Soumeya Belabbas | Artificial Intelligence | Best Researcher Award

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Soumeya Belabbas| Artificial Intelligence| Best Researcher Award

Soumeya Belabbas,University of Sciences and Technology Houari Boumediene,United state.

Soumeya has taught telecommunications systems and networks to fourth-year engineering students at the Higher National School of Information and Communication Technologies and Post in Algiers. Her teaching module includes mobile networks.

Publication Profile

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Education 

Soumeya Belabbas is currently pursuing her PhD in Telecommunication and Information Processing at the University of Sciences and Technology Houari Boumediene in Algiers, Algeria, a journey she embarked on in December 2017. Her doctoral research focuses on multi-variable acoustic modeling for assessing pathological speech understanding, showcasing her deep commitment to advancing the field of telecommunications and speech processing. In addition to her ongoing doctoral studies, Soumeya holds a Master’s degree in Telecommunications, Networks, and Multimedia, which she earned in June 2017 from the same university. This advanced degree equipped her with a robust foundation in telecommunications, further honed through her bachelor’s degree in Telecommunications and Electronics, completed in June 2015. Her academic journey began with a Baccalaureate Diploma in Experimental Sciences, awarded in July 2012, which laid the groundwork for her subsequent specialization and research. Soumeya has also actively participated in professional development activities, such as a workshop on FPGA systems in April 2024, reflecting her continuous pursuit of knowledge and expertise in her field.

Soumeya Belabbas has cultivated a solid foundation in both research and teaching throughout her academic career. As a part-time teacher at the Higher National School of Information and Communication Technologies and Post in Algiers, Algeria, she has been responsible for educating fourth-year engineering students in telecommunications systems and networks, with a specific focus on mobile networks. Her teaching role, undertaken in June 2024, has allowed her to share her extensive knowledge and experience with the next generation of engineers. Alongside her teaching duties, Soumeya has made significant contributions to the field of telecommunications and information processing through her research. She has published multiple articles and presented her findings at international conferences, showcasing her expertise in improving mobile speech recognition systems and pathological speech classification. Her role as a researcher and educator demonstrates her commitment to advancing the field and fostering academic growth.

Soumeya Belabbas’s research is centered on the cutting-edge domains of telecommunications and speech processing, with a particular emphasis on addressing challenges in automatic speech recognition and classification. Her work is dedicated to developing innovative solutions for individuals with voice disorders, aiming to enhance the robustness and accuracy of speech recognition systems. Her current PhD research project, titled “Multi-variable Acoustic Modeling for Assessing Pathological Speech Understanding,” exemplifies her focus on improving pathological speech classification systems. This project involves a comprehensive three-stage framework that incorporates advanced techniques such as speech enhancement, multi-stream approaches, and deep machine learning algorithms like convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks. Additionally, Soumeya’s interests extend to signal processing, image processing, and audio speech processing, leveraging machine learning and deep learning methodologies to tackle complex problems in these areas. Her dedication to these research areas is reflected in her numerous publications and presentations, which contribute to the advancement of knowledge and technology in telecommunications and speech processing.

Skills

Soumeya Belabbas possesses a diverse and robust skill set that spans various aspects of telecommunications, information processing, and programming. She is proficient in multiple programming languages, including C/C++, Python, Perl, MATLAB, VHDL, PHP, HTML5, and CSS3, which enable her to develop sophisticated algorithms and models for speech processing and recognition. Her technical expertise extends to the use of HTK tools, essential for hidden Markov model training and evaluation in speech recognition systems. Soumeya is also skilled in managing and operating different operating systems, including Windows Server and Linux (Ubuntu), ensuring that she can work in a versatile computing environment. Her strong foundation in signal and audio speech processing is complemented by her experience with machine learning and deep learning techniques, which she applies to enhance the performance of speech recognition systems. Additionally, Soumeya is well-versed in the use of various software tools and platforms essential for research and development in her field, making her a well-rounded and highly capable researcher and education .

