Publications

2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 |2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | before 2000

2024

• G. Sprint, M. Schmitter-Edgecombe, and D. Cook. HDTwin: Building a human digital twin using large language models for cognitive diagnosis. JMIR Formative Research, to appear.
• C. DeSmet, C. Greeley, and D. Cook. Hydra-TS: Enhancing human activity recognition with multi-objective synthetic time series data generation. IEEE Sensors, to appear.
• L. Besser, L. Wiese, D. Cook, J. Holt, S. Magzamen, B. Minor, D. Mitsova, J. Park, O. Sablan, M. Tourelle, and C. Williams. Rural Roads to Cognitive Resilience (RRR): A prospective cohort study protocol. IEEE Access, to appear.
• M. Schmitter-Edgecombe, C. Luna, S. Dai, and D. Cook. Capturing cognitive capacity in the everyday environment across a continuum of cognitive decline using a smartwatch n-back task and Ecological Momentary Assessment. Neuropsychology, to appear.
• C. DeSmet and D. Cook. HydraGAN: A cooperative agent model for multi-objective data generation. ACM Transactions on Intelligent Systems and Technology, 15(3):1-21, 2024.
• S. Pimento, H. Agarwal, B. Minor, S. Karia, D. Cook, M. Schmitter-Edgecombe, S. Farias, R. Lorabi, and A. Weakley. Interactive-Wear: An intelligent watch application to aid memory for intentions and everyday functioning in older adults with cognitive impairments. IEEE International Conference on AI for Medicine, Health, and Care, 2024.
• J. Nie, M. Schmitter-Edgecombe, and D. Cook. Speech analysis in older adults for neuropsychological status prediction. International Neuropsychological Society, 2024.
• M. Schmitter-Edgecombe, C. Luna, S. Dai, and D. Cook. Predicting daily cognition and lifestyle behaviors for older adults using smart home data and Ecological Momentary Assessment. The Clinical Neuropsychologist, to appear.

2023

• G. Wilson, J. Doppa, and D. Cook. CALDA: Improving multi-source time series domain adaptation with contrastive adversarial learning. IEEE Transactions on Pattern Analysis and Machine Learning, 45(12):14208-14221.
• T. Wang, T. Fischer, and D. Cook. The indoor predictability of human mobility. IEEE Transactions on Emerging Topics in Computing, 11(1):182-193, 2023.
• K. Wuestney, B. Lin, D. Cook, and R. Fritz. Modeling human frailty with a smart home-based approximation of entropy. ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, 2023.
• P. Seegmiller, J. Gatto, M. Basak, D. Cook, H. Ghasemzadeh, J. Stankovic, and S. Preum. The scope of in-context learning for the extraction of medical temporal constraints. International Workshop on Health Natural Language Processing, 2023.
• K. Luna, D. Cook, and M. Schmitter-Edgecombe. But will they use it? Predictors of adoption of an electronic memory aid in individuals with amnestic mild cognitive impairment. Neuropsychology, 37(8):955-965, 2023.
• D. Cook, L. Wiese, and M. Schmitter-Edgecombe. Testing the feasibility of tracking behavior and physiology patterns using wearable technology to detect potential cognitive risk among rural, multicultural older adults. AAIC, 2023.
• L. Wiese, I. Williams, J. Holt, C. Magzamen, L. Besser, J. Park, D. Mitsova, and D. Cook. Cane, muck, and community connections: Soil and air matters. Southern Gerontological Society Annual Scientific Conference, 2023.
• P. Seegmiller, J. Gatto, M. Basak, D. Cook, H. Ghasemzadeh, J. Stankovic, and S. Preum. The scope of in-context learning for the extraction of medical temporal constraints. International Workshop on Health Natural Language Processing, 2023.

2022

• T. Wang and D. Cook. Multi-person activity recognition in continuously monitored smart homes. IEEE Transactions on Emerging Topics in Computing, 10(2):1130-1141, 2022.
• M. Wilson, S. Fritz, M. Finlay, and D. Cook. Piloting smart home sensors to detect overnight respiratory and withdrawal symptoms in adults prescribed opioids. Pain Management Nursing, 12(11), 2022.
• B. Thomas, L. Holder, and D. Cook. Automated cognitive health assessment using partially-complete time series sensor data. Methods of Information in Medicine, 61(3/4):99-110, 2022.
• G. Sprint, M. Schmitter-Edgecombe, L. Holder, and D. Cook. Multimodal fusion of smart home and text-based behavior markers for clinical assessment prediction. ACM Transactions on Computing for Healthcare, 3(4):1-25, 2022.
• A. Ghods and D. Cook. PIP: Pictorial Interpretable Prototype learning for time series classification. IEEE Computational Intelligence, 2:34-45, 2022.
• S. Mirzadeh, A. Arefeen, J. Ardo, R. Fallahzadeh, B. Minor, J. Lee, J. Hildebrand, D. Cook, H. Ghasemzadeh, and L. Evangelista. Use of machine learning to predict medication adherence in individuals at risk for atherosclerotic cardiovascular disease. Smart Health, 2022.
• D. Cook, M. Strickland, and M. Schmitter-Edgecombe. Detecting smartwatch-based behavior change in response to a multi-domain brain health intervention. ACM Transactions on Computing for Healthcare, 3(3):1-18, 2022.
• S. Fritz, K. Wuestney, G. Dermody, and D. Cook. Nurse-in-the-loop smart home detection of health events associated with diagnosed chronic conditions: A case-event series. International Journal of Nursing Studies Advances, 4:100081, 2022.
• E. Ashari, N. Chaytor, D. Cook, and H. Ghasemzadeh. Memory-aware active learning in mobile sensing systems. IEEE Transactions on Mobile Computing, 21(1):181-195, 2022.
• M. Schmitter-Edgecombe and D. Cook. Partnering a compensatory application with activity-aware prompting to improve use in individuals with amnestic mild cognitive impairment: A randomized controlled pilot clinical trial. Journal of Alzheimer's Disease, 85(1):73-90, 2022.
• G. Wilson, J. Doppa, and D. Cook. Domain adaptation under behavioral and temporal shifts for natural time series mobile activity recognition. SIGKDD Workshop on Mining and Learning from Time Series, 2022.
• J. Briscoe, A. Gebremedhin, L. Holder, and D. Cook. Adversarial creation of a smart home testbed for novelty detection. AAAI Spring Symposium on Designing AI for Open Worlds, 2022.
• K. Wuestney, J. Ramirez, D. Cook, and R. Fritz. Smart home data visualization for proactive health monitoring of community dwelling older adults. Gerontological Society of America 2022 Annual Scientific Meeting, 2022.

2021

• D. Cook and M. Schmitter-Edgecombe. Fusing ambient and mobile sensor features into a behaviorome for predicting clinical health scores. IEEE Access, 2:65033-65043, 2021. code
• C. DeSmet and D. Cook. Recent developments in privacy-preserving mining of clinical data. ACM/IMS Transactions on Data Science, 2(4):1-32, 2021.
• M. Schmitter-Edgecombe, C. Luna, K. Brown, R. Cunningham, C. Sumida, L. Holder, and D. Cook. Pilot clinical trial: Electronic memory and management aid / smart home partnership increases aid use at three-month follow-up in individuals with mild cognitive impairment. Alzheimer's & Dementia, 17, 2021.
• J. Dahmen and D. Cook. Indirectly-supervised anomaly detection of clinically-meaningful health events from smart home data. ACM Transactions on Intelligent Systems and Technology, 12(2):1-18, 2021. code
• T. Wang and D. Cook. sMRT: Multi-resident tracking in smart homes with sensor vectorization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(8):2809-2821, 2021. code
• M. Schmitter-Edgecombe, C. Luna, and D. Cook. Technologies for health assessment, promotion, and intervention: Focus on aging and functional health. In J. J. Randolph (ed.), Positive Neuropsychology: An Evidence-Based Perspective on Promoting Cognitive Health. Springer, 2021.
• A. Ghods and D. Cook. A survey of deep network techniques all classifiers can adopt. Data Mining and Knowledge Discovery, 35:46-87, 2021.
• A. Ghods, A. Shahrokni, H. Ghasemzadeh, and D. Cook. Remote monitoring of the performance status and burden of symptoms of patients with gastrointestinal cancer via a consumer-based activity tracker: Quantitative cohort study. Journal of Medical Internet Research, 7(4):e22931, 2021.

2020

• G. Wilson, J. Doppa, and D. Cook. Multi-source deep domain adaptation with weak supervision for time-series sensor data. SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020. code
• Y. Ma, A. Campbell, D. Cook, J. Lach, S. Patel, T. Ploetz, M. Sarrafzadeh, D. Spruijt-Metz, H. Ghasemzadeh. Transfer learning for activity recognition in mobile health. KDD Workshop on Applied Data Science for Healthcare, 2020.
• N. Raghunath, M. Schmitter-Edgecombe, and D. Cook. Learning-enabled robotic assistive support for persons with memory impairment: Comparing older and younger adults' perceptions of the system. Gerontechnology, 19(3), 2020.
• A. Akbari, P. Alinia, H. Ghasemzadeh, and R. Jafari. Transfer learning for wearable computers. Wearable Sensors, pages 435-459, 2020.
• Y. Zhang, A. Srivastava, and D. Cook. Machine learning algorithm for activity-aware demand response considering energy savings and comfort requirements. IET Smart Grid, 3(5):730-737, 2020.
• A. Fellger, D. Weeks, E. Crooks, G. Sprint, and D. Cook. Wearable device-independent next day activity and next night sleep prediction for rehabilitation populations. IEEE Journal of Translational Engineering in Health and Medicine, 8:1-9, 2020.
• G. Sprint, D. Cook, and R. Fritz. Behavioral differences between subject groups identified using smart homes and change point detection. IEEE Journal of Biomedical and Health Informatics, 2020.
• G. Wilson and D. Cook. A survey of unsupervised deep domain adaptation. ACM Transactions on Intelligent Systems and Technology, 11(5):51, 2020.
• V. Tseng, J. Ying, S. Wong, D. Cook, and J. Liu. Computational intelligence techniques for combating COVID-19: A survey. IEEE Computational Intelligence Magazine, 15(4):10-22, 2020.
• N. Raghunath, J. Dahmen, K. Brown, M. Schmitter-Edgecombe, and D. Cook. Creating a digital memory notebook application for individuals with mild cognitive impairment to support everyday functioning. Disability and Rehabilitation: Assistive Technology, 4:421-431, 2020.
• S. Aminikhanghahi, M. Schmitter-Edgecombe, and D. Cook. Context-aware delivery of ecological momentary assessment. IEEE Journal of Biomedical and Health Informatics, 4(4):1206-1214, 2020.
• M. Schmitter-Edgecombe, C. Sumida, and D. Cook. Bridging the gap between performance-based assessment and self-reported everyday functioning: An ecological momentary assessment aproach. The Clinical Neuropsychologist, 34(4):678-699, 2020.
• B. Lin and D. Cook, Analyzing sensor-based individual and population behavior patterns via inverse reinforcement learning., Sensors, 20(18):5207, 2020.
• B. Lin and D. Cook. Using continuous sensor data to formalize a model of in-home activity patterns. Journal of Ambient Intelligence and Smart Environments, 12(3):183-201, 2020.
• C. Culman, S. Aminikhanghahi, and D. Cook. Easing power consumption of wearable activity monitoring with change point detection. Sensors, 20(1):310, 2020.
• T. Wang and D. Cook. Toward unsupervised multiresident tracking in ambient assisted living: Methods and performance metrics. Assistive Technology for the Elderly, 2020.
• Y. Wang and D. Cook. BraIN: A bidirectional generative adversarial network for image captions.
• A. Akbari, P. Alinia, H. Ghasemzadeh, D. Cook, and R. Jafari. Transfer learning for wearable computers. Wearable Sensors, pages 435-459, 2020.

