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About: The goal of Artificial Intelligence (AI) is to understand intelligence by constructing computational models of intelligent behavior. This entails developing and testing falsifiable algorithmic theories of (aspects of) intelligent behavior, including sensing, representation, reasoning, learning, decision-making, communication, coordination, action, and interaction. AI is also concerned with the engineering of systems that exhibit intelligence. Machine learning is concerned with the scientific study, exploration, design, analysis, and applications of algorithms that learn concepts, predictive models, behaviors, action policies, etc. from observation, inference, and experimentation and the characterization of the precise conditions under which classes of concepts and behaviors are learnable. Learning algorithms can also be used to model aspects of human and animal learning. Machine learning integrates and builds on advances in algorithms and data structures, statistical inference, information theory, signal processing as well as insights drawn from neural, behavioral, and cognitive sciences. Data mining is concerned with the applications of statistical machine learning for exploratory analysis and predictive modeling from large data sets. Causal discovery is concerned with algorithms for eliciting the underlying causal (as opposed to the merely predictive) relationships from observational and experimental data.

Areas of Strength: Some areas of strength in Artificial Intelligence in the college include: knowledge representation and inference; machine learning (especially, statistical machine learning, neural networks e.g., deep learning, learning predictive models from sequence data, spatial data, network data, temporal data, relational data); genetic algorithm/evolutional computation; fuzzy logic; eliciting causal effects from experimental and observational data, including temporal and relational data; and applications in bioinformatics, health informatics, social informatics, learning analytics, text analytics, image analytics, and computational discovery, among others.

Faculty: Chao-Hsien Chu, Lee GilesVasant Honavar, Sharon Huang, Ting-Hao Kenneth Huang, Dongwon Lee, Jessie Li, Fenglong MaPrasenjit Mitra, Sarah RajtmajerDavid ReitterJames Wang, Suhang Wang, Ting WangShomir Wilson, Amulya Yadav, John Yen, Zihan Zhou

About: Rapid advances in technologies for collecting data leading to the transformation of many historically data poor disciplines, e.g., biological sciences, health sciences, social sciences into increasingly data-rich disciplines. This has led to exponential increases in the volume, velocity, and variety of data, i.e., “big data”. New discoveries are increasingly driven by our ability to acquire, share, integrate and analyze, and construct and simulate predictive models of natural and built systems from big data. The emerging focus on Big Data is concerned with the exploration, development, and applications of scalable algorithms, infrastructures, and tools for organizing, integrating, retrieving, analyzing,and visualizing, large, complex, heterogeneous data.

Creative applications of big data analytics are enabling biologists to gain insights into how living systems acquire, encode, process, and transmit information; neuroscientists to uncover the neural bases of cognition; health scientists to not only diagnose and treat diseases but also help individuals make healthy choices; economists to understand markets; security analysts to uncover threats to national security; social scientists to study the evolution and dynamics of social networks; and scholars to gain new understandings of literature, arts, history, and cultures through advances in the digital humanities. 

Areas of Strength: Some areas of strength in Big Data in the college include information retrieval and search, scalable machine learning, learning predictive models, semantic complex event processing (CEP) from distributed, heterogeneous data, big data privacy and security, discovery Informatics, and big data applications in informatics (including Health Informatics, Security Informatics, Social Informatics).

Faculty: Lee Giles, Vasant Honavar, Sharon Huang, Jessie Li, Fenglong MaPrasenjit Mitra, Anna SquicciariniJames Wang, Suhang Wang, Amulya Yadav, Zihan Zhou

About: Health Informatics is concerned with the design, development, evaluation, adoption, and application of Health Information Technology (HIT) innovations in healthcare services delivery, management, and planning aiming to improve access to, quality, outcomes, and efficiency of healthcare. Bioinformatics is concerned with the development and applications of informatics methods and tools for analysis, interpretation, modeling, and visualization of complex biological data (including molecular sequence, structure, expression, and interaction Fundata). Brain informatics is concerned with the development and applications of informatics tools and methods for analysis, interpretation, modeling, and visualization of brain data (including connectivity, activity, and behavior).

Areas of Strength: Some areas of strength in Bioinformatics include the development of effective tools for analysis and prediction of protein-protein, protein-RNA, protein-DNA interfaces and interactions, for predicting epitopes and designing vaccines, and for annotating genomes. Some areas of focus in brain informatics include the development and application of methods for analysis and interpretation of brain connectivity and activity data (especially Functional Magnetic Resonance Imaging (fMRI) and Electroencephalogram (EEG) data). Some areas of strength in Health Informatics include development and application of methods and tools for integrative analyses of electronic health records, genomic, and contextual (socio-economic, environmental, and demographic) data for predictive and causal modeling of health outcomes, personalized treatments, design and evaluation of personal and mobile health (mHealth) applications for real time monitoring and detection for heart attack, stress, sleep quality, fall and other medical conditions, healthcare systems engineering, human-centered design interventions, text analytics, network analytics, and machine learning to improve health services delivery.