Publications 📚📝

Soumeya Belabbas| Artificial Intelligence| Best Researcher Award

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Soumeya Belabbas| Artificial Intelligence| Best Researcher Award

Soumeya Belabbas,

Dr.Chunhui Li, PhD (📧 chunhuili@lbl.gov | 🌐 LinkedIn | 🐱 GitHub) is a post-doctoral fellow at Lawrence Berkeley National Laboratory, specializing in deep learning and molecular dynamics simulations. Dr. Li’s research focuses on developing machine learning models for rapid predictions in geoscience and battery applications, with significant speed improvements over conventional methods.

Publication Profile

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Orcid

Education and Experience 🎓

Dr.Chunhui Li earned a Doctor of Philosophy in Mechanical Engineering (🎓 Washington State University, July 2019) with a dissertation on simulation studies of protein-particle interactions in battery applications, advised by Prof. Jin Liu. He also holds a Master of Engineering in Mechanical Engineering (🎓 Harbin Institute of Technology, July 2013) with a thesis on grinding trajectories of SiC Aspherical, advised by Prof. Feihu Zhang, and a Bachelor of Engineering in Manufacturing Engineering (🎓 Harbin Institute of Technology, July 2011). Since July 2020, Dr. Li has been working at Lawrence Berkeley National Laboratory, focusing on rapid prediction of liquid structure using deep learning and developing machine learning surrogates for Geoscience Models.

Professional Development

Dr.Chunhui Li has honed technical skills in molecular dynamics simulation (🖥️ GROMACS, LAMMPS, VASP), machine learning/deep learning/data science (🧠 PyTorch, TensorFlow, Scikit-Learn, Pandas, Numpy), and programming languages (💻 Python, Java, C/C++, Shell script, MATLAB, Perl). He has designed and managed high-throughput molecular dynamics simulations, curated extensive datasets, and developed efficient AI models, showcasing impressive speed improvements over traditional methods.

Research Focus

Dr. Li’s research focuses on the application of machine learning and molecular dynamics to geoscience and battery applications. His notable work includes developing deep learning models for rapid prediction of time-averaged structural properties, creating AI-based surrogates for geochemistry models, and improving the ion conductivity of solid polymer electrolytes through protein-coated nanofillers. Dr.Li’s collaborative efforts with experimental researchers have led to significant advancements in computational physics and material science.

Publications 📚📝

ChunhuiLi | Molecular Dynamics | Best Researcher Award

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ChunhuiLi | Molecular Dynamics | Best Researcher Award

Dr. ChunhuiLi , lawrence berkeley national laboratory, United States.

Dr.Chunhui Li, PhD (📧 chunhuili@lbl.gov | 🌐 LinkedIn | 🐱 GitHub) is a post-doctoral fellow at Lawrence Berkeley National Laboratory, specializing in deep learning and molecular dynamics simulations. Dr. Li’s research focuses on developing machine learning models for rapid predictions in geoscience and battery applications, with significant speed improvements over conventional methods.

Publication Profile

Google Scholar

Orcid

Education and Experience 🎓

Dr.Chunhui Li earned a Doctor of Philosophy in Mechanical Engineering (🎓 Washington State University, July 2019) with a dissertation on simulation studies of protein-particle interactions in battery applications, advised by Prof. Jin Liu. He also holds a Master of Engineering in Mechanical Engineering (🎓 Harbin Institute of Technology, July 2013) with a thesis on grinding trajectories of SiC Aspherical, advised by Prof. Feihu Zhang, and a Bachelor of Engineering in Manufacturing Engineering (🎓 Harbin Institute of Technology, July 2011). Since July 2020, Dr. Li has been working at Lawrence Berkeley National Laboratory, focusing on rapid prediction of liquid structure using deep learning and developing machine learning surrogates for Geoscience Models.

Professional Development

Dr.Chunhui Li has honed technical skills in molecular dynamics simulation (🖥️ GROMACS, LAMMPS, VASP), machine learning/deep learning/data science (🧠 PyTorch, TensorFlow, Scikit-Learn, Pandas, Numpy), and programming languages (💻 Python, Java, C/C++, Shell script, MATLAB, Perl). He has designed and managed high-throughput molecular dynamics simulations, curated extensive datasets, and developed efficient AI models, showcasing impressive speed improvements over traditional methods.

