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

 

 

 

Elena Zaitseva | Data Mining | Best Researcher Award

Elena Zaitseva | Data Mining | Best Researcher Award

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

Publication profile

Scopus
Googlscholar
Orcid

Education And Experiance

  • ๐ŸŽ“ย MSc in Computer Scienceย (1989) โ€“ Radioengineering Institute, Minsk, Belarus.
  • ๐ŸŽ“ย Ph.D. in Computer Scienceย (1994) โ€“ State University of Informatics and Radioelectronics, Belarus.
  • ๐ŸŽ“ย Associate Professor in Applied Informaticsย (1998) โ€“ Belarus State Economic University.
  • ๐ŸŽ“ย Professor in Applied Informaticsย (2015) โ€“ University of ลฝilina, Slovakia.
  • ๐Ÿ‘ฉโ€๐Ÿซย Teaching: Courses on Applied Informatics, C++, Neural Networks, Reliability Analysis, and Decision-Making Systems.
  • ๐Ÿง‘โ€๐Ÿ’ปย 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 Developmentย 

๐ŸŒย Elena Zaitsevaย is a prominent member of various international organizations, including theย Gnedenko Forumย andย IEEE Czechoslovakia Section Reliability Society, where she chairs significant committees. She has been co-editor and editorial board member for several journals, such asย Mathematical Problems in Engineeringย andย Innovative Technologies and Scientific Solutions for Industries. Her leadership extends to steering technical chapters inย 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.ย ๐ŸŒŸ

Research Focusย 

Awards and Honors

  • ๐Ÿ†ย Chairย of IEEE Czechoslovakia Section Reliability Society Chapter (2018 โ€“ Present).
  • ๐ŸŽ–๏ธย Chairย of ESRA Technical Chapter on Information Technologies and Communication (2011 โ€“ Present).
  • ๐Ÿ“œย Memberย of Editorial Boards for numerous international journals, includingย CERESย andย Mathematical Problems in Engineering.
  • ๐Ÿ…ย Recognized for leadership inย Gnedenko Forumย and European safety initiatives.
  • ๐ŸŒŸย Renowned for her impactful contributions toย reliability and statistical studiesย in academia and industry.

Publoication Top Notes

  • Review of artificial intelligence and machine learning technologies: Classification, restrictions, opportunities, and challengesย (Cited by: 173, Year: 2022)ย ๐ŸŒŸ๐Ÿค–
  • Construction of a reliability structure function based on uncertain dataย (Cited by: 93, Year: 2016)ย ๐Ÿ“Š๐Ÿ”
  • Reliability analysis of multi-state system with application of multiple-valued logicย (Cited by: 84, Year: 2017)ย โš™๏ธ๐Ÿงฎ
  • Review of some applications of unmanned aerial vehicles technology in the resource-rich countryย (Cited by: 70, Year: 2021)ย ๐Ÿš๐ŸŒ
  • Multiple-valued logic mathematical approaches for multi-state system reliability analysisย (Cited by: 66, Year: 2013)ย ๐Ÿ”ข๐Ÿ“
  • Importance analysis by logical differential calculusย (Cited by: 65, Year: 2013)ย ๐Ÿ“–โšก
  • A review of continuous authentication using behavioral biometricsย (Cited by: 59, Year: 2016)ย ๐Ÿ–ฅ๏ธ๐Ÿ”‘

Mr. Krish Kumar Raj | Data Mining Awards | Best Researcher Award

Mr. Krish Kumar Raj | Data Mining Awards | Best Researcher Award

Mr. Krish Kumar Raj, The University of the South Pacific, Fiji

Krish Kumar Raj is a diligent Electrical and Electronics Engineer, currently pursuing a Masterโ€™s Degree in Engineering Science at the University of the South Pacific. With a keen interest in power systems, domestic wiring standards, neural networks, and digital control systems, Krish has hands-on experience in hardware and simulation-based research, particularly in bearing fault diagnosis using deep learning strategies. With a Bachelor’s degree in Electrical and Electronics Engineering, Krish has acquired a diverse skill set encompassing power electrical drives, digital signal processing, and mechatronics. His work experience includes internships in electrical engineering firms and contributions to projects such as the development of a low-cost emergency ventilator during the Covid-19 pandemic. Krish is proficient in programming languages like Python and MATLAB, and he holds certifications in machine learning. He is known for his problem-solving abilities, leadership skills, and ability to work under pressure. Outside of work, Krish enjoys swimming, futsal, soccer, table tennis, and hiking.

