Research Projects
1. Bridging Machine Learning and Control Theory
This research explores the fundamental aspects of control theory and machine learning communities. On the one hand, control techniques are crucial for safety-critical systems; the robustness to uncertainty and disturbance is typically introduced by a model-based design equipped with sensing, actuation, and feedback. On the other hand, learning techniques have achieved state-of-the-art performance for various artificial intelligence tasks (computer vision, natural language processing, and Go). The developments of next-generation intelligent systems such as self-driving cars, advanced robotics, and smart health and education require leveraging these control and learning techniques efficiently and safely.
1.1 Mechanism-Informed Multiscale Modeling, Learning, and Experimentation of High Entropy Alloys
Fueled by breakthrough technology developments, the biological, biomedical, material, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance our understanding. The recent rise of machine learning as a powerful technique to integrate multimodality, multi-fidelity data, and reveal correlations between intertwined phenomena presents an exceptional opportunity. However, machine learning alone ignores the fundamental laws of physics and can result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data, and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large datasets from different sources and different levels of resolution.
This research explores that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. This project explores the intrinsic physical mechanism explicitly how the alloys show different glass-forming ability under different processing techniques (i.e., cast, meltspin, and sputtering).
This research explores that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. This project explores the intrinsic physical mechanism explicitly how the alloys show different glass-forming ability under different processing techniques (i.e., cast, meltspin, and sputtering).
2. Smart and Connected Health
Artificial Intelligence (AI) provides a promising future to solve current practice of medicine, which is at the crossroad of two major trends. The first is a failed business model, with increasing expenditures and jobs allocated to healthcare, but with reduced life expectancy and high infant, childhood, and maternal mortality in the United States. This exemplifies a paradox that is not at all confined to American medicine: investment of more human capital with worse human health outcomes. The second is the generation of data in massive quantities but the limits on analysis of such data by humans alone, which have clearly been exceeded, necessitating an increased reliance on machines. Accordingly, at the same time that there is more dependence than ever on humans to provide healthcare, AI algorithms are desperately needed to help.
2.1 Wearable and Mobile Technology for Behavior Change Intervention
More people than ever are living longer with chronic conditions such as obesity, type 2 diabetes, heart disease, and most cancer. Behavior change for effective self-management such as adherence to long-term therapy can improve health outcomes and quality of life in people living with chronic illnesses. The science of developing behavior change interventions with impact for patients aims to optimize the reach, effectiveness, adoption, implementation, and maintenance of interventions and rigorous evaluation of outcomes and processes of behavior change. The development of new services and technologies offers opportunities to enhance the scope of delivery of interventions to support behavior change and self-management at scale. Herein, this research focuses on developing, evaluating, and deploying wearable and mobile technology with embedded machine learning and control theory approaches to assist decision-making in behavior change intervention development automatically.
Selected Publications:
Selected Publications:
- Baglione, Anna N., Mehdi Boukhechba, Laura E. Barnes, Jiaqi Gong, and Kristen J. Wells. "Leveraging Mobile Sensing to Understand and Develop Intervention Strategies to Improve Medication Adherence." IEEE Pervasive Computing (2020).
- Boukhechba, Mehdi, Sonia Baee, Alicia L. Nobles, Jiaqi Gon g, Kristen Wells, and Laura E. Barnes. "A social cognitive theory-based framework for monitoring medication adherence applied to endocrine therapy in breast cancer survivors." In 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp. 275-278. IEEE, 2018.
- Gong, Jiaqi, Yu Huang, Philip I. Chow, Karl Fua, Matthew S. Gerber, Bethany A. Teachman, and Laura E. Barnes. "Understanding behavioral dynamics of social anxiety among college students through smartphone sensors." Information Fusion 49 (2019): 57-68.
- Boukhechba, Mehdi, Jiaqi Gong, Kamran Kowsari, Mawulolo K. Ameko, Karl Fua, Philip I. Chow, Yu Huang, Bethany A. Teachman, and Laura E. Barnes. "Physiological changes over the course of cognitive bias modification for social anxiety." In 2018 IEEE EMBS international conference on biomedical & health informatics (BHI), pp. 422-425. IEEE, 2018.
- Huang, Yu, Jiaqi Gong, Mark Rucker, Philip Chow, Karl Fua, Matthew S. Gerber, Bethany Teachman, and Laura E. Barnes. "Discovery of behavioral markers of social anxiety from smartphone sensor data." In Proceedings of the 1st Workshop on Digital Biomarkers, pp. 9-14. 2017.
2.2 Multimodal Sensing and Modeling for Human Movement Research
Motor and sensory rehabilitation are performed under the mechanisms involving the brain, spinal cord, central pattern generators (CPG), and muscles and joints, and are influenced by other contextual factors. However, to date, we still lack a complete understanding of how rehabilitation facilitates the neuroplasticity that enables brain reorganization during recovery from stroke. This shortcoming is due in large part to the technical limitations in converging rehabilitation neuroscience and artificial intelligence techniques such as deep neural networks.
