Tesfay Gidey | Statistical Modeling | Best Researcher Award

Dr. Tesfay Gidey | Statistical Modeling | Best Researcher Award

Dr. Tesfay Gidey at Addis Ababa Science and Technology University, Ethiopia

๐Ÿ‘จโ€๐ŸŽ“ Professional Profile

ย ๐Ÿ“š Early Academic Pursuits

Dr. Tesfay Gidey Hailuโ€™s academic journey began with a strong foundation in Statistics at Addis Ababa University, where he earned his BSc with a minor in Computer Science. This rigorous curriculum equipped him with vital skills in statistical methods and computer programming, laying the groundwork for his future studies. He furthered his education with an MSc in Health Informatics and Biostatistics at Mekelle University, delving into advanced topics such as epidemiology and public health, which ignited his passion for applying data-driven solutions in healthcare.

๐Ÿ’ผ Professional Endeavors

Dr. Gidey has demonstrated remarkable leadership in academia, serving as Associate Dean at Addis Ababa Science and Technology University (AASTU) for multiple departments. His responsibilities included driving research initiatives, enhancing curriculum quality, and overseeing student services. Additionally, his role as Head of Department at Jimma University showcased his commitment to improving departmental operations and student satisfaction. His extensive experience in project management underscores his ability to lead technology projects effectively.

๐Ÿ”ฌ Contributions and Research Focus

Dr. Gidey’s research is centered on critical areas such as signal processing, indoor localization, and machine learning. His Ph.D. work at the University of Electronic Science and Technology of China focused on applying advanced algorithms to real-world problems, particularly in indoor positioning systems. His contributions to quality control in manufacturing industries and the modeling of public health data further highlight his dedication to leveraging data for impactful solutions.

๐ŸŒ Impact and Influence

Through his academic and professional endeavors, Dr. Gidey has significantly influenced the fields of Information and Communication Engineering and data science. His ability to bridge theory and practical application positions him as a valuable asset in technology-driven environments. His commitment to mentoring students and faculty alike has fostered a culture of innovation and continuous improvement within the institutions he has served.

๐Ÿ“Š Academic Citations

Dr. Gidey’s scholarly work has gained recognition within academic circles, with several publications focusing on his research areas. His contributions to journals as a reviewer and author have enriched the body of knowledge in his fields, emphasizing the importance of interdisciplinary approaches to problem-solving.

๐Ÿ› ๏ธ Technical Skills

Dr. Gidey is proficient in a variety of programming languages, including Python, R, Java, and C++. He is skilled in using statistical tools such as SAS, SPSS, and advanced machine learning packages. His expertise in data visualization and business intelligence tools, like Tableau and Power BI, equips him to extract meaningful insights from complex datasets.

๐Ÿ‘ฉโ€๐Ÿซ Teaching Experience

With years of teaching experience, Dr. Gidey has effectively communicated complex subjects to students, ensuring they grasp fundamental concepts in information technology and data science. His ability to adapt his teaching methods to diverse learning styles has contributed to high levels of student engagement and success.

๐ŸŒŸ Legacy and Future Contributions

Dr. Gidey is dedicated to advancing the fields of data science and information engineering through research, teaching, and community engagement. His vision includes fostering collaborations between academia and industry, particularly in developing innovative solutions for public health and manufacturing. As he continues his career, Dr. Gidey aims to leave a lasting legacy that inspires future generations of engineers and data scientists.

๐Ÿ“ˆ Conclusion

With a strong foundation in academic excellence, professional leadership, and research innovation, Dr. Tesfay Gidey Hailu stands poised to make significant contributions to the field of Information and Communication Engineering. His passion for data and commitment to actionable results ensure he will continue to drive progress and inspire those around him.

