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

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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. 🌱🔬

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 🏢🎓

  • Ph.D. Candidate in Environmental Engineering (2022-Present) – USC, Los Angeles, USA 🇺🇸

    • GPA: 3.95/4
    • Advisor: Prof. Constantinos Sioutas

  • M.Sc. in Mechanical Engineering (Fluid Mechanics) (2017-2020) – University of Tehran, Iran 🇮🇷

    • GPA: 3.77/4
    • Supervisors: Dr. Alireza Riasi, Prof. Kayvan Sadeghy

  • B.Sc. in Mechanical Engineering (2012-2016) – K. N. Toosi University of Technology, Iran 🇮🇷

  • Research Assistant – USC Aerosol Lab (2023–Present) 🏭🌫️

    • 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 – Hydro-kinetic Energy Lab, University of Tehran (2017–2022) 🔬💧

    • Investigated fluid mechanics phenomena related to blood hammer effects in arteries

  • Teaching Assistant – USC & University of Tehran (2018–2024) 📚👨‍🏫

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

Professional Development 🚀

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. 🌿📊

Research Focus 🔍

Mehdi’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 🌫️📊 – Studying PM2.5 and ultrafine particle pollution in urban environments
  • Pollution Source Apportionment 🏭⚖️ – Analyzing emissions from vehicles, industries, and natural sources
  • Aerosol Science 🌪️💨 – Investigating the toxicity and health impacts of airborne particles
  • Machine Learning in Environmental Studies 🤖📉 – Utilizing data science to model pollution trends
  • Climate and Environmental Justice 🌎⚖️ – Advocating for equitable air quality policies in urban communities

Awards & Honors 🏆

  • Outstanding Research Assistant Award – USC, Sonny Astani Department of Civil and Environmental Engineering (2024) 🏅
  • Fellowship Award – USC (2022-2023) 🎓💰 (Recognized for academic excellence in Environmental Engineering)
  • National Fellowship for Master’s Studies – University of Tehran (2017) 📖🏆
  • Top 0.15% Rank in National Entrance Exam – Iran (Competitive ranking in Mechanical Engineering)

Publication Top Notes:

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

 

 

 

Elena Zaitseva | Data Mining | Best Researcher Award

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Elena Zaitseva | Data Mining | Best Researcher Award

Prof. Dr. Elena Zaitseva, University of Zilina , Slovakia.

Publication profile

Scopus
Googlscholar
Orcid

Education And Experiance

  • 🎓 MSc in Computer Science (1989) – Radioengineering Institute, Minsk, Belarus.
  • 🎓 Ph.D. in Computer Science (1994) – State University of Informatics and Radioelectronics, Belarus.
  • 🎓 Associate Professor in Applied Informatics (1998) – Belarus State Economic University.
  • 🎓 Professor in Applied Informatics (2015) – University of Žilina, Slovakia.
  • 👩‍🏫 Teaching: Courses on Applied Informatics, C++, Neural Networks, Reliability Analysis, and Decision-Making Systems.
  • 🧑‍💻 Research: Focus on multiple-valued logic, reliability analysis, and data mining applications.

Suitability For The Award

Prof. Dr. Elena Zaitseva is an exceptionally qualified candidate for the Best Researcher Award due to her remarkable academic career, innovative contributions to multiple research domains, and leadership roles in international scientific communities. With over three decades of professional experience, she has made significant advancements in applied informatics, reliability analysis, and multiple-valued logic, among other fields. Her work seamlessly bridges theoretical research and practical applications, particularly in data mining, healthcare reliability, and decision support systems.

Professional Development 

🌐 Elena Zaitseva is a prominent member of various international organizations, including the Gnedenko Forum and IEEE Czechoslovakia Section Reliability Society, where she chairs significant committees. She has been co-editor and editorial board member for several journals, such as Mathematical Problems in Engineering and Innovative Technologies and Scientific Solutions for Industries. Her leadership extends to steering technical chapters in European Safety and Reliability Association (ESRA). Through her dedication to professional excellence, she mentors researchers worldwide, advances computational reliability, and fosters interdisciplinary collaboration. Her innovative spirit is reflected in her contributions to the reliability and biomedical informatics communities. 🌟

Research Focus 

Awards and Honors

  • 🏆 Chair of IEEE Czechoslovakia Section Reliability Society Chapter (2018 – Present).
  • 🎖️ Chair of ESRA Technical Chapter on Information Technologies and Communication (2011 – Present).
  • 📜 Member of Editorial Boards for numerous international journals, including CERES and Mathematical Problems in Engineering.
  • 🏅 Recognized for leadership in Gnedenko Forum and European safety initiatives.
  • 🌟 Renowned for her impactful contributions to reliability and statistical studies in academia and industry.

