Faculty Research Areas

Computer and Information Sciences

  • Ashok Basawapatna: Educational Technologies
  • Doyoung Park: Computational Biology and visualization, and Image Processing
  • Mohamed Khalefa: Databases, Query Compilation and Optimization, Cloud Computing, Big Data Systems
  • Naresh Gupta: Modeling and Optimization
  • Renu Balyan: Natural Language Processing (NLP) & Deep Learning, Text Mining - Health & Education, Health Literacy
  • Shebuti Rayana: Data Mining, Anomaly Detection


  • David Ralston: Ergodic Thoery and Analysis
  • Frank Sanacory: Banach Space Geometry, Functional Analysis, Applied Data Science, Data Science Ethics
  • Geta Techanie: Point set topology and the continuum hypothesis
  • Jennie D'Ambroise: Schrodinger Equation
  • Lan Zhao: Applied mathematics
  • Maureen Dolan: Operation Research and Enviornmental Modeling

  • Myong-Hi Kim: Dynamics and polynomial root approximation methods

  • Nick Werner: Ring Theory

  • Yogesh More: Algebraic Geometry and Number Theory


Collaborative NSF Grant (Oct, 2023 - Sept 2025):

Total Grant Fund: $399,897; Old Westbury: $122,598

Title: Orchestration of Network Slicing for 5G-Enabled IoT Devices Using Reinforcement Learning

Project Investigators: Renu Balyan, Assistant Professor, Math/CIS (; Kanwalinderjit Kaur (California State University Bakersfield); Sagnika Ghosh (Tennessee State University)

Overview:  This research aims to create a system that can effectively manage IoT devices connected to the 5G network. Managing a multitude of IoT devices with diverse requirements is a complex task, making manual management challenging. Some devices require fast data transmission for activities like watching videos or playing virtual-reality games, while others need a quick response time for tasks like self-driving cars or monitoring devices. The solution to these problems is network slicing which involves dividing the network into smaller parts to handle different types of devices and services. However, the challenges inherent to network slicing are efficiently managing network resources, coordinating, and optimizing different parts of the network. This project addresses these challenges by designing a system that can automatically manage the resources of 5G-enabled IoT devices. The potential benefits of this approach are that it simplifies the network and reduces cost, saves energy, balances the workload, optimizes mobility, and makes the network easier to manage. This research advances the field by laying a solid groundwork for studying machine learning and network automation in devices that are part of the 5G-enabled IoT network. Furthermore, by employing and mentoring students from underrepresented backgrounds in STEM, this project will aim to bridge the gap in institutions across the US. This project will train the next generation of scholars from minority-serving universities and marginalized communities and help in workforce development in the fields of 5G and reinforcement learning (RL). The project leaders will also reach out to K-12 to promote education and engage with a diverse range of students, including women.

NSF Grant (Sept, 2022 - Aug 2025):

Total Grant Fund: $594,390.00

Title:  ACOSUS: An AI-driven COunseling System for Underrepresented Transfer Students

Project Investigators: Xiwei Wang - PI (Northeastern Illinois University); Co-PIs: Shebuti Rayana, Assistant Professor, Math/CIS (;   Palvi Aggarwal (University of Texas El Paso);  Yun Wan (University of Houston - Victoria), and Sherrene Bogle (California State Polytechnic University- Humboldt)

Overview: The use of information systems to assist underrepresented community college STEM students in their upskilling transfer to universities and job market success is a critical but challenging goal of higher education. Existing research on student counseling and advising is primarily concerned with investigating factors influencing student success, which are mostly restricted to demographic and academic variables. The intervention formats are restricted to a variety of programs such as mentorship and living-learning communities. The overarching goal of this proposal is to investigate the research challenges underlying the design of an innovative AI-driven student advising system, complementary to university counseling, that will provide personalized readiness assessment and suggestions, with a primary focus on underrepresented transfer students in STEM majors. The new framework will take into account cognitive and behavioral data as well as social media influences on top of student academic performance. It will exploit techniques from natural language processing, machine learning, and recommender systems. The project will also build research capacity at participating Hispanic Serving Institutions by establishing a sustainable faculty collaboration network, expanding research domains at each institution, creating regular workshops, and providing underrepresented students with research-oriented training opportunities.

