AiPEX Lab

Artificial Intelligence in Products Engineered for X (AiPEX) Lab

Our research explores the use of machine learning methods that predictively improve the outcome of product design solutions through the acquisition, fusion and mining of large-scale, publicly-available data. It has been reported that 70-80% of the costs of a product are determined during the design phase. Here, the term product is used in a general sense to refer to physical/digital systems that are guided by user needs, and that require domain knowledge to create. Towards enhancing the efficiency of the design process and creating personalized solutions, our research focuses on three thrust areas, outlined below.

Our lifelong research and education goals are tightly integrated and focus on advancing personalization, both from a design and learning perspective.

Faculty

Conrad Tucker

Director of CMU-Africa and Associate Dean for International Affairs-Africa

Professor of Mechanical Engineering

Courtesy Appointments, Biomedical Engineering, Machine Learning, Robotics Institute

Dr. Conrad Tucker is a professor of mechanical engineering and holds courtesy faculty appointments in Machine Learning, Robotics, and Biomedical Engineering at Carnegie Mellon University. His research focuses on the design and optimization of systems through the acquisition, integration and mining of large scale, disparate data.

Dr. Tucker has served as PI/Co-PI on federally/non-federally funded grants from the National Science Foundation (NSF), the Air Force Office of Scientific Research (AFOSR), the Defense Advanced Research Projects Agency (DARPA), the Army Research Laboratory (ARL), the Office of Naval Research (ONR) via the NSF Center for eDesign, and the Bill and Melinda Gates Foundation (BMGF). In February 2016, he was invited by National Academy of Engineering (NAE) President Dr. Dan Mote, to serve as a member of the Advisory Committee for the NAE Frontiers of Engineering Education (FOEE) Symposium. He received his Ph.D., M.S. (Industrial Engineering), and MBA degrees from the University of Illinois at Urbana-Champaign, and his B.S. in Mechanical Engineering from Rose-Hulman Institute of Technology.

Email: conradt@andrew.cmu.edu

Google Scholar: Conrad Tucker

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Projects

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Research thrusts

Research Thrust 1: Product Feature Discovery and Quantification: It is often stated that product customization is limited by the difficulty of addressing individual needs and converting them into product specifications. This research thrust seeks to advance machine understanding of user needs through the mining of large scale, publicly-available data (e.g., social media networks such as Twitter) for scalable and efficient product customization.
Research Thrust 2: Conceptual Design Generation and Evaluation: This research thrust seeks to create scalable ways to generate personalized design concepts, based on individuals’ unique needs. The evaluation of generated designs is grounded in a fundamental understanding of our physical universe and the laws that govern it. We explore ways to teach machines how to learn the relationships between design concepts and the environments/constraints that they must operate in.
Research Thrust 3: Information Veracity and Security: Ascertaining the veracity and security of data in the information age is a challenge both for humans (e.g., communicating within social media networks) and machines (e.g., training data for artificial neural networks). A lack of data veracity has the potential to “fool” both machines, as well as humans into achieving unintended outcomes/output. We explore ways to ensure the veracity of data used to train machine learning models in order to avoid garbage-in, garbage-out outcomes during the design process.

Project videos

Below, you'll find videos related to research and projects from the AiPEX Lab. ▲ Back to the top

Research team

Junghun Lee

Doctorate

Research interests: design, AI
Email: junghunl@andrew.cmu.edu

Junghun Lee on LinkedIn

Jennifer Evans

Doctorate

Co-Advisor: Jon Cagan
Research interests: AI, Machine Learning applied to problem solving, intuitive decision making, innovative design
Email: jennifee@andrew.cmu.edu

Conan Guo

Doctorate

Research interests: Robotics, Deep Learning
Email: ccguo@andrew.cmu.edu

Conan Guo on LinkedIn

Daniel Nguyen

Doctorate

Research interests: Deep Learning, controls, dextrous manipulation
Email: dtn2@andrew.cmu.edu

Daniel Nguyen on LinkedIn

James Cunningham

Doctorate

Research interests: Deep Learning/Reinforcement Learning
Email: jamescun@andrew.cmu.edu

Ananyananda Dasari

Doctorate

Research interests: Deep Learning, Machine Vision, Healthcare
Email: adasari@andrew.cmu.edu