Publoication Top Notes

  • Review of artificial intelligence and machine learning technologies: Classification, restrictions, opportunities, and challenges (Cited by: 173, Year: 2022) 🌟🤖
  • Construction of a reliability structure function based on uncertain data (Cited by: 93, Year: 2016) 📊🔍
  • Reliability analysis of multi-state system with application of multiple-valued logic (Cited by: 84, Year: 2017) ⚙️🧮
  • Review of some applications of unmanned aerial vehicles technology in the resource-rich country (Cited by: 70, Year: 2021) 🚁🌍
  • Multiple-valued logic mathematical approaches for multi-state system reliability analysis (Cited by: 66, Year: 2013) 🔢📐
  • Importance analysis by logical differential calculus (Cited by: 65, Year: 2013) 📖⚡
  • A review of continuous authentication using behavioral biometrics (Cited by: 59, Year: 2016) 🖥️🔑

Dr. Julius Olaniyan | Machine Learning Award |Best Researcher Award

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Dr. Julius Olaniyan | Machine Learning Award |Best Researcher Award

Dr. Julius Olaniyan, Bowen University, Nigeria 

Olaniyan Julius in Odo-Owa, Kwara State, Nigeria. He is a Lecturer II in the Computer Science Department at Bowen University, Iwo, Osun State, Nigeria. Julius holds a Ph.D. in Computer Science (2023) and has extensive experience in software development, data analysis, and teaching. He has worked in several institutions, including Landmark University, Federal Polytechnic Auchi, and Feghas Solutions Ltd. Over his career, he has developed various applications using programming languages such as C, C++, Java, Python, and PHP. Julius specializes in Artificial Intelligence, Computer Vision, Natural Language Processing, and Machine Translation. A devoted husband and father of three, Julius is passionate about advancing AI and its application in healthcare and education. He has contributed to several innovative research papers in the field of computer science and AI.

Professional Profile:

Google Scholar

Summary of Suitability for Award:

Dr. Olaniyan has demonstrated outstanding proficiency and expertise in the fields of Artificial Intelligence, Computer Vision, Natural Language Processing, and Machine Translation, with a solid academic background in Computer Science. He holds a Ph.D. in Computer Science from Landmark University, and has published extensively in high-impact journals and conferences. His work on cataract detection using deep learning, as well as his innovative contributions in areas like speech refinement and emotion recognition, highlights his commitment to advancing technology for real-world applications. Furthermore, his ability to collaborate across interdisciplinary research teams and contribute to several peer-reviewed articles reflects his academic rigor and leadership.

🎓Education: 

Olaniyan Julius completed his Ph.D. in Computer Science at Landmark University (2023). He also holds a Master’s in Computer Science (M.Tech) from the Federal University of Technology, Akure (2019), where he also earned a Postgraduate Diploma (PGD) in 2012. Julius started his academic journey with a Bachelor’s in Computer Science from the Federal University of Oye Ekiti (2022). His earlier qualifications include a Higher National Diploma (HND) in Computer Science from Auchi Polytechnic (2006), and a National Diploma (ND) in the same field (2000). Julius completed his Secondary Education at Orota Community High School, Odo-Owa (1994) and his Primary Education at St. Thomas Catholic School (1988). His strong educational foundation in Computer Science has shaped his successful academic and professional career.

🏢Work Experience:

Olaniyan Julius has a diverse career in academia and industry. He is currently a Lecturer II at Bowen University, Nigeria. Previously, he served as a Lecturer II at Landmark University (2023-2024) and as a Data Analyst at Federal Polytechnic Auchi (2013-2022). His industry experience includes working as a Software Developer/Business Developer at Feghas Solutions Ltd. (2009-2012) and a Tutor/Application Developer at Pesoka Systems Ltd. (2008). Julius also has teaching experience from his time as a Lecturer during his NYSC service at Maritime Academy of Nigeria (2007-2008). His early career included roles like Data Processing Officer at Ajaokuta Steel Company (2002-2004) and School Database Admin at Sani Bello Secondary School (2001). Julius’s experience spans academic teaching, research, software development, data analysis, and project management.