2019

• S. Aminikhanghahi, T. Wang, and D. Cook. Real-time change point detection with application to smart home time series data. IEEE Transactions on Knowledge and Data Engineering, 31(5):1010-1023, 2019. code
• C. Pereyda, N. Raghunath, B. Minor, G. Wilson, M. Schmitter-Edgecombe, and D. Cook. Cyber-physical support of daily activities: A robot / smart home partnership. ACM Transactions on Cyber Physical Systems, 2019:21.
• D. Cook. Sensors in support of aging-in-place: The good, the bad, and the opportunities. National Academies Workshop on Mobile Technology for Adapting Aging, 2019.
• G. Wilson, C. Pereyda, N. Raghunath, G. de la Cruz, S. Goel, S. Nesaei, B. Minor, M. Schmitter-Edgecombe, M. Taylor, and D. Cook. Robot-enabled support of daily activities in smart home environments. Cognitive Systems Research, 54:258-272, 2019.
• S. Aminikhanghahi and D. Cook. Enhancing activity recognition using CPD-based activity segmentation. Pervasive and Mobile Computing, 53:75-89, 2019.
• J. Dahmen and D. Cook. SynSys: A synthetic data generation system for healthcare applications. Sensors, 19(4):1181, 2019.
• A. Ghods, K. Caffrey, B. Lin, K. Fraga, R. Fritz, M. Schmitter-Edgecombe, C. Hundhausen, and D. Cook. Iterative design of visual analytics for a clinician-in-the-loop smart home. IEEE Journal of Biomedical and Health Informatics, 23(4):1742-1748, 2019.
• A. Ghods and D. Cook Activity2Vec: Learning ADL embeddings from sensor data with a sequence-to-sequence model. KDD Workshop on Mining and Learning from Time Series, 2019.
• T. Wang and D. Cook. Towards unsupervised multi-resident tracking in ambient assisted living: Methods and performance metrics. In N. Suryadevara and N. Mukhopadhyay (eds.), Assistive Technologies for the Elderly. Elsevier, 2019.
• S. Mirzadeh, J. Ardo, R. Fallahzadeh, B. Minor, L. Evangelista, D. Cook, and H. Ghasemzadeh. LabelMerger: Learning activities in uncontrolled environments. International Conference on Transdisciplinary AI, 2019.
• G. Wilson and D. Cook. Multi-purposing domain adaptation discriminators for pseudo labeling confidence. KDD Workshop on Adversarial Learning Methods for Machine Learning and Data Mining, 2019.
• Y. Wang and D. Cook. Bidirectional imputation of sensor-based time series. KDD Workshop on Mining and Learning from Time Series, 2019.
• G. Sprint, D. Weeks, J. Conci, and D. Cook. Utilizing consumer-grade wearable sensors for unobtrusive rehabilitation outcome prediction. IEEE International Conference on Biomedical and Health Informatics, 2019.

2018

• D. Cook, M. Schmitter-Edgecombe, L. Jonsson, and A. Morant. Technology-enabled assessment of functional health. IEEE Reviews on Biomedical Engineering, 12:319-332, 2018.
• D. Weeks, G. Sprint. J. Dahmen, A. La Fleur, and D. Cook. Implementing wearable sensors for continuous assessment of daytime heart rate response in inpatient rehabilitation. Telemedicine and e-Health, 24:1014-1020, 2018.
• A. Aramendi, A. Goenaga, D. Cook, and A. Basarab. Using smart offices to predict occupational stress. International Journal of Industrial Ergonomics, 67:13-26, 2018.
• A. Aramendi, A. Weakley, M. Schmitter-Edgecombe, D. Cook, A. Aztiria, A. Basarab, and M. Barrenechea. Smart home-based prediction of multi-domain symptoms related to Alzheimer's disease. Journal of Biomedical and Health Informatics, 22(6):1720-1731, 2018.
• A. Aramendi, A. Weakley, A. Goenaga, M. Schmitter-Edgecombe, and D. Cook. Automatic assessment of functional health decline in older adults based on smart home data. Journal of Biomedical Informatics, 81:119-130, 2018.
• D. Cook, G. Sprint, R. Fritz, and G. Duncan. Using smart city technology to make healthcare smarter. Proceedings of the IEEE, 106(4):708-722, 2018.
• J. Dahmen, B. Minor, D. Cook, T. Vo, and M. Schmitter-Edgecombe. Design of a smart home-driven digital memory notebook to support self-management of activities for older adults. Gerontechnology, 17(2):113-123, 2018.
• A. Mokhtari, S. Aminikhanghahi, Q. Zhang, and D. Cook. Fall detection in smart home sensors using UWB sensors and unsupervised change detection. Journal of Reliable Intelligent Environments, 4(3):131-139, 2018.
• W. M. Kirk, M. Fuchs, Y. Huangfu, N. Lima, P. O'Keeffe, B. Lin, T. Jobson, S. Pressley, V. Walden, D. Cook, and B. Lamb. Indoor air quality and wildfire smoke impacts in the Pacific Northwest. Science and Technology for the Built Environment, 24(2), 2018.
• A. Musser, B. Lin, D. Cook, B. Jobson, M. Kirk, N. Lima, P. O'Keeffe, S. Pressley, V. Walden, Y. Huangfu, and B. Lamb. Indoor air toxic gases levels in a net-zero energy house under multiple venilation system settings. Conference of the International Society of Indoor Air Quality and Climate, 2018.
• A. Musser, B. Lin, D. Cook, B. Jobson, M. Kirk, N. Lima, P. O'Keeffe, S. Pressley, V. Walden, Y. Huangfu, and B. Lamb. Simulations of indoor air quality based on future climate conditions. Conference of the International Society of Indoor Air Quality and Climate, 2018.
• A. Musser, B. Lin, D. Cook, B. Jobson, M. Kirk, N. Lima, P. O'Keeffe, S. Pressley, V. Walden, Y. Huangfu, and B. Lamb. The major role of temperature on indoor concentrations of air toxic VOCs in 9 houses based on in-situ high time resolution measurements. Conference of the International Society of Indoor Air Quality and Climate, 2018.

2017

• B. Minor, J. Doppa, and D. Cook. Learning activity predictors from sensor data: Algorithms, evaluation, and applications. IEEE Transactions on Knowledge and Data Engineering, 29(12):2744-2757, 2017.
• G. Sprint, D. Cook, D. Weeks, J. Dahmen, and A. La Fleur. Analyzing sensor-based time series data to track changes in physical activity during inpatient rehabilitation. Sensors, 17:2219-2238, 2017.
• K. Feuz and D. Cook. Collegial activity learning between heterogeneous sensors. Knowledge and Information Systems, 53(2):337-364, 2017.
• P. Alinia, C. Cain, R. Fallahzadeh, A. Shahrokni, and H. Ghasemzadeh. How accurate is your activity tracker? A comparative study of step counts in low-intensity physical activities. Journal of Medical Internet Research, 5(8):e106, 2017.
• B. Lin, Y. Huangfu, N. Lima, T. Jobson, M. Kirk, P. O'Keeffe, S. Pressley, V. Walden, B. Lamb, and D. Cook. Analyzing the relationship between human behavior and indoor air quality. Journal of Sensor and Actuator Networks. 6(13), 2017.
• J. Dahmen, D. Cook, X. Wang, and W. Honglei. Smart secure homes: A survey of smart home technologies that sense, assess, and respond to security threats. Journal of Reliable Intelligent Environments, 2017.
• B. Minor and D. Cook. Forecasting occurrences of activities. Pervasive and Mobile Computing, 38(1):77-91, 2017.
• S. Aminikhanghahi and D. Cook. A survey of methods for time series change point detection. Knowledge and Information Systems, 51(2):339-367, 2017.
• J. Williams and D. Cook. Forecasting behavior in smart homes based on past sleep and wake patterns. Technology and Health Care, 25:89-110, 2017.
• J. Dahmen, R. Fellows, D. Cook, and M. Schmitter-Edgecombe. An analysis of a digital variant of the Trail Making Test using machine learning techniques. Technology and Health Care, 25(2):251-264, 2017.
• J. Dahmen, B. Thomas, D. Cook, and X. Wang. Activity learning as a foundation for security monitoring in smart homes. Sensors, 17:737, 2017.
• R. Fellows, J. Dahmen, D. Cook, and M. Schmitter-Edgecombe. Multicomponent analysis of a digital trail making test. The Clinical Neuropsychologist, 31(1):154-167, 2017.
• K. Feuz and D. Cook. Modeling skewed class distributions by reshaping the concept space. AAAI Conference on Artificial Intelligence, 2017.
• S. Aminikhanghahi, R. Fallahzadeh, D. Cook, and L. Holder. Thyme: Improving smartphone prompt timing through activity awareness. IEEE International Conference on Machine Learning and Applications, 2017.
• G. Sprint, A. La Fleur, J. Dahmen, V. Stilwill, A. Meisen-Vehrs, D. Weeks, and D. Cook. Continuous assessment of daytime heart rate response during inpatient rehabilitation. American Congress of Rehabilitation Medicine Annual Conference, 2017.
• S. Fritz and D. Cook. Identifying varying health states in smart home sensor data: An expert-guided approach. World Multiconference on Systems, Cybernetics and Informatics, 2017.
• R. Fallahzadeh, B. Minor, L. Evangelista, D. Cook, and H. Ghasemzadeh. Mobile sensing to improve medication adherence. ACM/IEEE International Conference on Information Processing in Sensor Networks, 2017.
• J. Dahmen, A. LaFleur, G. Sprint, D. Cook, and D. Weeks. Using wrist-worn sensors to measure and compare physical activity changes for patients undergoing rehabilitation. Workshop on Sensing Systems and Applications Using Wrist Worn Smart Devices, 2017.
• G. Sprint, V. Borisov, D. Cook, and D. Weeks. Measuring changes in gait and vehicle transfer ability during inpatient rehabilitation with wearable inertial sensors. Workshop on Pervasive Health Technologies, 2017.
• M. Schmitter-Edgecombe, D. Cook, A. Weakley, and P. Dawadi. Using smart environment technologies to monitor and assess everyday functioning and deliver real-time intervention. In T. Parsons and R. Kane (eds.), The Role of Technology in Clinical Neuropsychology, Oxford University Press, 2017.
• S. Aminikhanghahi and D. Cook. Using change point detection to automate daily activity segmentation. Workshop on Context and Activity Modeling and Recognition, 2017.