Jessica Ezemba

Doctorate

Research interests: Product design, function/physics based design, graphic neural networks, generative design
Email: jezemba@andrew.cmu.edu

Jessica Ezemba’s LinkedIn

Madeleine Weaver

Masters

Research interests: human-robot interaction, biomimicry, machine learning

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Publications

Engineering and STEM

Peer Reviewed Journal Publications

Cunningham, J., Shu, D., Simpson, T., & Tucker, C. (2020). A sparsity preserving genetic algorithm for extracting diverse functional 3D designs from deep generative neural networks. Design Science, 6, E11. doi:10.1017/dsj.2020.9
Shu, D., Cunningham, J., Stump, G., Miller, S. W., Yukish, M. A., Simpson, T. W., & Tucker, C. S. (2020). 3D Design Using Generative Adversarial Networks and Physics-Based Validation. Journal of Mechanical Design, 142(7).
Ostrander, J. K., Tucker, C. S., Simpson, T. W., & Meisel, N. A. (2020). Evaluating the Use of Virtual Reality to Teach Introductory Concepts of Additive Manufacturing. Journal of Mechanical Design, 142(5).
Prakash, A., Kumar, S., Mahan, T., Williams, G., McComb, C., Menold, J., & Tucker, C. S. (2020). Detection of System Compromise in Additive Manufacturing Using Video Motion Magnification. Journal of Mechanical Design, 142(3).
Lopez, C. E., Cunningham, J., Ashour, O., & Tucker, C. S. (2020). Deep Reinforcement Learning for Procedural Content Generation of 3D Virtual Environments. Journal of Computing and Information Science in Engineering, 1-33.
Tucker, C. S. (2020). Workshop Report: Artificial Intelligence and the Future of STEM and Societies-Carnegie Mellon University. 10.13140/RG.2.2.22464.07687
Cunningham, J. D., Simpson, T. W., & Tucker, C. S. (2019). An Investigation of Surrogate Models for Efficient Performance-Based Decoding of 3D Point Clouds. Journal of Mechanical Design, 141(12).
Stump, G. M., Miller, S. W., Yukish, M. A., Simpson, T. W., & Tucker, C. (2019). Spatial Grammar-Based Recurrent Neural Network for Design Form and Behavior Optimization. Journal of Mechanical Design, 141(12).
Panchal, J. H., Fuge, M., Liu, Y., Missoum, S., & Tucker, C. (2019). Machine Learning for Engineering Design. Journal of Mechanical Design, 141(11).
Lim, S., & Tucker, C. S. (2019). Mining Twitter data for causal links between tweets and real-world outcomes. Expert Systems with Applications: X, 3, 100007.
Victoria Zhao, Z., Lopez, C. E., & Tucker, C. S. (2019). Evaluating the Impact of Idea Dissemination Methods on Information Loss. Journal of Computing and Information Science in Engineering, 19(3).
Lopez, C. E., Ashour, O., & Tucker, C. S. (2019, August). Reinforcement Learning Content Generation for Virtual Reality Applications. In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers Digital Collection.
Cunningham, J. D., Miller, S. W., Yukish, M. A., Simpson, T. W., & Tucker, C. S. Deep Reinforcement Learning for Transfer of Control Policies. In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers Digital Collection.
Wang, H., & Tucker, C. (2019, August). Pixel to Stroke Sketch Generation Using Reinforcement Learning. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 59179, p. V001T02A013). American Society of Mechanical Engineers.
Lopez, C. E., Zhao, Z. V., & Tucker, C. S. (2019, August). Semantic Network Differences Across Engineering Design Communication Methods. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 59179, p. V001T02A030). American Society of Mechanical Engineers.
