Multiple Graduate Research Assistant Positions
The Center for Secure Information Systems (CSIS) at the George Mason University is seeking multiple PhD students who will work as GRAs in the field of deep learning, machine learning, and their applications to cybersecurity. Please refer to our recent publications listed below and GRA students will be working for similar topics, including but not limited to i) enhancing generative models such as generative adversarial networks and ii) enhancing knowledge graph embedding for scalability and accuracy. They will also conduct many practical research projects for cybersecurity applications, such as fake data synthesis. Below is a list of recent publications on these topics.
Generative adversarial networks and their application to data privacy:
Noseong Park, Mahmoud Mohammadi, Kshitij Gorde, Sushil Jajodia, "Data Synthesis based on Generative Adversarial Networks," the 44th International Conference on Very Large Data Bases (VLDB), 2018
David K. Park, Seungjoo Yoo, Hyojin Bahng, Jaegul Choo, Noseong Park, "MEGAN: Mixture of Experts of Generative Adversarial Networks for Image Generation for Multimodal Image Generation," the 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018
Noseong Park, Ankesh Anand, Joel Ruben Antony Moniz, Kookjin Lee, Jaegul Choo, David K. Park, Tanmoy Chakraborty, Hongkyu Park, and Youngmin Kim, "MMGAN: Manifold Matching Generative Adversarial Network for Generating Images," the 24th International Conference on Pattern Recognition (ICPR), 2018
Knowledge graph query processing and embedding:
Hyunjoong Kang, Sanghyun Hong, Kookjin Lee, Noseong Park and Soonhyun Kwon, "On Integrating Knowledge Graph Embedding into SPARQL Query Processing," the 25th IEEE International Conference on Web Services (ICWS), 2018
Francesco Parisi, Noseong Park, Andrea Pugliese, V.S. Subrahmanian, "Top-k User-Defined Vertex Scoring Queries in Edge-Labeled Graph Databases," to appear in ACM Transactions on the WEB, 2018
Successful candidates should have at least one proven record of experience on the posted fields and be capable of solving research problems. Detailed advice will be provided but preference will be given to self-motivated students who can perform literature survey and design algorithms by themselves beyond the advice. Successful candidates may also have mature programming skills for Python (with various machine learning and data mining libraries such as Scikit-Learn), Tensorflow, and/or Java.
The initial GRA contract
will be for one year but can be extended until the end of PhD study depending on performance. The GRA support will consists of the standard support package of Volgenau School of Engineering. GRAs will be advised by Dr. Noseong Park ([log in to unmask];