2016

• G. Sprint, D. Cook, R. Fritz, and M. Schmitter-Edgecombe. Using smart homes to detect and analyze health events. Computer, 49(11):29-37, 2016.
• B. Thomas and D. Cook. Activity-aware energy-efficient automation of smart buildings. Energies, 9(8):624, 2016.
• G. Sprint, D. Cook, and M. Schmitter-Edgecombe. Unsupervised detection and analysis of changes in everyday physical activity data. Journal of Biomedical Informatics, 63:54-65, 2016.
• E. Van Etten, A. Weakley, M. Schmitter-Edgecombe, and D. Cook. Subjective cognitive complaints and objective memory performance influence prompt preference for instrumental activities of daily living. Gerontechnology, 14(3):169-176, 2016.
• Y. Hu, D. Tilke, T. Adams, A. Crandall, D. Cook, and M. Schmitter-Edgecombe. Smart home in a box: Usability study for a large scale self-installation of smart home technologies. Journal of Reliable Intelligent Environments, 2:93-106, 2016.
• R. Fritz, C. Corbett, R. Vandermause, and D. Cook. The influence of culture on older adults' adoption of smart home monitoring. Gerontechnology, 14(3):146-156, 2016.
• P. Dawadi, D. Cook, and M. Schmitter-Edgecombe. Modeling patterns of activities using activity curves. Pervasive and Mobile Computing, 28(C):51-68, 2016.
• B. Das, D. Cook, N. Krishnan, and M. Schmitter-Edgecombe. One-class classification-based real-time activity error detection in smart homes. IEEE Journal of Selected Topics in Signal Processing, 10(5):914-923, 2016. Data used for this study
• P. Dawadi, D. Cook, and M. Schmitter-Edgecombe. Automated clinical assessment from smart home-based behavior data. IEEE Journal of Biomedical and Health Informatics, 20(4):1188-1194, 2016.
• B. Thomas, A. Crandall, and D. Cook. A genetic algorithm approach to motion sensor placement in smart environments. Journal of Reliable Intelligent Environments, 2(1):3-16, 2016.
• A. Crandall and D. Cook. Current state of the art of smart environments and labs from an AAL point of view. In F. Florez-Revuelta and A. Chaaraoui (Eds.), Ambient Assisted Living: Technologies and Applications, IET, 2016.
• N. Roy, A. Misra, and D. Cook. Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments. Journal of Ambient Intelligence and Humanized Computing, 7(1):1-19, 2016.
• S. Aminikhanghahi and and D. Cook. Activity transition detection by relative density ratio estimation. Florida Artificial Intelligence Research Symposium, 2016.
• G. Sprint and D. Cook. Designing wearable sensor-based analytics for quantitative mobility assessment. IEEE International Conference on Smart Computing, 2016.
• G. Sprint, D. Cook, R. Fritz, and M. Schmitter-Edgecombe. Detecting health and behavior change by analyzing smart home sensor data. IEEE International Conference on Smart Computing, 2016.
• K. Bouchard, L. Holder, and D. Cook. Extracting generalizable spatial features from smart phone datasets. AAAI Workshop on Artificial Intelligence Applied to Assistive Technologies and Smart Environments, 2016.
• G. Sprint and D. Cook. Quantitative assessment of lower limb and cane movement with wearable inertial sensors. The Engineering in Medicine and Biology Conference, 2016.
• J. Williams and D. Cook. Using time series techniques to forecast and analyze wake and sleep behavior. KDD Workshop on Mining and Learning from Time Series, 2016. video
• G. Sprint, D. Cook, and D. Weeks. Patient similarity and joint features for rehabilitation outcome prediction. IJCAI Workshop on Knowledge Discovery in Healthcare Data, 2016.
• R. Fallahzadeh, S. Aminikhanghahi, A. Gibson, and D. Cook. Toward personalized and context-aware prompting for smartphone-based intervention. International Conference of the IEEE Engineering in Medicine and Biology Society, 2016.

2015

• G. Sprint, D. Cook, D. Weeks, and V. Borisov. Predicting functional independence measure scores during rehabilitation with wearable inertial sensors. IEEE Access, volume 3, 2015.
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• D. Cook, P. Dawadi, and M. Schmitter-Edgecombe. Analyzing activity behavior and movement in a naturalistic environment using smart home techniques. IEEE Journal of Biomedical and Health Informatics, 19(6):1882-1892, 2015.
• A. Weakley, J. Williams, M. Schmitter-Edgecombe, and D. Cook. Neuropsychological test selection for cognitive impairment classification: A machine learning approach. Journal of Clinical and Experimental Neuropsychology, 37(9):899-916, 2015.
• K. Robertson, C. Rosasco, K. Feuz, M. Schmitter-Edgecombe, and D. Cook. Prompting technologies: A comparison of time-based and context-aware transition-based prompting. Technology and Health Care, 23:745-746, 2015.
• K. Feuz and D. Cook. Transfer learning across feature-rich heterogeneous feature spaces via feature-space remapping. ACM Transactions on Intelligent Systems and Technology, 6(1):3, 2015.
• B. Minor, D. Cook, and J. Doppa. Data-driven activity prediction: Algorithms, evaluation methodology, and applications. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2015.
• G. Sprint, D. Cook, and D. Weeks. Towards automating clinical assessments: A survey of the Timed Up and Go (TUG). IEEE Transactions on Reviews in Biomedical Engineering, 8:64-77, 2015.
• K. Feuz, D. Cook, C. Rasasco, K. Robertson, and M. Schmitter-Edgecombe. Automated detection of activity transitions for prompting. IEEE Transactions on Human Machine Systems, 45(5):575-585, 2015.
• D. Cook and N. Krishnan. Activity Learning from Sensor Data. Wiley, 2015.
• G. Acampora, D. Cook, P. Rashidi, and A. Vasilakos. Healthcare data analytics. Data Analytics for Healthcare, C. Aggarwal and C. Reddy, editors, 2015.
• E. Nazerfard and D. Cook. CRAFFT: An activity prediction model based on Bayesian networks. Journal of Ambient Intelligence and Humanized Computing, 6:193-205, 2015.
• B. Das, N. Krishnan, and D. Cook. RACOG and wRACOG: Two probabilistic oversampling methods. IEEE Transactions on Knowledge and Data Engineering, 27(1):222-234, 2015.
• M. Schmitter-Edgecombe and D. Cook. Smart homes for monitoring and assessing everyday functioning and for real-time intervention. In The Role of Technology in Clinical Neuropsychology, Oxford University Press, 2015.
• P. Dawadi and D. Cook. Monitoring everyday abilities and cognitive health using pervasive technologies: Current state and prospect. In E. Wouters, J. van Hoof, and G. Demeris (Eds.), Handbook of Smart Homes, Health Care and Well Being, Springer, 2015.
• G. Sprint and D. Cook. Enhancing the CS1 student experience with gamification. IEEE Integrated STEM Education Conference, 2015. Winner, best paper award.
• A. Salah, B. Krose, D. Cook. Behavior analysis for elderly. Human Behavior Understanding, pages 1-10, Springer, 2015.

2014

• D. Cook and N. Krishnan. Mining the home environment. Journal of Intelligent Information Systems, 43(3):503-519, 2014.
• K. Robertson, C. Rosasco, K. Feuz, D. Cook, and M. Schmitter-Edgecombe. Prompting technologies: Is prompting during activity transition more effective than time-based prompting? Archives of Clinical Neuropsychology, 29(6):598, 2014.
• K. Feuz and D. Cook. Heterogeneous transfer learning for activity recognition using heuristic search techniques. International Journal of Pervasive Computing and Communications, 10(4):393-418, 2014. Named Outstanding Paper of 2014.
• N. Krishnan and D. Cook. Activity recognition on streaming sensor data. Pervasive and Mobile Computing, 20:138-154, 2014.
• B. Das, N. Krishnan, and D. Cook. Handling imbalanced and overlapping classes in a smart environments prompting dataset. Data Mining for Service, pages 199-219, Springer, 2014.
• P. Dawadi, M. Schmitter-Edgecombe, and D. Cook. Smart home-based longitudinal functional assessment. ACM UbiComp Workshop on Smart Health Systems and Applications, 2014.
• G. Sprint, V. Borisov, D. Cook, and D. Weeks. Wearable sensors in ecological rehabilitation environments. ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014.
• B. Thomas and D. Cook. CARL: Activity-aware automation for energy efficiency. ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014.
• B. Minor and D. Cook. Regression tree classification for activity prediction in smart homes. ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014.