Professional Profile:

Google Scholar

๐Ÿ“š Education:

Krish Kumar Raj holds an Honors Bachelorโ€™s degree in Electrical & Electronics Engineering with a commendable Cumulative GPA of 3.58. His academic journey has equipped him with a deep expertise in various facets of engineering, particularly in Power Systems, Neural Networks, and Digital Control. With a keen interest in leveraging these skills towards practical innovation, Krish is dedicated to pushing the boundaries of traditional engineering practices. Through his academic achievements and hands-on experience, he has demonstrated a strong commitment to excellence and a passion for contributing to advancements in his field.

๐Ÿ‘จโ€๐Ÿซ Employment & Experience:

Krish Kumar Raj’s professional journey encompasses diverse roles, reflecting his dedication to both academia and practical application. As a Part-time Tutor at the University of the South Pacific (USP) in Suva, he imparted knowledge to budding engineers, while also serving as a Lab Demonstrator, providing invaluable practical experience in electrical engineering. Complementing his academic endeavors, Krish gained real-world insights through internships in Electrical Contracting and Industrial Maintenance. These experiences not only enriched his understanding of the field but also honed his troubleshooting skills and ability to apply theoretical knowledge to practical scenarios.

๐Ÿ’ป Technical Skillset:

Krish Kumar Raj boasts a versatile technical skill set that spans various software and hardware platforms. Proficient in MATLAB, Python, and AUTOCAD, he navigates complex programming and design tasks with ease. Furthermore, his hands-on experience extends to working with Arduino, Raspberry Pi, and Programmable Logic Controllers (PLC), showcasing his ability to implement innovative solutions in hardware projects. Krish’s expertise also encompasses high and low voltage circuits, demonstrating his competency in handling diverse electrical systems and configurations with precision and proficiency.

Publication Top Notes:

  1. A state-space model for induction machine stator inter-turn fault and its evaluation at low severities by PCA
    • Published: 2021
    • Journal: IEEE Asia-Pacific Conference on Computer Science and Data Engineering
    • Cited by: 5
  2. ECG Multi Class Classification Using Machine Learning Techniques
    • Published: 2023
    • Journal: IEEE International Symposium on Medical Measurements and Applications
    • Cited by: 3
  3. Open Circuit (OC) and Short Circuit (SC) IGBT switch fault detection in three-phase standalone photovoltaic inverters using shallow neural networks
    • Published: 2022
    • Journal: 25th International Conference on Electrical Machines and Systems (ICEMS)
    • Cited by: 3
  4. A LSTM-based Neural Strategy for Diagnosis of Stator Inter-turn Faults with Low Severity Level for Induction Motors
    • Published: 2022
    • Journal: 25th International Conference on Electrical Machines and Systems (ICEMS)
    • Cited by: 3
  5. Enhanced Fault Detection in Bearings Using Machine Learning and Raw Accelerometer Data: A Case Study Using the Case Western Reserve University Dataset
    • Published: 2024
    • Journal: Information

 

 

 

 

