Our mission at ASPIRE is to advance science and technology needed to effectively understand, predict, and respond to evolving emergencies by leveraging integrative research on the interaction of social, psychological, informational, and economic processes


The current focus of the ASPIRE Collaboratory is predictive intelligence for pandemic prevention. The recent COVID-19 pandemic illustrates the central role of human decision making and behavior in the spread of such a transmissible disease.  People’s decisions regarding social isolation, social distancing, mask wearing, hand washing, and vaccination vary in complex ways.  People have different individual mindsets, and these can vary across different regions and subgroups, so different groups of people respond differently to messaging and mandates and those responses change over time.  There is also an ongoing scientific debate about the degree to which pandemic information or the perceived credibility of information sources influences the degree to which people change their behavior.  ASPIRE aims to address a Grand Challenge: To support improved pandemic intelligence, prediction, explanation, and countermeasures, the fundamental interdependent evolution of infection, behavior, and information needs to be rigorously understood at multiple levels drawing upon multiple disciplines.  An interdisciplinary science of computational theories and models needs to address the mutually adaptive dynamics of information flows, human behavior, and the transmission and evolution of pathogens.


Peter Pirolli

Peter is a senior research scientist at IHMC who joined the Institute in 2017. Previously he was a Research Fellow in the Interactive Intelligence Area at the Palo Alto Research Center (PARC), where he studied human information interaction. Before joining PARC, he was a Professor in the School of Education at the University of California at Berkeley.

Peter received his doctorate in cognitive psychology from Carnegie Mellon University in 1985.  He received a bachelor’s degree in psychology and anthropology from Trent University. He has been elected as a Fellow of the American Association for the Advancement of Science, the American Psychological Association, the Association for Psychological Science, the National Academy of Education, and the ACM Computer-Human Interaction Academy.

Peter is the author of “Information Foraging Theory: Adaptive Interaction with Information.” Peter is currently an Associate Editor for Human Computer Interaction. For publications please visit my personal website.

Archna Bhatia

Dr. Archna Bhatia is a Research Scientist in Speech and Natural Language Processing at IHMC Ocala. She has held postdoctoral researcher positions at the Language Technologies Institute at Carnegie Mellon University, and at the University of Colorado at Boulder. She earned her Ph.D. in linguistics from the University of Illinois at Urbana-Champaign in 2011.

Dr. Bhatia’s research is focused on (i) speech and natural language processing for health applications; (ii) natural language processing for applications such as cybersecurity, information extraction and cognitive modeling; and (iii) understanding/modeling constructions/linguistic phenomena to improve semantic parsing and natural language understanding.

In the health domain, she has worked on developing noninvasive techniques for detection and monitoring of physiological, psychological and/or neurological conditions. For example, she has developed an approach to detect stress and predict individuals’ response to stress based on the speech and language they produce (Bhatia et al., 2021). Previously, she developed a noninvasive speech based method for detection and monitoring of ALS based on divergence from the asymptomatic speech (Bhatia et al., 2017a; Bhatia et al., 2017c).

In other applications, she develops NLP-based systems that integrate human acquired knowledge, such as linguistics and social psychology, with machine learning. For example, she is currently working on extraction of individuals’ beliefs and sentiments from the textual content they produce on social media, using NLP and social psychology (Pirolli et al., 2020) to predict behavior. On a NASA funded project, she worked on developing a system that can help the crew in a space mission by automatically identifying the step operators are on in a technical procedure based on their conversations and information extracted from technical manuals using natural language processing and machine learning based analyses and provide recommendations accordingly.  Recently, she worked in a team that developed a human language technology pipeline for active defenses against social engineering attacks that makes use of NLP, computational sociolinguistics and metadata analysis (Bhatia et al., 2020; Dalton et al., 2020; Dorr et al., 2020).

To compute deep semantic representations of sentences, she has attempted to capture the richness of lexical semantics focusing on verb particle constructions, a type of multiword expressions using lexical resources such as WordNet, and has worked on incorporating the acquired knowledge into an ontology and lexicon to improve semantic parsing (Bhatia et al., 2018; Bhatia et al., 2017b).

Dr. Bhatia is a nominated officer (2021-2023) in the Standing Committee for the SIGLEX-MWE Section of the Special Interest Group on the Lexicon of the Association for Computational Linguistics (ACL).

Research funding sources: NASA STTR (Co-Investigator), ONR (Co-PI), DARPA, IARPA, NIH, NSF, Tampa VA

Konstantinos Mitsopoulos

Konstantinos joined IHMC as a Research Scientist in November 2022. He will be collaborating with Dr. Peter Pirolli, among others. His research aims to better understand the psychological and cognitive factors that influence human decision-making, in order to identify patterns and trends that can inform the design of new machine learning algorithms and models. 

He earned a bachelor’s degree in physics at the University of Athens; a master’s degree in machine learning at University College London; and a Ph.D. in computational neuroscience at Birkbeck College in the United Kingdom. 

Prior to joining IHMC, Konstantinos worked as a Project Scientist at the Robotics Institute at Carnegie Mellon University, where he focused on developing autonomous agents that can efficiently and effectively collaborate with humans to accomplish tasks with shared goals. Previously, he served as a postdoctoral researcher at the psychology department at Carnegie Mellon University, where he worked on numerous projects at the intersection of cognitive science and machine learning. 