🏅Awards:

Olaniyan Julius has received numerous accolades throughout his academic and professional journey. His Ph.D. dissertation was highly recognized, contributing to his recognition as an emerging scholar in Computer Science. He was awarded a best student award during his time at Landmark University and has been recognized by the Federal Polytechnic Auchi for his outstanding performance as a Data Analyst. Julius’s commitment to education and research has earned him several institutional commendations for his efforts in developing AI-driven solutions in healthcare and education. His research in Artificial Intelligence and Machine Translation has garnered him recognition at international conferences. He is also an active member of several professional organizations in computer science and artificial intelligence. Julius’s leadership and contributions to academic and professional initiatives have cemented his reputation as a passionate educator and researcher.

🔬Research Focus:

Olaniyan Julius specializes in Artificial Intelligence (AI), with a focus on Computer Vision, Natural Language Processing (NLP), and Machine Translation. His work primarily involves using deep learning techniques to create solutions for healthcare (e.g., cataract detection) and education (e.g., student performance evaluation). Julius is dedicated to developing hybrid AI models that combine traditional methods with transformative learning approaches. His research in audio signal denoising and speech-to-speech translation aims to enhance communication and multilingual interaction. He is passionate about designing AI-powered systems that can automate and optimize processes, improving outcomes in health diagnostics and online learning environments. Julius’s work on emotion detection in virtual classrooms and the integration of CNN models for speech emotion recognition represents a significant contribution to the AI field. His interdisciplinary research approach holds promise for real-world AI applications in various domains.

Publication Top Notes: 

  • “Utilizing an Attention-Based LSTM Model for Detecting Sarcasm and Irony in Social Media”
  • “Implementation of Audio Signals Denoising for Perfect Speech-to-Speech Translation Using Principal Component Analysis”
  • “Advancements in Accurate Speech Emotion Recognition Through the Integration of CNN-AM Model”
  • “Transformative Transparent Hybrid Deep Learning Framework for Accurate Cataract Detection”
  • “Parallel Attention Driven Model for Student Performance Evaluation”

 

 

 

 

Mohammadreza Mahmoudi | Data Science | Best Researcher Award

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Dr. Mohammadreza Mahmoudi | Data Science | Best Researcher Award

Professor, Fasa University, Iran 

Dr. Mohammadreza Mahmoudi is an esteemed researcher with a robust background in mathematical statistics and applied probability. His contributions span several impactful projects, including advanced statistical methods and applications in diverse fields. His research excellence and distinguished academic career make him a strong candidate for the Best Researcher Award.

Professional Profile:

Scopus

Summary of Suitability for the Research for Best Researcher Award: 

Dr. Mohammadreza Mahmoudi stands out as a prime candidate for the Best Researcher Award due to his exceptional contributions to mathematical statistics and applied probability. His extensive research on periodograms, statistical properties of simple processes, and advanced non-parametric methodologies demonstrates a deep expertise in his field. Dr. Mahmoudi’s accolades, including being a top student at all levels of his education and his role as an Advisory Board Member of ScienceVier Canada, underscore his recognition and influence in statistical research. His robust teaching experience and impactful projects further solidify his suitability for this prestigious award, highlighting his dedication to advancing statistical science and education.

🎓Education:

Dr. Mahmoudi completed his Ph.D. in Mathematical Statistics (Applied Probability) from Shiraz University in 2016, following a Master of Science in Mathematical Statistics and a Bachelor of Science in Statistics from the same institution. His educational journey reflects a profound commitment to advancing statistical science.

🏢Work Experience:

Dr. Mahmoudi has served as a researcher and educator in statistical methodologies, specializing in areas such as time series analysis, stochastic processes, and nonparametric methodologies. He has been actively involved in teaching a broad range of statistical courses at Shiraz University and has contributed to several high-impact research projects.

