Dr. Woong-Je Sung is a Research Engineer in the School of Aerospace Engineering at the Georgia Institute of Technology. His field of research is machine learning and its application into modeling-and-simulation.

  • Doctor of Philosophy, Aerospace Engineering, 2012, Georgia Institute of Technology, Atlanta, GA - USA
  • Master of Science, Aerospace Engineering, 1999, Seoul National University, Seoul - Korea
  • Bachelor of Science, Aerospace Engineering, 1997, Seoul National University, Seoul - Korea

CSAR-X Compound Helicopter Study (Sponsor: USAF)

  • Task: Generation of numerical analysis model of tandem rotorcraft configuration and performance assessment as a part of multi-configuration comparative study

Extremely Short Take-Off and Landing Vehicle Study (Sponsor: NASA)

  • Task: Creation of technology exploration interface using surrogate models enabling rapid design space exploration to achieve the required ESTOL performance

Autonomous Logistics Delivery Systems (Sponsor: ONR)

  • Task: Mathematical modeling of aerodynamic performance of the ALDS around the event of wing deployent and itgration with projectile physics and rocket booster models

Personal Aerial Vehicle Study (Sponsor: Korean Aerospace Research Institute)

  • Task: Aerodynamic design and analysis of various flying car concepts and identification of technical constraints that guarantees economic viability

NEXTGEN Large Civil Tilt-Rotor Study (Sponsor: NASA)

  • Task: Synthesis of multiple flight modes for vehicle sizing and performance estimation

Smart Campus (Sponsor: Georgia Tech)

  • Role: Technical Lead
  • Project overview: Generation of campus-wide energy demand model and application of predictive analytics for various energy consumption terms considering building characteristics, socio-economic factors, and weather data to establish energy-saving plant operation strategy

Digital Factory of the Future (Sponsor: Boeing)

  • Role: Technical Lead
  • Project overview: Analysis of patterns for mishaps of drilling robots and creation of prediction model for cycle time for drilling operation by application of machine learning techniques

Automated Surrogate Modeling (Sponsor: Siemens)

  • Role: Technical Lead
  • Project overview: Research on meta-machine learning to identify and fine-tune the best surrogate model for a given data set using Bayesian optimization and nested tree-based search

Automated Knowledge Extraction and Representation (Sponsor: Siemens)

  • Role: Technical Lead
  • Project overview: Application of Deep-Q learning to provide real-time, contextual, in-product command recommendation for CAD software, Siemens NX

Deep Data Fusion (Sponsor: Airbus)

  • Role: Technical Lead
  • Project overview: Development of deep data fusion framework to enable synthesis between wind tunnel test data and numerical analysis results with the objective of enabling a modeling and simulation task that seamlessly harness all available data with identified uncertainties

Development of FORESIGHT (Sponsor: Georgia Tech)

  • Role: Developer
  • Project overview: Development of comprehensive campus energy data browser and integration with predictive analytics model that enables users to navigate vast sensory measurement space and anticipate energy consumption of near future for a central chilled water plant

Environmentally Responsible Aviation Dashboard (Sponsor: NASA)

  • Role: Developer
  • Project overview: Development of GUI to enables users to navigate through all possible options in hierarchical and high dimensions of combinatorial technology space

Interactive Technology Portfolio (Sponsor: NASA ARMD)

  • Role: Developer
  • Project overview: Development of GUI that parses any given relational database and create a force-directed diagram of all elements in hierarchical fashion

Plant Mission Predictor Enhanced Flexibility (Sponsor: GE)

  • Role: Developer
  • Project overview: Development of a series of tools that extract the legacy tool’s detailed model parameters, interpret them, and generate VENSIM input file

Journal Papers

  1. Rajaram, D., Puranik, T. G., Ashwin Renganathan, S., Sung, W., Fischer, O. P., Mavris, D. N., & Ramamurthy, A. (2021). Empirical assessment of deep gaussian process surrogate models for engineering problems. Journal of Aircraft, 58(1), 182-196.

  2. Hong, S.K., Ahn, C.H. and Sung, W.J., 2005. Aerodynamic Modeling of Side Jet Influence on Body-Tail Configuration. Computational Fluid Dynamics Journal, 13(4), p.82.

  3. Hong, S.K., Lee, K.S. and Sung, W.J., 2001. Characteristics of Supersonic Nozzle and Jet Impingement. Journal of the Korea Institute of Military Science and Technology, 4(2), pp.256-262.

