Name: Vincent Onyame
Pronouns:
Institution: University of South Carolina
Department: Epid/Bios
Biography:
I was born and raised in Accra, Ghana where I grew up in a family of four. As I aged through the levels of school, I became aware that my parents did not go to school, and I have the biggest opportunity to be the first college graduate in my family. It also became clear to me that I want a career in STEM due to my penchant for mathematics. This motivated me to major in science in high school and Actuarial Science, for my Bachelors in college. I worked in insurance as an Underwriter and taught mathematics in high school before entering graduate school for my MSc Mathematics at East Tennessee State University. At ETSU, classes and projects in courses like Machine Learning and Multivariate Statistics fueled my interest in Data Science applications in healthcare. The quest to gain deeper knowledge and expertise in data science applications in healthcare led me to study for a PhD in Biostatistics at the University of South Carolina where I plan to work on Missing Data and Bayesian applications in Machine Learning. I intend to work as a researcher or an academic in Machine Learning and Data Science in healthcare after my PhD.
Academic Status: PhD Student
Year in program: 2nd
Research Area/Department: Data Science; other
Other, specify: BIOSTATISTICS
Major/Specialty: Biostatistics
Degrees Earned or in Progress: Bsc/Actuarial Science/2010 M.S. Mathematics(statistics)/2021
What courses or academic preparation have you completed to prepare for a summer internship experience?
Multivariate Statistics Bayesian Data and Computational Analysis
Have you published any research or worked on research/technical projects? Yes
Where has your research been published or where have you conducted research/technical projects? I conducted research in Missing Data Analysis and also currently working bayesian application in machine learning. “”Performance Comparison of Multiple Imputation Methods for Quantitative Variables for Small and Large Data with Differing Variability”” (2021). Electronic Theses and Dissertations. Paper 3915. https://dc.etsu.edu/etd/3915
Please describe your research/academic interests:
My academic interest is in the field of Missing Data Analysis and Bayesian Applications in Machine Learning.
Computational and Data Science Areas:
Computational Science Applications, i.e., Bioscience, Cosmology, Chemistry, Environmental Science, Nanotechnology, Climate, etc.; Data Analytics and Visualization; Machine Learning and AI
Research Synergy:
I have developed an interest in computational science, AI/machine learning, and data sciences due to their potential in tackling complex problems and advances in human knowledge. This research area that provides powerful tools and methodologies to solve the most challenging and complex problems facing the world today aligns with the multi-disciplinary research being carried out at the DOE laboratories. The DOE labs have been leading the way in finding solutions to some of the most pressing problems in the world in the fields of energy, bioscience, and national security. The capabilities of data science and machine learning to evaluate large-scale, complex systems and make predictions or optimize results is what piques my interest in them. I know the fusion of AI with data science technologies is aiding the analysis of meaningful insights from large and diverse datasets as this is relevant to the work done in the DOE labs, where advanced analytics and high-performance computers are being used in the advancement of technology and science. Leading innovation and creating cutting-edge technology are hallmarks of the DOE labs. I’m excited to contribute to this excellence-focused culture by using my knowledge in data science and machine learning to create practical solutions.
Motivation:
I am motivated to pursue research interests in machine learning and data science because of its capabilities in solving the unending problems of energy. My desire to make a significant contribution to ground-breaking research and innovation that might influence our society’s future is what drew me to the Department of Energy (DOE) labs. The DOE labs represent the pinnacle of excellence and innovation in machine learning and data science. I am particularly inspired by the DOE’s mission to address the nation’s critical issues related to energy security, environmental integrity, and global competitiveness. Working at DOE labs would provide an unparalleled opportunity to be at the forefront of technological advancements and to work alongside experts committed to solving real-world problems.
Lightning Talk Title: Data Science and Machine Learning Application in Energy and Environmental Problems