PhD Scholar at Xi’an Jiaotong University, China, Singapore
Mr. Muhammad Usman Aslam is an aspiring data scientist with a strong foundation in data analytics, machine learning, and expertise in the R programming language. He is passionate about leveraging emerging technologies to drive innovation in the high-tech industry, focusing on data-driven decision-making and predictive modeling. Currently pursuing a Ph.D. in Statistics from Xiโan Jiaotong University (China), his academic background includes an MS from COMSATS (Lahore) and a BS (Hons) in Statistics from GCU Faisalabad, where he earned a Silver Medal.
Mr. Aslam has contributed to significant research, including publications on process dispersion monitoring in semiconductor manufacturing and clinical predictions for COVID-19 patients using deep learning. His research interests span statistical quality control, business intelligence, and fuzzy number theory. He has teaching experience as a lecturer and research assistant and holds proficiency in statistical software such as R, Python, SPSS, and Minitab. His innovative approach positions him as a promising talent in data science.
Professional Profile:
Education๐
Mr. Muhammad Usman Aslam has a solid academic background in statistics and data science. He is currently pursuing a Ph.D. in Statistics at Xiโan Jiaotong University in China, focusing on advanced statistical methodologies. Before this, he completed his Masterโs degree in Statistics at COMSATS University Lahore in 2019, where he deepened his knowledge in statistical analysis and data interpretation.
He also holds a Bachelorโs degree with Honors in Statistics from Government College University Faisalabad, where he excelled as a Silver Medalist, showcasing his academic excellence. His educational journey includes a strong emphasis on research, with notable projects like his thesis on revisiting Process Capability Indices and analyzing risk factors for cardiovascular diseases. Additionally, he has engaged in coursework and training in various statistical tools and programming languages, equipping him with the necessary skills to excel in data analytics and machine learning. His educational achievements reflect his dedication and commitment to advancing his expertise in the field.
Professional Experience ๐ฅ
Mr. Muhammad Usman Aslam has accumulated valuable professional experience in the field of statistics and data science, contributing to both academia and research. He has been serving as a Lecturer and Research Assistant at Punjab College in Mian Channu since April 2018, where he engages in teaching statistical concepts while supervising student research projects. Prior to this role, he held lecturing positions at other educational institutions, including Superior College in Arifwala and Punjab College, showcasing his commitment to educating future statisticians.
In addition to his teaching responsibilities, Mr. Aslam has actively participated in research projects that leverage his expertise in data analytics and machine learning. His work includes innovative studies on statistical quality control, clinical predictions using deep learning, and the analysis of factors affecting public health. Through his multifaceted roles, Mr. Aslam is dedicated to advancing statistical knowledge and its practical applications, preparing students for careers in data-driven environments.
Research Interest ๐ฌ
Mr. Muhammad Usman Aslam’s research interests lie at the intersection of statistics, data science, and machine learning. His primary focus is on developing innovative methodologies for statistical analysis, particularly in quality control and predictive modeling. He is currently revisiting Process Capability Indices (PCIs) for univariate variable control charts by incorporating fuzzy concepts, aiming to enhance the precision of quality assessments in manufacturing processes.
Additionally, Mr. Aslam is passionate about applying deep learning techniques to healthcare, exemplified by his work on clinical predictions for COVID-19 patients using deep stacking neural networks. He has also explored determinants of contraceptive utilization among married women, reflecting his interest in social issues through a statistical lens. His analytical skills extend to environmental studies, where he has investigated public awareness and responses to environmental pollution. Overall, Mr. Aslamโs research endeavors aim to drive innovation in data-driven decision-making across various sectors, demonstrating his commitment to impactful statistical applications.
Awards and Honors ๐
Mr. Muhammad Usman Aslam has received recognition for his academic excellence and contributions to the field of statistics and data science. He was honored as a Silver Medalist during his Bachelorโs degree in Statistics at Government College University Faisalabad, showcasing his outstanding performance and dedication to his studies.
In addition to academic accolades, Mr. Aslam has made significant contributions to research, as evidenced by his publications in reputable journals. His work on innovative statistical methodologies and machine learning applications has garnered attention within the academic community.
Mr. Aslam’s commitment to continuous learning and professional development is reflected in his pursuit of a Ph.D. in Statistics at Xiโan Jiaotong University, China. He actively participates in various academic and research forums, further establishing his reputation as a promising data scientist. Through his achievements and ongoing efforts, Mr. Aslam exemplifies a commitment to excellence in research and a passion for advancing knowledge in his field.
Conclusion
Muhammad Usman Aslam is a strong candidate for an award in Excellence in Innovation, especially given his innovative research in data science, his contributions to real-world problems in healthcare and manufacturing, and his proficiency with cutting-edge statistical tools. However, his candidacy could be strengthened by showcasing more practical applications of his research and highlighting additional leadership roles in driving innovation beyond academic settings. His research interests and accomplishments certainly make him a noteworthy contender for recognition in the field of innovation.
Publications top noted๐
- Control charts in healthcare quality monitoring: a systematic review and bibliometric analysis
Authors: M. Waqas, S.H. Xu, S. Hussain, M.U. Aslam
Year: 2024
Citation: International Journal for Quality in Health Care, mzae060.
- 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
Year: 2024
Citation: Medicine 103 (27), e38766.
- 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
Year: 2024
Citation: Ain Shams Engineering Journal, 102904.
- 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
Year: 2024
Citation: New Generation Computing, 1-32.
- A redescending M-estimator approach for outlier-resilient modeling
Authors: A. Raza, M. Noor-ul-Amin, A. Ayari-Akkari, M. Nabi, M.U. Aslam
Year: 2024
Citation: Scientific Reports 14 (1), 7131.
- 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
Year: 2024
Citation: AIP Advances 14 (1).
- Fuzzy control charts for individual observations to analyze variability in health monitoring processes
Authors: M.U. Aslam, S.H. Xu, M. Noor-ul-Amin, S. Hussain, M. Waqas
Year: 2024
Citation: Applied Soft Computing 164, 111961.
- Transforming healthcare performance monitoringโA cutting-edge approach with generalized additive profiles: GAMs for healthcare quality monitoring
Authors: M. Waqas, S.H. Xu, M.U. Aslam, S. Hussain, G. Masengo
Year: 2024
Citation: Medicine 103 (37), e39328.
- 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.U. Aslam
Year: 2024
Citation: Scientific Reports 14 (1), 13811.
- EXPRESS: Clinical Predictions of COVID-19 Patients Using Deep Stacking Neural Network
Authors: S. Hussain, X. Songhua, M.U. Aslam, F. Hussain
Year: 2023
Citation: Journal of Investigative Medicine: the Official Publication of the American โฆ