  4. Hong, S.K. and Sung, W.J., 2001. Numerical Analysis on the Flow Characteristics of Side Jet Thruster. Journal of computational fluids engineering, 6(3), pp.27-31

Conference Papers

  1. Zhang, Yao, Woong Je Sung, and Dimitri N. Mavris. “Application of convolutional neural network to predict airfoil lift coefficient.” 2018 AIAA/ASCE/AHS/ASC structures, structural dynamics, and materials conference. 2018.

  2. Achour, Gabriel, et al. “Development of a conditional generative adversarial network for airfoil shape optimization.” AIAA Scitech 2020 Forum. 2020.

  3. Rajaram, D., Puranik, T. G., Renganathan, A., Sung, W. J., Pinon-Fischer, O. J., Mavris, D. N., & Ramamurthy, A. (2020). Deep Gaussian process enabled surrogate models for aerodynamic flows. In AIAA Scitech 2020 Forum (p. 1640).

  4. Mavris, Dimitri N., Michael G. Balchanos, Olivia J. Pinon-Fischer, and Woong Je Sung. “Towards a digital thread-enabled framework for the analysis and design of intelligent systems.” In 2018 AIAA Information Systems-AIAA Infotech@ Aerospace, p. 1367. 2018.

  5. Duncan, Scott, Michael Balchanos, Woongje Sung, Juhyun Kim, Yongchang Li, Yanal Issac, Dimitri Mavris, and Adam Coulon. “Towards a data calibrated, simulation-based campus energy analysis environment for situational awareness and future energy system planning.” In Energy Sustainability, vol. 45875, p. V002T11A010. American Society of Mechanical Engineers, 2014.

  6. Mavris, Dimitri N., Michael Balchanos, WoongJe Sung, and Olivia J. Pinon. “Towards a Big Data-enabled Virtual Experimentation Framework for Sustainability-Oriented, Large-scale Infrastructure Planning.”

  7. Mavris, D. N., Balchanos, M., Sung, W., & Pinon, O. J. (2016, June). A data mining and visual analytics perspective on sustainability-oriented infrastructure planning. In International Conference on Data Mining and Big Data (pp. 330-341). Springer, Cham.

  8. Kirby, M., Ran, H., Dufresne, S., Sung, W., Mavris, D., & Burdette, G. (2009, September). Advanced vehicles modeling for the next generation air transportation system (nextgen vehicle integration nra). In 9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO) and Aircraft Noise and Emissions Reduction Symposium (ANERS) (p. 7119).

  9. Zhang, W., Terrier, V., Fei, X., Markov, A., Duncan, S., Balchanos, M., Sung, W.J., Mavris, D.N., Loper, M.L., Whitaker, E. and Riley, M., 2018, December. Agent-based modeling of a stadium evacuation in a smart city. In 2018 Winter Simulation Conference (WSC) (pp. 2803-2814). IEEE.

  10. Fu, H., Kampezidou, S., Sung, W., Duncan, S., & Mavris, D. N. (2018). A Data-driven Situational Awareness Approach to Monitoring Campus-wide Power Consumption. In 2018 International Energy Conversion Engineering Conference (p. 4414).

  11. Kim, J. H., Sung, W., Balchanos, M. G., Duncan, S. J., & Mavris, D. N. (2017). Modeling the Impact of Thermal Demand Response on a Campus Chilled Water Plant and Assessment of Campus-wide Load Shift Potential. In 15th International Energy Conversion Engineering Conference (p. 4717).

  12. Mavris, D. N., Balchanos, M., Sung, W., & Pinon, O. J. Towards a Big Data-enabled Virtual Experimentation Framework for Sustainability-Oriented, Large-scale Infrastructure Planning.


  1. International Air Transport Association (IATA), The IATA Technology Roadmap Report, Contributed to the IATA Technology Roadmap Report, 3rd Edition, June 2009


  1. W. Sung, A neural network construction method for surrogate modeling of physics-based analysis, Ph.D. Thesis, School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, May 2012
  2. W. Sung, Aero-Structural Optimization of High Aspect Ratio Wing using Response Surface Method, Masters Thesis, Seoul National University, Seoul, Korea, Feb 1999

  • Member, American Institute of Aeronautics and Astronautics (AIAA)
  • Member, Institute of Electrical and Electronic Engineers (IEEE)