2013

• P. Dawadi, D. Cook, and M. Schmitter-Edgecombe. Automated cognitive health assessment using smart home smart monitoring of complex tasks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(6):1302-1313. Data used for this study.
• G. Acampora, D. J. Cook, P. Rashidi, and A. Vasilakos. A survey on ambient intelligence in health care. Proceedings of the IEEE, 101(12):2470-2494, 2013.
• P. Rashidi, D. Cook. COM: A method for mining and monitoring human activity patterns in home-based health monitoring systems. ACM Transactions on Intelligent Systems and Technology, 4(4):64:1-64:20, 2013.
• P. Dawadi, D. Cook, M. Schmitter-Edgecombe, and C. Parsey. Automated assessment of cognitive health using smart home technologies. Technology and Health Care, 21:323-343, 2013.
• D. Cook, A. Crandall, B. Thomas, and N. Krishnan. CASAS: A smart home in a box. IEEE Computer, 46(6):26-33, 2013.
• D. Cook, N. Krishnan and Z. Wemlinger. Learning a taxonomy of predefined and discovered activity patterns. Journal of Ambient Intelligence and Smart Environments, 5(6):621-637, 2013.
• D. Cook, N. Krishnan, and P. Rashidi. Activity discovery and activity recognition: A new partnership. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 43(3):820-828, 2013.
• D. Cook, K. Feuz, and N. Krishnan. Transfer learning for activity recognition: A survey. Knowledge and Information Systems, 36:537-556, 2013.
• D. Cook and L. Holder. Automated activity-aware prompting for activity initiation. Gerontechnology, 11(4):1-11, 2013.
• A. Seelye, M. Schmitter-Edgecombe, D. Cook, and A. Crandall. Naturalistic assessment of everyday activities and prompting technologies in mild cognitive impairment. Journal of the International Neuropsychological Society, 19(4):442-452, 2013.
• C. Chen, D. Cook, and A. Crandall. The user side of sustainability: Modeling behavior and energy usage in the home. Pervasive and Mobile Computing, 9(1):161-175, 2013.
• N. Roy, A. Misra, and D. Cook. Infrastructure-assisted smartphone-based ADL recognition in multi-inhabitant smart environments. Proceedings of the IEEE International Conference on Pervasive Computing and Communication, 2013.
• B. Das, N. Krishnan, and D. Cook. wRACOG: A Gibbs sampling-based oversampling technique. Proceedings of the IEEE International Conference on Data Mining, 2013.
• B. Das, N. Krishnan, and D. Cook. Handling class overlap and imbalance to detect prompt situations in smart homes. Proceedings of the ICDM Workshop on Data Mining in Biomedical Informatics and Healthcare, 2013.
• K. Feuz and D. Cook. Real-time annotation tool (RAT). Proceedings of the AAAI Workshop on Activity Context-Aware System Architectures, 2013.
• J. Williams, A. Weakley, D. Cook, and M. Schmitter-Edgecombe. Machine learning techniques for diagnostic differentiation of mild cognitive impairment and dementia. Proceedings of the AAAI Workshop on Expanding the Boundaries of Health Informatics Using AI, 2013.
• A. Crandall and D. Cook. Behaviometrics for identifying smart home residents. In Human Aspects in Ambient Intelligence (T. Bosse, D. Cook, M. Neerincx, and F. Sadri, editors), Atlantis / Springer, pages 55-71, 2013.

2012

• B. Das, D. Cook, M. Schmitter-Edgecombe, and A. Seelye. PUCK: An automated prompting system for smart environments. Personal and Ubiquitous Computing, 16(7):859-873, 2012.
• A. Seelye, M. Schmitter-Edgecombe, B. Das, and D. Cook. Application of cognitive rehabilitation theory to the development of smart prompting technologies. Reviews in Biomedical Engineering, 5:29-44, 2012.
• D. Cook. How smart is your home? Science, 335:1579-1581, 2012.
• D. De, W.Z. Song, S. Tang, D. Cook, and S. Das. Actisen: Activity-aware sensor network in smart environments. Pervasive and Mobile Computing, 8(5):730-750, 2012.
• D. Cook. Learning setting-generalized activity models for smart spaces. IEEE Intelligent Systems, 27(1):32-38, 2012.
• D. Cook and S. Das. Pervasive computing at scale: Transforming the state of the art. Pervasive and Mobile Computing, 8(1):22-35, 2012.
• L. Chen, J. Hoey, C. Nugent, D. Cook, and Z. Yu. Sensor-based activity recognition: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 42(6):790-808, 2012.
• A. Aztiria, J. Augusto, and D. Cook. Discovering frequent user-environment interactions in intelligent environments. Personal and Ubiquitous Computing, 16(1):91-103, 2012.
• H. Fang, R. Srinivasan, and D. Cook. Feature selection for human activity recognition in smart home environments. International Journal of Innovative Computing, Information and Control, 8(5):3525-3535, 2012.
• B. Das, N. Krishnan, and D. Cook. Automated activity interventions to asist with activities of daily living. Agents and Ambient Intelligence, IOS Press, 2012.
• B. Das, A. Seelye, B. Thomas, D. Cook, L. Holder, and M. Schmitter-Edgecombe. Using smart phones for context-aware prompting in smart environments. Proceedings of the CCNC CeHPSA Workshop, 2012.
• L. Zulas, A. Crandall, M. Schmitter-Edgecombe, and D. Cook. Caregiver needs from elder care assistive smart homes: Nursing assessment. Proceedings of the International Conference of the Human Factors and Ergonomics Society, 2012.
• A. Crandall, L. Zulas, N. Krishnan, K. Feuz, and D. Cook. Visualizing your ward: Bringing smart home data to caregivers. Proceedings of the CHI Workshop on Emerging Technologies for Healthcare and Aging, 2012.
• A. Crandall and D. Cook. Smart home in a box: A large scale smart home deployment. Proceedings of the Workshop on Large Scale Intelligent Environments, 2012.
• E. Nazerfard and D. Cook. Bayesian network structure learning for activity prediction in smart homes. Proceedings of the International Conference on Intelligent Environments, 2012.
• S. Dernbach, B. Das, N. Krishnan, B. Thomas, and D. Cook. Simple and complex activity recognition through smart phones. Proceedings of the International Conference on Intelligent Environments, 2012.
• C. Chen and D. Cook. Behavior-based home energy prediction. Proceedings of the International Conference on Intelligent Environments, 2012.

2011

• P. Rashidi, D. Cook, L. Holder, and M. Schmitter-Edgecombe. Discovering activities to recognize and track in a smart environment. IEEE Transactions on Knowledge and Data Engineering, 23(4):527-539, 2011.
• P. Rashidi and D. Cook. Activity knowledge transfer in smart environments. Pervasive and Mobile Computing, special issue on activity recognition, 7(3):331-343, 2011.
• M. Schmitter-Edgecombe, C. Parsey, and D. Cook. Cognitive correlates of functional performance in older adults: Comparison of self-report, direct observation and performance-based measures. Journal of the International Neuropsychological Society, 17(5):853-864, 2011.
• D. Cook, M. Schmitter-Edgecombe, and L. Holder. Gerontechnology education: Beyond the barriers. IEEE Pervasive Computing, 10(4):59-63, 2011.
• D. Cook and L. Holder. Sensor selection to support practical use of health-monitoring smart environments. Data Mining and Knowledge Discovery, 10:1-13, 2011.
• P. Rashidi and D. Cook. Ask me better questions: Active learning queries based on rule induction. Proceedings of the International Conference on Knowledge Discovery and Data Mining, 2011.
• B. Das and D. Cook. Data mining challenges in automated prompting systems. Workshop on Interacting with Smart Objects, 2011.
• Y. Sahaf, N. Krishnan, and D. Cook. Defining the complexity of an activity. Proceedings of the AAAI Workshop on Activity Context Representation: Techniques and Languages, 2011.
• P. Dawadi, D. Cook, C. Parsey, M. Schmitter-Edgecombe, and M. Schneider. An approach to cognitive assessment in smart homes. KDD Workshop on Medicine and Healthcare, 2011.
• C. Chen and D. Cook. Energy outlier detection in smart environments. Proceedings of the AAAI Workshop on Artificial Intelligence and Smarter Living: The Conquest of Complexity, 2011.
• V. Jakkula and D. Cook. Detecting anomalous sensor events in smart home data for enhancing the living experience. Proceedings of the AAAI Workshop on Artificial Intelligence and Smarter Living: The Conquest of Complexity, 2011.
• P. Rashidi and D. Cook. Domain selection and adaptation in smart homes. Proceedings of the International Conference on Smart Homes and Health Telematics, 2011.
• B. Das and D. Cook. An automated prompting system for smart environments. Proceedings of the International Conference on Smart Homes and Health Telematics, 2011.
• E. Nazerfard and D. Cook. Using association rule mining to discover temporal relations of daily activities. Proceedings of the International Conference on Smart Homes and Health Telematics, 2011.
• A. Crandall and D. Cook. Tracking systems for multiple smart home residents. Human Behavior Recognition Technologies, IGI Global, 2011.
• C. Chen and D. Cook. Novelty detection in human behavior through analysis of energy utilization. Human Behavior Recognition Technologies, IGI Global, 2011.
• S. Tang, D. De, W. Song, D. Cook, and S. Das. ActSee: Activity-aware radio duty-cycling for sensor networks in smart environments. Proceedings of the International Conference on Networked Sensing Systems, 2011.