Faculty: John M. Carroll, Chao-Hsien Chu, Xinning GuiVasant Honavar, Sharon Huang, Fenglong MaJohn Yen

About: Cognitive science is concerned with the scientific study of the mind. Cognitive Science examines the nature and the working of cognitive processes particularly as they relate to a given context whether computational, physical, biological, neural, or social. It includes research on intelligence and behavior, especially focusing on how information is represented, processed, and transformed to support a wide range of tasks such as goal-oriented and opportunistic reasoning, learning, language-based communication, interacting with technology or analyzing data, or judging risks in security and safety.  It comprises areas of perception, memory, reasoning, language, emotion, and decisions in brains (of humans or other animals) and machines (computers, robots, agents).  Cognitive science models the mind at different levels: computational, neuro-physical, or social.

Cognitive science has emerged as a transdisciplinary research area that has basic-level roots in philosophy, psychology, computer science, neuroscience, linguistics, anthropology, and sociology.  At the same time cognitive science has also inextricably evolved and advanced through the use of technologies that provide windows into the mind: affective computing, eye tracking, linguistic and behavioral coding and analytics, decision aids, neuro-physiological measurement apparatus (e.g., fMRI and EEG neuro-imagining), dynamic modeling and alignment of brain networks, wearable-ubiquitous computing, and brain-computer interaction technologies, to name a few.

Areas of Strength: Some areas of strength in Cognitive Science in the college include computational discourse analysis and psycholinguistics, decision-making, memory and learning, multi-agent interaction, intelligent associates, distributed cognition, pattern recognition, knowledge acquisition, situation awareness, analogical problem solving and knowledge transfer, cognitive hemispheric asymmetry and face / image recognition, cognitive modeling and models of perception and language, multi-modal capturing and analysis of neural, visual, and cognitive processes (e.g., for cyber analysts), dynamic network modeling, brain network alignment, and temporal causality analysis of neural events.

Some of the practical areas in which IST faculty members have applied principles of cognitive science are:  emergency crisis management, DoD C3I (communications, control, communications, and intelligence), estimating the success of natural-language communications, peer-to-peer patient support in online forums, image analyst work, intelligence analyst work, software design, cyber situation awareness/cyber security work, police cognition, fighter pilot performance, medical decision making, and data triage and intrusion detection of network analysts.

Faculty: John M. Carroll, Vasant Honavar, Sharon Huang, David Reitter, Frank RitterJohn Yen

Affiliated Faculty: Ping Li,

About: Community informatics involves the emerging field addressing how information communication technologies impact interactions among community members at a local, national and global level in relation to their social, cultural, economic and community development.   For example, how does the ability to create communities of interest impact commerce (e.g., via e-commerce), political dialogs and power, exchange and distribution of popular culture, and empower social change, etc. The deployment of information systems and communications provides an enabler for developing countries and regions for social and economic development.

Closely related to community informatics is the area of social informatics, which seeks to understand the impact of information technology in organizational work design and effectiveness (e.g., the social aspects of people and society and technical aspects of organizational structure and processes). Social Informatics seeks to understand and optimize the utilization of information technology to improve individual and organizational effectiveness, productivity and wellbeing.

Areas of Strength: Some areas of strength in the college include; geo-spatial activity awareness, community information technology workshops for community learning, crowd-sourcing for citizen science, location-sensitive mobile applications, community planning for emergency management, alternate/community currencies, and distributed, digital inequality, diversity in STEM education and the technology workforce, and computer supported collaborative work.

Faculty: Guoray Cai, John M. Carroll, Xinning Gui, Yubo KouCarleen Maitland, Mary Beth Rosson, Andrea Tapia, Lynette Yarger, John Yen

About: HCD integrates, applies and develops human and computational sciences through creating and evaluating interactive systems. HCD researchers study specific fields of human practice/work domains, using ethnography, survey and interview methods, laboratory experiments, field deployments, and session logging/data mining. They create advanced user interfaces and applications incorporating mobile and collaborative technologies, interactive visualizations, and a wide range of interactions. They study the social, cognitive and affective aspects of the user experience, as well as consequences for communities, organizations, and society. HCD research increases the chance that new information technology can actually be used and enjoyed by people for real purposes. HCD researchers are user advocates first and technology advocates second.

Areas of Strength: The HCD group at Penn State has special strengths in scenario-based design, including design tools and methods, and design rationale. We have current research projects directed at understanding user experiences and investigating new tools for Massive Online Open Courses (MOOCs), collaborative information visualization and analysis, municipal geo-deliberation and decision making, the management of online safety by families with teenagers, data analytic approaches to codifying community issues, discussion, and sentiment, and smart camera-based prosthetics for visually impaired people. We are investigating mobile interactions and applications to support time-banking and other peer-to-peer exchange systems, behavior change for health and wellness, health tracking and cross-generational health collaborations within families, and community heritage, events, and crisis management.