By making a dynamical systems hypothesis regarding the neurologically impaired arms, we propose to develop a sequential model based on variational auto-encoders to infer the control dynamics of the neurological pathway from cerebral cortex, spinal CPG, to muscles and joints and establish the spectrum of motor and sensory impairment reflective the underlying influencing mechanisms of neurological and musculoskeletal factors thus enhancing clinical probabilistic reasoning and decision making. The infrastructure of the neural networks will be designed to integrate data-driven (e.g., 128 EEG channel, kinetics and kinematics variables from rehabilitation robot, and wearable motion and EMG sensor data) and model-based (e.g., control pathway from the cerebral cortex to muscles and joints) methods for enabling explainable neural networks.
Selected Publications:
- Mandalapu, Varun, Nutta Homdee, Joseph M. Hart, John Lach, Stephan Bodkin, and Jiaqi Gong. "Developing computational models for personalized ACL injury classification." In 2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 1-4. IEEE, 2019.
- Dandu, Sriram Raju, Matthew M. Engelhard, Asma Qureshi, Jiaqi Gong, John C. Lach, Maite Brandt-Pearce, and Myla D. Goldman. "Understanding the physiological significance of four inertial gait features in multiple sclerosis." IEEE journal of biomedical and health informatics 22, no. 1 (2017): 40-46.
- Gong, Jiaqi, Myla D. Goldman, and John Lach. "Deepmotion: a deep convolutional neural network on inertial body sensors for gait assessment in multiple sclerosis." In 2016 IEEE Wireless Health (WH), pp. 1-8. IEEE, 2016.
- Gong, Jiaqi, Yanjun Qi, Myla D. Goldman, and John Lach. "Causality analysis of inertial body sensors for multiple sclerosis diagnostic enhancement." IEEE journal of biomedical and health informatics 20, no. 5 (2016): 1273-1280.
- Gong, Jiaqi, Philip Asare, Yanjun Qi, and John Lach. "Piecewise linear dynamical model for action clustering from real-world deployments of inertial body sensors." IEEE Transactions on Affective Computing 7, no. 3 (2016): 231-242.
3. Smart and Connected Education
After my many-year experience in smart and connected health, I started reconsidering health issues from another angle of view, primarily through the plausible causal pathways that explain the widely accepted but poorly understood positive association between education and health. Therefore, I started developing this STEM educational research project on the first day of my tenure-track journey. Now, this project combines interdisciplinary expertise (e.g., educational and developmental psychology, ubiquitous computing, curriculum development) and university-wide resources (e.g., university administration, program directors, IT and student database support) to understand STEM persistence in students from underrepresented backgrounds with the goal of developing a rigorous research program and evaluation plan for existing and future intervention programs.
3.1 Contextualization to Practice: Exploring the Emotional Lives of Underrepresented Students
Contemporary models of STEM education emphasize students’ holistic development including in their cognitive, psychomotor, social and affective domains. These models are inspiring new research directions in the assessment and understanding and promotion of students’ learning engagement, motivation, and emotions. However, the characteristics of the emotional lives (academic and daily-life behavior) of the underrepresented groups remain largely unknown. While asking students questions about these issues is useful, there is a more direct way to study them - by measuring students' learning behavior through ubiquitous sensing and computing systems. A unique element of this study is that in addition to surveys, the investigators will monitor students' emotional lives through multimodal data mining (e.g., smartwatch, smartphone, learning tracking system) to capture emotional context and dynamics in daily school life.
Selected Publications:
Selected Publications:
- Mandalapu, Varun, and Jiaqi Gong. "Understanding Affective Dynamics of Learning Toward a Ubiquitous Learning System." GetMobile: Mobile Computing and Communications 23, no. 2 (2019): 9-15.
- Mandalapu, Varun, and Jiaqi Gong. "Studying Factors Influencing the Prediction of Student STEM and Non-STEM Career Choice." In Proceedings of the 12th International Conference on Educational Data Mining. 2019.
- Mandalapu, Varun, and Jiaqi Gong. "Towards better affect detectors: Detecting changes rather than states." In International Conference on Artificial Intelligence in Education, pp. 199-203. Springer, Cham, 2018.
3.2 AI-empowered Data Storytelling
There has been a rapid increase in the amount and type of data collected and various data-analytics methodologies developed in science, technology, and engineering, especially with the rise of machine learning techniques. The ability to utilize data and analytics to tell stories to demonstrate the power of data and communicate the value of data to influence stakeholders is becoming key to any STEM project's success. Therefore, STEM graduates should have the ability to comprise data analytic skills and communication expertise. The proposed project will test an innovative approach to teach STEM graduate students to build storytelling competency with data to meet this training need.
Selected Publications:
Selected Publications:
- Lee, Heera, Varun Mandalapu, Andrea Kleinsmith, and Jiaqi Gong. "Distinguishing Anxiety Subtypes of English Language Learners Towards Augmented Emotional Clarity." In International Conference on Artificial Intelligence in Education, pp. 157-161. Springer, Cham, 2020.