Prakash, S. K. A., Mahan, T., Williams, G., McComb, C., Menold, J., & Tucker, C. S. (2019, August). On the Analysis of a Compromised Additive Manufacturing System Using Spatio-Temporal Decomposition. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 59193, p. V02BT03A018). American Society of Mechanical Engineers.
Jain, P., & Tucker, C. (2019, August). Mobile Based Real-Time Occlusion Between Real and Digital Objects in Augmented Reality. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 59179, p. V001T02A085). American Society of Mechanical Engineers.
Lopez, C. E., Ashour, O. M., & Tucker, C. (2019, June). An Introduction to the CLICK Approach: Leveraging Virtual Reality to Integrate the Industrial Engineering Curriculum. In ASEE annual conference & exposition.
Lopez, C. E., Miller, S. R., & Tucker, C. S. (2019). Exploring biases between human and machine generated designs. Journal of Mechanical Design, 141(2).
Lopez, C., Miller, S., & Tucker, C. S. Exploring Biases between Human and Machine Generated Designs. Journal of Mechanical Design. doi: 10.1115/1.4041857. (PDF).
Lim, S., Tucker, C. S., Jablokow, K., & Pursel, B. (2018). A semantic network model for measuring engagement and performance in online learning platforms. Computer Applications in Engineering Education. (PDF).
Kang, S., & Tucker, C. S. (2018). Exploring the Correlation Between New Function Attributes Mined from Different Product Domains and Market Sales. The Engineering Economist, 63(2), 113-142. (PDF).
Lesniak, K., & Tucker, C. S. (2018). Dynamic Rendering of Remote Indoor Environments Using Real-Time Point Cloud Data. ASME Journal of Computing and Information Science in Engineering, 18(3), 031006. (PDF).
Tuarob, S., & Tucker, C. S. (2018). Automated Discovery of Product Feature Inferences within Large Scale Implicit Social Media Data. ASME Journal of Computing and Information Science in Engineering, 18(2), 021017. (PDF).
Dering, M., Tucker, C. S., & Kumara, S. (2018). An Unsupervised Machine Learning Approach To Assessing Designer Performance During Physical Prototyping.ASME Journal of Computing and Information Science in Engineering, 18(1), 011002. (PDF).
Tuarob, S., Strong, R., Chandra, A., & Tucker, C. S. (2018). Discovering Discontinuity in Big Financial Transaction Data. ACM Transactions on Management Information Systems (TMIS), 9(1), 3.
Dering, M., & Tucker, C. S. (2017). A Convolutional Neural Network Model for Predicting a Product’s Function, Given Its Form. ASME Journal of Mechanical Design, 139(11), 111408. (PDF).
Lim, S. & Tucker, C. S. (2017). Mitigating Online Product Rating Biases through the Discovery of Optimistic, Pessimistic, and Realistic Reviewers. ASME Journal of Mechanical Design, 139(11), 111409. (PDF).
Singh, A., & Tucker, C. S. (2017). A machine learning approach to product review disambiguation based on function, form and behavior classification. Decision Support Systems, 97, 81-91. (PDF).
Bezawada, S., Hu, Q., Gray, A., Brick, T., & Tucker, C. S. (2017). Automatic Facial Feature Extraction for Predicting Designers’ Comfort with Engineering Equipment during Prototype Creation. ASME Journal of Mechanical Design, 139(2), 021102. (PDF).