Outside of work, Konstantinos is an avid volleyball player with many years of experience competing in multiple countries. He has a passion for Latin dances and enjoys learning and practicing various styles such as salsa, bachata and kizomba. 

Brody Mather

Brodie is a research associate at IHMC whose research and development focuses on problems at the cross section of natural language processing and cybersecurity. As co-inventor of ask detection and stance detection with Bonnie Dorr, he has been able to design tools that aid in the understanding of individuals through the processing of textual messages.

For example, the ask detection system is able to identify “asks” that a social engineer makes of a potential victim in order to defend against such attacks. Stance detection allows for the automatic extraction of beliefs that individuals hold and their attitudes towards those beliefs.

Brodie is a Ph.D. student at the University of West Florida, where his work focuses on the development and evaluation of a general framework for domain-specialization of stance detection. His earlier cybersecurity work with Larry Bunch focused on development of a human-centered visualization tool that captures and displays network traffic. He is co-author of multiple papers in AAAI, COLING, and EMNLP.

Choh Man Teng

Choh Man came to the IHMC in 2000. Before that, she was in Sydney, and before that Rochester, N.Y., and way before that, Hong Kong.

Choh Man likes bicycles, kayaks, and Volkswagens that pose as lawn ornaments. In her spare time, she ponders issues in machine learning and uncertain reasoning.

Here are some of the projects she is working on.

Intelligent data understanding: Imperfections in data can arise from many sources. Data polishing copes with noisy data by pinpointing the locations of the imperfections and determining appropriate replacement values. This is one step beyond filtering which simply eliminates the noisy data. Polishing is information-efficient: it recovers the corrupted data.

Reasoning under uncertainty: Our everyday reasoning involves a lot of uncertainty. Nonmonotonic reasoning systems model the type of commonsense and scientific discovery process where conclusions asserted earlier may have to be retracted later. Uncertainty and uncertain reasoning are characterized using a combination of logic, statistics, and other formal constructs.

Kathleen M. Carley

Kathleen M. Carley is a professor in the School of Computer Science in the department – Institute for Software Research – at Carnegie Mellon University. She also has courtesy appointments at:

She is the director of the Center for Computational Analysis of Social and Organizational Systems (CASOS), a university wide interdisciplinary center that brings together network analysis, computer science, and organization science (www.casos.ece.cmu.edu). Kathleen M. Carley’s research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining system for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. Dr. Carley is the director of the center for Computational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, including students, post doctoral fellows, research staff, and faculty. She is the founding co-editor of the journal Computational and Mathematical Organization Theory which she now co-edits with Dr. Terrill Frantz. She has co-edited several books in the computational organizations and dynamic network area.

Christian Lebiere

Christian Lebiere is a Research Faculty in the Psychology Department at Carnegie Mellon University, having received his Ph.D. from the CMU School of Computer Science. During his graduate career, he studied connectionist models and was the co-developer of the Cascade-Correlation neural network learning algorithm. Since 1991, he has worked on the development of the ACT-R hybrid cognitive architecture and was co-author with John R. Anderson of the 1998 book The Atomic Components of Thought. The ACT-R cognitive architecture is used by a large international community of researchers in Cognitive Science and Artificial Intelligence. Most recently, Dr. Lebiere has been involved with John Laird and Paul Rosenbloom in defining the Common Model of Cognition, a community-wide effort to consolidate and formalize the scientific progress resulting from the 50-year research program in cognitive architectures. Dr. Lebiere is a founding member of the Biologically Inspired Cognitive Architectures Society, of the International Conference on Cognitive Modeling, and of the Editorial Board of the Journal of Artificial General Intelligence and the Journal of Biologically Inspired Cognitive Architectures. His work has won the Technion Prediction Tournament and best paper awards at several conferences. His research has been supported by NSF, ONR, AFOSR, ARL, NASA, DARPA, IARPA, DMSO, and DTRA. His main research interests are cognitive architectures and their applications to psychology, artificial intelligence, human-computer interaction, decision-making, intelligent agents, network science, cognitive robotics and human-machine teaming.

Mark Orr

Mark Orr is a research associate professor in the Network Systems Science and Advanced Computing division. Orr was originally trained as a cognitive psychologist at the University of Illinois at Chicago. Orr received augmentation to this training with postdoctoral fellowships in computational modeling (Carnegie Mellon), neuroscience (Albert Einstein College of Medicine), and epidemiology/complex systems (Columbia University). Over the past decade, he has become heavily involved in understanding dynamic processes and drivers of risky behavior and decision making, primarily in a public health context, at the scale of the individual and populations. Orr is now currently expanding these ideas into other contexts and for other applications (e.g., DoD, DOE, DHS).


A computational cognitive model of behaviors and decisions that modulate pandemic transmission: Expectancy-value, attitudes, self-efficacy, and motivational intensity

A General Framework for Domain-Specialization of Stance Detection: A Covid-19 Response Use Case

Cognitive modeling for computational epidemiology

Mining Online Social Media to Drive Psychologically Valid Agent Models of Regional Covid-19 Mask Wearing



Computational Theory of the Co-evolution of Pandemics, (Mis)information, and Human Mindsets and Behavior

Improving Computational Epidemiology with Higher Fidelity Models of Human Behavior

January 2023 COVID Information Commons Webinar: Research LightningTalks and Q&A

GPCE Seminar Series: Mark Orr, Biocomplexity Institute, UVA