Research Focus

Dr. Li’s research focuses on the application of machine learning and molecular dynamics to geoscience and battery applications. His notable work includes developing deep learning models for rapid prediction of time-averaged structural properties, creating AI-based surrogates for geochemistry models, and improving the ion conductivity of solid polymer electrolytes through protein-coated nanofillers. Dr.Li’s collaborative efforts with experimental researchers have led to significant advancements in computational physics and material science.

Publications 📚📝

MohammadSadeq Mottaqi | Machine Learning | Best Researcher Award

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MohammadSadeq Mottaqi | Machine Learning | Best Researcher Award

Dr. MohammadSadeq Mottaqi, City University of New York, United States.

Dr. MohammadSadeq Mottaqi is a Ph.D. candidate in Biochemistry at the City University of New York, with a background in Biotechnology from the University of Tehran. Proficient in Python and R, Mohammad excels in bioinformatics tools like PyMol and Gromacs. His research spans machine learning-based drug action prediction, molecular dynamics simulations, and protein-ligand interactions. He has published on topics such as SARS-CoV-2 infection responses and cyanobacterial genera identification. Mohammad’s technical prowess extends to data analysis, visualization, and high-performance computing, reflecting his commitment to advancing biotechnological and computational research. 🧬🔬💻

Publication Profile

Scopus

orcid

Education 🎓

Ph.D. in Biochemistry
City University of New York, New York, NY
Aug 2022 – Present

Bachelor of Science in Biotechnology
University of Tehran, Tehran, Iran
Sep 2017 – March 2022

Experience 💼

Graduate Assistant
City University of New York, New York, NY
Aug 2022 – Present

  • Assisting in bioinformatics and computational biochemistry research.

Research Experience

  • Conducted molecular dynamics simulations of mutated HER2 proteins.
  • Investigated protein-ligand interactions using Python and PyMOL.
  • Developed machine learning models for predicting drug actions.

 

Professional Development

Mr.Mohammad Sadeq Mottaqi is currently immersed in his Ph.D. journey in Biochemistry at the City University of New York, focusing on computational biochemistry. His research expertise includes pioneering machine learning approaches for predicting drug actions and analyzing transcriptomics data to advance personalized medicine. Mohammad’s impactful contributions extend to molecular dynamics simulations of protein interactions and investigating the molecular mechanisms of SARS-CoV-2 mutations. With a foundation in biotechnology from the University of Tehran, he blends technical skills in Python, R, and bioinformatics tools like Gromacs and PyMol. Mohammad is dedicated to pushing the boundaries of bioinformatics, bridging theoretical insights with practical applications in healthcare and environmental management.🌱

Research Focus

Mr. MohammadSadeq Mottaqi is a biochemist and computational scientist specializing in bioinformatics and machine learning applications in drug discovery and molecular biology. His research includes predictive modeling for drug actions, molecular dynamics simulations, and protein-ligand interactions. Mohammad has contributed significantly to understanding SARS-CoV-2 through machine learning approaches and automated identification of toxigenic cyanobacterial genera for water quality control. His work, cited extensively, showcases his expertise in integrating computational tools and biotechnological research, making impactful advancements in these fields. 🌐🔬💻📊

Publications 📚📝

  • Automated identification of toxigenic cyanobacterial genera for water quality control purposes, Journal of Environmental Management, 2024 🌿
  • Contribution of machine learning approaches in response to SARS-CoV-2 infection, Informatics in Medicine Unlocked, 2021 🦠
  • Mutations in SARS-CoV-2; Consequences in structure, function, and pathogenicity of the virus, Microbial Pathogenesis, 2021 🧬

FrankBove | Environmental Health | Best Researcher Award

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FrankBove | Environmental Health | Best Researcher Award

Dr. FrankBove, ATSDR/CDC, United States.