Lesniak, K., Tucker, C. S., Bilén, S.G., Terpenny, J., & Anumba, C. (2017). Immersive Distributed Design through Real Time Capture, Translation and Rendering of 3D Mesh Data. ASME Journal of Computing and Information Science in Engineering (JCISE), 17(3), 17(3), 031010 (The conference version of this paper won the Best Paper Award at the 2016 ASME IDETC/CIE Conference under the Virtual Environments and Systems (VES) division).(PDF).
Ma, J., Tucker, C. S., Okudan Kremer, G. E., & Jackson, K. L. (2017). Exposure to Digital and Hands-on Delivery Modes in Engineering Design Education and Their Impact on Task Completion Efficiency. Journal of Integrated Design and Process Science, DOI:10.3233/jid-2016-0021. 1-18.
Bodnar, T., Dering, M. L., Tucker, C. S., & Hopkinson, K. M. (2017). Using Large-Scale Social Media Networks as a Scalable Sensing System for Modeling Real-Time Energy Utilization Patterns. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(10), 2627-2640. (PDF)
Lim, S. & Tucker, C. S. (2016). A Bayesian Sampling Method for Product Feature Extraction from Large Scale Textual Data. ASME Journal of Mechanical Design, 138(6), 061403. (PDF).
Munoz, D. & Tucker, C. S. (2016). Modeling the Semantic Structure of Textually-Derived Learning Content and its Impact on Recipients’ Response States. ASME Journal of Mechanical Design, 138(4), 042001. (PDF).
Kang, S., & Tucker, C. S. (2015). An automated approach to quantifying functional interactions by mining large-scale product specification data. Journal of Engineering Design, 1-24. (PDF).
Behoora, I., & Tucker, C. S. (2015). Machine Learning Classification of Design Team Members' Body Language Patterns For Real Time Emotional State Detection.Design Studies, 39, 100-127. (PDF).
K. Kotobi, P. B. Mainwaring, C. S. Tucker, & Bilén, S.G. (2015). Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio. Electronics, 4(2), 221–238.
Tuarob, S., & Tucker, C. S. (2015). Automated Discovery of Lead Users and Latent Product Features By Mining Large Scale Social Media Networks. ASME Journal of Mechanical Design, 137(7), 071402. (PDF).
Tuarob, S., & Tucker, C. S. (2014). Quantifying Product Favorability and Extracting Notable Product Features Using Large Scale Social Media Data. ASME Journal of Computing and Information Science in Engineering, 15(3), 031003. (PDF).
Tucker, C. S., Pursel, B., & Divinsky, A. (2014). Mining Student-Generated Textual Data in MOOCS And Quantifying Their Effects on Student Performance and Learning Outcomes. ASEE Computers in Education Journal (CoEd), 5(4), 84-95. (PDF).
Kang, S., Sane, C., Vasudevan, N., & Tucker, C. S. (2013). Product Resynthesis: Knowledge Discovery Of The Value Of End- Of-Life Assemblies And Subassemblies. ASME Journal of Mechanical Design, 136(1), 011004. (PDF).
Tucker, C. S., & Kim, H. M. (2011). Trend Mining for Predictive Product Design. ASME Journal of Mechanical Design, 133(11), 111008. (PDF).
Tucker, C. S., & Kim, H.M., Barker, D.E., and Zhang, Y. (2010). A Relief F Attribute Weighting and X-Means Clustering Methodology for Top-Down Product Family Optimization. Engineering Optimization Journal, 42(7), 593-616. (PDF)
Tucker, C. S., & Kim, H. M. (2009). Data-Driven Decision Tree Classification for Product Portfolio Design Optimization. ASME Journal of Computing And Information Science In Engineering (JCISE), 9(4), 041004. (PDF)
Tucker, C. S., & Kim, H. M. (2008). Optimal Product Portfolio Formulation by Merging Predictive Data Mining with Multilevel Optimization. ASME Journal of Mechanical Design, 130(4), 041103. (PDF).