 

๐Ÿ“– Top Noted Publications

Theories and Methods for Indoor Positioning Systems: A Comparative Analysis, Challenges, and Prospective Measures
  • Author: Tesfay Gidey Hailu; Xiansheng Guo; Haonan Si; Lin Li; Yukun Zhang
    Journal: Sensors
    Year: 2024
Ada-LT IP: Functional Discriminant Analysis of Feature Extraction for Adaptive Long-Term Wi-Fi Indoor Localization in Evolving Environments
  • Author: Tesfay Gidey Hailu; Xiansheng Guo; Haonan Si; Lin Li; Yukun Zhang
    Journal: Sensors
    Year: 2024
Measurement Science and Technology
  • Author: Bright Awuku; Ying Huang; Nita Yodo; Eric Asa
    Journal: Measurement Science and Technology
    Year: 2024
Measurement Science and Technology
  • Author: Xun Zhang; Guanghua Xu; Xiaobi Chen; Ruiquan Chen; Jieren Xie; Peiyuan Tian; Sicong Zhang; Qingqiang Wu
    Journal: Measurement Science and Technology
    Year: 2024
Machine Learning: Science and Technology
  • Author: Omer Subasi; Sayan Ghosh; Joseph Manzano; Bruce Palmer; Andrรฉs Marquez
    Journal: Machine Learning: Science and Technology
    Year: 2024
ย MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection
  • Author: Tesfay Gidey Hailu; Taye Abdulkadir Edris
    Journal: Intelligent Information Management
    Year: 2023

Jinlu Liu | Bayesian Analysis | Best Researcher Award

Mr. Jinlu Liu, Bayesian Analysis, Best Researcher Award

Mr. Jinlu Liu at University of Edinburgh, United Kingdom

Professional Profile

Orcid Profile
Scopus Profile

Summary

Jinlu Liu is an accomplished statistician and researcher specializing in Bayesian nonparametric mixture models and their applications in single-cell sequencing data. With a PhD in Statistics and Artificial Intelligence from the University of Edinburgh, Jinlu has developed innovative methods for cell subtype detection and neuronal connectivity modeling. His expertise spans advanced statistical modeling, data analysis, and computational techniques, and he has made significant contributions through publications, poster presentations, and awards.

Education

  • PhD in Statistics and Artificial Intelligence, University of Edinburgh (Sep 2019 โ€“ Feb 2024)
    Research focused on constructing Bayesian nonparametric mixture models for single-cell sequencing data, with applications in detecting cell subtypes and genetic markers.
  • MSc in Data Science (Distinction), University of Edinburgh (Sep 2018 โ€“ Sep 2019)
    Specialized in Biomedical Data Science, Incomplete Data Analysis, Bayesian Theory, and Natural Language Processing.
  • BSc in Mathematics with Statistical Science (First), University College London (Sep 2015 โ€“ Aug 2018)
    Studied Mathematical Methods, Real Analysis, Bayesian Statistics, and Optimization Algorithms.

Professional Experience

  • Postdoctoral Researcher, Centre for Discovery Brain Sciences, University of Edinburgh (Feb 2024 โ€“ Present)
    Modeling neuronal connectivity using MAPseq data and Bayesian mixture models.
  • Mathematics Tutor, University of Edinburgh (Sep 2019 โ€“ Feb 2024)
    Tutored various courses, including Multivariate Analysis, Nonparametric Regression Models, and Statistical Computing.
  • Information Analyst, Public Health Scotland, National Health Service (Jan 2022 โ€“ Jul 2022)
    Developed time series algorithms and optimized R code for patient waiting times.

Research Interests

Jinlu Liu’s research interests include Bayesian nonparametric methods, statistical modeling of biological data, and computational algorithms for data analysis. He is particularly focused on applications in single-cell sequencing, neuronal connectivity, and healthcare data analysis. His work aims to develop efficient and reliable statistical methods to advance understanding in these fields.

Key Skills

  • Advanced proficiency in R, LaTeX, OpenBUGS, JAGS, and Stan
  • Intermediate skills in Python, SQL, and SAS
  • Expertise in Bayesian modeling, statistical computing, and data analysis
  • Proficient in using Slack, Zoom, and Microsoft Teams

๐Ÿ“– Publication Topย Noted