Dr.Frank J. Bove, Sc.D, is a distinguished senior epidemiologist specializing in environmental and occupational health. He has contributed significantly to epidemiologic studies, including those related to Camp Lejeune and PFAS contamination. His career at ATSDR and various adjunct faculty positions highlight his expertise in public health and epidemiology. Bove’s work is recognized through numerous publications, workshops, and panel memberships. His efforts in environmental health have shaped policies and advanced scientific understanding in the field 🌍📊.

Publication Profile

Scopus

Education and Experience

Dr. Frank J. Bove holds a B.A. in Political Science and Philosophy from the University of Pennsylvania (1973) and an M.S. (1984) and Sc.D. (1987) in Environmental Health Science and Epidemiology from the Harvard School of Public Health. His extensive career includes roles as a senior epidemiologist at ATSDR, research scientist at the New Jersey Department of Health, and adjunct faculty at Georgia State University and Drexel University. He has also contributed to public health through positions at the Commonwealth of Massachusetts and Tufts University 📚🎓.

Professional Development

Dr. Bove has led critical epidemiological research projects, supervised technical aspects of hazardous waste worker surveillance, and reviewed protocols for epidemiologic studies. He has been a member of various expert panels, including those for the CDC, EPA, and National Academy of Sciences. His work on environmental health risks, particularly in drinking water contamination, has influenced public health policies and research directions 🧪🔍.

Research Focus

Frank J. Bove’s research focuses on environmental health epidemiology, particularly the health effects of hazardous waste exposure, drinking water contaminants, and occupational health risks. His studies on reproductive outcomes, cancer risks, and developmental disorders have provided valuable insights into public health impacts and helped shape regulatory guidelines and public health initiatives 🧬💧.

Publications 📚📝

  1. Causes of death among United States decedents with ALS: An eye toward delaying mortality (2023) 📄 [3 citations]
  2. Evaluating the Effectiveness of State-Level Policies on Childhood Blood Lead Testing Rates (2023) 📄 [2 citations]
  3. Incidence of amyotrophic lateral sclerosis in the United States, 2014–2016 (2022) 📄 [20 citations]
  4. Prevalence of amyotrophic lateral sclerosis (ALS), United States, 2016 (2022) 📄 [19 citations]
  5. Per-and polyfluoroalkyl substances multi-site study (2021) 📄 [1 citation]
  6. Reconstructing historical VOC concentrations in drinking water for epidemiological studies at a U.S. military base: Summary of results (2016) 📄 [12 citations]
  7. Evaluation of contaminated drinking water and male breast cancer at Marine Corps Base Camp Lejeune, North Carolina: A case control study (2015) 📄 [19 citations]
  8. Mortality study of civilian employees exposed to contaminated drinking water at USMC Base Camp Lejeune: A retrospective cohort study (2014) 📄 [20 citations]

MarcellaRocha | Data Science | Best Researcher Award

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MarcellaRocha | Data Science| Best Researcher Award

Dr. MarcellaRocha, LAIS/UFRN, Brazil.

 Dr.Marcella Rocha Andrade Da is a researcher at the Universidade Federal do Rio Grande do Norte in Natal, Brazil 🇧🇷. With a PhD in Electrical and Computer Engineering, she has extensive experience in Computing, Machine Learning, Artificial Intelligence, and Natural Language Processing. Her expertise includes Digital Image Processing, Flutter, Digital Electronics, Python, C++, Dart, Matlab, and SQL. Marcella excels in Data Science, Data Engineering, Data Mining, and Big Data, utilizing tools like Power BI, PySpark, Hive, ADAC, and Teradata. She is known for solving complex problems, leading Agile teams, and collaborating with cross-functional groups. 📈💡

Publication Profile

Scopus

Education and Experience 🎓💼

  • PhD in Electrical and Computer Engineering 🎓
  • Master in Computer Engineering from UFRN (2017-2019) 🎓
  • Bachelor’s Degree in Computer Engineering from IESB (2010-2014) 🎓
  • Data Scientist II: Big Data, SQL, Python, PySpark, Hive, ADAC, SAS, Teradata, Power BI, Financial data 💾
  • Data Scientist: Big Data and Language Model, SQL, Python, PySpark, Azure, Hive, ADAC, Power BI 💾
  • Machine Learning Research: Data Science, Machine Learning, NLP, Data and text mining, Healthcare data, Python/R programming 🧠💻

Professional Development 🌱🚀

Dr.Marcella has developed studies and research in Data Science, Machine Learning, and NLP, focusing on Data and text mining in the healthcare domain. She has consistently demonstrated her ability to deliver innovative solutions and manage cross-functional teams effectively.

