Winnie Wezi Mkandawire
Winnie Wezi Mkandawire is an interdisciplinary research-oriented free-lancing early career Bioinformatician/Data Scientist; a recipient of young researcher’s membership award for the American Association for the Advancement of Science, 2020; and a member of women in Data Science and women in Machine Learning, Massachusetts, USA.
She pursued MSc in Bioinformatics and Computational Science majoring in Data Science (Fulbright Scholarship, Worcester Polytechnic Institute, USA, 2021), MicroMasters in Statistics and Data Science (Massachusetts Institute of Technology (MIT), 2021), Postgraduate diploma in Bioinformatics (Kamuzu University of Health Sciences (KUHeS), yet to graduate), BSc in Biomedical Sciences (Mzuzu University, 2014), and is currently pursuing PhD in Basic Sciences with a major in Bioinformatics from University of Massachusetts Medical School (UMMS). Winnie is a multi-grants awardee in both independent as well as collaborative research projects and has worked with both national and international research and academic institutions on various interdisciplinary projects which employed the application of Data Science techniques. As a Data Scientist, Winnie has worked on various projects on the application of machine learning, deep learning, Natural Language Processing and Computer Vision—to solve various problems in the medical, health, business and agriculture sectors. One of the projects she worked on was application of a novel semi-supervised learning algorithm to elucidate mechanisms underlying drug resistance in HIV.
Her research interests are in the application of statistical, machine learning and deep learning methods in descriptive, diagnostic, predictive and prescriptive analytics. She believes interdisciplinary multifactorial approaches can be a better key in addressing most challenges affecting the world today.
Linear regression for machine learning
Day 2, 1:00-4:30 pm
Linear Regression is one of the fundamental supervised-machine learning algorithms. While it is relatively simple and might not seem fancy enough when compared to other Machine Learning algorithms, it remains widely used across various domains such as Biology, Social Sciences, Finance, and …Read more »