The faculty at NIU are actively involved in a number of research projects, and students, both undergraduate and graduate, are encouraged to get involved. Specific focus areas in the department include the following.
Research in this cluster spans machine learning, deep learning, graph neural networks, generative AI and large language models, natural language processing and dialogue systems, and AI methods for scientific and biological data. Faculty also work on foundational topics, including optimization theory and federated learning. Applications span healthcare, education, and interdisciplinary collaboration with the natural, social, and engineering sciences.
Faculty: Hamed Alhoori, Reva Freedman, Zhishuai Guo, Lei Zhang, Jie Zhou
As computing systems become increasingly interconnected, securing data, networks, and critical infrastructures has become a fundamental challenge. Cybersecurity research at NIU focuses on protecting cyber-physical systems, cloud platforms, and next-generation networks from evolving and sophisticated threats. This includes areas such as IoT and smart infrastructure security, 5G systems security, quantum communication security, and the use of machine learning to detect and respond to attacks. Research efforts are supported by the Center of Excellence in Cybersecurity and Future Networks (CECFN), which advances innovation in cybersecurity, future networks, and critical infrastructure protection. Students are encouraged to engage in hands-on research that addresses real-world challenges in safety-critical environments.
Faculty: Nur Imtiazul Haque, Elmahedi Mahalal, Hisham Kholidy
This cluster investigates how people make sense of complex data, from collection and analysis to interactive visual exploration and knowledge discovery. Faculty develop visual analytics methods for exploring multivariate and relational datasets, build tools that support computational reproducibility and data provenance in scientific workflows and study how interactive systems can improve human reasoning and decision-making.
Faculty: Hamed Alhoori , David Koop, Maoyuan Sun
As AI components are embedded in safety-critical applications, this cluster addresses the engineering challenges they create. Research focuses on requirements specification for ML-enabled software, safety assurance and traceability, automated vulnerability detection and the design of dependable software architectures that remain trustworthy as AI capabilities and system requirements change.
Faculty: Mona Rahimi
Faculty in this cluster develop algorithms, distributed architectures, and scalable computing solutions for large-scale scientific problems. Applications include real-time image reconstruction for proton computed tomography, a technique for improving cancer treatment planning, and GPU-accelerated scientific visualization. Research in this area is supported by close collaboration with Argonne National Laboratory.
Faculty: Kirk Duffin, Nicholas Karonis
This cluster applies pattern recognition, machine learning, and computational methods to biological data at scale. Research areas include automated analysis and annotation of multidimensional microscopy images, 3D neuron reconstruction, and comparative genomics. This work enables discoveries in neuroscience, cellular biology and genome evolution.
Faculty: Jie Zhou, Minmei Hou