Research Focus 🔍🔬

Dr.Marcella’s research focuses on Machine Learning, Natural Language Processing, and Data Science, with applications in healthcare data analysis. She is particularly interested in text analysis, authorship recognition, and leveraging AI for public health improvements.

Publications 📚📝

  1. Half dose ChAdOx1 nCoV-19 vaccine was equivalent to full doses to reduce moderate and severe COVID-19 cases (Galvão-Lima, L.J., de Medeiros Júnior, N.F., Jesus, G.S., … Valim, V., Valentim, R.A.M., IJID Regions, 2023) 🌍🦠
    • 0 Citations 📉
  2. A text as unique as a fingerprint: Text analysis and authorship recognition in a Virtual Learning Environment of the Unified Health System in Brazil (Rocha, M.A.D., Morais, P.S.G.D., Barros, D.M.D.S., … Dias-Trindade, S., Valentim, R.A.D.M., Expert Systems with Applications, 2022) ✍️🔍
    • 4 Citations 📈
  3. A text as unique as fingerprint: AVASUS text analysis and authorship recognition (Rocha, M.A.D., Nóbrega, G.A.S.D., De Medeiros Valentim, R.A., Alves, L.P.C.F., ACM International Conference Proceeding Series, 2020) ✍️🔍
    • 4 Citations 📈

Ali Elashery | Metallurgical Engineering | Best Researcher Award

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Ali Elashery | Metallurgical Engineering | Best Researcher Award

Dr.AliElashery, CURAL- University of Quebec at Chicoutimi, Canada.

Dr.Ali Elashery is a dedicated and skilled Metallurgist with a wealth of experience in both academic and industrial settings. Currently serving as a Senior Metallurgist at CURAL (Rio Tinto Laboratory) in the Department of Applied Science at UQAC, Canada, he has demonstrated exceptional expertise in developing new metallurgical processes and materials. Ali’s commitment to enhancing efficiency and quality in laboratory practices has significantly contributed to his organization’s success. He is passionate about leveraging his knowledge and skills for the growth of any prospective organization. 🌟

Publication Profile:

Scopus

Education and Experience:

Dr.Ali Elashery earned his Ph.D. in Metallurgical Engineering from the University of Quebec at Chicoutimi (UQAC), focusing on the hot deformation of 6xxx extruded aluminum alloys. Prior to this, he obtained a Master’s degree in Metallurgical Engineering from Cairo University, specializing in the welding of Urea-Grade duplex stainless steels. He also holds a Bachelor’s degree in Metallurgical and Material Science Engineering from Cairo University. Ali’s professional journey includes significant roles such as Senior Metallurgist and Research Assistant at CURAL (Rio Tinto Laboratory), as well as Teaching and Research Assistant positions at Cairo University. His extensive experience spans developing new alloys, conducting quality control, and managing laboratory operations. 🎓🔬

Professional Development:

Throughout his career, Dr.Ali Elashery has actively pursued professional development to enhance his skills and qualifications. He is a member of the Engineering Intern (EIT) program in Ontario, Canada, and the Canadian Institute of Mining (CIM). He has successfully completed the National Professional Practice Examination (NPPE) and holds various certifications in AutoCAD, uncertainty measurements, and ISO 17025 internal auditing. Additionally, Ali is certified in multiple non-destructive testing methods, including ultrasonic, radiographic, magnetic particle, and liquid penetrant testing. These credentials reflect his commitment to maintaining high standards in metallurgical practices and continuous learning. 📜🏅

