학부 연구생 모집에 관심있는 학생은 hyunsuk@sejong.ac.kr로 메일주세요.

Welcome to MAIN LAB

MAchine Intelligence and Networking (MAIN) lab에서는 지능형 의사결정, 설명 가능한 인공지능 등의 기계학습/인공지능 관련 분야에 관한 연구와 함께, 인공지능 기술을 지능형 사물인터넷과 같은 미래 무선 통신 네트워크에 응용하는 연구를 수행하고 있습니다.

MAchine Intelligence and Networking (MAIN) lab develops machine learning (ML) methods for intelligent decision-making and explainable AI and applies AI/ML for wireless networks.

Professor

Hyun-Suk Lee, Ph.D.

Assistant Professor,
School of Intelligent Mechatronics Engineering,
Sejong University, Seoul, Korea


Room 523, Daeyang AI Center
hyunsuk@sejong.ac.kr
+82-2-3408-1958


Education

  • Ph.D., Electrical & Electronic Engineering, Yonsei University, Seoul, Korea, Feb. 2018
  • B.S., Electrical & Electronic Engineering, Yonsei University, Seoul, Korea, Feb. 2012

Experience

  • Assistant Professor, School of Intelligent Mechatronics Engineering, Sejong University, Sep. 2020-Present
  • Postdoctoral Research Fellow, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Sep. 2019-Aug. 2020
  • Postdoctoral Research Associate, School of Electrical and Electronic Engineering, Yonsei University, Mar. 2018-Aug. 2020

Research Areas

Intelligent Decision-Making

MAIN LAB에서는 기계가 의사 결정을 수행할 때 고려해야하는 다양한 문제점들을 해결하는 연구를 진행하고 있습니다. 예를 들어, 의사 결정을 통해 얻어지는 보상을 최대화 하기 위한 보다 효율적이고 효과적인 방법, 혹은 의사 결정으로 인해 발생할 수 있는 위험성을 최소화하는 방법 등에 관해 연구하고 있습니다.

We try to resolve the challenges in intelligent decision-making. We develop a more effective and efficient way to maximize the rewards from the decisions and a method to confidently minimize the unsafety situations due to the decisions.

Explainable Artificial Intelligence (XAI)

MAIN LAB에서는 사람이 이해할 수 없는 형태의 딥러닝 모델을 다양한 해석 모듈을 통해 사람이 이해할 수 있는 형태로 표현하는 설명 가능한 인공지능 기술 (XAI)에 관하여 연구하고 있습니다.

We develop a new interpretability module to explain the black-box deep learning models in a form of some expressions that be understood by humans.

Future Wireless Networks

MAIN LAB에서는 다양한 미래 무선 네트워크 관련 분야에 관하여 연구하고 있습니다. 특히, 인공지능/기계학습 기술을 무선 네트워크 분야에 응용하여 AR/VR, 지능형 사물인터넷 등 통신 기술이 필요한 미래 서비스 지원을 위해 발생할 수 있는 문제를 해결하는 연구를 진행하고 있습니다.

We study the challenges in future wireless networks to support future services such as AR/VR and intelligent IoT systems. Especially, we focus on applying AI/machine learning methods to solve such challenges.


Publications and Patents

Selected Machine Learning-Related Papers

  1. H.-S. Lee, C. Shen, W. Zame, J.-W. Lee, and M. van der Schaar, "SDF-Bayes: Cautious optimism in safe dose-finding clinical trials with drug combinations and heterogeneous patient groups," to appear in AISTATS 2021.
  2. H.-S. Lee, Y. Zhang, W. Zame, C. Shen, J.-W. Lee, and M. van der Schaar, "Robust recursive partitioning for heterogeneous treatment effects with uncertainty quantification," in Proc. NeurIPS 2020, Dec. 2020.
  3. H.-S. Lee and J.-W. Lee, "Adaptive wireless power transfer beam scheduling for non-static IoT devices using deep reinforcement learning," IEEE Access, Vol. 8, pp. 206659-206673 Nov. 2020.
  4. W. R. Zame, I. Bica, C. Shen, A. Curth, H.-S. Lee, S. Bailey, J. Weatherall, D. Wright, F. Bretz, M. van der Schaar, "Machine learning for clinical trials in the era of COVID-19," Statistics in Biopharmaceutical Research - Special Issue on Covid-19, Vol. 12, No. 4, pp. 506-517, 2020.
  5. H.-S. Lee, J.-Y. Kim, and J.-W. Lee, "Resource allocation in wireless networks with deep reinforcement learning: A circumstance-independent approach," IEEE Systems Journal, Vol. 14, No. 2, pp. 2589-2592, June 2020.
  6. H.-S. Lee, C. Shen, J. Jordon, and M. van der Schaar, "Contextual constrained learning for dose-finding clinical trials," in Proc. AISTATS 2020, June 2020.
  7. H.-S. Lee and J.-W. Lee, "Contextual learning based wireless power transfer beam scheduling for IoT devices," IEEE Internet of Things Journal, Vol. 5, No. 5, pp. 9606–9620, Dec. 2019.
  8. H.-S. Lee, C. Tekin, M. van der Schaar, and J.-W. Lee, "Adaptive contextual learning for unit commitment in microgrids with renewable energy sources," IEEE Journal of Selected Topics in Signal Processing, Vol. 12, No. 4, pp. 688-702, Aug. 2018.

