Muhammad Usman Aslam is an aspiring Data Scientist with a robust foundation in data analytics and machine learning. He possesses deep expertise in R programming and is passionate about leveraging emerging technologies to drive innovation in the high-tech industry. Muhammad is eager to contribute his proficiency in data-driven decision-making and predictive modeling to a dynamic team while continuously learning and expanding his skills in data science.
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Google Scholar Profile
Muhammad Usman Aslam has contributed significantly to various fields through his research publications. His work on process dispersion monitoring, clinical predictions of COVID-19 patients using deep stacking neural networks, and comprehensive reviews on factors related to cardiovascular diseases have been well-received and published in reputable journals.
Muhammad Usman Aslam’s author metrics reflect his emerging influence in the field. Since 2019, he has garnered 2 citations, holds an h-index of 1. These metrics underscore his growing impact and the recognition his work is beginning to receive within the academic community.
Education
Muhammad is currently pursuing a PhD in Statistics at Xi’an Jiaotong University (XJTU) in Xi’an, China, which he began in 2021. He holds an MS in Statistics from COMSATS University, Lahore, completed in 2019, and a BS Honors in Statistics from Government College University Faisalabad (GCUF), where he graduated as a Silver Medalist in 2013.
Research Focus
Muhammad’s research interests include data analytics, business intelligence, machine learning, statistical quality control, and fuzzy number theory. His work aims to advance these fields through innovative methodologies and practical applications, particularly in the high-tech industry.
Professional Journey
Muhammad’s professional journey includes roles as a Research Assistant at XJTU since 2021, a Lecturer and Research Assistant at Punjab College in Mian Channu from 2016 to 2021, and a Lecturer at Superior College in Arifwala from 2015 to 2016. These roles have provided him with extensive experience in teaching and research, further honing his skills and expertise.
Honors & Awards
Muhammad Usman Aslam has received recognition for his academic excellence, including being awarded a Silver Medal during his BS Honors in Statistics at Government College University Faisalabad.
Publications Noted & Contributions
Muhammad has co-authored several notable publications:
Title: A redescending M-estimator approach for outlier-resilient modeling
- Authors: A. Raza, M. Noor-ul-Amin, A. Ayari-Akkari, M. Nabi, M. Usman Aslam
- Journal: Scientific Reports
- Volume: 14 (1)
- Article: 7131
- Cited by: 1
- Year: 2024
Title: Process dispersion monitoring: Innovative AEWMA control chart in semiconductor manufacturing
- Authors: I. Khan, M. Noor-ul-Amin, M. U. Aslam, A. M. Mostafa, B. Ahmad
- Journal: AIP Advances
- Volume: 14 (1)
- Cited by: 1
- Year: 2024
Title: Fuzzy Control Charts for Individual Observations to Analyze Variability in Health Monitoring Processes
- Authors: M. U. Aslam, S. Xu, M. Noor-ul-Amin, S. Hussain, M. Waqas
- Journal: Applied Soft Computing
- Article: 111961
- Year: 2024
Title: Global contribution of statistical control charts to epidemiology monitoring: A 23-year analysis with optimized EWMA real-life application on COVID-19
- Authors: M. Waqas, S. H. Xu, M. U. Aslam, S. Hussain, K. Shahzad, G. Masengo
- Journal: Medicine
- Volume: 103 (27)
- Article: e38766
- Year: 2024
Title: Designing an efficient adaptive EWMA model for normal process with engineering applications
- Authors: Z. Rasheed, M. Khan, S. M. Anwar, M. U. Aslam, S. A. Lone, S. A. Almutlak
- Journal: Ain Shams Engineering Journal
- Article: 102904
- Year: 2024
Title: Joint monitoring of mean and variance using Max-EWMA control chart under lognormal process with application to engine oil data
- Authors: F. A. Almulhim, S. Malik, M. Hanif, A. A. Hassaballa, M. Nabi, M. Usman Aslam
- Journal: Scientific Reports
- Volume: 14 (1)
- Article: 13811
- Year: 2024
Title: Optimal Prognostic Accuracy: Machine Learning Approaches for COVID-19 Prognosis with Biomarkers and Demographic Information
- Authors: S. Hussain, X. Songhua, M. U. Aslam, F. Hussain, I. Ali
- Journal: New Generation Computing
- Pages: 1-32
- Year: 2024
Title: EXPRESS: Clinical Predictions of COVID-19 Patients Using Deep Stacking Neural Network
- Authors: S. Hussain, X. Songhua, M. U. Aslam, F. Hussain
- Journal: Journal of Investigative Medicine: The Official Publication of the American …
- Year: 2023
Research Timeline
Muhammad’s research timeline includes significant milestones such as his MS thesis on revisiting the Process Capability Indices (PCIs) for univariate variable control charts, completed in 2019. His research report on the statistical analysis of cardiovascular disease risk factors was completed during his BS Honors in 2013. He also conducted an environmental research report analyzing factors affecting pollution in Faisalabad in 2012.
Collaborations and Projects
Throughout his career, Muhammad has collaborated with various researchers and institutions. Notable projects include his contributions to research on process dispersion monitoring in semiconductor manufacturing, clinical predictions using neural networks, and the determinants of contraceptive utilization among married women. His collaborative work has significantly advanced the understanding and application of statistical and machine learning techniques in these areas.