Musfiqur Sazal

School of Computing and Information Sciences

Lecture Information:
  • August 15, 2019
  • 10:00 AM
  • CASE 349

Speaker Bio

Musfiqur Sazal is a Ph.D. candidate at the School of Computing and Information Sciences, Florida International University, under the supervision of Professor Giri Narasimhan. His research interest lies in causality, Bayesian inference, and applied machine learning. He holds a bachelor degree in Computer Science and Engineering from Khulna University of Engineering & Technology in 2013. He was employed as a software engineer in Samsung Research and Development from 2013 to 2015 before joining FIU. He has published papers in different conferences including ICCABS. He was awarded an NSF travel grant in 2018 and 2019.


Inferring causality is the process of connecting a cause with an effect. Identifying even a single causal relationship from data is more valuable than observing dozens of correlations in a data set. The study of causality is not new in many areas of science, but in recent years with the advances in artificial intelligence, Bayesian networks, causal calculus, data science, and machine learning, the question has become “how to draw a causal conclusion in a data-driven way?”. Given a sufficiently large and rich data set, the theoretical foundations of causality allows us to go well beyond merely discovering statistical associations in data, but to infer causal relationships in a quantitative manner and to even explore “what-if” questions, which can have a profound impact on data-driven decision making in any domain. Learning causal inference has been compared to human level intelligence.

The goal of this dissertation is to develop a causal framework for the highly dynamic, interdependent, and complex data sets generated from microbiome studies. A microbiome is a community of microbes including bacteria, archaea, protists, fungi and viruses that share an environmental niche. Microbiomes have been referred to as a social network because of the complex set of potential interactions between its various taxonomic members. Microbiome studies have recently been augmented with the collection of multiomics data that allows a glimpse into different aspects of the microbial community. This dissertation will explore the application of the causality to data from microbiome studies. Data from these studies also include multiomics data sets and data from longitudinal studies. This work will improve our understanding of microbial communities inside human bodies and in environmental settings, and will help develop therapies when imbalances arise in these communities.