Connectionists: GRA positions of George Mason University in deep learning, machine learning, and constrained optimization

Sat Sep 15 15:55:31 EDT 2018

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, constrained optimization, 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, ii) enhancing knowledge graph embedding
for scalability and accuracy, iii) solving optimization problems in
cybersecurity and social networks (e.g., optimally allocating security
analysts against security alarms, finding a set of social sensors to detect
outbreaks in social media, etc.). 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:

   1.

   Noseong Park, Mahmoud Mohammadi, Kshitij Gorde, Sushil Jajodia, "Data
   Synthesis based on Generative Adversarial Networks
   <https://secure-web.cisco.com/1NlDilNdGJXekNkmX5w4q_zJa5N3M6ekGszW_FGf-xbG0M-ZA8Xlpcq4fjnQCtUwaQceYhyOmkE8Y8SVwqj1LfK2ZAu7Z_IUMFBtmWvzA8YXx_Ey-R9jCK66LMyty2GlWc2yVFRTO8XxPBEMekFzZerrqPg9yLteIJeHkjPiPpQIwq8b3MHY53YS9H1P_Xwb8IkEHv1emHr_pItNn9-JLDbqyVWLjQOMsF6FmLlfNVcqUjJppyLcerTRJ6Rb_lmSzxiCHfddh_R_o7jVDtBkauq38LP11ZRlhu2HocgFVFnRL2mzEv4lTVCUvmn2HJpV-Arz3mPXfzm_4m1fS5d9Y_wH_PMD6a8r8KqaB_KIMMDQZogs0eDjGvovCTY_V6qgAZYIoCtJQCzwVIFiaoFA5Bfdd-MR8aBLgbTLImT9UdekWp66DRB5jllLDqQ0fnXhMSzHfzXlF6MvkOWi2P147nQ/https%3A%2F%2Fwww.dropbox.com%2Fs%2Fh3tgggwo3s7jjn7%2Fvldb2018.pdf%3Fdl%3D0>,"
   the 44th International Conference on Very Large Data Bases (VLDB), 2018
   2.

   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
   <https://secure-web.cisco.com/15OnYvOi3OFX7L0S1L9LZrM3pGjSP_77C8PPdtQ57opVU8CzTZRe6ArUUsBZsqGcyIywVRbbEQxAx1G2tBiRI-edRrxQaGU7LXnMv3_ts8k-2CxIt5Y57BiKO667wFAaTcrs3_HpgFMOR8CxpEifHPLKznhV1lM0LRfeYZMwdVaTo6WomQFu9LYz8PyYlwzBBE34M6pavXCf3SvtDuw8xyaZ6KRbhwQHcDEMXoeSRyjAzewZvaqg1J3e8C7BTKM2UGOk2w8JlgNR_3TFkX5mrN8SUXhqht0Za4JXIKRDngFGQD3bxEjwVEgb5kC25MrxueWLGd6UlbjMH7LM1MozD7ibwH0wg_VTt0XTWG0MdwPRskZ7iQWYSI2ljMxc38BYLvKuQTHAESpScj47No5uI6pCncIhONLDwwScp7vnUqjViKDOZb-GkW8E7d8wRKIkpNwAKlQqDRyVQhe2Z4e5hDA/https%3A%2F%2Farxiv.org%2Fpdf%2F1805.02481.pdf>,"
   the 27th International Joint Conference on Artificial Intelligence
    (IJCAI), 2018
   3.