Publications
Journals
[J16] L. L. Ruiz, Jiaqi Gong., "Self-Powered Cardiac Monitoring: Maintaining Vigilance with Multi-Modal Harvesting and E-Textiles," in IEEE Sensors Journal, doi: 10.1109/JSEN.2020.3017706.
[J15] Cheah, Charissa SL, Salih Barman, Kathy TT Vu, Sarah E. Jung, Varun Mandalapu, Travis D. Masterson, Ryan J. Zuber, Lee Boot, and Jiaqi Gong. "Validation of a Virtual Reality Buffet environment to assess food selection processes among emerging adults." Appetite (2020): 104741.
[J14] Baglione, Anna N., Mehdi Boukhechba, Laura E. Barnes, Jiaqi Gong, and Kristen J. Wells. "Leveraging Mobile Sensing to Understand and Develop Intervention Strategies to Improve Medication Adherence." IEEE Pervasive Computing (2020).
[J13] Jiaqi Gong, Yu Huang, Philip I. Chow, Karl Fua, Matthew S. Gerber, Bethany A. Teachman, and Laura E. Barnes. "Understanding Behavioral Dynamics of Social Anxiety Among College Students Through Smartphone Sensors." Information Fusion 49 (2019): 57-68. (Impact Factor: 10.7)
[J12] Ma, Rui, Jiaqi Gong, Guocheng Liu, and Qi Hao. "Enabling Cognitive Pyroelectric Infrared Sensing: From Reconfigurable Signal Conditioning to Sensor Mask Design." IEEE Transactions on Industrial Informatics (2019). (Impact Factor: 7.4)
[J11] Fan, Dawei, Luis Lopez Ruiz, Jiaqi Gong, and John Lach. "EHDC: An Energy Harvesting Modeling and Profiling Platform for Body Sensor Networks." IEEE Journal of Biomedical and Health Informatics (2017). (Impact Factor: 4.2)
[J10] Sriram Raju Dandu; Matthew M. Engelhard; Asma Qureshi; Jiaqi Gong; John Lach; Maite Brandt-Pearce; Myla Goldman, “Understanding the Physiological Significance of Four Inertial Gait Features in Multiple Sclerosis,” IEEE Journal of Biomedical and Health Informatics, 2017. (Impact Factor: 4.2)
[J9] Jiaqi Gong, John Lach, Yanjun Qi and Myla D. Goldman, “Causality Analysis of Inertial Body Sensors for Multiple Sclerosis Diagnostic Enhancement”, Journal of Biomedical and Health Informatics, Vol. 20, No. 5, Sep. 2016. (Impact Factor: 4.2)
[J8] Jiaqi Gong, Philip Asare, John Lach, and Yanjun Qi, “Piecewise Linear Dynamical Model for Action Clustering from Real-World Deployments of Inertial Body Sensors,” IEEE Transactions on Affective Computing, Vol. 7, No. 3, Sep. 2016. (Impact Factor: 6.3)
[J7] Mandalapu, Varun, Benjamin Ghaemmaghami, Renee Mitchell, and Jiaqi Gong. "Understanding the relationship between healthcare processes and in-hospital weekend mortality using MIMIC III." Smart Health 14 (2019): 100084.
[J6] Mandalapu, Varun, and Jiaqi Gong. "Understanding Affective Dynamics of Learning Toward A Ubiquitous Learning System." GetMobile: Mobile Computing and Communications 23, no. 2 (2019): 9-15.
[J5] Karen M., Rose, John Lach, Yelena Perkhounkova, Jiaqi Gong, Sriram Raju Dandu, Robert Dickerson, Ifat Afrin Emi, Dawei Fan, Janet Specht, and John Stankovic. "Use of Body Sensors to Examine Nocturnal Agitation, Sleep, and Urinary Incontinence in Individuals with Alzheimer's Disease." Journal of gerontological nursing 44, no. 8 (2018): 19-26.
[J4] Jiaqi Gong, Qi Hao, and Fei Hu, “Transform-Invariant Feature Based Functional MR Image Registration and Neural Activity Modeling,” International Journal of Computational Biology and Drug Design, Vol. 6, No. 3, pp. 175-189, 2013
[J3] Jiaqi Gong, Junbin Gong, and Jinwen Tian, “Using Star Trackers for Space Surveillance,” Journal of SPIE Newsroom, Vol. 27, pp. 1-3, 2010
[J2] Weiting Li, Shugang Hou, Kai Lan, and Jiaqi Gong, “Key Technologies and Research Progress of the Adaptive Managed Pressure Drilling,” Natural Gas Industry, Vol. 11, pp. 018-021, 2009
[J1] Jiaqi Gong, Lin Wu, Junbin Gong, Jie Ma and Jinwen Tian, “Flower Algorithm for Star Pattern Recognition in Space Surveillance with Star Trackers,” Optical Engineering, Vol. 48, No. 12, pp. 124401/1-8, 2009
Conferences
[C34] Lee, Heera, Varun Mandalapu, Andrea Kleinsmith, and Jiaqi Gong. "Distinguishing Anxiety Subtypes of English Language Learners Towards Augmented Emotional Clarity." In International Conference on Artificial Intelligence in Education, pp. 157-161. Springer, Cham, 2020.
