Hyeryung Jang

Contact me: hrjang357 at gmail

Bio, Long CV

I am currently a research associate in the Department of Informatics of King's College London (KCL), London, United Kingdom, working with Prof. Osvaldo Simeone, since March, 2018. I received M.S. and B.S. in Electrical Engineering from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea, in 2012 and 2010, respectively. I received Ph.D in Electrical Engineering from KAIST in early 2017, working with Prof. Yung Yi and Prof. Jinwoo Shin, and then worked as a post-doctoral researcher at KAIST (2017).

My recent research interests lie in mathematical modeling and analysis of communication systems, with a specific focus on applying learning, inference, and control of probabilistic graphical models to communication systems. More generally, the research area covered by my recent and future works is Networked Machine Learning, which takes inspiration from the human brain to carry out supervised, unsupervised, and reinforcement learning tasks in large-scale communication networks. My past research works include network economics, game theory, and distributed algorithms in communication networks. Here are few examples of my recent/past research topics.

Learning for Brain-inspired Computing - learning algorithms for dynamic exponential family models [ICASSP2019], and probabilistic Spiking Neural Networks [SPM2019]

Learning and Inference of Graphical Models (with applications to large-scale networks) - algorithms for communication efficient structure learning [MOBIHOC2018], for sampling efficient reinforcement learning [IJCAI2019], for fast adaptable parameter learning [SPAWC2019], and for multi time-scale parameter learning [ISIT2017]

Optimal and Distributed Parameter Control in Graphical Models - optimal CSMA [INFOCOM2014, TWC2017], coordination maximization [MOBIHOC2016, TCNS2018]

Game Theory and Economics: Networked Market - network economics, pricing of ISPs [JSAC2017, SDP2013]


You can also find my papers listed in Google Scholar or dblp.


  • [P4] Hyeryung Jang, Hyungseok Song, and Yung Yi, "On Cost-efficient Learning of Data Dependency", submitted for journal review, 2019. (Part of this work has been published MOBIHOC 2018)
  • [P3] Nicolas Skatchkovsky, Hyeryung Jang, and Osvaldo Simeone, "Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence", submitted for conference publication, 2019. (arXiv preprint version is available at arXiv:1910.09594)
  • [P2] Sangwoo Park, Hyeryung Jang, Osvaldo Simeone, and Joonhyuk Kang, "Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning", under the review of IEEE Transactions on Signal Processing (TSP), 2019. (Part of this work has been published SPAWC 2019)
  • [P1] Hyeryung Jang, Hyungwon Choi, Yung Yi, and Jinwoo Shin, "Adiabatic Persistent Contrastive Divergence Learning", under the revision of ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS), 2019. (Part of this work has been published ISIT 2017)




  • [T2] Hyeryung Jang, "Optimization and Learning of Graphical Models: A Stochastic Approximation Approach", Ph.D. Thesis in Electrical Engineering from Korea Advanced Institute of Science and Technology (KAIST), 2017. (Advisor: Prof. Yung Yi, Co-Advisor: Prof. Jinwoo Shin)
    • Abstract: This thesis mainly addresses the problem of optimization and learning in graphical models via stochastic approximation theory. First, in various multi-agent networked environments, the system can benefit from coordinating actions of two interacting agents at some cost of coordination, where a primary goal is to develop distributed algorithms maximizing the coordination effect. Examples include a wireless sensor networks with duty cycling, where a sensor node consumes a certain amount of energy when it is awake, but a coordinated operation of sensors enables some meaningful tasks, e.g., sensed data forwarding. Such pair-wise coordinations and node-wise costs in the network can be captured by graphical model framework, which becomes the problem of finding the optimal graph parameter. In this thesis, I present various distributed algorithms that require only one-hop message passing and locally-observed information, which can be interpreted based on either Lagrange duality theory or game theory framework. The proposed algorithms are motivated by a stochastic approximation method that runs a Markov chain simulation incompletely over time, but provably guarantees the convergence to the optimal solution. Second, I discuss the problem of parameter learning in graphical models having latent variables, where the standard approach, i.e., Expectation Maximization algorithm, is computationally intractable for high dimensional data, in both expectation and maximization steps. Since the substitution of one step to a faster surrogate for combating against intractability can often cause failure in convergence, I propose a new learning algorithm (called Adiabatic Persistent Contrastive Divergence), which runs a few cycles of Markov chains in both steps. Using multi-time-scale stochastic approximation theory, the proposed algorithm provably ensures convergence to a correct optimum, and moreover, I demonstrate the theoretical findings through extensive experiments with synthetic data and/or real-world data sets.
  • [T1] Hyeryung Jang, "Economic Benefits of ISP-CDN and ISP-ISP Cooperation", M.S. Thesis in Electrical Engineering from Korea Advanced Institute of Science and Technology (KAIST), 2012. (Advisor: Prof. Yung Yi)




  • Ph.D: Electrical Engineering, KAIST, 2012.3~2017.2 (under supervision of Prof. Yung Yi and Prof. Jinwoo Shin)
  • M.S: Electrical Engineering, KAIST, 2010.3~2012.2 (under supervision of Prof. Yung Yi)
  • B.S: Electrical Engineering, KAIST, 2006.3~2010.2
  • Highschool: Korea Minjok Leadership Academy (KMLA), 2004.3~2006.2

