Biometrics Research Group
Principal Investigator: Anil K. JainWebsite |
The Biometrics Research Group focuses on the study of biometrics. The field of biometrics examines the unique physical or behavioral traits that can be used to determine a person’s identity. Biometric recognition is the automatic recognition of a person based on one or more of these traits. The word “biometrics” is also used to denote biometric recognition methods. For example, fingerprint, face, or iris biometric features are sometimes described as single biometrics. Biometric technology can prevent fraud, enhance security, and curtail identity theft.
Computer Vision Lab
Principal Investigator: Xiaoming LiuWebsite |
Computer Vision Lab focuses on vision problems such as 3D reconstruction, 3D object detection, biometrics, facial analysis and modeling, and deepfake detection.
Data Science and Engineering
Principal Investigator: Jiliang Tang and Hui LiuWebsite | Github | X
The Data Science and Engineering lab focuses on graph machine learning, trustworhty AI, and their applications in education and biology
HAAIL
Principal Investigator: Mohammad GhassemiWebsite |
The Human Augmentation and Artificial Intelligence Laboratory (HAAIL), performs research to enable robust machine learning in circumstances where: (1) data are limited, (2) data are noisy, and (3) humans are actively involved in sensitive decision-making procedures. Our laboratory is particularly interested in the healthcare and financial application areas, where these three circumstances are exceedingly common.
HAL
Principal Investigator: Vishnu BoddetiWebsite |
The Human Analysis Lab in the Computer Science and Engineering Department at Michigan State University works at the intersection of computer vision, biometrics and machine learning. Our current research focusses on building trustworthy machine learning systems.
HLR Lab
Principal Investigator: Parisa KordjamshidiWebsite | Github |
The HLR lab researchs natural language processing, machine learning and combining vision and language. We investigate innovative methodologies, particularly neuro-symbolic techniques to interplay between learning and reasoning. We develop techniques that integrate structured and formal knowledge in statistical and neural learning. Leveraging the capabilities of large vision and language models, we are committed to enhancing their robustness and reliability in tackling complex reasoning tasks. We develop research software and build prototypes to facilitate designing AI systems. Moreover, we aim to apply our techniques and tools on real world-problems and conduct multi-disciplinary research to impact the society for making the world a better place to live.
ILLIDAN Lab
Principal Investigator: Jiayu ZhouWebsite | Github |
The Intelligent Data Analytics (ILLIDAN) Lab at Michigan State University, directed by Prof. Jiayu Zhou, conducts cutting-edge research on machine learning methodologies for big data analytics. The main research theme of ILLIDAN Lab is convergent data science: enhancing decision making for data science through establishing the closed-loop flow of informatics among key components of human, data, and analytics.
OPTML
Principal Investigator: Sijia LiuWebsite | Github | X
The OPTimization and Trustworthy Machine Learning (OPTML) group, based in the Michigan State University’s CSE Department, conducts innovative research spanning artificial intelligence (AI), machine learning (ML), optimization, computer vision, security, and signal processing. Presently our primary focus is to advance the trustworthiness, generality, and scalability of AI/ML algorithms and systems, particularly for emerging foundational vision and language models. Our research efforts are dedicated to both foundational and application-oriented dimensions.
NLP & CSS Lab
Principal Investigator: Kristen JohnsonGithub |
The NLP & CSS Lab focuses on the development of machine learning and natural language processing models which leverage both linguistics and social science theories for the understanding of real-world and computational social science phenomena. Currently the main focus of the lab is bias detection and mitigation, guided from both optimization and theory-driven perspectives.
ACTION Lab
Principal Investigator: Yu KongWebsite |
ACTION Lab is dedicated to teaching machines to better perceive the visual world. We are particularly interested in video understanding, vision-language modeling, and open world recognition problems. We focus on discovering fundamental principles and algorithms for solving these problems. Our goal is to improve machine intelligence and explore new ways to make machines learn to change the world for social good.