Faculty: Syed Billah, Guoray Cai, John Carroll, Xinning GuiSteven Haynes, Yubo KouMary Beth Rosson, Luke Zhang

About: Information economics and policy concerns the production, distribution and use of information and associated policies. Areas of research include; e-commerce, the “bundling” and “valuation” of information goods and services, how information and information technology systems affect an economy and economic decisions, personal and organizational privacy, and the “right to be forgotten”.   Aspects of information economics are related to game theory concepts such as on-line auctions, dynamic automated resource allocation, intelligent agents, and other concepts.

Areas of Strength: We conduct active research in economics of information security, and the public policy aspects of online privacy and the “right to be forgotten”.  We also conduct research at the interface between public policy and regulations, and the interface between public policy and information technology standards.

Faculty: Edward Glantz, Carleen Maitland, Anna Squicciarinni

About: Multi-sensor information fusion seeks to combine information from multiple sensors and sources to achieve inferences that are not feasible from a single sensor or source. The proliferation of micro and nano-scale sensors, wireless communication, and ubiquitous computing enables the assembly of information from sources including physical sensors, humans acting as observers, on-line data sources, and model data.   This information can be used for a wide variety of applications such as: environmental monitoring, crisis management, medical diagnosis, monitoring and control of manufacturing processes and intelligent buildings.  A key problem is how to integrate or fuse information from heterogeneous sources.  Techniques for such information fusion are drawn from a broad set of disciplines including: statistical estimation, signal and image processing, artificial intelligence, and information sciences.  Major issues involve architectures for distributed sensing and processing, selection and integration of algorithms, the role of the human-in-the-loop for analysis and decision-making, degree of automation and computer-aided cognition.   A related research area, data visualization, seeks to explore how to use advanced visualization and human-machine interaction to support understanding and analysis of large and complex data sets.

Areas of Strength: The Center for Network-Centric Cognition and Information Fusion (NC2IF) provides a focus on sensor and information fusion and data visualization.   Researchers associated with the center explore the information chain from energy detection via sensors and human observation to physical modeling, signal and image processing, pattern recognition, knowledge creation, information infrastructure, and human decision-making—all in the context of organizations and the nation.  See  Our research focuses on the gap between the collection of reports and data in computer systems and the knowledge and decisions in the minds of computer users.  The NC2IF is also host to the Extreme Events Laboratory (EEL) illustrated. The Extreme Events Laboratory is designed primarily to support research and experimentation in the areas of hard and soft data fusion, visualization, and sonification (viz., the translation of data into sound to use human hearing for data analysis, classification and anomaly detection).   This facility allows our researchers to run end-to-end experiments that improve situational awareness and enhance our ability to optimally leverage all available sensors, human observers, and technology in order to escape "information overload" and extract the true meaning hidden  within the vast mountains of available data.

Faculty: Guoray Cai, Nicklaus Giacobe, Jacob Graham, Sharon Huang, Jeff Rimland, John Yen, Luke Zhang

About: Network science is an interdisciplinary area which studies complex networks such as biological networks, computer networks, cognitive networks, social networks, economic and business networks. The field uses many theories and methods including data mining, entity recognition, graph theory, information extraction, network flow, statistical mechanics, visualization, and social structure and is primarily concerned with large scale networks. The National Research Council defines network science as "the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena.”

Faculty: Chao–Hsien Chu, Lee Giles, Vasant Honavar, Dongwon Lee, Jessie Li, Carleen Maitland, Prasenjit Mitra, Sarah RajtmajerDavid Reitter, Amulya Yadav, John Yen, Luke Zhang

About: Cybersecurity is a broad research field, including computer security, network security, trustworthy computing, privacy, usability, regulations and public policy, and attack-resilient cyber-physical systems.  We take an interdisciplinary approach to conduct research to detect and remove threats of information misuse to the human society: mitigate risk, reduce uncertainty, and enhance predictability and trust.  Our research methodology is rooted in several disciplines including computer science, game theory, applied mathematics, cognitive science, control theory, economics, social sciences, and public policy. 

Areas of Strengths: Our main research strengths are in systems and software security, usability considerations in privacy and security, economics of information security, and privacy, security and privacy for mobile devices, sensors/sensor network, Internet of Things (IoT), cyber physical systems and cloud computing, cyber forensics.  The current research thrusts include cyber situational awareness, online privacy, secure cloud computing, building secure mobile devices. 

Faculty: John Carroll, Chao-Hsien Chu, Megan Costello, Peter Forster, Marc Friedenberg, Nicklaus Giacobe, Michael Hills, Dongwon Lee, Peng Liu, Sarah RajtmajerDon Shemanski, Anna Squicciarini, Ting WangShomir Wilson, Dinghao Wu, Xinyu Xing, Heng Xu, John Yen

Affiliated Faculty: Alan Sonsteby