🏆Awards and Grants:

Dr. Mahmoudi has been recognized as a top student during his Ph.D., M.Sc., and B.Sc. periods at Shiraz University. He has also been elected as an Advisory Board Member of ScienceVier Canada, showcasing his expertise and influence in the field of statistics.

Publication Top Notes:

  1. “Machine learning models for predicting interactions between air pollutants in Tehran Megacity, Iran”
    • Year: 2024
    • Journal: Alexandria Engineering Journal
  2. “Solving optimal control problems governed by nonlinear PDEs using a multilevel method based on an artificial neural network”
    • Year: 2024
    • Journal: Computational and Applied Mathematics
  3. “The removal of methylene blue from aqueous solutions by polyethylene microplastics: Modeling batch adsorption using random forest regression”
    • Year: 2024
    • Journal: Alexandria Engineering Journal
  4. “Meteorological Drought Prediction Based on Evaluating the Efficacy of Several Prediction Models”
    • Year: 2024
    • Journal: Water Resources Management
  5. “Spatial and temporal assessment and forecasting vulnerability to meteorological drought”
    • Year: 2024
    • Journal: Environment, Development and Sustainability
  6. “Assessment of Continuity Changes in Spatial and Temporal Trend of Rainfall and Drought”
    • Year: 2023
    • Journal: Pure and Applied Geophysics
  7. “Using the multiple linear regression based on the relative importance metric and data visualization models for assessing the ability of drought indices”
    • Year: 2023
    • Journal: Journal of Water and Climate Change
  8. “Dryland farming wheat yield prediction using the Lasso regression model and meteorological variables in dry and semi-dry region”
    • Year: 2023
    • Journal: Stochastic Environmental Research and Risk Assessment
  9. “Cyclic clustering approach to impute missing values for cyclostationary hydrological time series”
    • Year: 2023
    • Journal: Quality and Quantity
  10. “Statistical and Mathematical Modeling for Predicting Caffeine Removal from Aqueous Media by Rice Husk-Derived Activated Carbon”
    • Year: 2023
    • Journal: Sustainability (Switzerland)

 

 

Dr. Kittichai Lavangnananda | Data Science Awards | Best Researcher Award

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Dr. Kittichai Lavangnananda | Data Science Awards | Best Researcher Award

Dr. Kittichai Lavangnananda, University of Luxembourg, Thailand

Dr. Kittichai Lavangnananda holds a Ph.D. in Artificial Intelligence from Cardiff University, UK (1996), an M.Sc. in Computer Science from The University of Wales Cardiff, UK (1987), and a B.Sc. in Computer Science from The University of Hull, UK (1985). He currently serves as an Associate Professor at King Mongkut’s University of Technology Thonburi (KMUTT), where he also holds administrative positions as Associate Dean on Research, International Relations, and Academic Quality Assurance, and Head of Software Technology Division. His research interests include Computational Intelligence, Data Science, Evolutionary Computation, Machine Learning, Deep Learning, and Urban Planning. Kittichai has extensive international collaboration and experience in academia and technology development. 🤖

Professional Profile:

Scopus

🎓 Qualification:

Dr. Kittichai Lavangnananda is an accomplished academic with a Ph.D. in Artificial Intelligence from Cardiff University, UK (1996), complemented by an M.Sc. in Computer Science from The University of Wales Cardiff (1987), and a B.Sc. in Computer Science from The University of Hull, UK (1985). Currently serving as an Associate Professor at King Mongkut’s University of Technology Thonburi (KMUTT), he holds pivotal administrative roles including Associate Dean for Research, International Relations, and Academic Quality Assurance. Dr. Lavangnananda also heads the Software Technology Division, contributing significantly to the fields of Computational Intelligence, Data Science, Evolutionary Computation, Machine Learning, Deep Learning, and Urban Planning through his research and leadership.

👨‍🏫 Teaching Experience:

Dr. Kittichai Lavangnananda is an Associate Professor at King Mongkut’s University of Technology Thonburi (KMUTT), where he serves as the Associate Dean of Research, International Relations, and Academic Quality Assurance, and Head of the Software Technology Division. With a Ph.D. in Artificial Intelligence from Cardiff University, UK, and extensive international experience, his teaching spans key areas including Artificial Intelligence, Data Structures and Algorithms, Human-Computer Interaction, Qualitative & Model-based Reasoning, and Object-oriented programming. He brings a wealth of knowledge and practical insight to his educational role, fostering a deep understanding of complex computational concepts among his students.