2010

• S. Deleawe, J. Kusznir, B. Lamb, and D. Cook. Predicting air quality in smart environments. Journal of Ambient Intelligence and Smart Environments, 2(2):145-154, 2010.
• D. Cook, A. Crandall, G. Singla, and B. Thomas. Detection of social interaction in smart spaces. Journal of Cybernetics and Systems, special issue on social awareness in smart spaces, 41(2):90-104, 2010.
• E. Kim, S. Helal, and D. Cook. Human activity recognition and pattern discovery. IEEE Pervasive Computing, 9(1):48-53, 2010.
• G. Singla, D. Cook, and M. Schmitter-Edgecombe. Recognizing independent and joint activities among multiple residents in smart environments. Ambient Intelligence and Humanized Computing Journal, 1(1):57-63, 2010.
• C. Corley, D. Cook, A. Mikler, and K. Singh. Text and structural data mining of influenza mentions in web and social media. International Journal of Environmental Research and Public Health, 7(2):596-615, 2010.
• B. Das, C. Chen, N. Dasgupta, D. Cook, and A. Seelye. Automated prompting in a smart home environment. Proceedings of the ICDM Workshop on Data Mining for Service, 2010.
• E. Nazerfard and D. Cook. Discovering temporal features and relations of activity patterns. Proceedings of the ICDM Workshop on Data Mining for Service, 2010.
• P. Rashidi and D. Cook. An adaptive sensor mining framework for pervasive computing applications. Lecture Notes in Computer Science, 5840:154-174, 2010.
• C. Corley, D. Cook, A. Mikler, and K. Singh. Using web and social media for influenza surveillance. Advances in Computational Biology, Springer, 2010.
• P. Rashidi and D. Cook. Mining sensor streams for discovering human activity patterns over time. Proceedings of the IEEE International Conference on Data Mining, 2010.
• P. Rashidi and D. Cook. Mining and monitoring patterns of daily routines for assisted living in real world settings. Proceedings of the ACM International Health Informatics Symposium, 2010.
• E. Nazerfard, L. Holder, and D. Cook. Conditional random fields for activity recognition in smart environments. Proceedings of the ACM International Health Informatics Symposium, 2010.
• P. Rashidi and D. Cook. Home to home transfer learning. Proceedings of the AAAI Plan, Activity, and Intent Recognition Workshop, 2010.
• P. Rashidi and D. Cook. Multi home transfer learning for resident activity discovery and recognition. Proceedings of the International Workshop on Knowledge Discovery from Sensor Data, 2010.
• C. Chen, B. Das, and D. Cook. Energy prediction based on resident's activity. Proceedings of the International Workshop on Knowledge Discovery from Sensor Data, 2010.
• R. Srinivasan, C. Chen, and D. Cook. Activity recognition using actigraph sensor. Proceedings of the International Workshop on Knowledge Discovery from Sensor Data, 2010.
• J. Kusznir and D. Cook. Designing lightweight software architectures for smart environments. Proceedings of the International Conference on Intelligent Environments, 2010.
• C. Chen, B. Das, and D. Cook. A data mining framework for activity recognition in smart environments. Proceedings of the International Conference on Intelligent Environments, 2010.
• A. Crandall and D. Cook. Using a hidden Markov model for resident identification. Proceedings of the International Conference on Intelligent Environments, 2010.
• A. Aztiria and D. Cook. Automatic modeling of frequent user behaviours in intelligent environments. Proceedings of the International Conference on Intelligent Environments, 2010.
• V. Jakkula and D. Cook. Outlier detection in smart environment structured power datasets. Proceedings of the International Conference on Intelligent Environments, 2010.
• A. Elfaham, H. Hagras, S. Helal, H. Shantonu, J. Lee, and D. Cook. A fuzzy based verification agent for the PerSim human activity simulator in ambient intelligence environments. Proceedings of the IEEE International Conference on Fuzzy Systems, 2010.

2009

• P. Rashidi and D. Cook. Keeping the resident in the loop: Adapting the smart home to the user. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 39(5):949-959, 2009.
• D. Cook, J. Augusto, and V. Jakkula. Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile Computing, 5(4):277-298, 2009.
• D. Cook, L. Holder, S. Thompson, P. Whitney, and L. Chilton. Graph-based analysis of nuclear smuggling data. Journal of Applied Security Research, 4(4), 2009.
• D. Brezeale and D. Cook. Learning video preferences using visual features and closed captions. IEEE Multimedia, 16(3):39-47, 2009.
• D. Cook, H. Hagras, V. Callaghan, and A. Helal. Making our environments intelligent. Journal of Pervasive and Mobile Computing, 5:556-557, 2009.
• A. Crandall and D. Cook. Coping with multiple residents in a smart environment. Journal of Ambient Intelligence and Smart Environments, 1(4):323-334, 2009.
• G. Singla, D. Cook, and M. Schmitter-Edgecombe. Tracking activities in complex settings using smart environment technologies. International Journal of BioSciences, Psychiatry and Technology, 1(1):25-35, 2009.
• S. Szewcyzk, K. Dwan, B. Minor, B. Swelove, and D. Cook. Annotating smart environment sensor data for activity learning. Technology and Health Care, special issue on Smart Environments: Technology to support health care, 2009.
• D. Cook and M. Schmitter-Edgecombe. Assessing the quality of activities in a smart environment. Methods of Information in Medicine, 2009.
• D. Cook. Multi-agent smart environments. Journal of Ambient Intelligence and Smart Environments, 1:47-51, 2009.
• D. Cook and W. Song. Ambient intelligence and wearable computing: Sensors on the body, in the home, and beyond. Journal of Ambient Intelligence and Smart Environments, 3:1-4, 2009.
• D. Cook and A. Crandall. Learning activity models for multiple agents in a smart space. Handbook of Ambient Intelligence and Smart Environments, Elsevier, 2009.
• C. You, L. Holder, and D. Cook. Learning patterns in the dynamics of biological networks. Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2009.
• C. Corley, A. Mikler, K. Singh, and D. Cook. Monitoring influenza trends through mining social media. Proceedings of the International Conference on Bioinformatics and Computational Biology, 2009.
• N. Ketkar, L. Holder, and D. Cook. gRegress: Extracting features from graph transactions for regression. Proceedings of the International Joint Conference on Artificial Intelligence, 2009.
• N. Ketkar, L. Holder, and D. Cook. Faster computation of the direct product kernel for graph classification. Proceedings of the Symposium on Computational Intelligence and Data Mining, 2009.
• N. Ketkar, L. Holder, and D. Cook. Empirical comparison of graph classification algorithms. Proceedings of the Symposium on Computational Intelligence and Data Mining, 2009.
• D. Cook, M. Schmitter-Edgecombe, A. Crandall, C. Sanders, and B. Thomas. Collecting and disseminating smart home sensor data in the CASAS project. Proceedings of the CHI Workshop on Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research, 2009.
• A. Mendez-Vazquez, S. Helal, and D. Cook. Simulating events to generate synthetic data for pervasive spaces. Proceedings of the CHI Workshop on Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research, 2009.
• G. Singla and D. Cook. Interleaved activity recognition for smart environments. Proceedings of the International Conference on Intelligent Environments, 2009.
• P. Rashidi and D. Cook. Transferring learned activities in smart environments. Proceedings of the International Conference on Intelligent Environments, 2009.

2008

• A. Helal, M. Schmalz, and D. Cook. Smart home-based health platform for behavioral monitoring and alteration of diabetes patients. Journal of Diabetes Science and Technology, 3(1):1-8, 2008.
• J. Kukluk, L. Holder, and D. Cook. Inference of edge replacement graph grammars. International Journal on Artificial Intelligence Tools, 2008.
• C. D. Corley, A. R. Mikler, D. Cook, and K. Singh. Dynamic intimate contact social networks and epidemic interventions. International Journal of Environmental and Healthcare Biotechnology, 2008.
• V. Jakkula and D. Cook. Anomaly detection using temporal data mining in a smart home environment. Methods of Information in Medicine, 2008.
• D. Brezeale and D. Cook. Automatic video classification: A survey of the literature. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 38(3):416-430, 2008.
• V. Jakkula and D. J. Cook. Enhancing smart home algorithms using temporal relations. Technology and Aging, IOS Press, 2008.
• V. Jakkula, A. Crandall, and D. J. Cook. Enhancing anomaly detection using temporal pattern discovery. Advanced Intelligent Environments, Springer, 2008.
• C. You, L. Holder, and D. Cook. Graph-based data mining in dynamic networks: Empirical comparison of compress-based and frequency-based subgraph mining. Proceedings of the Workshop on Analysis of Dynamic Networks, 2008.
• P. Rashidi and D. Cook. An adaptive sensor mining model for pervasive computing applications. Proceedings of the KDD Workshop on Knowledge Discovery from Sensor Data, 2008.
• C. You, L. Holder, and D. Cook. Graph-based temporal mining of metabolic pathways with microarray data. Proceedings of the SIGKDD Workshop on Data Mining in Bioinformatics, 2008.
• A. Aztiria, J. Augusto, A. Izaguirre, and D. Cook. Learning accurate temporal relations from user actions in intelligent environments. Proceedings of the Symposium of Ubiquitous Computing and Ambient Intelligence, 2008.
• C. You, L. Holder, and D. Cook. Temporal and structural analysis of biological networks in combination with microarray data. Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2008.
• W. Davis, A. Kalyanaraman, and D. Cook. An informatic theoretic approach for the discovery of irregular and repetitive patterns in genomic data. Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2008.
• C. You, L. Holder, and D. Cook. Dynamic graph-based relational learning of temporal patterns in biological networks changing over time. Proceedings of the International Conference on Bioinformatics and Computational Biology, 2008.
• P. Rashidi and D. Cook. Adapting to resident preferences in smart environments. Proceedings of the AAAI Workshop on Advances in Preference Handling, pages 78-84, 2008.
• G.Singla, D. Cook, and M. Schmitter-Edgecombe. Incorporating temporal reasoning into activity recognition for smart home residents. Proceedings of the AAAI Workshop on Spatial and Temporal Reasoning, pages 53-61, 2008.
• S. Lockwood and D. Cook. Computer, light on!. Proceedings of the International Conference on Intelligent Environments, 2008.
• A. Crandall and D. Cook. Attributing events to individuals in multi-inhabitant environments. Proceedings of the International Conference on Intelligent Environments, 2008.
• P. Rashidi and D. Cook. Keeping the intelligent environment resident in the loop. Proceedings of the International Conference on Intelligent Environments, 2008.
• J. Tilton, D. Cook, and N. Ketkar. The integration of graph based knowledge discovery with image segmentation hierarchies for data analysis, data mining and knowledge discovery. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2008.