   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
   <https://secure-web.cisco.com/1BoZMK9GrfHOekK2VKALzXjysHXrSlyBrliVFjqP4beJu6cG1sVBBVcCsBUcnIjriKS9XbWhpIMm3bplkHI7ydMm2ZIUtQrBj-XFaAOxUuw9UaredT8TiUZeuwKrlpWkjEQr-3ZlIy6Qd6ChycJYbDSB7TUYQJWtNYPawOSbOiNKTTjXLjLbRNrRu3TXmurXV8eKm35JMo_T7_REj-M20N8_Q0a3hjgL2NU8gu04FMN38IKcditNqDR-Rn_pXUqrHkR_GW2hh-etjW6vPfl0Bf44BVIYbfuGlJlXPEDMqW8m69YP4BpxiMWrwWTgkoSU9XatnfyiVWdDmNNkIZz8jEtU-I2sX5dbz6-7yWYCMx9eG7Yl-d1-yettQQveZBECqDe22Q70j4AvALgX_4mMOdSiXyEUsfZmRn37gvjbLlzIMDrzudyO9bXEl3spA8IIQmbOju5QcC76iH-a_sEPSEw/https%3A%2F%2Farxiv.org%2Fpdf%2F1707.08273.pdf>,"
   the 24th International Conference on Pattern Recognition (ICPR), 2018

Knowledge graph query processing and embedding:

   1.

   Hyunjoong Kang, Sanghyun Hong, Kookjin Lee, Noseong Park and Soonhyun
   Kwon, "On Integrating Knowledge Graph Embedding into SPARQL Query
   Processing
   <https://secure-web.cisco.com/1P8uPamdj1vavmpm4Tc3q_Em0KEJL6eLzd0_Ek5Sa7sT-CoQs6b3rUbwFmxDCT-pERVwgPzs1lP3WD2BC0X-w8tRc33IUSIZQG9Qj7gt2ZzjLh7CTinWMI07wNV6Lkne8BLbVP5WJOmXgfNmnzRpg3wLAwGIddoWn9cVsriY_K0K3t11k906y3uBNtMM0XUPUh44I1R1SerUonck9zQUFxQArr4mmhdmdzJCB8TyfMmiBPf9mbVhbNEaJX4aBy5vJIiXQtysy0l3-IPRBkQf_daZKykGKnZX7wgFtngMv-BMJTkJLO9ZB5b06phi2Cu54PpxacqRvhO2EnYgJwvd3eXvIjPnAh_8T016aFxAomt6IaXBuiaj-ooUcBZecNUa1wChCpw3ah9ehj2JnwEr-c1kJC87ZZdrhlGhKRLe_FG9nfixrABg_oqWexA19MCXZkZiXxSqo9wyjcWHN6D8NmQ/https%3A%2F%2Fwww.dropbox.com%2Fs%2Fnhp9htnd81bnrrf%2Ficws2018_short.pdf%3Fdl%3D0>,"
    the 25th IEEE International Conference on Web Services (ICWS), 2018
   2.

   Francesco Parisi, Noseong Park, Andrea Pugliese, V.S. Subrahmanian, "Top-k
   User-Defined Vertex Scoring Queries in Edge-Labeled Graph Databases
   <https://secure-web.cisco.com/1q_EYbjPt2XJ-ZVHGW9ikSMe1BdTx42OpXCch5Sc9oECKOC4-e9NoqSQx-7PHIy1CXOn6WV2aUa5j38trW2HCeoVgLWFqN9K_nzEVznLRIKO4VqVfxH8_2SrLpoCCuPBTT6z35QNc0yFwmdTrhLQNMhnCTnRm5X1Exz_kHsf_lMGBNA7NP9bFPlEoesBPn6x6TldDNURAH0w8R0yFoEl9-uWiLlUtQFkakEqRN0tgZ3c-p-s2GnSSK8UtkSoTAGgTKMChc2KhAm8GFRi8yis8MiEZWOoScw8RcuUvq5B9_9Ljxy51nL0OJNV_EQKQWfM_64UiQRsStNVnrTiU0xaRo45CRDqotkVIf9fRPMIsotTVDi-oOR8uGvpaxTiGOh0Ep1hqk_dBFk7sWCghY7QICgu2EohHfKwKAQAL5xcKIZ6IzN7c6rxdCROfmFfkYvVrOMwu49uu775DSmDHHW9Nxw/https%3A%2F%2Fwww.dropbox.com%2Fs%2Fi1f3aq3dqt9t00k%2FTWEB2017.pdf%3Fdl%3D0>,"
   to appear in ACM Transactions on the WEB, 2018

Constrained optimization and prediction-drive optimization:

   1.