Honors and Awards

  • BK21 Plus Scholarship, Republic of Korea, 2017
  • Qualcomm-KAIST Innovation Award, Qualcomm, 2016
  • National Scholarship, Republic of Korea, 2006~2016

Work Experiences

  • 2018.03~Present: Research Associate, Centre for Telecommunications Research, Department of Informatics, King's College London, London, England, United Kingdom (Host: Prof. Osvaldo Simeone)
  • 2017.03~2018.02: Post-doctoral Researcher, BrainKorea21 Plus, Information & Electronics Research Institute, KAIST, Republic of Korea (Host: Prof. Yung Yi)
  • 2015.06~2015.10: Research Intern, Center of Non-Linear Studies, Los Alamos National Laboratory, NM, United State (Host: Dr. Michael Chertkov)

Project Experiences

  • 2018.04~Present: Intel's Neuromorphic Research Community (INRC), Intel
    • Attend workshops with leading researchers in the fields of neuroscience-inspired Artificial Intelligence and neuromorphic computing
  • 2018.04~Present: FOG-aided wireless networks for communication, cacHing and cOmputing: theoRetical and algorithmic fouNdations (FOGHORN), European Research Council (ERC)
    • Develop fundamental theoretical insights, via network information theory, communication theory and machine learning, on the optimal performance and operation of fog-aided wireless networks
  • 2017.07~2018.03: Research on Learning-based Service Improvement Framework in Large-scale Online Request System, Naver, Republic of Korea
    • Design and analyze Reinforcement Learning based service improvement framework in large-scale online request system
    • Implement deep reinforcement learning algorithm for service improvement in large-scale online request system
  • 2014.10~2015.06: Bosch-KAIST Smart Car Project: Look Ahead: Shared Sensing for Cooperative Cars, Bosch, German - Korea
    • Design and implement MAC protocol for real-time video transmission between vehicles
  • 2013.11~2016.10: Research on Horizontal and Vertical Decoupling in Big Wireless Networks: Theory and Implementation, National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT & Future Planning (MSIP), Republic of Korea
    • Research on distributed MAC protocol design for CSMA-based wireless networks (from optimization and game theory perspective)
  • 2011.06~2011.10: Research on Smart Network based B2B2C Service Modeling and Economic Analysis, Korea Telecom, Republic of Korea
    • Research on analysis of economic value of pricing between Internet Service Providers, Content Providers, and users

Presentation Experiences

  • "Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing", at IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brighton, UK, May, 2019. [poster]
  • "Information-Theoretic Learning of Probabilistic Spiking Neural Networks", at INRC Workshop, Reykjavik, Iceland, September, 2018. [poster]
  • "Learning Data Dependency with Communication Cost," at ACM Mobile Ad Hoc Networking and Computing (MOBIHOC), Los Angeles, USA, June 27, 2018. [slide]
  • "Adiabatic Persistent Contrastive Divergence Learning", at IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, June 30, 2017. [slide]
  • "Optimization and Learning of Graphical Models: A Stochastic Approximation Approach", at Korea Computer Congress (KCC): Spotlight Session for Young Women Scholars, Jeju, Republic of Korea, June 20, 2017. [slide]
  • "Distributed Coordination Maximization over Networks: A Stochastic Approximation Approach", at ACM Mobile Ad Hoc Networking and Computing (MobiHoc), Paderborn, Germany, July 7, 2016. [slide]
  • "Distributed Learning for Utility Maximization over CSMA-based Wireless Multihop Networks", at IEEE International Conference on Computer Communications (INFOCOM), Toronto, Canada, April 29, 2014. [slide]
  • "On the Interaction between ISP Revenue Sharing and Network Neutrality", at ACM International Conference on emerging Networking Experiments and Technologies (CoNEXT) Student workshop, Philadelphia, USA, November 30, 2010. [poster]

Teaching Experiences

  • Teaching Assistant, Communication Systems (6CCS3COS), KCL, Fall 2018
  • Teaching Assistant, Optimization in Communication Networks (EE650), KAIST, Spring 2016, Spring 2013
  • Teaching Assistant, Programming Structures for Electrical Engineering (EE209), KAIST, Fall 2014
  • Teaching Assistant, Computer Networks (EE323), KAIST, Spring 2014, Spring 2011
  • Teaching Assistant, Data Structure and Algorithms for Electrical Engineering (EE205), KAIST, Fall 2013, Fall 2011
  • Teaching Assistant, Economics in Communication Networks (EE655), KAIST, Spring 2012

Reference Available on Request

  • Professor Yung Yi: Professor at the School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), South Korea, yiyung at kaist dot edu, +82 (0) 42 350 3486
  • Professor Jinwoo Shin: Associate Professor at the School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), South Korea, jinwoos at kaist dot ac dot kr
  • Professor Osvaldo Simeone: Professor at the Department of Informatics, King's College London (KCL), United Kingdom, osvaldo dot simeone at kcl dot ac dot uk
  • Professor Se-Young Yun: Assistant Professor at the Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), South Korea, yunseyoung at kaist dot ac dot kr