Prof Dr. Sathiyabhama Balasubramaniam | Data Mining | Women Researcher Award

Prof Dr. Sathiyabhama Balasubramaniam | Data Mining | Women Researcher Award

Prof Dr. Sathiyabhama Balasubramaniam, Sona College of Technology, India

๐ŸŽ“ Dr. B. Sathiyabhama, with qualifications including a B.E., M.Tech., and Ph.D. from the National Institute of Technology, Tiruchirappalli, brings nearly three decades of teaching experience to the table. Her expertise spans Data Mining, Big Data Analytics, Computational Intelligence, and Health Informatics. With 63 publications to her name, she’s a prolific researcher. As Head of the Centre for Data Mining and Database System Design, she leads research and consultancy projects. Dr. Sathiyabhama’s leadership and technical prowess earned her recognition in the AICTE-UKIERI Leadership Development Programme 2020 and a WINNER title in the IITB-ISRO-AICTE Mapathon. ๐Ÿ†

Professional Profile:

Scopus

Google Scholar

๐ŸŽ“ Qualification:

Dr. B. Sathiyabhama holds a B.E., M.Tech., and Ph.D. from the National Institute of Technology, Tiruchirappalli. She completed her M.Tech project internship at the Bioinformatics Centre, IISC, Bangalore, where she obtained a University rank.

๐Ÿ“š Experience:

With 29 years and 10 months of teaching experience, Dr. Sathiyabhama’s expertise spans various domains including Data Mining, Big Data Analytics, Computational Intelligence, Health Informatics, and more.

๐Ÿ“ Publications:

She has contributed to 63 international and national journal and conference publications, demonstrating her research prowess.

๐Ÿ† Major Contributions:

Dr. Sathiyabhama serves as the Head of the Centre for Data Mining and Database System Design, driving research, development, and consultancy projects. She has organized and coordinated various conferences and conventions and actively contributes to professional society activities.

๐Ÿ… Awards and Recognitions:

She was selected as one of the 100 participants for the AICTE-UKIERI Leadership Development Programme 2020 and was recognized as a WINNER in the IITB-ISRO-AICTE Mapathon, showcasing her leadership and technical skills.

Research Interest :

๐Ÿ” Dr. B. Sathiyabhama’s research interests lie at the intersection of Big Data Analytics, Healthcare, and Data Mining. With a keen focus on leveraging data-driven insights to revolutionize healthcare practices, she delves into the vast realms of Big Data to uncover patterns and trends that can enhance medical decision-making and patient outcomes. Her passion for exploring the synergies between technology and healthcare drives her quest to harness the power of data for the betterment of society. ๐Ÿฅ๐Ÿ’ป

๐Ÿ“šย Publication Impact and Citations :

Scopus Metrics:

  • ๐Ÿ“ย Publications: 34 documents indexed in Scopus.
  • ๐Ÿ“Šย Citations: A total of 295 citations for his publications, reflecting the widespread impact and recognition of Dr. B. Sathiyabhama’s research within the academic community.

Google Scholar Metrics:

  • All Time:
    • Citations: 799 ๐Ÿ“–
    • h-index: 15ย  ๐Ÿ“Š
    • i10-index: 25 ๐Ÿ”
  • Since 2018:
    • Citations: 595 ๐Ÿ“–
    • h-index: 13 ๐Ÿ“Š
    • i10-index: 17 ๐Ÿ”

๐Ÿ‘จโ€๐Ÿซ A prolific researcher with significant impact and contributions in the field, as evidenced by citation metrics. ๐ŸŒ๐Ÿ”ฌ

Publications Top Notes :

  1. A survey on partition clustering algorithms
    • Published in International Journal of Enterprise Computing and Business Systems in 2011.
    • 103 citations.
  2. Bearing fault diagnosis using wavelet packet transform, hybrid PSO and support vector machine
    • Published in Procedia Engineering in 2014.
    • 69 citations.
  3. A novel feature selection framework based on grey wolf optimizer for mammogram image analysis
    • Published in Neural Computing and Applications in 2021.
    • 60 citations.
  4. T2FL-PSO: Type-2 fuzzy logic-based particle swarm optimization algorithm used to maximize the lifetime of Internet of Things
    • Published in IEEE Access in 2021.
    • 57 citations.
  5. Energy and delay aware data aggregation in routing protocol for Internet of Things
    • Published in Sensors in 2019.
    • 55 citations.