State University of New York, Canton
Welcome to the Data Science Innovations Lab at State University of New York, Canton, directed by Dr. Mehdi Ghayoumi, Assistant Professor of Cybersecurity and Director of the Data Science Minor Program. We focus on advancing research in Artificial Intelligence, Machine Learning, and Cybersecurity to create real-world solutions.
Our lab is a hub for innovation, bridging academia and industry across sectors like healthcare, robotics, and digital media. Equipped with cutting-edge facilities and collaborative teams, we strive to push the boundaries of modern data science.
Through a collaborative environment, we encourage creativity and interdisciplinary engagement. Our dedicated researchers and students work to advance theoretical foundations and applied technologies that can positively impact society.
At the Data Science Innovations Lab, we leverage the transformative power of big data and advanced analytics to address pressing real-world challenges. Our aim is to pioneer cutting-edge tools and methods, contributing significantly to the broader scientific community and society.
We cultivate a culture of excellence and collaboration. Our researchers and students from diverse backgrounds come together to develop robust solutions in cybersecurity, healthcare, robotics, and more. With state-of-the-art resources and a commitment to interdisciplinary research, we aim to make impactful contributions to fields like AI and machine learning.
By nurturing talent and promoting innovative thinking, our lab strives to inspire the next generation of data scientists and engineers, guiding them toward meaningful careers in both academia and industry.
Description: This project explores advanced biometric systems that integrate multiple modalities—such as facial recognition, voice authentication, and eye tracking—to enhance security and privacy in digital environments. Drawing on Dr. Ghayoumi’s work in “A review of multimodal biometric systems,” the research investigates efficient fusion methods (e.g., adaptive fuzzy logic or deep learning) to improve accuracy and robustness against spoofing attacks. Students will gain experience in signal processing, data fusion algorithms, and system design, with the goal of developing next-generation authentication solutions applicable to banking, healthcare, and beyond.
Description: Building on the lab’s track record in facial expression and affective computing research (“Towards Formal Multimodal Analysis of Emotions”), this project examines how robots can perceive and interpret human emotions in real time. Using convolutional neural networks (CNNs) and other deep learning architectures, students will work on data collection, annotation, and model training to enable socially adept robots. By integrating multimodal cues—facial expressions, voice tone, and gestures—this initiative aims to create more empathetic and adaptive machines that can enhance user engagement in fields such as healthcare assistance, customer service, and education.
Description: Inspired by ongoing lab work in eye tracking and Alzheimer’s detection (“Extending the Frontiers of Eye Tracking”), this project targets the development of machine learning models to analyze subtle behavioral and physiological markers. Students will employ bidirectional LSTMs, attention mechanisms, and time-series analysis to detect early signs of Alzheimer’s or other cognitive impairments. Collaborations with healthcare professionals will offer unique data sources, and participants will gain skills in biomedical signal processing, patient privacy considerations, and system validation—all aimed at improving outcomes through early, accurate diagnostics.
Description: Capitalizing on expertise shared in “Generative Adversarial Networks in Practice,” this project explores how GANs can produce realistic synthetic datasets for various applications—ranging from healthcare to cybersecurity—while preserving individual privacy. Students will learn how to architect GAN models, train them on sensitive datasets, and evaluate their performance. By balancing data utility with privacy, the project provides a powerful tool for research and industry partners who need large, high-quality datasets but must adhere to strict confidentiality and compliance requirements.
Description: Drawing on the lab’s work in access control and policy modeling (“An autonomous model to enforce security policies based on user’s behavior”), this project researches how AI-driven systems can automatically adapt security policies based on real-time user behavior. Students will analyze user interactions—logins, network usage patterns, file access—to detect anomalies and dynamically reinforce threat responses. Emphasis is placed on designing scalable solutions for enterprise environments, enhancing both trust and effectiveness of security protocols. Participants will gain hands-on skills in network monitoring, machine learning for threat detection, and secure system engineering.
Description: As AI systems increasingly shape critical decisions (in healthcare, finance, etc.), there is growing demand for interpretable models. This project investigates novel frameworks that integrate explainability into deep learning architectures (CNNs, LSTMs, Transformers), aligning with the lab’s broader goals of human-centric AI. Students will develop and evaluate XAI techniques—such as saliency maps and attention-based methods—and apply them to tasks like emotion detection, health analytics, or social robotics. By making AI decisions transparent and justifiable, the project aims to foster user trust and facilitate regulatory compliance, ensuring ethical and responsible AI deployment.
Lab Director
Scientific Advisor
Entrepreneur Advisor
Scientific Advisor
Project Manager
Scientific Advisor
Data Collection Assistant
Data Science Researcher
Data Science Researcher
Data Science Researcher
We are proud to be part of the State University of New York (SUNY) system, recognized for its dedication to high-quality education, research, and public service. As a member of the SUNY Canton community, our lab benefits from a strong network of academic resources, collaborative opportunities, and a shared vision of innovation.
SUNY’s robust support ensures our projects receive the necessary resources, fostering an environment where interdisciplinary research and practical learning thrive side by side.
We are honored to receive grant support that advances our innovative research projects:
This grant empowers our entrepreneurial research and development efforts, helping the team explore market opportunities and commercial potential for advanced data science solutions.
This award from AWS Startups supports our emerging ventures and accelerates cloud-based innovation. It helps us build scalable solutions that empower data-driven products and services, fostering our entrepreneurial efforts within the Lab.
We appreciate the support and collaboration of these institutions and organizations:
Our lab members publish leading-edge research in renowned venues. Recent works include:
We welcome passionate students, scholars, and industry professionals who aspire to shape the future of data science. Whether you’re interested in our current projects or exploring new research ideas, our lab is the perfect environment to innovate and excel.
Contact UsInterested in partnering or learning more about our research? Please fill out this form and we will get back to you soon.