Mentors
Dr. Kecheng Yang
Kecheng Yang is an Assistant Professor in the Department of Computer Science at Texas State University. He received his Ph.D. and M.S. degrees in Computer Science from the University of North Carolina at Chapel Hill in 2018 and 2015, respectively, both with Prof. James H. Anderson. Before that, he received his B.E. degree in Computer Science and Technology from Hunan University in 2013. His research interests include real-time and cyber-physical systems, scheduling theory and resource allocation algorithms, and heterogeneous multiprocessor platforms. His work has been published in a variety of top-tier conferences and journals, such as RTSS, DAC, ICCAD, TPDS, TCAD, and has won two Outstanding Paper Awards.
Dr. Anne H.H. Ngu
Anne H.H. Ngu is currently a Full Professor and Ph.D. Program Director with the Department of Computer Science at Texas State University. From 1992-2000, she worked as a Senior Lecturer in the School of Computer Science and Engineering, University of New South Wales, Australia. She had held research scientist positions with Telcordia Technologies; Microelectronics and Computer Technology (MCC); University of California, Berkeley; Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia and the Tilburg University, The Netherlands. She was a summer faculty scholar at Lawrence Livermore National Laboratory from 2003-2006. Dr. Ngu has published over 120 technical papers in journals and refereed conferences in computer science. Dr. Ngu’s main research interests are in data analytics and management, smart health, scientific workflows, and service computing. She was a winner of the 2013 NCWIT Undergraduate Research Mentoring Award and the Presidential Distinction Award for Services, Texas State University in 2017 and 2014.
Dr. Tao Hou
Tao Hou is an Assistant Professor in the Department of Computer Science at Texas State University. His research interests span quite a few areas, including network security, system security, machine learning for cybersecurity and adversarial machine learning. He is especially interested in research problems that arise from practical domains, with a focus on both experimental/empirical study and sound theoretical footings. More recently, he is working mostly on traffic fingerprinting, web security, binary analysis and IoT security. His research results have been published in premier conferences and journals (e.g., IEEE INFOCOM, ACM CCS, IEEE/ACM Transactions on Networking, IEEE Transactions on Dependable and Secure Computing). In addition, his work on deep-learning network traffic has been awarded the Best Paper Award in 2019 IEEE GlobalSIP.
Dr. Heena Rathore
Heena Rathore is an Assistant Professor in the Computer Science Department at Texas State University. Academically, she has worked as an assistant professor of instruction at The University of Texas at San Antonio and as a visiting assistant professor at Texas A&M University, Texarkana. She also worked as a data scientist and program manager at Hiller Measurements in Austin. Before that, she worked as a postdoctoral researcher for the US-Qatar Joint Collaborative Project between Temple University, the University of Idaho, and Qatar University. She received her Ph.D. in computer science and engineering on a Tata Consultancy Services Research scholarship at the Indian Institute of Technology Jodhpur, India. Her research interests include cognitive AI, cybersecurity of cyber-physical systems, and biologically inspired systems. She has been the winner of several prestigious awards, including Educationist Empowering India, IEEE Region 5 Outstanding Individual Achievement Award, IEEE Central Texas Section Achievements Award, IIT Alumni Award for Recognizing Excellence in Young Alumni, NI Global Engineering Impact Award, and others.
Dr. Lu Wang
Lu Wang is currently an Assistant Professor in Computer Science at Texas State University. Her primary research interests are developing and applying Machine Learning, Data Mining and Statistical methods (e.g., Multi-task Learning, Survival Analysis, Clustering, Risk Factor Analysis and Causal Discovery) on various data including gene expression, electronic health/medical records (EHRs/EMRs), and DNA sequencing reads for both cognitive disorders (e.g., delirium, Alzheimer’s disease, dementia, major depressive disorder) and chronic diseases (e.g., cancer, obesity, hypertension). Inspired by the human factors approach, she also designs and develops Human-Centered Artificial Intelligence tools for users to integrate, visualize, analyze, and interpret health data in order to improve the interoperability and accessibility of AI-assisted healthcare decision support. She has published almost 30 journal and conference papers including 20 first-authored ones in the top venues including proceedings of International Conference on Data Mining (IEEE ICDM), Data Mining and Knowledge Discovery (DMKD Journal), ACM Transactions on Computer-Human Interaction (TOCHI), Journal of Medical Internet Research (JMIR) Medical Informatics, American Medical Informatics Association (AMIA) Informatics Summit, International Conference on Machine Learning and Applications (IEEE ICMLA), EMBS International Conference on Biomedical and Health Informatics (IEEE EMBS BHI), International Conference on Bioinformatics and Biomedicine (IEEE BIBM), Alzheimer’s Association International Conference (AAIC), International Symposium on Bioinformatics Research and Applications (ISBRA), International Conference on Bioinformatics and Bioengineering (IEEE BIBE), Neurocomputing journal, etc. One of her first authored papers, which was published on IEEE ICMLA, was a best paper top 3 finalist in 2017.