Research Focus:

Dr.Ali Elashery’s research interests are diverse and impactful, focusing on physical metallurgy, material science, and metal forming. He has made significant contributions to the development of recrystallization-resistant alloys and the welding of stainless steels. His expertise extends to quality control, heat treatment, mechanical testing, and microstructure analysis. Ali has published numerous journal and conference papers, presented his findings at various international conferences, and received multiple awards for his research, including best poster awards and student paper prizes. His work aims to advance metallurgical processes and enhance the understanding of material properties for industrial applications. 📚🔧

Publication Top Notes:

  • Hot deformation behavior and processing maps for an Al-Mg-Si-Zr-Mn alloy
    📆 2024 | 📚 Journal of Alloys and Metallurgical Systems | 🔢 1 citation
  • Microstructure, tensile and bending properties of extruded Al–Mg–Si 6xxx alloys with individual and combined additions of Zr and Mn
    📆 2024 | 📚 Materials Science and Engineering: A | 🔢 0 citations
  • Microstructure and texture evolution during high-temperature compression of Al-Mg-Si-Zr-Mn alloy
    📆 2023 | 📚 Materials Characterization | 🔢 4 citations
  • Nucleation and transformation of Zr-bearing dispersoids in Al–Mg–Si 6xxx alloys
    📆 2023 | 📚 Journal of Materials Research | 🔢 4 citations
  • Effect of Si Level on the Evolution of Zr-Bearing Dispersoids and the Related Hot Deformation and Recrystallization Behaviors in Al–Si–Mg 6xxx Alloys
    📆 2022 | 📚 Advanced Engineering Materials | 🔢 4 citations
  • Improving the dispersoid distribution and recrystallization resistance of a Zr-containing 6xxx alloy using two-step homogenization
    📆 2022 | 📚 Philosophical Magazine | 🔢 6 citations
  • Evolution of Zr-Bearing Dispersoids during Homogenization and Their Effects on Hot Deformation and Recrystallization Resistance in Al-0.8%Mg-1.0%Si Alloy
    📆 2021 | 📚 Journal of Materials Engineering and Performance | 🔢 12 citations

WeiSun | Financial Intermediation | Best Researcher Award

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WeiSun | Financial Intermediation | Best Researcher Award

Dr. WeiSun, Saginaw Valley State University, United States.

📚Dr.Wei Sun is an Assistant Professor at Saginaw Valley State University with a rich academic and professional background in finance and telecommunications. Fluent in English and Chinese, he specializes in financial management, real estate finance, and risk analysis, bringing extensive research and teaching experience to his role.

Publication Profile

Scopus

Education 🎓

  • Ph.D. in Business Administration (Finance), University of Memphis, 2021
  • M.S. in Business Administration (Finance), University of Memphis, 2020
  • Master of Business Administration, Ashland University, 2012
  • B.S. in Electronic and Information Engineering, Dalian University of Technology, 2003

Experience 💼

  • Assistant Professor, Saginaw Valley State University, 2022 – Present
  • Visiting Lecturer, Texas A&M University – Central Texas, 2021 – 2022
  • Instructor, University of Memphis, 2017 – 2020
  • Senior Telecommunication Engineer, China Unicom, Rizhao Branch, 2003 – 2008

Professional Development 📈

Dr.Wei has enhanced his skills with certification from the Financial Infrastructure Stability and Cyber-security (FISC) Center at the University of Memphis. He has presented at major finance conferences and served as an Associate Editor for Humanities & Social Sciences Communications.

Research Focus 🔍

Dr.Wei’s research delves into financial intermediation, bank risk-taking, financial inclusion, and the dynamics of stock market behavior. His work aims to provide insights into the roles of state-owned banks, financial disclosure readability, and neighborhood economic impacts, contributing significantly to the field of finance.

Publication Top Notes

  • Financial intermediation around national elections: Evidence of state-owned banks as credit smoothers
    • 2024 📅
    • Cited by: 0 📊
  • Trade-time clustering
    • 2023 📅
    • Cited by: 1 📊