   Bo An, Haipeng Chen, Noseong Park, and V.S. Subrahmanian, "Data-Driven
   Frequency-Based Airline Profit Maximization
   <https://secure-web.cisco.com/1C2Mw63EgaGBVTfUk5mrIPDc2WCGqzI1qYtFIDFXWhzkjmMzKziloRng93hqM0DhAdEVcul4LiR6RHl4JLe4KXnBHadTLgaFXipdErKaJzlYLNXUWv2yWcvwunVkt3923ZnLF6gSBqeVRgexDS5sdgzpjcmKxo0PeWtr5LIt_rAc1xMEuguS7GU46Iu3yPWNmsMQb0eDNCHjxFQs8glNRi_BQiyBJxMnKrqrWwflNxjUUDwO4l7UPUQcogpzzhsue7_GkJQ5YirlMhcGhLy7XQ3lbxnlphGl473lw5wxzmB3a_vRCQFbdtm6bab2cCZetnRAj6LKmVWsX3fHeEMEaN86OyYBLDodwYWSV0_-mmjUrKPsFCts7tgPPHU4lA1MxzpextdY_582vDg_o1dK-mrXvwmtWC-TEUOYun1oL8fIehw6l3QpmVzif_uwjuRwFzmbH6Q9VsTQAGBVCimYYQg/https%3A%2F%2Fwww.dropbox.com%2Fs%2Fry4svnr37y08hzt%2FTIST2017.pdf%3Fdl%3D0>,"
   ACM Transactions on Intelligent Systems and Technology, vol. 8, no. 4, 2017
   2.

   Bo An, Haipeng Chen, Noseong Park, and V.S. Subrahmanian, "MAP:
   Maximizing Airline Profits using an Ensemble Game-Theoretic Forecasting
   Approach
   <https://secure-web.cisco.com/1toLz7OzmdOiO9N1MLWwhaIQj_cwowqbwP1hd4i2G_1VQ3UkMPhR-_dt37nx0nfKAVlBoF4GKMsfDcN6McZUw__oTMpSq6qSRYfciJ-mR40aTOzNeEgtmGjEotozcwxayd1tLgasFCzJtYQHcGNV8HMMCRswaRPKE7-7pO4S3kJIgOBiH7paL1lOA4rFNpDnxIaw9LLAA38J7gzmyIf7yeC9dT9HTelhv0mwKQ7_dHGuRzLYX8-J6_KrSP7MmYa4w6Pb9GmwjmTtx0RojOA3L63xqQPww0hDHLsr62R01xZwfnRvZNteBiXggUpTGyxvDDBB6TtxPTLy-oc6sNE1SoLHPip3cqbEDzUfaiZE4V1482fPh43Pe1Z3KUoGcfXayB69DDjRlrpwFzLGkpvhBPCPT7DpKwGXwKtiYBIZZoGUAXz-q9qoSZWBGRyEJ1pagJ1LoSYNcNo2giD6nawcnnw/https%3A%2F%2Fwww.dropbox.com%2Fs%2Flxuwniprea1uzbr%2Fairline.pdf%3Fdl%3D0>,"
   ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2016
   3.