[C33] Varun Mandalapu and Jiaqi Gong. "Studying Factors Influencing the Prediction of Student STEM and Non-STEM Career Choice" In: The 12th International Conference on Educational Data Mining, Michel Desmarais, Collin F. Lynch, Agathe Merceron, & Roger Nkambou (eds.) 2019, pp. 607 - 610
[C32] Mandalapu V, Hart JM, Bodkin SG, Lach J, Homdee N, Jiaqi Gong. Developing Computational Models for Personalized ACL Injury Classification. 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2019, Best Student Paper Award
[C31] Charissa S. L. Cheah, Stephen P. Kaputsos, Varun Mandalapu, Truc Tran, Salih Barman, Sarah E. Jung, Kathy T. T. Vu, Travis D. Masterson, Ryan Zuber, Lee Boot, Jiaqi Gong. Neurophysiological Variations in Food Decision-Making within Virtual and Real Environments. 2019 IEEE International Conference on Biomedical and Health Informatics. Chicago, USA.
[C30] Alam Ridwan, David Peden, Jiaqi Gong, John Lach. Non-Invasive Inference of Minute Ventilation Using Wearable ECG and Gaussian Process Regression. 2019 IEEE International Conference on Biomedical and Health Informatics. Chicago, USA.
[C29] Varun Mandalapu and Jiaqi Gong, “Towards Better Affect Detectors: Detecting Changes rather than States”, 19th International Conference on Artificial Intelligence in Education, London, UK, June 2018
[C28] Mehdi Boukhechba, Jiaqi Gong, Kamran Kowsari, Mawulolo Ameko, Karl Fua, Philip Chow, Yu huang, Bethany Teachman, Laura Barnes, “Physiological Changes Over the Course of Cognitive Bias Modification for Social Anxiety”, 2018 IEEE International Conference on Biomedical and Health Informatics. March 4-7, 2018, Las Vegas, NV, USA
[C27] Mehdi Boukhechba, Sonia Baee, Alicia Nobles, Jiaqi Gong, Kristen Wells, Laura Barnes, “A Social Cognitive Theory-Based Framework for Monitoring Medication Adherence Applied to Endocrine Therapy in Breast Cancer Survivors”, 2018 IEEE International Conference on Biomedical and Health Informatics. March 4-7, 2018, Las Vegas, NV, USA
[C26] Benjamin Ghaemmaghami, Ridwan Alam, Jiaqi Gong, David Peden, John Lach, “Non-Invasive Minute Ventilation Monitoring for Respiratory Health Applications”, 2018 IEEE International Conference on Biomedical and Health Informatics. March 4-7, 2018, Las Vegas, NV, USA
[C25] Haoyu Wang, Jiaqi Gong, Yan Zhuang, Haiying Shen, and John Lach, “Healthedge: Task Scheduling for Edge Computing with Health Emergency and Human Behavior Consideration in Smart Homes,” IEEE International Conference on Big Data, Dec. 11-14, 2017, Boston, MA, USA.
[C24] Zhang, Jinghe, Jiaqi Gong, and Laura Barnes. "HCNN: Heterogeneous Convolutional Neural Networks for Comorbid Risk Prediction with Electronic Health Records." In Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2017 IEEE/ACM International Conference on, pp. 214-221. IEEE, 2017.
[C23] Fan, Dawei, Jiaqi Gong, Benjamin Ghaemmaghami, Anyi Zhang, John Lach, and David B. Peden. "Characterizing and Calibrating Low-Cost Wearable Ozone Sensors in Dynamic Environments." In Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2017 IEEE/ACM International Conference on, pp. 300-301. IEEE, 2017.
[C22] Alam, Ridwan, Joshua Dugan, Nutta Homdee, Neeraj Gandhi, Benjamin Ghaemmaghami, Harshitha Meda, Azziza Bankole, Martha Anderson, Jiaqi Gong, Tonya Smith-Jackson, and John Lach, "BESI: reliable and heterogeneous sensing and intervention for in-home health applications." In Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2017 IEEE/ACM International Conference on, pp. 147-156. IEEE, 2017.
[C21] Alam, Ridwan, Jiaqi Gong, Mark Hanson, Azziza Bankole, Martha Anderson, Tonya Smith-Jackson, and John Lach. "Motion biomarkers for early detection of dementia-related agitation." In Proceedings of the 1st Workshop on Digital Biomarkers, pp. 15-20. ACM, 2017.
[C20] Huang, Yu, Jiaqi Gong, Mark Rucker, Philip Chow, Karl Fua, Matthew S. Gerber, Bethany Teachman, and Laura E. Barnes. "Discovery of Behavioral Markers of Social Anxiety from Smartphone Sensor Data." In Proceedings of the 1st Workshop on Digital Biomarkers, pp. 9-14. ACM, 2017.