2007

• D. Cook, L. Holder, and G. M. Youngblood. Graph-based analysis of human transfer learning using a game testbed. IEEE Transactions on Knowledge and Data Engineering, 19(11):1-14, 2007.
• K. Gopalratnam and D. Cook. Online sequential prediction via incremental parsing: The Active LeZi algorithm. IEEE Intelligent Systems, 22(1), 2007.
• J. Kukluk, L. Holder and D. J. Cook. Inference of node replacement graph grammars. Intelligent Data Analysis, 11(4), 2007.
• G. M. Youngblood and D. Cook. Data mining for hierarchical model creation. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 37(4):561-572, 2007.
• D. Cook and S. K. Das. How smart are our environments? An updated look at the state of the art. Pervasive and Mobile Computing, 3(2):53-73, 2007.
• D. Cook. Making sense of sensor data. IEEE Pervasive Computing, 2007.
• V. Jakkula and D. Cook. Prediction models for a smart home based health care system. Proceedings of the First International Workshop on Smart Homes for Tele-Health, 2007.
• V. Jakkula, A. Crandall, and D. Cook. Knowledge discovery in entity based smart environment resident data using temporal relations based data mining. Proceedings of the ICDM Workshop on Spatial and Spatio-Temporal Data Mining, 2007.
• J. Kukluk, L. Holder, and D. Cook, Inference of edge replacement graph grammars. Proceedings of the Florida Artificial Intelligence Research Symposium, 2007.
• C. Corley, L. Brown, A. Mikler, D. Cook, and K. Singh. Generating social networks of intimate contacts for the study of public health intervention strategies. Proceedings of the IEEE Seventh International Symposium on BioInformatics and BioEngineering, 2007.
• W. Davis, A. Kalyanaraman, and D. Cook. An information theoretic approach for the discovery of irregular and repetitive patterns in genomic data. Proceedings of the International Conference on Computational Systems Bioinformatics, 2007.
• V. Jakkula and D. Cook. Mining sensor data in smart environments for temporal activity prediction. Proceedings of the First International Workshop on Knowledge Discovery from Sensor Data, 2007.
• J. Kukluk, L. Holder, and D. Cook. Inference of node and edge replacement graph grammars. Proceedings of the ICML Workshop on Challenges and Applications of Grammar Induction, 2007.
• V. Jakkula and D. Cook. Temporal pattern discovery for anomaly detection in smart homes. Proceedings of the International Conference on Intelligent Environments, 2007.
• V. Jakkula and D. Cook. Using temporal relations in smart home data for activity prediction. Proceedings of the ICML Workshop on the Induction of Process Models, 2007.
• J. Kukluk, C. You, L. Holder and D. Cook. Learning node replacement graph grammars in metabolic pathways. International Conference on Bioinformatics and Computational Biology, 2007.
• V. Jakkula and D. Cook. Learning temporal relations in smart home data. Proceedings of the Second International Conference on Technology and Aging, 2007.
• P. Rashidi, G. M. Youngblood, D. Cook, and S. Das. Inhabitant guidance of smart environments. Proceedings of the International Conference on Human-Computer Interaction, 2007.
• D. J. Cook, G. M. Youngblood, and G. Jain. Algorithms for smart spaces. The Engineering Handbook on Smart Technology for Aging, Disability and Independence, A. Helal, M. Mokhtari and B. Abdulrazak, Editors, John Wiley and Sons, 2007.

2006

• D. Cook and L. Holder (eds.). Mining Graph Data. John Wiley and Sons, December 2006.
• D. Cook. Health monitoring and assistance to support aging in place. Journal of Universal Computer Science, 12(1):15-29, 2006.
• J. Coble, D. Cook, and L. Holder. Structure discovery in sequentially-connected data streams. International Journal on Artificial Intelligence Tools, 2006.
• N. Ketkar, L. Holder, and D. Cook, Mining in the proximity of subgraphs. Proceedings of LinkKDD, 2006.
• C. D. Corley, D. Cook, L. Holder, and K. P. Singh. Graph-based data mining in epidemia and terrorism data. Proceedings of the Conference on Quantitative Methods and Statistical Applications in Defense and National Security, 2006.
• C.-C Tseng and D. Cook, Mining from time series human movement data. Proceedings of the Conference on Systems, Man, and Cybernetics, 2006.
• V. Jakkula, M. Youngblood, and D. Cook. Identification of lifestyle behavior patterns with prediction of the happiness of an inhabitant in a smart home. Proceedings of the AAAI Workshop on Computational Aesthetics, 2006.
• G. Jain, D. Cook, and V. Jakkula. Monitoring health by detecting drifts and outliers for a smart environment inhabitant. Proceedings of the International Conference On Smart Homes and Health Telematics, 2006.
• J. Kukluk, L. Holder and D. Cook, Inference of node replacement recursive graph grammars. Proceedings of the SIAM Conference on Data Mining, April 2006.
• S. Das and D. Cook, Designing and modeling smart environments. Proceedings of the Workshop on Autonomic Computing and Communications, 2006.
• K. Ates, J. Kukluk, L. Holder, D. Cook and K. Zhang, Graph grammar induction on structural data for visual programming. Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence, November 2006.
• J. Kukluk, C. You, L. Holder and D. Cook, Discovering recursive patterns in biological networks. Dallas Area Bioinformatics and Computational Biology Workshop, August 2006.
• D. Brezeale and D. Cook. Using closed caption and visual features to classify movies by genre. Proceedings of the KDD/MDM Workshop, 2006.
• J. Potts, D. Cook, and L. Holder. Learning from supervised graphs. Applied Graph Theory in Computer Vision and Pattern Recognition (M. Last, A. Kandel, and H. Bunke, editors), Wiley, 2006.
• D. Cook, G. M. Youngblood, and G. Jain. Algorithms for smart spaces. Technology for Aging, Disability and Independence: Computer and Engineering for Design and Applications, Wiley, 2006.
• D. Cook, G. M. Youngblood, and S. K. Das. A multi-agent approach to controlling a smart environment. AI and Smart Homes, pages 165-182, Springer Verlag, 2006.
• S. K. Das and D. Cook. Designing Smart Home Environments: A paradigm based on learning and prediction. Wireless Mobile and Sensor Networks: Technology, Applications and Future Directions. (R. Shorey, A. Ananda, M. C. Chan, and W. T. Ooi, eds.), pages 337-356, Wiley, 2006.

2005

• S. Bandyopadhyay, U. Maulik, L. Holder and D. Cook (eds.). Advanced Methods for Knowledge Discovery from Complex Data. Springer, 2005.
• L. Holder, D. Cook, J. Coble and M. Mukherjee. Graph-based relational learning with application to security.. Fundamenta Informaticae Special Issue on Mining Graphs, Trees and Sequences, 66(1-2):83-101, 2005.
• M. Youngblood, D. Cook, and L. Holder. Managing adaptive versatile environments. Journal of Pervasive and Mobile Computing, 2005.
• J. Kukluk, L. Holder, and D. Cook, Algorithm and experiments in testing planar graphs for isomorphism. Journal of Graph Algorithms and Applications, 8(3), 2005.
• J. Coble, D. Cook, R. Rathi, and L. Holder. Iterative structure discovery in graph-based data. International Journal of Artificial Intelligence Techniques. 14(1-2), 2005.
• N. Ketkar, L. Holder, and D. Cook. Comparison of graph-based and logic-based multi-relational data mining. SIGKDD Explorations Special issue on Link Mining 7(2), 2005.
• J. Coble and D. Cook. Structure discovery in sequentially connected data. Proceedings of the Florida Artificial Intelligence Research Symposium, 2005. Recipient of the best paper award.
• K. Gee and D. Cook. Text classification using graph-encoded linguistic elements. Proceedings of the Florida Artificial Intelligence Research Symposium, 2005.
• R. Rathi and D. Cook. A serial partitioning approach to scaling graph-based knowledge discovery. Proceedings of the Florida Artificial Intelligence Research Symposium, 2005.
• J. Potts, D. Cook, and L. Holder. Learning from examples in a single graph. Proceedings of the Florida Artificial Intelligence Research Symposium, 2005.
• M. Youngblood, D. Cook, and L. Holder, Managing adaptive versatile environments. Proceedings of the IEEE International Conference on Pervasive Computing and Communications, 2005.
• N. Ketkar, L. Holder and D. Cook, Qualitative comparison of graph-based and logic-based multi-relational data mining: A case study. Proceedings of the ACM KDD Workshop on Multi-Relational Data Mining, August 2005.
• N. Ketkar, L. Holder, D. Cook, R. Shah and J. Coble, Subdue: Compression-based frequent pattern discovery in graph data. Proceedings of the ACM KDD Workshop on Open-Source Data Mining. August 2005.
• S. K. Das and D. Cook, Designing smart environments: A paradigm based on learning and prediction. Proceedings of First International Conference on Pattern Recognition and Machine Intelligence (PReMI'05), Kolkata, India, Dec 18-22, 2005.
• M. Youngblood, D. Cook, L. Holder and E. Heierman. Automation intelligence for the smart environment. Proceedings of the International Joint Conference on Artificial Intelligence, 2005.
• J. Potts, L. Holder, D. Cook, and J. Coble. Learning concepts from intelligence data embedded in a supervised graph. Proceedings of the International Conference on Intelligence Analysis, 2005.
• M. Youngblood, L. Holder, and D. Cook. A learning architecture for automating the intelligent environment. Proceedings of the Conference on Innovative Applications of Artificial Intelligence, 2005.
• L. Holder and D. Cook, Graph-based data mining. J. Wang (ed.), Encyclopedia of Data Warehousing and Mining, Idea Group Publishing, 2005.
• D. Cook, L. Holder, J. Coble and J. Potts, Graph-based mining of complex data. S. Bandyopadhyay, U. Maulik, L. Holder and D. Cook (eds.), Advanced Methods for Knowledge Discovery from Complex Data, Springer, 2005.

