Ph.D. Student, Department of Computer Science, Northern Illinois University
I have experience working in the IT industry for over a decade. I completed my bachelor’s degree in Computer Science from the University of Dhaka and my master’s in Computer Science from Northern Illinois University. I am pursuing my Ph.D. in Computer Science at Northern Illinois University.
I am proficient in working with machine learning models with advanced knowledge in natural language processing (NLP), artificial intelligence (AI), and information visualization (InfoViz). With my expertise in Python libraries (e.g., Spacy, TensorFlow, PyTorch, etc.) and JavaScript tools (e.g., D3.js), I am able to extend my work in different directions. I have developed several machine-learning models for clustering, topic modeling, and classification of social media data. I am currently working to expand non-destructive inspection (NDI) research boundaries by building image segmentation models to identify defects from ultrasonic and CT scans.
Expertise
- Image Segmentation
- Data Visualization
- Machine Learning
- Artificial Intelligence
- Data Analytics
- Natural Language Processing
Research Interests
- Machine Learning
- Artificial Intelligence
- Computer Vision
- Visual Analytics
- Social Media
Publications
Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic
A research paper published in the MDPI – Healthcare journal where me and the co-authors proposed a machine learning based approach to catch misinformation related search keywords and add a roadblock before redirecting users to possible harmful healthcare behaviors.
Analyzing Twitter Bot Activity on Academic Articles
Social Media & Society Conference 2020
A short paper analyzing the Twitter bot activity on academic articles was published at the Social Media & Society conference 2020. The co-authors and I analyzed the behavior of Twitter bots using the dataset from Altmetrics.
Visual Analysis of COVID-19 Vaccine Stance
We developed a web-based tool to analyze COVID-19 tweets visually. This interactive tool helps identify relations and hidden patterns in COVID-19 vaccine-related topics and public stance towards vaccination.
Public interest in science or bots? Selective amplification of scientific articles on Twitter
ASLIB Journal of Information Management
Since public interest in scientific findings can shape the decisions of policymakers, it is essential to identify the possibility of bot activity in the dissemination of any given scholarly article. Without arguing whether the social bots are good or bad and without arguing about the validity of a scholarly article, our work proposes a tool to interpret the public interest in an article by identifying the possibility of bot activity toward an article.
Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccination
Natural Language Processing Journal
The COVID-19 pandemic exposed significant weaknesses in the healthcare information system. The overwhelming volume of misinformation on social media and other socioeconomic factors created extraordinary challenges to motivate people to take proper precautions and get vaccinated. In this context, our work explored a novel direction by analyzing an extensive dataset collected over two years, identifying the topics de/motivating the public about COVID-19 vaccination.
Mixture-of-Experts for Multi-Domain Defect Identification in Non-Destructive Inspection
Despite their effectiveness, traditional UT methods rely on the manual interpretation of ultrasonic signals, which is time-consuming, labor-intensive, and subjective. Furthermore, processing such large-scale data, particularly across materials of varying thicknesses, significantly increases the computational demands of deep learning model optimization. To overcome these challenges, we propose an efficient sparse mixture-of-experts (MoE) model with a multi-level loss function and introduce four novel training objectives to improve computational efficiency and accuracy in identifying surface defects in composite aircraft materials.
Effective Defect Detection Using Instance Segmentation for NDI
Ultrasonic testing is a common Non-Destructive Inspection (NDI) method used in aerospace manufacturing. However, the complexity and size of the ultrasonic scans make it challenging to identify defects through visual inspection or machine learning models. Using computer vision techniques to identify defects from ultrasonic scans is an evolving research area. In this study, we used instance segmentation to identify the presence of defects in the ultrasonic scan images of composite panels that are representative of real components manufactured in aerospace.
VulSim: Leveraging Similarity of Multi-Dimensional Neighbor Embeddings for Vulnerability Detection
Natural Language Processing Journal
Despite decades of research in vulnerability detection, vulnerabilities in source code remain a growing problem, and more effective techniques are needed in this domain. To enhance software vulnerability detection, in this paper, we first show that various vulnerability classes in the C programming language share common characteristics, encompassing semantic, contextual, and syntactic properties. We then leverage this knowledge to enhance the learning process of Deep Learning (DL) models for vulnerability detection when only sparse data is available.
Current Projects
Anomaly Detection in Ultrasonic Images
We are working to identify anomalies in ultrasonic scan images. We are tackling this as a computer vision problem and using image segmentation models to classify the anomalies. This work can be groundbreaking in automating the manufacturing process of different safety-critical industries.
Identifying Objects in CT Scans
We are developing ML models to identify objects and regions of interest from CT scans. We are building fully unsupervised image segmentation models to prepare pseudo-labels for the scanned images. Additionally, clustering and edge detection techniques are used to improve the detections.
Education
Ph.D. in Computer Science – Northern Illinois University
Areas of research for the Ph.D. covers NLP, visual analytics, and big data analytics.
2022 – running
M.Sc. in Computer Science – Northern Illinois University
Major topics covered in the graduate program were Python programming, big data analytics, machine learning, data visualization, and artificial intelligence.
2019 – 2022
B.Sc. in Computer Science – University of Dhaka
Major topics covered in the undergraduate program were computer programming, network security, database management, operating systems, discrete mathematics, and calculus.
2003 – 2009
Work Experience
Visiting Student Researcher – Argonne National Laboratory
I am working with the Advanced Photon Source team at Argonne in advancing machine learning techniques.
2025 – 2025
Research Assistant – Northern Illinois University
I am currently working as a research assistant at the DATALab in the department of Computer Science of NIU. I am working on various machine learning-related projects with my colleagues at the lab. My present research involves the analysis of ultrasonic scans to identify flaws in airplane fuselage during the manufacturing process.
2022 – current
Teaching Assistant – Northern Illinois University
I worked as a teaching assistant for different Computer Science courses including C++ programming, Software Engineering, UNIX programming, Android programming etc. My responsibilities included assisting undergraduate students with their coursework and helping the professors with grading and evaluating.
2019 – 2022
Web Application Developer – Choobs Ltd.
I worked on web and mobile based solutions to automate the business models of several small and medium sized organizations. I have successfully led several teams to complete mid to large sized applications.
2011 – 2019
Founder & CEO – Creativity Unleashed
I have led several teams to provide software solutions to local and foreign organizations. I led the development of one of the country’s first e-commerce platform.
2006 – 2013
Programmer – SGC Soft Ltd.
I worked on several desktop and web-based software solutions for small and medium sized organizations.
2009 – 2009
Achievements
- Travel Scholarship: AAAI (2025)
- Google Cloud Research Grand: Google (2023)
- SIGIR Student Travel Grant: JCDL (2023)
- Finalist: The Virus Versus Hackathon (2020)
- Participation: The AIR Hackathon (2018)
- Finalist: Porsche NEXT OI (2018)
- Finalist: Human Beyond Digital Hackathon (2018)
- Finalist: Hôpitaux Universitaires de Genève (HUG) – Hackathon (2018)
- Champion: Mercedes-Benz Digital Challenge (2017)
Activities
- Program Committee Member – CIKM 2023, 2024, 2025
- Participated in STEM fest organized by NIU and present our work in NDI research (2022)
- Volunteered as a speaker at Inspiring Youth Bangladesh (2018)
- Participated in e-commerce Expo organized by the Bangladesh Association of Software and Information Services (BASIS) (2012)
- Participated at Soft Expo organized by Bangladesh Association of Software and Information Services (BASIS) as entrepreneur (2010, 2011, 2012)