Peer-Reviewed Conference Publications

Cunningham, J., & Tucker, C. S. (2018, August). A Validation Neural Network (VNN) Metamodel For Predicting The Performance Of Deep Generative Designs. ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2018), DETC2018-86299
Lesniak, K., & Tucker, C. S. (2018, August). Real-Time Occlusion Between Real And Digital Objects In Augmented Reality. ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2018), DETC2018-86346
Dering, M., Lee, C., Hopkinson, K. M., & Tucker, C. S. (2018, August). A Deep Learning Model For Mining Object-Energy Correlations Using Social Media Image Data. ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2018), DETC2018-85417
Dering, M., Cunningham, J., Desai, R., Yukish, M., Simpson, T., & Tucker, C. S. (2018, August). A Physics-Based Virtual Environment for Enhancing the Quality of Deep Generative Designs. ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2018), DETC2018-86333
Zhao, V., & Tucker, C. S. (2018, August). Categorization of Information Loss during the Dissemination of Design Ideas. ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2018), DETC2018-86100
Ostrander, J., Tucker, C. S., Simpson, T., & Meisel, N. (2018, August). Evaluating the Effectiveness Of Virtual Reality As An Interactive Educational Resource For Additive Manufacturing. ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2018), DETC2018-86036
Lopez, C., Miller, S., and Tucker, C. S. (2018, August). Human Validation Of Computer Vs Human Generated Design Sketches. ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2018), DETC2018-85698
Dering, M. L., & Tucker, C. S. (2017, December). Generative adversarial networks for increasing the veracity of big data. In Big Data (Big Data), 2017 IEEE International Conference on (pp. 2595-2602). IEEE.
Dering, M., & Tucker, C. S. (2017, November). Implications of Generative Models in Government. AAAI 2017 Fall Symposium Series, Washington, D.C.
Lim, S., Jablokow, K., Tucker, C. S., & Pursel, B. (2017, August). Quantifying The Mismatch Between Course Content And Students’ Dialogue In Online Learning Environments. Proceedings of the 2017 ASME IDETC/CIE, DETC2017- 67339. Best Paper Award at the 2017 ASME IDETC/CIE Conference under the Design Education Conference (DEC) division.
Lesniak, K., & Tucker, C. S. (2017, August). Connecting the Real-World and the Virtual World through Dynamic Environment Rendering. Proceedings of the 2017 ASME IDETC/CIE, DETC2017-67738.
Lopez, C., & Tucker, C. S. (2017, August). From Mining Affective States To Mining Facial Keypoint Data: The Quest Towards Personalized Feedback. Proceedings of the 2017 ASME IDETC/CIE, DETC2017-67340.
Toh, C., Starkey, E., Tucker, C. S., & Miller, S. (2017, August). Mining For Creativity: Determining The Creativity Of Ideas Through Data Mining Techniques. Proceedings of the 2017 ASME IDETC2017/ DTM- 68304.
Starkey, E., Spencer, C., Lesniak, K., Tucker, C. S., & Miller, S. (2017, August). Do Technological Advancements Lead To Learning Enhancements? An Exploration In Virtual Product Dissection. Proceedings of the 2017 ASME IDETC2017/ DETC2017- 68237.
Dering, M., & Tucker, C. S. (2017, June). Board # 146 Early Predicting of Student Struggles Using Body Language Paper. 2017 ASEE Annual Conference & Exposition. Columbus, Ohio.
Lopez, C., & Tucker, C. S. (2017, June). Board # 91 When to Provide Feedback? Exploring Human-Co-Robot Interactions in Engineering Environments. 2017 ASEE Annual Conference & Exposition. Columbus, Ohio.
Lesniak, K., Tucker, C. S., Bilén, S., Terpenny, J. & Anumba, C. (2016, August). Networked, Real Time Translation of 3D Mesh Data to Immersive Virtual Reality Environments. Proceedings of the 2016 ASME IDETC/CIE, DETC 2016-59762, Best Paper Award at the 2016 ASME IDETC/CIE Conference under the Virtual Environments and Systems (VES) division.