   Noseong Park, Edoardo Serra, Tom Snitch, and V.S. Subrahmanian, “APE: A
   Data-Driven, Behavioral Model Based Anti-Poaching Engine,
   <https://secure-web.cisco.com/1wAnTnoN_QwRBIb9DJ8JMsqvFh9NtcmqyJrGrSjuHCLeZMXAI_Pugd7yZosfq2lfLDqgxavzXiAoDJFAqwkIqxtRnWb_VXkyz0LcQ5LEYs4TZk2-Cl05mfm7e5BBf2hUjghm1TUD-a_iElbgYKv3ZWS6BLrXM8fVQbjAE8DHaxR3MB80nCN9wQ2hnYvLyjPuyRmRaQ0_0p6LzUnKsq1ottw1naNr9aCjgo7UPhKsQILrEEGMQ5avJfy1l2_gvWSWkmOdzw2itMScWijpOlHEjPB1-3UQ56AL3H2BFXahTtP8S06mBsrn7qGe4wzX1znLcKTCblLpgbdVi_aJ7ETxL_7za8nmsRJy5-QU4r6TElizz7myCxXev6x5Vz1TU3BieeWE4Eu_SnNkUmNEtzZ0AlTQeB4gRfR9MN9SuLXSnH-YDC_darDA9H_jgNX_5RjKhhbp4X2ipaBpVNmAgzrKNQA/https%3A%2F%2Fwww.dropbox.com%2Fs%2Fvhu2zfbrr2v4yyt%2Ftcss2015.pdf%3Fdl%3D0>”
   IEEE Transactions on Computational Social Systems, vol. 2, no. 2, pp. 1-23,
   2015 [NBC Nightly News]
   <http://secure-web.cisco.com/1IIRcPHqJs5p_8tiNwjaqBNXRMJbPIKclyB-lERjqd8K4zrk5IutboQdiXpTHaMO71t6DG82iPp54f28gSaBKHDH_GhxoHaNtx2IdzL3igaegVOn8JWkfUs8SXit-L8qTMkMwmCbqTHga3rTWSp3LExTMklwlTtXc3hC1Igp5EBCm-Z-C5B1TgUvAqtW35FWlqbQOO2JXAUEDnoIAep2y4gp2b3IDzeV8tv8J8OfSiy_7roMW5cJoE5wcQrgC0iJhIr6C3efFcy_zywdzYoE02urmKdDcJ51cvy0Yf9vud-0egD5lbYLQV0_Vncm90mOtsntZuBQjcIJdsqvO5r7eHMfTUhQL6jb7zIbuai9zJ2Q7v_JzCCiYC77XHUoCgOXrtvenkmjdnPhzs-lWU5QRR-3oHDaZMJUpuPmRrbfn1UR2kqnvk1pP60XPLjgsiXY6v9bVj00KyrDMRFXJxL5CAA/http%3A%2F%2Fwww.nbcnews.com%2Fnews%2Fworld%2Fair-shepherd-uses-drones-stop-elephant-rhino-poachers-africa-n335801>

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 (npark9 at gmu.edu;
https://sites.google.com/view/npark
<https://secure-web.cisco.com/1uiyKv7_wXj7ZRcRxQpF7sEGa0jh8YZrvK1DOCP-ODbJ9P1Rcjro4Q0Ztlw2YcDHklTcUHQ2h6w6lMrHabv9ZSv-0PoKwY2cRwlr3T8ztZ0uDnLsrZf1Cyh66DxxTTl_n3tqifNWHOp3pktYBnA9b0RMIaea_hNWKq10Pte-YMD0yf_c8DJ7-EsVRn5KZ4ZOGYBQBIltVkN1LfVO9G8cpXMLqNHcOEzkLGb8QrUtvlbuMhTYu2pNnEa4yPah3FBmM4qalpkzgV0ffZQ67D5xoH_T3iIbQz0gsOt-DPAhj12HwOmJ4TeAyalf8zch_O2c6Rm6Xuy4bjqFQ5Rg8RyKKqUGthbeSJw2pT0OD7Heo0fg2-5l6pG-gbBc1LGH554U3-xZbj3nIteYmsP-oaoJVk6Al6tkzQxr28dw0pQZ0jyaQK8Mgwm1HtS6d2VujKzaaKgYUnqh7zkwnYJW1QBDj0A/https%3A%2F%2Fsites.google.com%2Fview%2Fnpark>).
Please send your CV if interested.

-------------------------------------------------

Noseong Park, PhD *| *Assistant Professor

Center for Secure Information Systems

Information Sciences and Technology

424, Research Hall

George Mason University

<npark9 at gmu.edu>npark9 at gmu.edu* | * <https://sites.google.com/view/npark>Visit
Homepage <https://sites.google.com/view/npark>
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