2004

• Smart Environments: Technologies, Protocols and Applications (D. Cook and S. Das, editors), John Wiley and Sons.
• K. Gopalratnam and D. Cook. Active LeZi: An incremental parsing algorithm for sequential prediction. International Journal of Artificial Intelligence Tools, 14(1-2):917-930, 2004.
• D. Cook and S. Das. MavHome: Work in progress, IEEE Pervasive Computing, 2004.
• D. Cook, L. Holder, M. Huber, and R. Yerraballi. Enhancing computer science education with a wireless intelligent simulation environment. Journal of Computing in Higher Education, 16(1), 2004.
• J. Coble, D. Cook, L. Holder, and R. Rathi. Structure discovery from sequential data. Proceedings of the Florida Artificial Intelligence Research Symposium, 2004. Extended paper in International Journal of Artificial Intelligence Techniques, 14(1-2), 2005.
• A. Rakhshan, L. Holder and D. Cook. Structural web search engine. International Journal of Artificial Intelligence Tools, 13(1):27-33, 2004.
• I. Jonyer, L. Holder and D. Cook. MDL-based context-free graph grammar induction and applications. International Journal of Artificial Intelligence Tools, 13(1):45-64, 2004.
• S. Rao and D. Cook. Predicting inhabitant actions using action and task models with application to smart homes. International Journal of Artificial Intelligence Tools, 13(1):81-100, 2004.
• S. Das and D. Cook. Health monitoring in an agent-based smart home. Proceedings of the International Conference on Smart Homes and Health Telematics (ICOST), Singapore, September, 2004.
• E. Heierman, M. Youngblood, and D. Cook. Mining temporal sequences to discover interesting patterns. KDD Workshop on Mining Temporal and Sequential Data, 2004.
• I. Jonyer, L. Holder, and D. Cook, Attribute-value selection based on the minimum description length. Proceedings of the International Conference on Artificial Intelligence, 2004.
• L. Holder and D. Cook. Graph-based data mining. Encyclopedia of Data Warehousing and Mining, 2004.
• D. Cook and M. Youngblood. Smart homes. Encyclopedia of Human-Computer Interaction, 2004.
• S. K. Das and D. Cook, Health monitoring in an agent-based smart home by activity prediction. Toward a Human-Friendly Assistive Environment, D. Zhang and M. Mokhari (eds.), IOS Press, pages 3-14, 2004.

2003

• L. Holder and D. Cook. Graph-based relational learning: Current and future directions. SIGKDD Explorations special issue on Multirelational Data Mining, 5(1):90-93, 2003.
• D. Cook, N. Manocha, and L. Holder. Using a graph-based data mining system to perform web search. International Journal of Pattern Recognition and Artificial Intelligence, 2003.
• P. Sandanayake and D. Cook. Imitating agent game strategies using a scalable Markov model. International Journal of Pattern Recognition and Artificial Intelligence, 2003.
• G. Peterson and D. Cook. Incorporating decision-theoretic planning in a robot architecture. Robotics and Autonomous Systems, 42(2):89-106, 2003.
• D. Cook, S. Das, Karthik Gopalratnam, and Abhishek Roy. Health monitoring in an agent-based smart home. Proceedings of the International Conference on Aging, Disability and Independence Advancing Technology and Services to Promote Quality of Life, 2003.
• E. Heierman and D. Cook. Improving home automation by discovering regularly occurring device usage patterns. Proceedings of the International Conference on Data Mining, 2003. % 128 papers selected from 501
• Y. Wang, D. Cook, V. Papudesi, and M. Huber. User-guided reinforcement learning of robot assistive tasks for an intelligent environment. Proceedings of the Conference on Intelligent Robots and Systems, 2003.
• S. Rao and D. Cook. Identifying tasks and predicting actions in smart homes using unlabeled data. Proceedings of the Machine Learning Workshop on The Continuum from Labeled to Unlabeled Data, 2003.
• C. Noble and D. Cook, Graph-based anomaly detection. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003.
• F. Khawaja, D. Gjoni, M. Huber, D. Cook and M. Youngblood. Achieving faster convergence to the optimal policy by using knowledge of the reward structure. Proceedings of the Artificial Intelligence Applications Conference, 2003.
• I. Jonyer, L. Holder, and D. Cook. MDL-based context-free graph grammar induction. Proceedings of the Sixteenth International Conference of the Florida AI Research Society, May 2003.
• A. Rakhshan, L. Holder, and D. Cook. Structural web search engine. Proceedings of the Sixteenth International Conference of the Florida AI Research Society, May 2003.
• K. Gopalratnam and D. Cook. Active LeZi: An incremental parsing algorithm for device usage prediction in the smart home. Proceedings of the Florida Artificial Intelligence Research Symposium, 2003.
• R. Mehta, D. Cook, and L. Holder. Identifying inhabitants of an intelligent environment using a graph-based data mining systems. Proceedings of the Florida Artificial Intelligence Research Symposium, 2003.
• S. Rao and D. Cook. Improving the performance of action prediction through identification of abstract tasks. Proceedings of the Florida Artificial Intelligence Research Symposium, 2003.
• V. Papudesi, M. Huber, Y. Wang, and D. Cook. Integrating user commands and autonomous task performance in a reinforcement learning framework. Proceedings of the AAAI Spring Symposium on Human Interaction with Autonomous Systems in Complex Environments, 2003.
• D. Cook, M. Youngblood, E. Heierman, K. Gopalratnam, S. Rao, A. Litvin, and F. Khawaja. MavHome: An agent-based smart home. Proceedings of the Conference on Pervasive Computing, 2003.
• A. Roy, S. Bhaumik, A. Bhattacharya, K. Basu, D. Cook, and S. Das. Location aware resource management in smart homes. Proceedings of the Conference on Pervasive Computing, 2003.
• K. Gee and D. Cook. Using latent semantic indexing to filter spam. ACM Symposium on Applied Computing, Data Mining Track, 2003.

2002

• S. K. Das, D. Cook, A. Bhattacharya, E. O. Heierman, III, and T.-Y. Lin. The role of prediction algorithms in the MavHome smart home architecture. IEEE Wireless Communications Communications Special Issue on Smart Homes, 9(6):77-84, 2002.
• J. Ramirez, D. Cook, L. Peterson, and D. Peterson. Temporal pattern discovery in course-of-disease data. IEEE Engineering in Medicine and Biology.
• W. Harris, D. Cook, and F. Lewis. A matrix formulation for integration of planning and manufacturing. Journal of Intelligent Manufacturing.
• I. Jonyer, L. Holder, and D. Cook. Concept formation using graph grammars. Proceedings of the KDD Workshop on Multi-Relational Data Mining, 2002.
• J. Gonzalez, L. Holder, and D. Cook. Experimental comparison of graph-based relational concept learning with inductive logic programming systems. Proceedings of the Inductive Logic Programming Conference, 2002.
• J. Gonzalez, L. Holder, and D. Cook. Graph-based relational concept learning. Proceedings of the International Machine Learning Conference, 2002.
• S. Bandyopadhyay, U. Maulik, D. Cook, L. Holder, and Y. Ajmerwala. Enhancing structure discovery for data mining in graphical databases using evolutionary programming. Proceedings of the Florida Artificial Intelligence Research Symposium, pages 232-236, 2002.
• P. Sandanayake and D. Cook. Imitating agent game strategies using a scalable Markov model. Proceedings of the Florida Artificial Intelligence Research Symposium, 2002.
• L. Holder, D. Cook, J. Gonzalez, and I. Jonyer. Structural pattern recognition in graphs. Pattern Recognition and String Matching (D. Chen and X. Cheng, eds.), Kluwer Academic Publishers, 2002.

2001

• I. Jonyer, D. Cook, and L. Holder. Discovery and evaluation of graph-based hierarchical conceptual clusters. Journal of Machine Learning Research, 2:19-43, 2001.
• D. Cook, L. Holder, S. Su, R. Maglothin, and I. Jonyer. Structural mining of molecular biology data. IEEE Engineering in Medicine and Biology special issue on Advances in Genomics, 20(4):67-74, 2001.
• I. Jonyer, L. Holder, and D. Cook. Hierarchical conceptual structural clustering. International Journal on Artificial Intelligence Tools, 10(1-2):107-136, 2001.
• L. Holder and D. Cook. An integrated tool for enhancement of artificial intelligence curriculum. Journal of Computing in Higher Education, 12(2), 2001.
• D. Cook, L. Holder, G. Galal, and R. Maglothin. Approaches to parallel graph-based knowledge discovery. Journal of Parallel and Distributed Computing, 61(3):427-446, 2001.
• J. Ramirez, D. Cook, L. L. Peterson, and D. M. Peterson. Temporal pattern discovery in sparse course-of-disease data. Medical Data Mining and Knowledge Discovery (K.J. Cios, ed.), Springer-Verlag, 2001.
• J. Gonzalez, L. Holder, and D. Cook. Application of graph-based concept learning to the predictive toxicology domain. Proceedings of the Predictive Toxicology Challenge Workshop, 2001.
• N. Manocha, D. Cook, and L. Holder. Structural web search using a graph-based discovery system. Proceedings of the Florida Artificial Intelligence Research Symposium, 2001.
• D. Cook and L. Holder. A client-server interactive tool for integrated artificial intelligence curriculum. Proceedings of the FLAIRS Special Track on AI Education, 2001.
• J. R. Nayak and D. Cook. Approximate association rule mining. Proceedings of the Florida Artificial Intelligence Research Symposium, 2001.
• J. Gonzalez, L. Holder, and D. Cook. Graph-based concept learning. Proceedings of the Florida Artificial Intelligence Research Symposium, 2001.
• C. Hannon and D. Cook. Exploring the use of cognitive models in AI applications using the Stroop effect. Proceedings of the Florida Artificial Intelligence Research Symposium, 2001.

2000

• D. Cook and L. Holder. Graph-based data mining. IEEE Intelligent Systems, 15(2):32-41, 2000.
• W. Harris and D. Cook. Automatically generating plans for manufacturing. Journal of Intelligent Systems, 10(3):297-319, 2000.
• J. Ramirez, D. Cook, L. L. Peterson, and D. M. Peterson. An event set approach to sequence discovery in medical data. Intelligent Data Analysis, 4(6):513-530, 2000.
• J. Gonzalez, L. Holder, and D. Cook. Graph-based concept learning. Proceedings of the National Conference on Artificial Intelligence, 2000.
• I. Jonyer, L. Holder, and D. Cook. Graph-based hierarchical conceptual clustering in structural data. Proceedings of the National Conference on Artificial Intelligence, 2000.
• W. Harris, D. Cook, and F. Lewis. Combining representations from manufacturing, machine planning, and manufacturing resource planning. Proceedings of the AAAI Workshop on Representational Issues for Real-World Planning Systems, 2000.
• J. Gonzalez, I. Jonyer, L. Holder and D. Cook. Efficient mining of graph-based data. Proceedings of the AAAI Workshop on Learning Statistical Models from Relational Data, 2000.
• G. Peterson and D. Cook. Decision-theoretic planning in the Graphplan framework. Proceedings of the Artificial Intelligence Planning Symposium, 2000.
• A. Baritchi and D. Cook. Discovering structural patterns in telecommunications data. Proceedings of the Florida AI Research Symposium, pages 82-85, 2000.
• J. Gonzalez, L. Holder, and D. Cook. Structural knowledge discovery used to analyze earthquake activity. Proceedings of the Florida AI Research Symposium, pages 86-90, 2000.
• C. Hannon and D. Cook. A parallel approach to unified cognitive modeling of language. Proceedings of the Thirteenth Canadian Conference on Artificial Intelligence, 2000.
• C. Hannon and D. Cook. A parallel approach to modeling language learning and understanding in young children. Proceedings of the Florida AI Research Symposium, pages 209-213, 2000.
• J. Coble and D. Cook. Real-time learning when concepts shift. Proceedings of the Florida AI Research Symposium, pages 192-196, 2000.
• I. Jonyer, L. Holder, and D. Cook. Graph-based hierarchical conceptual clustering. Proceedings of the Florida AI Research Symposium, pages 91-95, 2000.

