Jennifer Jin
Contact
Bio
Dr. Jennifer Jin holds a Ph.D. in EECS from the University of California–Irvine and a Master's in computer science from the University of California-LA. Her Ph.D. research focused on advanced analytics methods involving semantic computing and machine learning. Her research interests encompass (1) software engineering for intelligent systems and (2) medical analytics utilizing machine learning. Dr. Jin is actively involved in the development of medical diagnosis systems that utilize CT scans and employ deep learning algorithms.
Education
Ph.D. Electrical Engineering and Computer Science, University of California – Irvine, 2019.
Master Computer Science, University of California - Los Angeles, 2011.
B.S. Computer Science, University of Texas, Dallas, 2009.
COURSES
CSE 4550- Software Engineering
CSE 4820- Senior Project
CSE 5720- Database Management Systems
CSE 6964- Masters Project
RESEARCH
Software Engineering for Intelligent Systems: Advancements in machine learning have made the development of intelligent systems increasingly feasible and affordable. Accordingly, software engineering theories and methods should also be refined and extended to support modeling the intelligent components of a system, their behavior, training high performing ML models, and maintaining the system intelligence over the system lifecycle.
Dr. Jin has been devising software engineering methods applicable to the development of large-scale industrial intelligent systems.
Machine Learning for Medical Analytics: Medical Analytics refers to a collection of software methods used for diagnosing diseases based on symptoms and medical images, including CT and MRI scans. With the advancements in deep learning and the availability of extensive CT and MRI data for research purposes, machine learning-based disease diagnosis systems are increasingly being employed in medical clinics.
Dr. Jin, in collaboration with physicians in university hospitals, has been developing medical analytics systems that utilize a range of ML algorithms, including Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). These systems aim to enhance medical diagnostics by combining the expertise of healthcare professionals with high-performing ML models. One recent development of the systems is Liver Cancer Diagnosis system with phased CT scans.
PUBLICATION
Journal
Flora, J., Khan, W., Jin, J., Jin, D., Hussain, A., Dajani, K. and Khan B. Usefulness of Vaccine Adverse Event Reporting System for Machine-Learning Based Vaccine Research: A Case Study for COVID-19 Vaccines. International Journal of Molecular Sciences. 2022; 23(15):8235
Wang, C.C.N., Jin, J., Chang, JG. et al. Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization. BMC Med Inform Decis Mak 20, 208, September, 2020. https://doi.org/10.1186/s12911-020-01227-6
Jin, J., Zhang, G., Sheu, P., Hayakawa, M. and Kitazawa, A. Influence Maximization in graph-based OLAP (GOLAP). Journal of Social Network Analysis and Mining, 9: 54, Dec. 2019. https://doi.org/10.1007/s13278-019-0598-2
Jin, J. and Hayakawa, M. Network Analysis for Graph-based OLAP (GOLAP). International Journal of Semantic Computing, 13(1), 2018.
Jin, J. OLAP and machine learning. Encyclopedia with Semantic Computing and Robotic Intelligence. 1(1), 2017.
Wang, S., Jin, J., Rivett, P. and Kitazawa, A. Technical Survey Graph Databases and Applications. International Journal of Semantic Computing 9(4), pp. 523-545, 2015.
Jin, J., Kim, M. and Rivett, P. Technology Outlook: Semantic Computing for Education. International Journal of Semantic Computing, 9(3), 2015.
Kim, J., Wang, G. and Bae, S.T. A survey of Big Data Technologies and How Semantic Computing Can Help. International Journal of Semantic Computing, 8(1), 2014.
Conference
Jin, J., Kim, S., Wilding, A., Christie, M., Villamor, S. Beaven, A. and Jin, D. Achieving Clinical-level Machine Learning System for Segmenting Organs on CT Scans. The American Roentgen Ray Society (ARRS) 2022, New Orleans. May. 2022.
Jin, J., Kim, S., Christie, M., Wilding, A., Villamor, S. Beaven, A. and Jin, D. Machine Learning System to Fully Automate the Detecting Hepatocellular Carcinoma with Liver Imaging Reporting and Data System (LI-RADS). The American Roentgen Ray Society (ARRS) 2022, New Orleans. May. 2022.
Kim, M. and Jin, J. “Autonomous Sprinkler System with MAPE-K”, International Conference on Computational Science and Computational Intelligence, Las Vegas. December 2022.
Alexander, B., Hou, Y., Khan, B. and Jin, J. "Learn Programming In Virtual Reality? A Case Study of Computer Science Students," 2022 IEEE Global Engineering Education Conference (EDUCON), 2022, pp. 270-275, doi: 10.1109/EDUCON52537.2022.9766621
Curtis, H., Park, J., Christie, M., Wilding, D., Villamor, S., Beaven, A., Smith, T., Kim, S., Jin, J., Jin, D. “Automating Liver Reporting and Data System (LI-RADS) to Classify Hepatocellular Carcinoma Using Artificial Intelligence.” Western Medical Research Conference. Carmel, California. January 20, 2023.
Park, J., Jin, J., Christie, M., Wilding, D., Villamor, S., Curtis, H., Beaven, A., Smith, T., Jin, D. “Organ Segementation on CT Imaging Through Machine Learning.” Western Medical Research Conference. Carmel, California. January 20, 2023.
Jin, J., Song, M., Kim, S. and Jin, D. High Performing Mask-RCNN Model for Segmenting Organs in Medical Images. IEEE International Conference on e-Health and Bioengineering (EHB) 2021, Online. Nov. 2021.
Jin, J., Kim, S., Wilding, A., Christie, M., Villamor, S. Beaven, A. and Jin, D. Achieving Clinical-level Machine Learning System for Segmenting Organs on CT Scans. The American Roentgen Ray Society (ARRS) 2022, New Orleans. May. 2022.
Jin, J., Kim, S., Christie, M., Wilding, A., Villamor, S. Beaven, A. and Jin, D. Machine Learning System to Fully Automate the Detecting Hepatocellular Carcinoma with Liver Imaging Reporting and Data System (LI-RADS). The American Roentgen Ray Society (ARRS) 2022, New Orleans. May. 2022.
Jin, J. Kim, S. and Jin, D. “Integrated Convolution Neural Network and Recurrent Neural Network based Deep Learning System for the Liver Imaging Reporting and Data System (LI-RADS),” RSNA 2020, Chicago. Nov. 2020.
Kim, J., Design and Evaluation of Mobile Applications with Full and Partial Offloading. The 7th International Conference on Grid and Pervasive Computing (GPC 2012), Hong Kong, May 2012.
Kim, J. Architectures with Full and Partial Offloading for Mobile Applications. IEEE International Conference on Internet (ICONI 2010), Mactan Island, Philippines, Dec. 2010.
Kim, J. Architecture patterns for Service-based Mobile Applications. IEEE International Conference on Service-Oriented Computing and Applications (SOCA 2010), Perth, Australia, Dec. 2010.