 2021 for “C-SAR: Class-Specific and Adaptive Recognition for Arabic Handwritten Cheques”
  • ๐Ÿ… Nomination for Best Paper Award โ€“ ICVS 2021 for “MARL: Multimodal Attentional Representation Learning for Disease Prediction”
  • ๐ŸŽ– Promoted to Professor โ€“ Qatar University, 2021
  • ๐Ÿ”ฌ Senior Member, IEEE & ACM โ€“ Recognized for contributions to AI and Computational Intelligence
  • ๐ŸŒ International Collaborations โ€“ Adjunct Professor at the University of Waterloo, fostering global research partnerships

๐Ÿ”ฌ Research Focus

Prof. Khaled Shabanโ€™s research lies at the intersection of Artificial Intelligence, Computational Intelligence, and Data Science. His work in Machine Learning-driven healthcare analytics, particularly in disease prediction and medical image analysis, is widely recognized. He has also made significant contributions to cybersecurity, cloud computing, and smart grid systems. His studies on optimization and knowledge discovery enhance IoT, AI-based automation, and intelligent computing solutions. Through numerous publications and projects, he has addressed real-world challenges in AI, energy-efficient computing, and adaptive learning systems, making his research impactful across academia and industry.

๐Ÿ“–ย Publication Top Notes

  • Urban Air Pollution Monitoring System with Forecasting Models

    • Year: 2016
    • Citations: 341
  • Fault Detection, Isolation, and Service Restoration in Distribution Systems: State-of-the-Art and Future Trends

    • Year: 2016
    • Citations: 321
  • Delay-Aware Scheduling and Resource Optimization with Network Function Virtualization

    • Year: 2016
    • Citations: 266
  • A Reliability-Aware Network Service Chain Provisioning with Delay Guarantees in NFV-Enabled Enterprise Datacenter Networks

    • Year: 2017
    • Citations: 224
  • Deep Learning Models for Sentiment Analysis in Arabic

    • Year: 2015
    • Citations: 150

 

 

Mr. Mohammad Mahdi Badami | Data Analysis | Young Scientist Award

Mr. Mohammad Mahdi Badami | Data Analysis | Young Scientist Award

Mr. Mohammad Mahdi Badami | University of Southern California | United States

Mehdi Badami is a dedicated Ph.D. candidate in Environmental Engineering at the University of Southern California (USC) under Prof. Constantinos Sioutas. His expertise lies in air quality improvement, with hands-on experience in air pollution monitoring using advanced instrumentation such as SMPS-CPC, OPS, and Aethalometer 51. He specializes in data-driven environmental assessments, employing Python for pollution source apportionment and emission trend analysis. His research contributes to community-centric environmental policies and sustainable air quality solutions. Passionate about environmental justice, he aims to bridge scientific research with real-world policy implementation. ๐ŸŒฑ๐Ÿ”ฌ

Professional Profile:

Google Scholar

Suitability for the Young Scientist Award

Mehdi Badami is a strong candidate for the Young Scientist Award due to his significant contributions to environmental engineering, particularly in air quality improvement. As a Ph.D. candidate at the University of Southern California (USC), his research focuses on air pollution monitoring and data-driven environmental assessments. His expertise in advanced instrumentation (e.g., SMPS-CPC, OPS, Aethalometer 51) and Python-based pollution source apportionment makes him a valuable asset to the field.

Education & Experience ๐Ÿข๐ŸŽ“

  • Ph.D. Candidate in Environmental Engineering (2022-Present) โ€“ USC, Los Angeles, USA ๐Ÿ‡บ๐Ÿ‡ธ

    • GPA: 3.95/4
    • Advisor: Prof. Constantinos Sioutas
  • M.Sc. in Mechanical Engineering (Fluid Mechanics) (2017-2020) โ€“ University of Tehran, Iran ๐Ÿ‡ฎ๐Ÿ‡ท

    • GPA: 3.77/4
    • Supervisors: Dr. Alireza Riasi, Prof. Kayvan Sadeghy
  • B.Sc. in Mechanical Engineering (2012-2016) โ€“ K. N. Toosi University of Technology, Iran ๐Ÿ‡ฎ๐Ÿ‡ท