Kang, S., & Tucker, C. S., (2016, August). Automated Mapping Of Product Features Mined From Online Customer Reviews To Engineering Product Characteristics.Proceedings of the 2016 ASME IDETC/CIE, DETC 2016-59772.
Hu, Q., Bezawada, S., Gray, A., Tucker, C. S., & Brick, T.(2016, August). Exploring the Link between Task Complexity and Students’ Affective States during Engineering Laboratory Activities. Proceedings of the 2016 ASME IDETC/CIE, DETC 2016-59757.
S. Tuarob, Strong, R., Blomberg, J., Chandra, A., Chowdhary, P., Oh, S., & Tucker, C. S. (2015, June). Automatic Discovery of Service Name Replacements using Ledger Data. Proceedings of the 2015 IEEE International Conference on Services Computing 2015 (SCC 2015), New York, USA.
Bharathi, A., & Tucker, C. S. (2015, August). Investigating the Impact of Interactive Immersive Virtual Reality Environments in Enhancing Online Engineering Design Activities. Proceedings of the 2015 ASME IDETC/CIE, DETC 2015-47388.
Dering, M., & Tucker, C. S. (2015, August). A Computer Vision Approach for Automatically Mining and Classifying End of Life Products and Components. Proceedings of the 2015 ASME IDETC/CIE, DETC 2015-47401.
Dickens, B., Sellers, S., Harms, G., Startle, O., & Tucker, C. S. (2015, August). A Proposed Virtual Reality Approach for Minimizing Information Loss in Multi-User, Scalable Environments. Proceedings of the 2015 ASME IDETC/CIE, DETC 2015-47414.
Tuarob, S., & Tucker, C. S. (2015, August). A Product Feature Inference Model Based For Mining Implicit Customer Preferences Within Large Scale Social Media Networks. Proceedings of the 2015 ASME IDETC/CIE, DETC 2015-47225.
Kang, S., & Tucker, C. S. (2015, August). Automated Concept Generation Based On Function-Form Synthesis. Proceedings of the 2015 ASME IDETC/CIE, DETC 2015-47687.
Singh, A., & Tucker, C. S. (2015, August). Investigating the Heterogeneity of Product Feature Preferences Mined Using Online Product Data Streams. Proceedings of the 2015 ASME IDETC/CIE, DETC 2015-47439 .
Tucker, C. S., & Kumara, S. (2015, August). An Automated Object-Task Mining Model for Providing Students with Real Time Performance Feedback. Proceedings of the 2015 American Society of Engineering Education Conference.
Loken, E., Tucker, C. S., Oravecz, Z., & Linder, F. (2015, June). Psychometric Analysis of Residence and MOOC Assessments. Proceedings of the 2015 American Society of Engineering Education Conference.
T. Bodnar, Tucker, C. S., Hopkinson, K., & Bilen, S. (2014, October). Increasing the Veracity of Event Detection on Social Media Networks Through User Trust Modeling. Proceedings of the 2014 IEEE International Conference on Big Data. Washington, D.C.
Tucker, C. S., St. John, D., Behoora, I., & Marcireau, A. (2014, August). Open Source 3D Scanning and Printing for Design Conceptualization and Realization. 2014 ASME IDETC/CIE, DETC2014-34801.
Munoz, D., & Tucker, C. S. (2014, August). Assessing Students’ Emotional States: An Approach To Identify Lectures That Provide An Optimal Learning Experience. 2014 ASME IDETC/CIE, DETC2014-34702.
Tucker, C. S., Dickens, B., & Divinsky, A. (2014, August). Knowledge Discovery of Student Sentiments in MOOCs and their Impact on Course Performance. 2014 ASME IDETC/CIE, DEC2014-34797
Doll, K., & Tucker, C. S. (2014, August). Mining End-Of-Life Materials Suitable For Material Resynthesis And Discovering New Application Domains. 2014 ASME IDETC/CIE, DETC2014.
Tuarob, S., & Tucker, C. S. (2014, August). Discovering Next Generation Product Innovations By Identifying Lead User Preferences Expressed Through Large Scale Social Media Data. 2014 ASME IDETC/CIE, DETC2014.
John, S., & Tucker, C. S. (2014, August). Quantifying The Price And Demand Of Subassemblies In The End Of Life Strategy Of Product Resynthesis. 2014 ASME IDETC/CIE, DETC2014.