Collaborative NSF Grant (Sept, 2022 - Aug 2025):

Total Grant Fund: $598,925; Old Westbury: $140,215

Title: Privacy Preserving Tutoring System for Health Education of Low Literacy Hispanic Populations

Project Investigators: Renu Balyan, Assistant Professor, Math/CIS (; Francisco Iacobelli (Northeastern Illinois University; Zechun Cao (Texas A&M University-San Antonio); Sanaz Rahimi Moosavi (California State University Dominguez Hills Foundation)

Overview: This project will implement a computer tutor for low literacy Hispanic breast cancer survivors. Breast cancer is the leading cause of cancer-related deaths in Hispanics, and although research has shown that education can greatly mitigate stress and improve quality of life, few educational interventions for this population exist. To date, computer tutors that converse with their students have been tried mainly with a highly literate population in college settings, so their impact on low literacy Hispanics is unknown. The project will be carried out with two objectives in mind. First, development of a novel intelligent computer tutoring system that is customized so that it can effectively query and interact with Hispanic breast cancer survivors by adapting existing content that was created for this population in prior research. The second objective is to develop privacy-preserving algorithms that utilize robust end-to-end encrypted communication and can encrypt and decrypt distributed data in real time at a speed that does not hinder the interactions with the computer tutor.  This project will advance our understanding of the impact of designing AI powered tutors to address diversity and disparities in the access to information by a subset of low literacy individuals, as well as our understanding of privacy preserving algorithms that work in real-time with complex natural language processing models. More broadly, project outcomes will facilitate access to information for minority populations and will serve to build research capacity and train minority students in the participating teaching-oriented institutions.

The contributions of this development process will be threefold:

  1. to understand the role of culture and education in the interaction between low literacy Hispanic breast cancer survivors and intelligent tutoring systems;
  2. to develop a framework that facilitates the implementation of intelligent tutoring systems for minority populations;
  3. to develop accurate and low latency privacy preserving mechanisms for NLP model training and dialogue interfaces.

Summer Mini Grant - American Society for Engineering Education-ASEE (May 2022 - Sept, 2022): $10,000.00

Title: Investigating NLP and ML techniques for developing a secure and privacy-preserving health-centric ITS

Project Investigator: Renu Balyan, Assistant Professor, Math/CIS (

Overview: The tasks proposed in this mini-grant will advance the research goal of developing privacy-preserving NLP and ML techniques, where the participants will explore the NLP and ML techniques, master the use of Homomorphic Encryption, and the methodology behind building PPML solutions.

Summer Mini Grant - American Society for Engineering Education-ASEE (May 2022 - Sept, 2022): $10,000.00

Title: Exploratory data analysis for an AI-driven transfer student advising system: A pilot study

Co-Investigator: Shebuti Rayana, Assistant Professor, Math/CIS (

Overview: The goal of the mini-grant is to collect preliminary data and undertake exploratory analysis for building an innovative AI-driven student advising system that incorporates social and psychological variables. The system will give tailored assessments and recommendations for upskilling transfer and job market preparation, emphasizing minority transfer students in STEM disciplines.

NSF Grant (Oct, 2021 - Sept 2023): $299,498.00

Title: StEM: Stimulate, Engage and Motivate student research by enhancing the research capacity

Project Investigator (PI): Renu Balyan, Assistant Professor, Math/CIS (

Co-PIs: Shebuti Rayana, Mohamed Khalefa (Math/CIS); Kinning Poon, Solomon Chak, Christos Noutsos (Biological Sciences)

Overview: The goal of this project is to stimulate, engage, and motivate graduate and undergraduate STEM students, including from underrepresented minority groups to conduct data research in different domains, while supporting expansion and enhancement of research capacity to provide more research opportunities to the diverse student population and the faculty. This goal will be achieved by following a three-pronged approach to:

  • enhance and expand the technological infrastructure by acquiring new equipment to facilitate computational processing and big data analyses
  • embed research within the curriculum for existing courses via CURE and introduce new research-focused programs and courses
  • motivate and engage students in research activities and provide hands-on experience via research projects, training, seminars, and workshops through guidance and mentoring by academia and industry experts.

The award will improve the academic and professional skills of STEM students, in particular female and other underrepresented minority students, provide an opportunity to work with experienced research mentors and present/publish their work, create educational materials for long-term enhancement of academic curricula, and foster knowledge and promote the developmental skills to prepare students for data science and big data careers.


Spring 2023

Fall 2022

Spring 2022

Summer 2022

Summer Bridge Program (June 21, 2022 - June 24, 2022)

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