  • Research Assistant โ€“ USC Aerosol Lab (2023โ€“Present) ๐Ÿญ๐ŸŒซ๏ธ

    • Conducted air pollution measurements using state-of-the-art monitoring systems
    • Developed Python programs for data automation and pollution trend analysis
    • Led collaborations with institutions like Harvard, UCLA, and Dresden University
    • Mentored Ph.D. students on environmental research projects
  • Research Assistant โ€“ Hydro-kinetic Energy Lab, University of Tehran (2017โ€“2022) ๐Ÿ”ฌ๐Ÿ’ง

    • Investigated fluid mechanics phenomena related to blood hammer effects in arteries
  • Teaching Assistant โ€“ USC & University of Tehran (2018โ€“2024) ๐Ÿ“š๐Ÿ‘จโ€๐Ÿซ

    • Assisted in courses on climate change, air quality, fluid mechanics, and thermodynamics

Professional Development ๐Ÿš€

Mehdi Badami has actively contributed to the field of environmental engineering through cutting-edge research on air pollution, sustainability, and emission control. His extensive knowledge of aerosol science, atmospheric chemistry, and data analysis allows him to assess air quality trends with precision. He has developed innovative models for pollution source apportionment, worked on real-time monitoring systems, and collaborated with leading institutions to improve urban air quality. His passion for environmental justice drives his work towards creating actionable solutions that ensure healthier air for communities. His dedication extends beyond academia, as he actively engages in outreach and policy-driven initiatives. ๐ŸŒฟ๐Ÿ“Š

Research Focus ๐Ÿ”

Mehdiโ€™s research centers on air pollution control, environmental monitoring, and sustainable urban development. His work involves identifying and mitigating pollution sources through field measurements and computational models. He specializes in:

  • Air Quality Assessment ๐ŸŒซ๏ธ๐Ÿ“Š โ€“ Studying PM2.5 and ultrafine particle pollution in urban environments
  • Pollution Source Apportionment ๐Ÿญโš–๏ธ โ€“ Analyzing emissions from vehicles, industries, and natural sources
  • Aerosol Science ๐ŸŒช๏ธ๐Ÿ’จ โ€“ Investigating the toxicity and health impacts of airborne particles
  • Machine Learning in Environmental Studies ๐Ÿค–๐Ÿ“‰ โ€“ Utilizing data science to model pollution trends
  • Climate and Environmental Justice ๐ŸŒŽโš–๏ธ โ€“ Advocating for equitable air quality policies in urban communities

Awards & Honors ๐Ÿ†

  • Outstanding Research Assistant Award โ€“ USC, Sonny Astani Department of Civil and Environmental Engineering (2024) ๐Ÿ…
  • Fellowship Award โ€“ USC (2022-2023) ๐ŸŽ“๐Ÿ’ฐ (Recognized for academic excellence in Environmental Engineering)
  • National Fellowship for Masterโ€™s Studies โ€“ University of Tehran (2017) ๐Ÿ“–๐Ÿ†
  • Top 0.15% Rank in National Entrance Exam โ€“ Iran (Competitive ranking in Mechanical Engineering)

Publication Top Notes:

๐Ÿ“„ Design, optimization, and evaluation of a wet electrostatic precipitator (ESP) for aerosol collection โ€“ Atmospheric Environment (2023) โ€“ ๐Ÿ“‘ Cited by: 11
๐Ÿ“„ Size-segregated source identification of water-soluble and water-insoluble metals and trace elements of coarse and fine PM in central Los Angeles โ€“ Atmospheric Environment (2023) โ€“ ๐Ÿ“‘ Cited by: 7
๐Ÿ“„ Numerical study of blood hammer phenomenon considering blood viscoelastic effects โ€“ European Journal of Mechanics-B/Fluids (2022) โ€“ ๐Ÿ“‘ Cited by: 7
๐Ÿ“„ Development and performance evaluation of online monitors for near real-time measurement of total and water-soluble organic carbon in fine and coarse ambient PM โ€“ Atmospheric Environment (2024) โ€“ ๐Ÿ“‘ Cited by: 4
๐Ÿ“„ Numerical analysis of laminar viscoelastic fluid hammer phenomenon in an axisymmetric pipe โ€“ Journal of the Brazilian Society of Mechanical Sciences and Engineering (2021) โ€“ ๐Ÿ“‘ Cited by: 3
๐Ÿ“„ Urban emissions of fine and ultrafine particulate matter in Los Angeles: Sources and variations in lung-deposited surface area โ€“ Environmental Pollution (2025) โ€“ ๐Ÿ“‘ Cited by: 1