CS Talk - Fardina Fathmiul Alam

Event time: 
Thursday, February 2, 2023 - 10:30am
Zoom Presentation See map
Event description: 

CS Talk
Fardina Fathmiul Alam

Host: Holly Rushmeier

Title of Teaching Demo:  ”Understanding K-Means Clustering Machine Learning”


“There is a growing amount of data that is being generated today. Data scientists are attempting to turn large datasets from industries like business, healthcare, and social media into actionable insights and data-driven decisions. With the advent of machine learning (ML) and artificial intelligence (AI), machines are getting more and more advanced and their abilities are frequently pushed to the limit. Over the last decade, ML has made huge progress in technology in everything from photo recognition to self-driving cars. ”Unsupervised Machine Learning” draws inferences from datasets without labels. If we don’t know what we’re looking for, it’s great for finding patterns. Since labeled data is difficult or expensive to gather, many real-world datasets are unlabeled or partially labeled, making unsupervised learning approaches like k-means clustering valuable tools for dealing with large amounts of unlabeled data. Due to its simplicity, efficiency, and interpretation, K-means clustering is one of the most used unsupervised learning techniques for grouping related data points. In this lecture, I will talk about what is k-means clustering and how it works, which can help us to understand our data in a unique way by grouping or dividing data into groups. ”


Fardina Fathmiul Alam is a Ph.D. candidate in the Department of Computer Science at George Mason University (GMU) in Fairfax, Virginia. In addition, She works as a Graduate Research Assistant in Dr. Amarda Shehu’s Computational Biology Lab at GMU. Her research focuses on Machine Learning (ML), specifically the intersection of Deep Learning (DL) with bioinformatics and computational biology. She works on designing novel deep neural-network-based latent variable models to organize high-dimensional, non-linear molecular structure space (in terms of representation learning), and for that her application domain is Protein Structure data. Fardina already has a good number of publications in her work. In 2013, She received her undergraduate degree in Computer Science and Engineering from the Military Institute of Science and Technology (MIST) in Dhaka, Bangladesh. She earned her MS degree in Computer Science from Mason. She worked for about four years as a Full-Time Teaching Faculty (Lecturer) at the International University of Business, Agriculture, and Technology (IUBAT) in Dhaka, Bangladesh, before joining Mason. She also spent about 3 years at Mason as a Graduate Teaching Assistant for various graduate and undergraduate courses. Fardina intends to complete my Ph.D. by the end of the Spring of 2023.”