Lewis, K., Moore-Russo, D. Cormier, P., Olewnik, A., Kremer, G., Tucker, C. S., & Simpson, T. (2014, June). Assessment of Product Archaeology as a Framework for Contextualizing Engineering Design. Proceedings of the 2014 American Society of Engineering Education.
Tucker, C. S., Jackson, K., Kremer, G., & Schmidt, L.(2014, June). The Evolution of Tactile and Virtual Learning Preferences in Undergraduate Engineering Education. Proceedings of the 2014 American Society of Engineering Education.
Sane, C., & Tucker, C. S. (2013, August). Product Resynthesis as a Reverse Logistics Strategy for an Optimal Closed-Loop Supply Chain. Proceedings of the 2013 ASME IDETC/CIE, DETC2013-12587.
Vasudevan, N., & Tucker, C. S.(2013, August). Digital Representation of Physical Artifacts: The Effect of Low Cost, High Accuracy 3D Scanning Technologies on Engineering Education, Student Learning and Design Assessments. Proceedings of the 2013 ASME IDETC/CIE, DETC2013-12651.
Tuarob, S., & Tucker, C. S.(2013, August). Fad or Here to Stay: Predicting Product Market Adoption and Longevity Using Large Scale, Social Media Data. Proceedings of the 2013 ASME IDETC/CIE, DETC2013-12661.
Han, Y., & Tucker, C. S., Simpson, T. and Davidson, E.(2013, August). A Data Mining Trajectory Clustering Methodology for Modeling Indoor Design Space Utilization. Proceedings of the 2013 ASME IDETC/CIE, DETC2013-12690.
Lewis, K., Moore-Russo, D. Cormier, P., Olewnik, A., Kremer, G., Tucker, C. S., Simpson, T., & Ashour, O.(2013, August). The Assessment Of Product Archaeology As A Platform For Contextualizing Engineering Design. Proceedings of the 2013 ASME IDETC/CIE, DETC2013-12587
Lewis, K., Moore-Russo, D. Cormier, P., Olewnik, A., Kremer, G., Tucker, C. S., & Simpson, T.,(2013, June). The Development of Product Archaeology as a Platform for Contextualizing Engineering Design. Proceedings of the 2013 American Society of Engineering Education (ASEE) Conference.
Jackson, K., Tucker, C. S., & Kremer, G.(2013, June). Student Perceptions of Tactile and Virtual Learning Approaches: What Can We Learn from their Viewpoint. Proceedings of the 2013 American Society of Engineering Education (ASEE) Conference.
Tucker, C. S., & Kang, S.(2012, August). A Bisociative Design Framework For Knowledge Discovery Across Seemingly Unrelated Product Domains. Proceedings of the 2012 ASME IDETC/CIE, DETC2012-70764.
Crespo, J., Kremer, G., Tucker, C.S., & Medina, L. (2012, August). An Analysis of Complexity Measures in Product Design and Development. Proceedings of the 2012 ASME IDETC/CIE, DETC2012-1309.
Tucker, C. S., & Kim, H. M. (2012, August). Predicting Emerging Product Design Trends by Mining Publicly Available Customer Review Data. Proceedings of the 18th International Conference on Engineering Design (ICED11), Vol. 6 2011.
Tucker, C. S., & Kim, H. M. (2010, August). Trending Mining for Predictive Product Design. 2010 ASME IDETC/CIE, DETC2010-28364.
Tucker, C. S., Hoyle, C., Kim, H. M., & Chen, W. (2009, August). A Comparative Study of Data-Intensive Demand Modeling Techniques In Relation To Product Design and Development. Proceedings of the 2009 ASME IDETC/CIE, DETC2009-87049.
Tucker, C. S., Kim, H., Barker, D., & Zhang, Y. (2008, September). Data-Mining Driven Reconfigurable Product Family Design Framework for Aerodynamic Particle Separators. In 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference (p. 6065).
Tucker, C. S., & Kim, H. M. (2007, August). Product Family Concept Generation and Validation through Predictive Decision Tree Data Mining and Multi-level Optimization. Proceedings of the 2007 ASME Design Automation Conference.
Tucker, C. S., & Kim, H. M.(2006, September). Optimal Product Portfolio Formulation: Merging Predictive Data Mining with Analytical Target Cascading. Proceedings of the 11th AIAA/MAO Conference, Portsmouth, VA, USA.

