KSM Tozammel Hossain

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About Me

My name is Tozammel Hossain. I am currently an assistant professor in the Department of Information Science at the University of North Texas. Previously, I was an assistant research professor at the University of Missouri - Institute for Data Science and Informatics. I received my PhD in computer science at Virginia Tech and earned three years of experience as a postdoctoral associate at the University of Southern California-Information Sciences Institute (USC-ISI).

My research interests lie in applied machine learning and data science, with an emphasis on bioinformatics, health informatics, social science, and cybersecurity. Some of my recent research include devising methods for anticipating societal population-level events (e.g., flu outbreak and civil unrest), disease incidence, and cyber-attacks; inferring co-evolving entities and their interactions in complex systems; and modeling state and behavior in social systems (e.g., ideological leanings, polarization, and equity).

My career goal is to solve challenging problems in machine learning and data science.

Education

Virginia Tech

I earned my doctoral degree in the Department of Computer Science at Virginia Tech. I conducted my research in the Discovery Analytics Center led by Dr. Naren Ramakrishnan. My thesis was in the area of bioinformatics, specifically on modeling evolutionary constraints and improving multiple sequence alignments using couplings. I received a masters degree from the same department in 2014.

Bangladesh University of Engineering & Technology

I obtained my B.Sc. degree in Computer Science & Engineering in the Department of CSE at Bangladesh University of Engineering & Technology in 2007. I conducted my undergraduate thesis under the supervision of Dr. Saidur Rahman, and my topic was Applications of Graphs in Bioinformatics.

Experience

Publications

News Coverage

Invited Talk

Refereed Journal Papers

Refereed Conference Papers

Under Review

Archived

[arXiv ‘18] Florian Quinkert, Thorsten Holz,KSM Tozammel Hossain, Emilio Ferrara, and KristinaLerman. “RAPTOR: Ransomware Attack PredicTOR”. In:arXiv preprint arXiv:1803.01598 (2018).
[arXiv ‘16] KSM Tozammel Hossain†, Huijuan Shao†, Hao Wu, Maleq Khan, Anil Vullikanti, B Aditya Prakash, Madhav Marathe, and Naren Ramakrishnan. Forecasting the Flu: Designing Social Network Sensors for Epidemics. In: arXiv:1602.06866.(2016). (Published in ACM SIGKDD Workshop epiDAMIK ‘2018) [Link]

Research Projects

Below are some of the selected projects I am working on or have previously worked on.

Modeling Evolutionary Constraints in Proteins

This project aims to model evolutionary constraints in proteins. Evolutionary constraints shape the sequences, structures, and functions of protein families. We are interested in a type of evolutionary constraint, residue coupling, or correlated mutation, an important indicator for predicting protein structures and revealing functional insights into proteins. We are focusing on modeling a rich set of pairwise and higher-order residue couplings, emphasizing providing a mechanistic explanation for couplings and decomposing couplings of various orders. We also investigate a method for mining frequent episodes, called coupled patterns, in an alignment produced by a classical algorithm for proteins and RNAs. We also exploit the coupled patterns to improve the alignment quality concerning the exposition of couplings. NSF supports this project with a proposal for integrating, predicting, and generating mixed-mode information. This proposal is a collaboration between Carnegie Mellon University, Dartmouth College, Purdue University, and PNNL.

Modeling Ailment State of Users using Social Network Data

Contagions arise in many situations, such as biological (like Flu), social (memes, hashtag propagating on Twitter), etc. While epidemiological research has inspired researchers modeling social contagion, recent work has shown that there are key aspects along which social contagions differ from biological contagions. In this project, we reconcile the apparently contrasting behaviors with finer-grained modeling of biological phases as inferred from tweets. We propose a temporal topic model for inferring hidden biological states for users. Our work can be seen as a stepping stone to a better understanding of contagions in both biological and social spheres.

Designing Social Network Sensors for Epidemics

Early detection and modeling of a contagious epidemic can provide important guidance about quelling the contagion, controlling its spread, or the effective design of countermeasures. This project aims to design social network sensors—a small set of people who can be monitored to provide insight into the emergence of an epidemic in a larger population. Using the graph-theoretic notion of dominators, we develop an efficient and effective heuristic for lead-time detection. Using city-scale datasets generated by extensive microscopic epidemiological simulations involving millions of individuals, we illustrate the practical applicability of our methods and show significant benefits (up to 22 days more lead time) compared to other competitors.

Inferring Ideal Points for US Supreme Court Justices

In Supreme Court parlance and the political science literature, an ideal point positions a justice in a continuous space. It can be interpreted as a quantification of the justice policy preferences. We present an automated approach to infer such ideal points for justices of the US Supreme Court. This approach combines topic modeling over case opinions with judges’ voting (and endorsing) behavior. Furthermore, given a topic of interest, say the Fourth Amendment, the topic model can be optionally seeded with supervised information to steer the inference of ideal points. Applying this methodology to five years of cases provides exciting perspectives into the leaning of justices on crucial issues, coalitions underlying specific topics, and the role of swing justices in deciding the outcomes of cases.

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