1999

• D. Cook and R.C. Varnell. Adaptive parallel iterative deepening search. Journal of Artificial Intelligence Research, volume 9:167-194, 1999.
• S. Su, D. Cook, and L. Holder. Knowledge discovery in molecular biology: Identifying structural regularities in proteins. Intelligent Data Analysis, volume 3:413-436, 1999.
• G. Galal, D. Cook, and L. Holder. Exploiting parallelism in a scientific discovery system to improve scalability. Journal of the American Society for Information Science, 50(1):65-73, 1999.
• K.S. Tae, D. Cook, and L. Holder. Experimentation-driven knowledge acquisition for planning. Computational Intelligence, 15(3), 1999.
• D. Cook, Preface to the FLAIRS special issue. International Journal of Pattern Recognition and Artificial Intelligence, 13(2):1-2, 1999.
• P. Gmytrasiewicz, C.-C. Tseng, and D. Cook. Optimization of parallel search using machine learning and uncertain reasoning. Proceedings of the IJCAI Workshop on Statistical Machine Learning for Large-Scale Optimization, 1999.
• G. Peterson and D. Cook, Decision-theoretic layered robotic control architecture. Proceedings of the National Conference on Artificial Intelligence, 1999.
• R. Chittimoori, L. Holder, and D. Cook. Applying the Subdue substructure discovery system to the chemical toxicity domain. Proceedings of the Florida AI Research Symposium, 1999.
• D. Cook and P. Gmytrasiewicz. Controlling the parameters of parallel search using uncertainty reasoning. Proceedings of the AAAI Symposium on Search Strategy under Uncertain and Incomplete Information, 1999.
• R. Chittimoori, L. Holder, and D. Cook. Applying the Subdue substructure discovery system to the chemical toxicity domain. Proceedings of the AAAI Spring Symposium on Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools, 1999.
• J. Coble and D. Cook. Virtual environments: An agent-based approach. Proceedings of the AAAI Spring Symposium on Agents with Adjustable Autonomy, 1999.
• J. Coble and D. Cook. Virtual environments: An agent-based approach. Proceedings of the AAAI Spring Symposium on Intelligent Agents in Cyberspace, 1999.
• D. Cook and C. Hannon. Adaptive parallel search for theorem proving. Proceedings of the Florida AI Research Symposium, 1999.
• W. Briggs and D. Cook. Anytime planning for optimal tradeoff between deliberative and reactive planning, Proceedings of the Florida AI Research Symposium, 1999.
• S. Su, D. Cook, and L. Holder. Application of knowledge discovery to molecular biology: Identifying structural regularities in proteins. Proceedings of the Pacific Symposium on Biocomputing, 1999.
• D. Cook, G. Galal, and L. Holder. Improving scalability of scientific discovery systems by exploiting parallelism. in Pattern Discovery in Biological Data: Tools, Techniques and Application, J. Wang, B. Shapiro, and D. Shasha (eds.), Oxford University Press, 1999.

1998

• W. Harris, F. Lewis, and D. Cook. Machine planning for manufacturing. Journal of Intelligent Manufacturing, 9(5):413-430, 1998.
• J. Coble and D. Cook. Fault tolerant coordination of robot teams. Proceedings of the AAAI Fall Symposium on Cognitive Robotics, 1998.
• G. Peterson and D. Cook. Learning and planning in a robotic game. Proceedings of the AAAI Fall Symposium on Integrated Planning for Autonomous Agent Architectures, 1998.
• W. Harris and D. Cook. Machine planning to design manufacturing processes. Proceedings of the AAAI Fall Symposium on Integrated Planning for Autonomous Agent Architectures, 1998.
• S. Whisenhunt and D. Cook. Comparison of techniques to learn agent strategies in adversarial games. Proceedings of the Machine Learning Workshop on the Methodology of Applying Machine Learning, 1998.
• S. Taylor, D. Levine, K. Kavi, and D. Cook. A comparison of multithreading implementations. Yale Multithreaded Programming Workshop, 1998.
• L. Holder and D. Cook. Coupling two complementary knowledge discovery systems. Proceedings of the Florida AI Research Symposium, 1998.
• W. Harris and D. Cook. Integrating hierarchical and analogical planning. Proceedings of the Florida AI Research Symposium, 1998.
• R. C. Varnell and D. Cook. Integrating machine learning in parallel heuristic search. Proceedings of the Florida AI Research Symposium, 1998.
• G. Peterson and D. Cook. DFA learning of opponent strategies. Proceedings of the Florida AI Research Symposium, 1998.
• D. Cook, G. Galal, and L. Holder. Exploiting parallelism in knowledge discovery systems to improve scalability. Proceedings of the Thirty-First Hawaii International Conference on System Sciences, 1998.
• D. Cook, G. Galal, and L. Holder. Improving scalability of scientific discovery systems by exploiting parallelism. Pattern Discovery in Biological Data: Tools, Techniques and Application, Oxford University Press, 1998.

1997

• S. Djoko, D. Cook, and L. Holder. An empirical study of domain knowledge and its benefits to substructure discovery. IEEE Transactions on Knowledge and Data Engineering, 9(4), 1997.
• D. Cook and R. C. Varnell. Maximizing the benefits of parallel search using machine learning. Proceedings of the National Conference on Artificial Intelligence, 1997.
• G. Galal, D. Cook and L. Holder. Improving scalability in a scientific discovery system by exploiting parallelism. Proceedings of the International Conference on Knowledge Discovery and Data Mining, 1997.
• G. Galal and D. Cook. Exploiting parallelism in a scientific discovery system to improve scalability. Proceedings of the Tenth Annual Florida AI Research Symposium, 1997.
• D. Cook. Improving the performance of planning systems using parallel hardware and flexible social laws. Proceedings of the NSF Design and Manufacturing Grantees Conference, 1997.
• D. Cook. A hybrid approach to improving the performance of parallel search. in Parallel Processing for Artificial Intelligence, J. Geller (ed.), Elsevier Science Publishers, 1997.
• E. Mettala, D. Cook and K. Harbison. Scenario-based design of UGV RSTA algorithms. in Reconnaissance, Surveillance, and Target Acquisition for the Unmanned Ground Vehicle, O. Firschein and T. Strat (eds.), 1997.
• D. Cook, P. Gmytrasiewicz and L. Holder. Decision-theoretic multi-agent cooperative sensor planning. in Reconnaissance, Surveillance, and Target Acquisition for the Unmanned Ground Vehicle, O. Firschein and T. Strat (eds.), 1997.

1996

• D. Cook, P. Gmystrasiewicz and L. Holder. Decision-theoretic cooperative sensor planning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(18), 1996.
• D. Cook, L. Holder, and S. Djoko. Scalable discovery of informative structural concepts using domain knowledge. IEEE Expert, 10:59-68, 1996.
• D. Cook, P. Gmytrasiewicz and L. Holder. Multi-agent cooperative sensor planning. Proceedings of the Image Understanding Workshop, 1321-1332, 1996.
• E. Mettala, D. Cook, and K. Harbison. Application of the scenario-based engineering process to the Unmanned Ground Vehicle project. Proceedings of the Image Understanding Workshop, 627-641, 1996.
• K. S. Tae and D. Cook. Experimental knowledge acquisition for planning. Proceedings of the Conference on Machine Learning, 1996.
• W. Briggs and D. Cook. A clustering approach to resource allocation in multiagent systems. Proceedings of the Florida AI Research Symposium, pages 127-131, 1996.
• R. C. Varnell, D. Cook, and L. Peterson. Optimizing the performance of parallel heuristic search. Proceedings of the Florida AI Research Symposium, pages 385-389, 1996.
• K. S. Tae and D. Cook. Experimentation-driven incremental operator learning. Proceedings of the Florida AI Research Symposium, pages 204-208, 1996.
• D. Cook. Scaling up planning systems using parallel hardware and machine learning. Proceedings of the NSF Design and Manufacturing Grantees Conference, 1996.
• K. S. Tae and D. Cook. Knowledge acquisition for planning with incomplete information. Proceedings of the AAAI Spring Symposium on Planning with Incomplete Information for Robot Problems, 1996.

1995

• K. Woods, D. Cook, L. O. Hall, K. W. Bowyer, and L. Stark. Learning combination of evidence functions in object recognition. Journal of Artificial Intelligence Research, 3, 187-222, 1995.
• D. Cook and L. Holder. Knowledge discovery from structural data. Journal of Intelligence and Information Sciences, 5(3), 229-245, 1995.
• W. Briggs and D. Cook. Flexible social laws. Proceedings of the International Joint Conference on Artificial Intelligence, 1995.
• S. Djoko, D. Cook, and L. Holder. Analyzing the benefits of domain knowledge in substructure discovery. Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), 1995.
• J. Baumgartner, D. Cook, and B. Shirazi. Genetic solutions to the load balancing problem. Proceedings of the International Conference on Parallel Processing, 1995.
• S. Nerur and D. Cook. Maximizing the speedup of parallel search using HyPS. Proceedings of the Third International Workshop on Parallel Processing for Artificial Intelligence, 1995.
• L. Stark, K. W. Bowyer, K. Woods, L. Hall, and D. Cook. Application of learning techniques in a function-based recognition system. Symbolic Visual Learning, K. Ikeuchi and M. Veloso (eds.), Oxford University Press, 1995.