Healthcare

Peer Reviewed Journal Publications

Imani, F., Chen, R., Tucker, C., & Yang, H. (2019, August). Random Forest Modeling for Survival Analysis of Cancer Recurrences. In 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) (pp. 399-404). IEEE.
Lopez, C., & Tucker, C. (2019). Mining Facial Keypoint Data: The Quest Toward Personalized Engineering Applications. In Emotional Engineering, Vol. 7(pp. 97-112). Springer, Cham.
Lopez, C. E., & Tucker, C. S. (2019). The effects of player type on performance: A gamification case study. Computers in Human Behavior, 91, 333-345.
Lopez, C., & Tucker, C. (2018). Towards Personalized Adaptive Gamification: A Machine Learning Model for Predicting Performance. IEEE Transactions on Games. (PDF)
Lopez, C., & Tucker, C. S. (2018). The effects of player type on performance: A gamification case study. Computers in Human Behavior, DOI: https://doi.org/10.1016/j.chb.2018.10.005. (PDF).
Lopez, C., Tucker, S., Salameh, T., & Tucker, C. (2018). An unsupervised machine learning method for discovering patient clusters based on genetic signatures.Journal of biomedical informatics, 85, 30-39. (PDF).
Prakash, S. K. A., & Tucker, C. S. (2018). Bounded Kalman filter method for motion-robust, non-contact heart rate estimation. Biomedical Optics Express, 9(2), 873-897. (PDF).
Munoz, D. A., Kilinc, M. S., Nembhard, H. B., Tucker, C. S., & Huang, X. (2017). Evaluating the Cost-Effectiveness of an Early Detection of Parkinson's Disease through Innovative Technology. The Engineering Economist, DOI: http://dx.doi.org/10.1080 /0013791X.2017.1294718. (PDF).
Tuarob, S., Tucker, C. S., Kumara, S., Giles, C. L., Pincus, A. L., Conroy, D. E., & Ram, N. (2017). How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information. Journal of Biomedical Informatics, 68, 1-19. (PDF).
Lim, S., Tucker, C. S., & Kumara, S. (2017). An unsupervised machine learning model for discovering latent infectious diseases using social media data. Journal of Biomedical Informatics, 66, 82-94, DOI: http://dx.doi.org/10.1016/j.jbi.2016.12.007. (PDF).
Lopez, C., & Tucker, C. S. (2017). A Quantitative Method for Evaluating the Complexity of Implementing and Performing Game Features in Physically-Interactive Gamified Applications. Computers in Human Behavior, 71, 42-58, DOI: http://dx.doi.org/10.1016/j.chb.2017.01.036. (PDF).
Bharathi, A., Singh, A., Tucker, C. S., & Nembhard, H. (2016). Knowledge Discovery of Game Design Features By Mining User-Generated Feedback. Computers in Human Behavior, 60, 361-371. (PDF).
Tucker, C. S., Behoora, I., Black-Nembhard, H., Lewis, M., Sterling, N., & Huang, X. (2015). Machine Learning Classification of Medication Adherence in Patients with Movement Disorders Using Non-Wearable Sensors. Computers in Biology and Medicine, 66, 120-134. (PDF).
Tucker, C. S., Han, Y., Nembhard-Black, H., Lee, W., Lewis, M., Sterling, N. & Huang, X. (2015). A Data Mining Methodology For Predicting Early Stage Parkinson's Disease Using Non-Invasive, High-Dimensional Gait Sensor Data. IIE Transactions on Healthcare Systems Engineering, 5(4), 238-254. (PDF).
Tuarob, S., Tucker, C. S., Salathe, M. & Ram, N. (2014). An Ensemble Heterogeneous Classification Methodology for Discovering Health-Related Knowledge in Social Media Messages. Journal of Biomedical Informatics, 49, 255-268. (PDF).

Peer Reviewed Conference Publications

Broniatowski, D., & Tucker, C. S., (2016, June). The Role of Big Data and Associated Theory in Complex Engineered Systems. 2016 Council of Engineering Systems Universities (CESUN) Symposium.
Tuarob, S., Tucker, C. S., Salathe, M., & Ram, N. (2015, October). Modeling Individual-Level Infection Dynamics Using Social Network Information. The 24th ACM International Conference on Information and Knowledge Management (CIKM 2015), Melbourne, Australia.
Koffer, R., Ram, N., Brick, T. R., Almeida, D., & Tucker, C. S. (2015, November). Machine Learning and Gerdontology: Boosted Regression Trees Predict Age Differences in Stressor Experience. In Gerontologist (Vol. 55, pp. 461-462), 2001 Evans Rd, Cary, NC 27513 USA: Oxford University Press.
Yin, P., Ram, N., Lee, W., Tucker, C. S., Khandelwal, S., & Salathe, M. (2014, May). Two sides of a coin: Separating Personal Communication and Public Dissemination Accounts in Twitter. 2014 The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Tainan, Taiwan.
Bodnar, T., Barclay, V., Ram, N., Tucker, C. S., & Salathe, M. (2014, April). On the Ground Validation of Online Diagnosis with Twitter and Medical Records.PHDA2014/WWW2014.
Tuarob, S., Tucker, C. S., Salathe, M., & Ram, N. (2013, October). Discovering Health-Related Knowledge in Social Media Using Ensembles of Heterogeneous Features. The 22nd ACM International Conference on Information and Knowledge Management (CIKM 2013). San Francisco, CA, USA.
Manohar, G., & Tucker, C. S. (2013, August). A Privacy Preserving Data Mining Methodology for Dynamically Predicting Emerging Human Threats. Proceedings of the 2013 ASME IDETC/CIE. DETC2013-13155.

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Visit to CMU-Africa

View the photo slide show from the Upanzi Network Digital Public Goods Workshop.

The Upanzi Network Digital Public Goods Workshop - March 24-25, 2022

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