The ALPS Laboratory is a group of multidisciplinary researchers that conducts cutting-edge research at the intersection of machine learning, computational privacy, and cybersecurity. Our mission is to fully unleash the potential of data-driven algorithmic systems to support groundbreaking discoveries and critical decision-making. We look to the real world for practical and important problems. Then, we aim to develop effective, scalable analytical tools to empower such systems and mitigate their negative impact on individuals and society.

The Artificial Intelligence Research Laboratory conducts research in artificial intelligence, machine learning, data mining, information integration, and semantic web. Its projects explore applications in bioinformatics, engineering informatics, cheminformatics, materials informatics, social informatics, and related areas.

The Center for Big Data Analytics and Discovery Informatics aims to accelerate advances in a number of fields by addressing the challenges in data management, data integration, data analytics, and predictive modeling and simulation to realize the full potential of big data. Our researchers seek to understand how scientists, decision makers, and the public at large can use this wealth of information to drive not only scientific discovery but also decisions and policies.

The Penn State Center for Socially Responsible Artificial Intelligence is a multi-unit initiative that promotes the thoughtful development and application of AI and studies its impact on all areas of human endeavor. In addition to supporting research focused explicitly on AI for social good and mitigating threats from its misuse, through this center, Penn State encourages that all AI research and development activities consider social and ethical implications as well as intended and possible unintended consequences. 

The Crowd-AI Laboratory focuses on combining artificial intelligence with crowdsourcing to create systems that are more usable, robust, and intelligent. Our researchers work on real-time crowdsourcing, conversational agents, technical human-computer interaction, and natural language processing to understand how people can use automated technologies in context and how automated technologies serve people’s practical needs.

The Human Language Technologies Laboratory aims to solve problems to enable computers to do meaningful work with large volumes of text. Natural language is crucial to how humans communicate, synthesize, and store information. Our researchers develop methods for natural language processing and apply them to a variety of domains, including privacy, online social networks, web science, and digital libraries.

The Intelligent Information Systems Research Laboratory aims to explore and support all levels of research that will improve and enhance our ability to generate, manage, search, and mine information and knowledge. Current research covers internet database design and analysis, mobile web computing, web mining and navigation, web agents, novel and intelligent web tools, multimedia retrieval, web and internet models, web usage, automatic content analysis and digital libraries, web search, niche search engines, and information retrieval.

The Knowledge Visualization Laboratory seeks to understand the frontier issues in visual analytics, an area with a goal to augment data-driven decision-making by bridging computational capacities of computing systems and analytical skills of human beings. Our research focuses on the visualization-based tools that have become essential to human-data interaction by exploring the relevant cognitive theories, such as visual cognition, problem-solving theories, and learning theories, that impact human-computer interaction and decision-making.

The Penn State Information, Knowledge, and Web Laboratory—also known as PIKE—researches issues involving the management of and mining in data in diverse forms, including structured records, text, multimedia, social media, and web. Current and past projects have focused on developing computational techniques to detect and prevent fake news, creating a fairness-aware recommendation engine between cybersecurity job postings and job seekers' skills, and advancing machine learning methods to model the psychological phenomenon known as memory illusion.