[C19] Jiaqi Gong, Myla Goldman, and John Lach, “DeepMotion: A Deep Convolutional Neural Network on Inertial Body Sensors for Gait Assessment in Multiple Sclerosis,” Wireless Health Conference, Baltimore, MD, USA, Oct. 2016
[C18] Dawei Fan*, Jiaqi Gong1, and John Lach, “Eating Gestures Detection by Tracking Finger Motion,” Wireless Health Conference, Baltimore, MD, USA, Oct. 2016
[C17] Jiang Lu, Lei Wu, Ting Zhang, and Jiaqi Gong, “Robot-assisted Intelligent Emergency System for Individual Elderly Independent Living,” IEEE Global Humanitarian Technology Conference (GHTC), Seattle, WA, USA, 2016
[C16] Dawei Fan*, Luis Lopez Ruiz*, Jiaqi Gong1, and John Lach, “Profiling, Modeling, and Predicting Energy Harvesting for Self-Powered Body Sensor Platforms,” 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2016
[C15] Yan Zhuang*, Jiaqi Gong, Casey Kerrigan, Brad Bennett, John Lach and Shawn Russell, “Gait Tracker Shoe for Accurate Step-by-step Determination of Gait Parameters”, 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2016
[C14] Jiaqi Gong, John Lach, Yanjun Qi and Myla D. Goldman, “Causal Analysis of Inertial Body Sensors for Enhancing Gait Assessment Separability towards Multiple Sclerosis Diagnosis”, 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2015 (Best Paper Award Finalist)
[C13] Jiaqi Gong, Matt Engelhard*, Myla D. Goldman, John Lach, “Correlations between Objective Measures from Inertial Body Sensors and Subjective Measures from Clinical Evaluation”, BodyNets: 10th International Conference on Body Area Networks, Sydney, Australia, Sep. 2015
[C12] Jiaqi Gong, John Lach, “Motion Markers Discovery from Inertial Body Sensors for Enhancing Objective Assessment of Robotic Surgical Skills,” 4th International Symposium on Bioelectronics and Bioinformatics (ISBB), Beijing, China, October 2015
[C11] Jiaqi Gong, Karen Rose, Ifat Emi*, Janet Specht, Enamul Hoque, Dawei Fan*, Sriram Dandu*, Robert Dickson, Yelena Perkhounkova, John Lach, John Stankovic, “Home Wireless Sensing System for Monitoring Nighttime Agitation and Incontinence in Patients with Alzheimer’s Disease,” Wireless Health Conference, Bethesda, MD, USA, Oct. 2015
[C10] Jiaqi Gong, Philip Asare*, John Lach, and Yanjun Qi, “Piecewise Linear Dynamical Model for Actions Clustering from Inertial Body Sensors with Considerations of Human Factors,” BodyNets: 9th International Conference on Body Area Networks, London, UK, Sep. 2014 (Best Paper Award)
[C9] Jiaqi Gong and John Lach, “Reconfigurable Differential Accelerometer Platform for Inertial Body Sensor Networks,” IEEE Conference on Sensors, Baltimore, MD, USA, Nov. 2013 (Best Paper Finalist)
[C8] Jiang Lu*, Jiaqi Gong, Qi Hao, and Fei Hu, “Multi-Agent based Wireless Pyroelectric Infrared Sensor Networks for Multi-Human Tracking and Self-Calibration,” IEEE Conference on Sensors, Baltimore, MD, USA, Nov. 2013 (Best Student Paper Award)
[C7] Jiang Lu*, Jiaqi Gong, Qi Hao, and Fei Hu, “Space Encoding Based Compressive Multiple Human Tracking with Distributed Binary Pyroelectric Infrared Sensor Networks,” IEEE International Conference on Multisensor Fusion and Information Integration, Hamburg, Germany, Sep. 2012 (Best Paper Award Finalist)
[C6] Jiaqi Gong, Hui Ge and Zhongliang Jing, “Visual Analysis for 2-D Point Set Matching,” 23rd Chinese Control and Decision Conference, Mianyang, China, May 2011
[C5] Jiaqi Gong and Zhongliang Jing, “A Texture-Based Method for Autonomous Star Identification,” International Conference on Electric Information and Control Engineering, Wuhan, China, Apr. 2011
[C4] Kai Lan, Shugang Hou, Jiaqi Gong, Youming Xiong, and Chengkai Li, “Development of Downhole Explosion Monitoring System for Gas Drilling,” International Oil and Gas Conference and Exhibition, Beijing, China, June 2010
[C3] Jiaqi Gong, Jie Ma and Jinwen Tian, “A Flower Algorithm for Autonomous Star Pattern Recognition,” 47th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Orlando, Florida, USA, 2009
[C2] Jiaqi Gong, Lin Wu, Junbin Gong, Jie Ma, Jinwen Tian, “A Flower Algorithm for Autonomous Star Identification in Space Surveillance,” Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, Yichang, China, Sep. 2009
[C1] Lin Wu, Jiaqi Gong, Hua Cheng, Jie Ma, and Jinwen Tian, “New method of underwater passive navigation based on gravity gradient,” 5th International Symposium on Multispectral Image Processing & Pattern Recognition, Wuhan, China, Nov. 200
[J16] L. L. Ruiz, Jiaqi Gong., "Self-Powered Cardiac Monitoring: Maintaining Vigilance with Multi-Modal Harvesting and E-Textiles," in IEEE Sensors Journal, doi: 10.1109/JSEN.2020.3017706.
[J15] Cheah, Charissa SL, Salih Barman, Kathy TT Vu, Sarah E. Jung, Varun Mandalapu, Travis D. Masterson, Ryan J. Zuber, Lee Boot, and Jiaqi Gong. "Validation of a Virtual Reality Buffet environment to assess food selection processes among emerging adults." Appetite (2020): 104741.
[J14] Baglione, Anna N., Mehdi Boukhechba, Laura E. Barnes, Jiaqi Gong, and Kristen J. Wells. "Leveraging Mobile Sensing to Understand and Develop Intervention Strategies to Improve Medication Adherence." IEEE Pervasive Computing (2020).
[J13] Jiaqi Gong, Yu Huang, Philip I. Chow, Karl Fua, Matthew S. Gerber, Bethany A. Teachman, and Laura E. Barnes. "Understanding Behavioral Dynamics of Social Anxiety Among College Students Through Smartphone Sensors." Information Fusion 49 (2019): 57-68. (Impact Factor: 10.7)
[J12] Ma, Rui, Jiaqi Gong, Guocheng Liu, and Qi Hao. "Enabling Cognitive Pyroelectric Infrared Sensing: From Reconfigurable Signal Conditioning to Sensor Mask Design." IEEE Transactions on Industrial Informatics (2019). (Impact Factor: 7.4)
[J11] Fan, Dawei, Luis Lopez Ruiz, Jiaqi Gong, and John Lach. "EHDC: An Energy Harvesting Modeling and Profiling Platform for Body Sensor Networks." IEEE Journal of Biomedical and Health Informatics (2017). (Impact Factor: 4.2)
[J10] Sriram Raju Dandu; Matthew M. Engelhard; Asma Qureshi; Jiaqi Gong; John Lach; Maite Brandt-Pearce; Myla Goldman, “Understanding the Physiological Significance of Four Inertial Gait Features in Multiple Sclerosis,” IEEE Journal of Biomedical and Health Informatics, 2017. (Impact Factor: 4.2)
[J9] Jiaqi Gong, John Lach, Yanjun Qi and Myla D. Goldman, “Causality Analysis of Inertial Body Sensors for Multiple Sclerosis Diagnostic Enhancement”, Journal of Biomedical and Health Informatics, Vol. 20, No. 5, Sep. 2016. (Impact Factor: 4.2)
[J8] Jiaqi Gong, Philip Asare, John Lach, and Yanjun Qi, “Piecewise Linear Dynamical Model for Action Clustering from Real-World Deployments of Inertial Body Sensors,” IEEE Transactions on Affective Computing, Vol. 7, No. 3, Sep. 2016. (Impact Factor: 6.3)
[J7] Mandalapu, Varun, Benjamin Ghaemmaghami, Renee Mitchell, and Jiaqi Gong. "Understanding the relationship between healthcare processes and in-hospital weekend mortality using MIMIC III." Smart Health 14 (2019): 100084.
[J6] Mandalapu, Varun, and Jiaqi Gong. "Understanding Affective Dynamics of Learning Toward A Ubiquitous Learning System." GetMobile: Mobile Computing and Communications 23, no. 2 (2019): 9-15.
[J5] Karen M., Rose, John Lach, Yelena Perkhounkova, Jiaqi Gong, Sriram Raju Dandu, Robert Dickerson, Ifat Afrin Emi, Dawei Fan, Janet Specht, and John Stankovic. "Use of Body Sensors to Examine Nocturnal Agitation, Sleep, and Urinary Incontinence in Individuals with Alzheimer's Disease." Journal of gerontological nursing 44, no. 8 (2018): 19-26.
[J4] Jiaqi Gong, Qi Hao, and Fei Hu, “Transform-Invariant Feature Based Functional MR Image Registration and Neural Activity Modeling,” International Journal of Computational Biology and Drug Design, Vol. 6, No. 3, pp. 175-189, 2013
[J3] Jiaqi Gong, Junbin Gong, and Jinwen Tian, “Using Star Trackers for Space Surveillance,” Journal of SPIE Newsroom, Vol. 27, pp. 1-3, 2010
[J2] Weiting Li, Shugang Hou, Kai Lan, and Jiaqi Gong, “Key Technologies and Research Progress of the Adaptive Managed Pressure Drilling,” Natural Gas Industry, Vol. 11, pp. 018-021, 2009
[J1] Jiaqi Gong, Lin Wu, Junbin Gong, Jie Ma and Jinwen Tian, “Flower Algorithm for Star Pattern Recognition in Space Surveillance with Star Trackers,” Optical Engineering, Vol. 48, No. 12, pp. 124401/1-8, 2009
Conferences
[C34] Lee, Heera, Varun Mandalapu, Andrea Kleinsmith, and Jiaqi Gong. "Distinguishing Anxiety Subtypes of English Language Learners Towards Augmented Emotional Clarity." In International Conference on Artificial Intelligence in Education, pp. 157-161. Springer, Cham, 2020.
[C33] Varun Mandalapu and Jiaqi Gong. "Studying Factors Influencing the Prediction of Student STEM and Non-STEM Career Choice" In: The 12th International Conference on Educational Data Mining, Michel Desmarais, Collin F. Lynch, Agathe Merceron, & Roger Nkambou (eds.) 2019, pp. 607 - 610
[C32] Mandalapu V, Hart JM, Bodkin SG, Lach J, Homdee N, Jiaqi Gong. Developing Computational Models for Personalized ACL Injury Classification. 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2019, Best Student Paper Award
[C31] Charissa S. L. Cheah, Stephen P. Kaputsos, Varun Mandalapu, Truc Tran, Salih Barman, Sarah E. Jung, Kathy T. T. Vu, Travis D. Masterson, Ryan Zuber, Lee Boot, Jiaqi Gong. Neurophysiological Variations in Food Decision-Making within Virtual and Real Environments. 2019 IEEE International Conference on Biomedical and Health Informatics. Chicago, USA.
[C30] Alam Ridwan, David Peden, Jiaqi Gong, John Lach. Non-Invasive Inference of Minute Ventilation Using Wearable ECG and Gaussian Process Regression. 2019 IEEE International Conference on Biomedical and Health Informatics. Chicago, USA.
[C29] Varun Mandalapu and Jiaqi Gong, “Towards Better Affect Detectors: Detecting Changes rather than States”, 19th International Conference on Artificial Intelligence in Education, London, UK, June 2018
[C28] Mehdi Boukhechba, Jiaqi Gong, Kamran Kowsari, Mawulolo Ameko, Karl Fua, Philip Chow, Yu huang, Bethany Teachman, Laura Barnes, “Physiological Changes Over the Course of Cognitive Bias Modification for Social Anxiety”, 2018 IEEE International Conference on Biomedical and Health Informatics. March 4-7, 2018, Las Vegas, NV, USA
[C27] Mehdi Boukhechba, Sonia Baee, Alicia Nobles, Jiaqi Gong, Kristen Wells, Laura Barnes, “A Social Cognitive Theory-Based Framework for Monitoring Medication Adherence Applied to Endocrine Therapy in Breast Cancer Survivors”, 2018 IEEE International Conference on Biomedical and Health Informatics. March 4-7, 2018, Las Vegas, NV, USA
[C26] Benjamin Ghaemmaghami, Ridwan Alam, Jiaqi Gong, David Peden, John Lach, “Non-Invasive Minute Ventilation Monitoring for Respiratory Health Applications”, 2018 IEEE International Conference on Biomedical and Health Informatics. March 4-7, 2018, Las Vegas, NV, USA
[C25] Haoyu Wang, Jiaqi Gong, Yan Zhuang, Haiying Shen, and John Lach, “Healthedge: Task Scheduling for Edge Computing with Health Emergency and Human Behavior Consideration in Smart Homes,” IEEE International Conference on Big Data, Dec. 11-14, 2017, Boston, MA, USA.
[C24] Zhang, Jinghe, Jiaqi Gong, and Laura Barnes. "HCNN: Heterogeneous Convolutional Neural Networks for Comorbid Risk Prediction with Electronic Health Records." In Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2017 IEEE/ACM International Conference on, pp. 214-221. IEEE, 2017.
[C23] Fan, Dawei, Jiaqi Gong, Benjamin Ghaemmaghami, Anyi Zhang, John Lach, and David B. Peden. "Characterizing and Calibrating Low-Cost Wearable Ozone Sensors in Dynamic Environments." In Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2017 IEEE/ACM International Conference on, pp. 300-301. IEEE, 2017.
[C22] Alam, Ridwan, Joshua Dugan, Nutta Homdee, Neeraj Gandhi, Benjamin Ghaemmaghami, Harshitha Meda, Azziza Bankole, Martha Anderson, Jiaqi Gong, Tonya Smith-Jackson, and John Lach, "BESI: reliable and heterogeneous sensing and intervention for in-home health applications." In Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2017 IEEE/ACM International Conference on, pp. 147-156. IEEE, 2017.
[C21] Alam, Ridwan, Jiaqi Gong, Mark Hanson, Azziza Bankole, Martha Anderson, Tonya Smith-Jackson, and John Lach. "Motion biomarkers for early detection of dementia-related agitation." In Proceedings of the 1st Workshop on Digital Biomarkers, pp. 15-20. ACM, 2017.
[C20] Huang, Yu, Jiaqi Gong, Mark Rucker, Philip Chow, Karl Fua, Matthew S. Gerber, Bethany Teachman, and Laura E. Barnes. "Discovery of Behavioral Markers of Social Anxiety from Smartphone Sensor Data." In Proceedings of the 1st Workshop on Digital Biomarkers, pp. 9-14. ACM, 2017.
[C19] Jiaqi Gong, Myla Goldman, and John Lach, “DeepMotion: A Deep Convolutional Neural Network on Inertial Body Sensors for Gait Assessment in Multiple Sclerosis,” Wireless Health Conference, Baltimore, MD, USA, Oct. 2016
[C18] Dawei Fan*, Jiaqi Gong1, and John Lach, “Eating Gestures Detection by Tracking Finger Motion,” Wireless Health Conference, Baltimore, MD, USA, Oct. 2016
[C17] Jiang Lu, Lei Wu, Ting Zhang, and Jiaqi Gong, “Robot-assisted Intelligent Emergency System for Individual Elderly Independent Living,” IEEE Global Humanitarian Technology Conference (GHTC), Seattle, WA, USA, 2016
[C16] Dawei Fan*, Luis Lopez Ruiz*, Jiaqi Gong1, and John Lach, “Profiling, Modeling, and Predicting Energy Harvesting for Self-Powered Body Sensor Platforms,” 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2016
[C15] Yan Zhuang*, Jiaqi Gong, Casey Kerrigan, Brad Bennett, John Lach and Shawn Russell, “Gait Tracker Shoe for Accurate Step-by-step Determination of Gait Parameters”, 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2016
[C14] Jiaqi Gong, John Lach, Yanjun Qi and Myla D. Goldman, “Causal Analysis of Inertial Body Sensors for Enhancing Gait Assessment Separability towards Multiple Sclerosis Diagnosis”, 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2015 (Best Paper Award Finalist)
[C13] Jiaqi Gong, Matt Engelhard*, Myla D. Goldman, John Lach, “Correlations between Objective Measures from Inertial Body Sensors and Subjective Measures from Clinical Evaluation”, BodyNets: 10th International Conference on Body Area Networks, Sydney, Australia, Sep. 2015
[C12] Jiaqi Gong, John Lach, “Motion Markers Discovery from Inertial Body Sensors for Enhancing Objective Assessment of Robotic Surgical Skills,” 4th International Symposium on Bioelectronics and Bioinformatics (ISBB), Beijing, China, October 2015
[C11] Jiaqi Gong, Karen Rose, Ifat Emi*, Janet Specht, Enamul Hoque, Dawei Fan*, Sriram Dandu*, Robert Dickson, Yelena Perkhounkova, John Lach, John Stankovic, “Home Wireless Sensing System for Monitoring Nighttime Agitation and Incontinence in Patients with Alzheimer’s Disease,” Wireless Health Conference, Bethesda, MD, USA, Oct. 2015
[C10] Jiaqi Gong, Philip Asare*, John Lach, and Yanjun Qi, “Piecewise Linear Dynamical Model for Actions Clustering from Inertial Body Sensors with Considerations of Human Factors,” BodyNets: 9th International Conference on Body Area Networks, London, UK, Sep. 2014 (Best Paper Award)
[C9] Jiaqi Gong and John Lach, “Reconfigurable Differential Accelerometer Platform for Inertial Body Sensor Networks,” IEEE Conference on Sensors, Baltimore, MD, USA, Nov. 2013 (Best Paper Finalist)
[C8] Jiang Lu*, Jiaqi Gong, Qi Hao, and Fei Hu, “Multi-Agent based Wireless Pyroelectric Infrared Sensor Networks for Multi-Human Tracking and Self-Calibration,” IEEE Conference on Sensors, Baltimore, MD, USA, Nov. 2013 (Best Student Paper Award)
[C7] Jiang Lu*, Jiaqi Gong, Qi Hao, and Fei Hu, “Space Encoding Based Compressive Multiple Human Tracking with Distributed Binary Pyroelectric Infrared Sensor Networks,” IEEE International Conference on Multisensor Fusion and Information Integration, Hamburg, Germany, Sep. 2012 (Best Paper Award Finalist)
[C6] Jiaqi Gong, Hui Ge and Zhongliang Jing, “Visual Analysis for 2-D Point Set Matching,” 23rd Chinese Control and Decision Conference, Mianyang, China, May 2011
[C5] Jiaqi Gong and Zhongliang Jing, “A Texture-Based Method for Autonomous Star Identification,” International Conference on Electric Information and Control Engineering, Wuhan, China, Apr. 2011
[C4] Kai Lan, Shugang Hou, Jiaqi Gong, Youming Xiong, and Chengkai Li, “Development of Downhole Explosion Monitoring System for Gas Drilling,” International Oil and Gas Conference and Exhibition, Beijing, China, June 2010
[C3] Jiaqi Gong, Jie Ma and Jinwen Tian, “A Flower Algorithm for Autonomous Star Pattern Recognition,” 47th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Orlando, Florida, USA, 2009
[C2] Jiaqi Gong, Lin Wu, Junbin Gong, Jie Ma, Jinwen Tian, “A Flower Algorithm for Autonomous Star Identification in Space Surveillance,” Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, Yichang, China, Sep. 2009
[C1] Lin Wu, Jiaqi Gong, Hua Cheng, Jie Ma, and Jinwen Tian, “New method of underwater passive navigation based on gravity gradient,” 5th International Symposium on Multispectral Image Processing & Pattern Recognition, Wuhan, China, Nov. 200