🔬 Research Interests:

Dr. Kittichai Lavangnananda, Ph.D., is an Associate Professor at King Mongkut’s University of Technology Thonburi (KMUTT), where he serves as Associate Dean of Research, International Relations, and Academic Quality Assurance, and Head of the Software Technology Division. His research interests encompass Computational Intelligence, Data Science, Evolutionary Computation, Machine Learning, Deep Learning, Meta-heuristics, and Multi-Objective Optimization. With a strong background in Artificial Intelligence and extensive experience in academia and technology development, Dr. Lavangnananda contributes significantly to advancing knowledge in these fields through research, teaching, and collaborative projects both nationally and internationally.

Publication Top Notes:

  • Title: Scheduling Deep Learning Training in GPU Cluster Using the Model-Similarity-Based Policy
    • Publication Year: 2023
  • Title: Implementation of Predictive Model for Diarrhea among Afghanistan Children Based on Medical and Non-Medical Attributes
    • Publication Year: 2022
  • Title: Implementing Predictive Model for Child Mortality in Afghanistan
    • Publication Year: 2022
    • Citations: 2
  • Title: Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service
    • Publication Year: 2021
    • Citations: 7
  • Title: Application of Machine Learning in Assignment of Child Delivery Service in Afghanistan
    • Publication Year: 2021
    • Citations: 3

 

 

Prof. Mamede de Carvalho | Big Data Awards | Best Researcher Award

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Prof. Mamede de Carvalho | Big Data Awards | Best Researcher Award

Prof. Mamede de Carvalho, Faculdade de Medicina , Universidade de Lisboa, Portugal

Prof. Mamede de Carvalho is a distinguished medical professional renowned for his contributions to neurology and physiology. He obtained his MD from Nova Lisbon University in 1985, specializing in Neurology at the University Hospital in Lisbon in 1993. He earned his PhD in Neurology from the University of Lisbon in 2000, followed by a Habilitation in Neurosciences in 2007. Since 2010, he has served as a Full Professor of Physiology at the University of Lisbon, where he has made significant advancements in clinical neurology, particularly in ALS and neuromuscular disorders. Prof. de Carvalho’s leadership roles include Vice-Dean at the Faculty of Medicine and President of the Reynaldo dos Santos Technological Center in Lisbon. He also directed the Neuromuscular Unit at CHLN – Hospital de Santa Maria from 2009 to 2019, further cementing his impact on neurology research and practice.

🌐 Professional Profile:

Scopus

Orcid

Google Scholar

🎓 Education

Prof. Mamede de Carvalho is a distinguished medical professional with a robust academic background. He obtained his MD from Nova Lisbon University in Lisbon, Portugal, in 1985, followed by specialization in Neurology at the University Hospital in Lisbon in 1993. He earned his PhD in Neurology from the University of Lisbon in 2000 and completed his Habilitation in Neurosciences at the same institution in 2007. Since 2010, he has served as a Full Professor of Physiology at the University of Lisbon, contributing significantly to the fields of neurology and neuroscience through his research and academic leadership.

🌐 Professional Experience & Leadership

Prof. Mamede de Carvalho is a distinguished figure in neurology and physiology, having served as the President of the Reynaldo dos Santos Technological Center in Lisbon, Portugal, from 2017 to 2022. Prior to this, he held the position of Vice-Dean at the Faculty of Medicine – University of Lisbon from 2015 to 2022. With extensive expertise, he also served as the Director of the Neuromuscular Unit at CHLN – Hospital de Santa Maria in Lisbon from 2009 to 2019, contributing significantly to advancements in clinical neurology and neuromuscular disorders.

🔬 Clinical Research & Funding

Prof. Mamede de Carvalho is a pioneering figure in clinical neurology research, renowned for his contributions to advancements like Transcranial Magnetic Stimulation and the Threshold Technique for Axonal Excitability. His leadership has been instrumental in securing significant funding for projects focused on amyotrophic lateral sclerosis (ALS) and other neurodegenerative diseases, including grants from JPND and FCT.

Publication Top Notes: