Diane Woodbridge

Diane Woodbridge

Associate Professor

Full-Time Faculty
101 Howard 606
(415) 422-4812

Biography

Diane Woodbridge is an associate professor in the MS in Data Science program at the University of San Francisco. Her research interests include scalable database management systems, data fusion, and machine learning focusing on remote health monitoring (IoT in Healthcare). Prior to joining USF, Professor Woodbridge was with the scalable analysis and visualization department at Sandia National Laboratories.

Research Areas

  • Scalable database management systems
  • Data fusion
  • Machine learning

Education

  • PhD, Computer Science, University of California, Los Angeles
  • MS, Computer Science, University of California, Los Angeles
  • BS, Computer Science and Engineering, Sogang University, South Korea

Awards & Distinctions

  • USF Center for Research, Artistic, and Scholarly Excellence (CRASE) Interdisciplinary Action Group Grant, 2021

  • Anita Borg Institute for Women and Technology Systers Pass-it-on Award, 2018

  • Jesuit Foundation Grant, 2017

  • Best Paper Award, ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, 2014.

  • Best Paper Award, Conference on Mobile Computing, Applications, and Services (MobiCASE), 2009.

  • NIH/National Library of Medicine Medical Informatics Training Program Fellowship, 2009-2012.

Selected Publications

  • Donthireddy, S.K.R., Suh, J.H. and Woodbridge, D.M.K. Orthotic Prescription for Pediatric Flexible Flat Feet using Convolutional Neural Networks. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1105-1108). IEEE, 2022.

  • Sethi, C., Vellera, M., Woodbridge, D.M.K. and Ahnn, J.J. Bundle Recommender from Recipes to Shopping Carts-Optimizing Ingredients, Kitchen Gadgets and their Quantities. Workshop on Online Recommender
    Systems and User Modeling (ORSUM) jointly with the 16th ACM Conference on Recommender Systems (RecSys). ACM, 2022.

  • Palacios, Victor, Diane Myung-Kyung Woodbridge, and Jean L. Fry. "Machine Learning-based Meal Detection Using Continuous Glucose Monitoring on Healthy Participants: An Objective Measure of Participant Compliance to Protocol." In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 7032-7035. IEEE, 2021.

  • Kaur, Hashneet, Patrick Ka-Cheong Poon, Sophie Yuefei Wang, and Diane Myung-kyung Woodbridge. "Depression Level Prediction in People with Parkinson’s Disease during the COVID-19 Pandemic." In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 2248-2251. IEEE, 2021.

  • Pedram, Mahdi, Seyed Iman Mirzadeh, Seyed Ali Rokni, Ramin Fallahzadeh, Diane Myung-Kyung Woodbridge, Sunghoon Ivan Lee, and Hassan Ghasemzadeh. "LIDS: Mobile System to Monitor Type and Volume of Liquid Intake." IEEE Sensors Journal 21, no. 18 (2021): 20750-20763.

  • Woodbridge, Diane Myung-Kyung, and Kevin Bengtson Wong. "A scalable medication intake monitoring system." In Big Data in Psychiatry# x0026; Neurology, pp. 217-240. Academic Press, 2021.

  • Bolleddula, Nithish, Geoffrey Yau Chun Hung, Daren Ma, Hoda Noorian, and Diane Myung-kyung Woodbridge. "Sensor Selection for Activity Classification at Smart Home Environments." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 3927-3930. IEEE, 2020.

  • Cheon, Andy, Stephanie Yeoju Jung, Collin Prather, Matthew Sarmiento, Kevin Wong, and Diane Myung-kyung Woodbridge. "A Machine Learning Approach to Detecting Low Medication State with Wearable Technologies." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4252-4255. IEEE, 2020.

  • Fozoonmayeh, Donya, Hai Vu Le, Ekaterina Wittfoth, Chong Geng, Natalie Ha, Jingjue Wang, Maria Vasilenko, Yewon Ahn, and Diane Myung-kyung Woodbridge. "A scalable smartwatch-based medication intake detection system using distributed machine learning." Journal of medical systems 44, no. 4 (2020): 1-14.

  • Kim, Paul, Ziyu Fan, Lance Fernando, Jacques Sham, Crystal Sun, Yixin Sun, Brian Wright, Xi Yang, Nicholas Ross, and Diane Myung-kyung Woodbridge. "Controversy Score Calculation for News Articles." In 2019 First International Conference on​ Transdisciplinary AI (TransAI), pp. 56-63. IEEE, 2019.

  • A. Sharma, S. Singh, B. Wright, A. Perry, D. M.-K. Woodbridge, and A. Popa, “Scalable motor movement recognition from electroencephalography using machine learning,” in 2019 IEEE Annual Computer Software and Applications Conference (COMPSAC), 2019

  • P. Agrawal, D. Bhargavi, X. Han, N. Tevathia, A. Popa, N. Ross, D. M.-K. Woodbridge, B. Zimmerman-Bier, W. Bosl, et al., “A scalable automated diagnostic feature extraction system for EEGs,” in 2019 IEEE Annual Computer Software and Applications Conference (COMPSAC), 2019.

  • D. L. John, E. Kim, K. Kotian, K. Y. Ong, T. White, L. Gloukhova, D. M.-k. Woodbridge, and N. Ross, “Topic modeling to extract information from nutraceutical product reviews,” in 2019 IEEE Annual Consumer Communications & Networking Conference (CCNC), 2019.

  • A. Q. Keck, M. Romero, R. Sandor, D. M.-k. Woodbridge, and P. Intrevado, “Predicting unethical physician behavior at scale: A distributed computing framework,” in IEEE Smart World Congress, 2019.

  • L. Li, Z. Li, L. G. Reichmann, and D. M.-k. Woodbridge, “A scalable and reliable model for real-time air quality prediction,” in IEEE Smart World Congress, 2019.

  • N. Hua, V. Suarez, R. Reilly, P. Trinh, P. Intrevado, and D. M.-k. Woodbridge, “The impact of bike-sharing ridership on air quality: A scalable data science framework,” in IEEE International Conference on Smart City Innovations, 2019.

  • N. Lin, E. Liu, F. Tenorio, X. Yang, and D. M.-k. Woodbridge, “Distributed data analytics framework for cluster analysis of parking violation,” in IEEE International Conference on Smart City Innovations, 2019.

  • X. Lian, S. Melancon, J.-R. Presta, A. Reevesman, B. J. Spiering, and D. M.-k. Woodbridge, “Scalable real-time prediction and analysis of san francisco fire department response times,” in IEEE International Conference on Ubiquitous Intelligence and Computing, 2019.

  • Ma, J., Ovalle, A.,  Woodbridge, D.M. (2018) Medhere : A Smartwatch-based Medication Adherence Monitoring System using Machine Learning and Distributed Computing. Presented at International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

  • Dong, C., Du, L., Ji, F., Song, Z., Zheng, Y., Intrevado, P., Woodbridge, D. (2018) Forecasting Smart Meter Energy Usage using Distributed Systems and Machine Learning. Presented at IEEE International Conference on Smart City (SmartCity).

  • Howard, A., Lee, Tim., Mahar, S., Intervdo., Woodbridge, D. (2018). Distributed Data Analytics Framework for Smart Transportation. Presented at IEEE International Conference on Smart City (SmartCity).

  • Ma, J., Ovalle, A., Woodbridge, D.M. (2018). Medication adherence monitoring using machine learning. IEEE International Conference on Biomedical and Health Informatics (BHI), Las Vegas.

  • Chen, L., Li, R., Liu, Y., Zhang, R., Woodbridge, D.M. (2017). Machine learning-based product recommendation using Apache Spark. IEEE UIC International Workshop on Data Science and Computational Intelligence (DSCI), San Francisco.

  • Brost, R., Phillips, C., Robinson, D., Stracuzzi, D., Wilson, A. and Woodbridge, D.M. (2015). "Computing Quality Scores and Uncertainty for Approximate Pattern Matching in Geospatial Semantic Graphs." Statistical Analysis and Data Mining (SADM), 8(5-6), 340-352.

  • Woodbridge, D.M., Wilson, A.T., Rintoul, M.D. and Goldstein, R.H. (2015). Time Series Discord Detection in Medical Data using a Parallel Relational Database. Presented at IEEE International Conference on Bioinformatics and Biomedicine, Washington, DC.

  • Brost, R.C., McLendon, W.C., III, Parekh, O., Rintoul, M.D., Strip, D.R. and Woodbridge, D.M. (2014). A Computational Framework for Ontologically Storing and Analyzing Very Large Overhead Image Sets. Presented at ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, Dallas.

  • Lindsay, S. and Woodbridge, D.M. Spacecraft State-of-health (SOH) Analysis via Data Mining. (2014). Presented at AIAA International Conference on Space Operations (SpaceOps), Pasadena.

  • Suh, M., Nahapetian, A., Woodbridge, J., Rofouei, M. and Sarrafzadeh, M. (2012). "Machine Learning-Based Adaptive Wireless Interval Training Guidance System." Mobile Networks and Applications, 17(2), 163-177.

  • Suh, M., Woodbridge, J., Moin, T., Lan, M., Alshurafa, N., Samy, L., Mortazavi, B., Ghasemzadeh, H., Bui, A., Ahmadi, S. and Sarrafzadeh, M. (2012). Dynamic Task Optimization in Remote Diabetes Monitoring Systems. Presented at IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology (HISB), San Jose.

  • Suh, M., Moin, T., Woodbridge, J., Lan, M., Ghasemzadeh, H., Ahmadi, S., Bui, A., and Sarrafzadeh, M. (2012). Dynamic Self-adaptive Remote Health Monitoring System for Diabetics. Presented at International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), San Diego.

  • Lan, M., Samy, L., Alshurafa, N., Suh, M., Ghasemzadeh, H., Sarrafzadeh, M. and Macabasco-O'Connell, A. (2012). WANDA: An End-to-End Remote Health Monitoring and Analytics System for Heart Failure Patients. Presented at Wireless Health Conference, San Diego.

  • Suh, M., Chen, C.A., Woodbridge, J., Tu, M.K., Kim, J.I., Nahapetian, A., Evangelista, L.S. and Sarrafzadeh, M. (2011). "A Remote Patient Monitoring System for Congestive Heart Failure." Journal of Medical Systems, 35(5), 1165-1179.

  • Suh, M., Woodbridge, J., Lan, M., Bui, A., Evangelista, L.S. and Sarrafzadeh, M. (2011). Missing Data Imputation for Remote CHF Patient Monitoring. Presented at International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Boston.

  • Suh, M., Evangelista, L.S., Chen, C.A., Han, K., Kang, J., Tu, M.K., Chen, V., Nahapetian, A. and Sarrafzadeh, M. (2010). An Automated Vital Sign Monitoring System for Congestive Heart Failure Patients. Presented at ACM SIGHIT International Health Informatics Symposium (IHI), Miami.

  • Suh, M., Evangelista, L.S., Chen, V., Hong, W.S., Macbeth, J., Nahapetian, A., Figueras, F.J. and Sarrafzadeh, M. (2010). WANDA B.: Weight and Activity with Blood Pressure Monitoring System for Heart Failure Patients. Presented at The Second International IEEE WoWMoM Workshop on Interdisciplinary Research on E-Health Services and Systems (IREHSS), Lucca, Italy.

  • Suh, M.K., Lee, K., Heu, A., Nahapetian, A. and Sarrafzadeh, M. (2009). Bayesian Networks-Based Interval Training Guidance System for Cancer Rehabilitation. Presented at Conference on Mobile Computing, Applications, and Services (MobiCASE), San Diego.

  • Dorman, K., Yahyanejad, M., Nahapetian, A., Suh, M., Sarrafzadeh, M., McCarthy, W. and Kaiser, W. (2009). Nutrition Monitor: A Food Purchase and Consumption Monitoring Mobile System. Presented at Conference on Mobile Computing, Applications, and Services (MobiCASE), San Diego.

  • Suh, M., Lee, K., Nahapetian, A. and Sarrafzadeh, M. (2009). Interval Training Guidance System with Music and Wireless Group Exercise Motivations. Presented at IEEE Symposium on Industrial Embedded Systems (SIES), Lausanne, Switzerland.

  • Suh, M., Rofouei, M., Nahapetian, A., Kaiser, W.J. and Sarrafzadeh, M. (2009). Optimizing Interval Training Protocols Using Data Mining Decision Trees. Presented at Body Sensor Networks (BSN).