International Journals

  1. B.-H. Lee, H.-S. Lee, S. Moon, and J.-W. Lee, "Enhanced random access for massive machine type communications," to appear in IEEE Internet of Things Journal.
  2. H.-S. Lee and J.-W. Lee, "Adaptive wireless power transfer beam scheduling for non-static IoT devices using deep reinforcement learning," IEEE Access, Vol. 8, pp. 206659-206673 Nov. 2020.
  3. W. R. Zame, I. Bica, C. Shen, A. Curth, H.-S. Lee, S. Bailey, J. Weatherall, D. Wright, F. Bretz, M. van der Schaar, "Machine learning for clinical trials in the era of COVID-19," Statistics in Biopharmaceutical Research - Special Issue on Covid-19, Vol. 12, No. 4, pp. 506-517, 2020.
  4. K.-W. Kim, H.-S. Lee, and J.-W. Lee, "Opportunistic waveform scheduling for wireless power transfer with multiple devices," IEEE Transactions on Wireless Communications, Vol. 19, No. 9, pp. 5651-5665, Sept. 2020.
  5. H.-S. Lee, J.-Y. Kim, and J.-W. Lee, "Resource allocation in wireless networks with deep reinforcement learning: A circumstance-independent approach," IEEE Systems Journal, Vol. 14, No. 2, pp. 2589-2592, June 2020.
  6. H.-S. Lee and J.-W. Lee, "EHLinQ: Distributed scheduler for D2D communication with RF energy harvesting," IEEE Systems Journal, Vol. 14, No. 2, pp. 2281-2292, June 2020.
  7. H.-S. Lee and J.-W. Lee, "Adaptive traffic management and energy cooperation in renewable energy powered cellular networks," IEEE Systems Journal, Vol. 14, No. 1, pp. 132-143, Mar. 2020.
  8. H.-S. Lee and J.-W. Lee, "Contextual learning based wireless power transfer beam scheduling for IoT devices," IEEE Internet of Things Journal, Vol. 5, No. 5, pp. 9606–9620, Dec. 2019.
  9. D.-Y. Kim, H.-S. Lee, K.-W. Kim, and J.-W. Lee, "Dual amplitude shift keying with double half-wave rectifier for SWIPT," IEEE Wireless Communications Letters, Vol. 8, No. 4, pp. 1020-1023, Aug. 2019.
  10. H.-S. Lee and J.-W. Lee, "Resource and task scheduling for SWIPT IoT systems with renewable energy sources," IEEE Internet of Things Journal, Vol. 6, No. 2, pp. 2729-2748, Apr. 2019.
  11. K.-W. Kim, H.-S. Lee, and J.-W. Lee, "Waveform design for fair wireless power transfer with multiple energy harvesting devices," IEEE Journal on Selected Areas in Communications, Vol. 37, No. 1, pp. 34-47, Jan. 2019.
  12. S. Moon, H.-S. Lee, and J.-W. Lee, "SARA: Sparse code multiple access-applied random access for IoT devices," IEEE Internet of Things Jorunal, Vol. 5, No. 4, pp. 3160-3174, Aug. 2018.
  13. H.-S. Lee, C. Tekin, M. van der Schaar, and J.-W. Lee, "Adaptive contextual learning for unit commitment in microgrids with renewable energy sources," IEEE Journal of Selected Topics in Signal Processing, Vol. 12, No. 4, pp. 688-702, Aug. 2018.
  14. H.-S. Lee and J.-W. Lee, "Task offloading in heterogeneous mobile cloud computing: Modeling, analysis, and cloudlet deployment," IEEE Access, Vol. 6, pp. 14908-14925, Mar. 2018.
  15. H.-S. Lee and J.-W. Lee, "QC2LinQ: QoS and channel-aware distributed link scheduler for D2D communication," IEEE Transactions on Wireless Communications, Vol. 15, No. 12, pp. 8565-8579, Dec. 2016.

International Conferences and Workshops

  1. H.-S. Lee, C. Shen, W. Zame, J.-W. Lee, and M. van der Schaar, "SDF-Bayes: Cautious optimism in safe dose-finding clinical trials with drug combinations and heterogeneous patient groups," to appear in AISTATS 2021.
  2. H.-S. Lee, Y. Zhang, W. Zame, C. Shen, J.-W. Lee, and M. van der Schaar, "Robust recursive partitioning for heterogeneous treatment effects with uncertainty quantification," in Proc. NeurIPS 2020, Dec. 2020.
  3. H.-S. Lee, C. Shen, J. Jordon, and M. van der Schaar, "Contextual constrained learning for dose-finding clinical trials," in Proc. AISTATS 2020, June 2020, Palermo, Italy.
  4. D.-H.Bae, H.-S. Lee, and J.-W. Lee, "Low Latency Uplink Transmission," in Proc. ICEIC 2017, Jan. 2017, Phuket, Thailand.
  5. H.-S. Lee, C. Tekin, M. van der Schaar, and J.-W. Lee, "Contextual learning for unit commitment with renewable energy sources," in Proc. IEEE GlobalSIP 2016, Dec. 2016, Washington DC, USA.
  6. H.-S. Lee and J.-W. Lee, "Energy cooperation and traffic management in cellular networks with renewable energy," in Proc. IEEE Globecom 2016, Dec. 2016, Washington DC, USA.
  7. H.-S. Lee and J.-W. Lee, "QoS and channel-aware distributed link scheduling for D2D communication," in Proc. WiOpt 2016, May 2016, Arizona, USA.
  8. H.-S. Lee, D.-H.Bae, and J.-W. Lee, "Energy or traffic: which one to transfer," in Proc. IEEE VTC 2015-Fall, Sept. 2015, Boston, USA.
  9. H.-S. Lee and J.-W. Lee, "QoS and channel-aware subchannel scheduling for D2D multicast communication," in Proc. ITC-CSCC 2015, June 2015, Seoul, Korea.

Domestic Journals

  1. D.-H. Bae, H.-S. Lee, J.-W. Lee, "Low latency uplink transmission scheme in mobile communication networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 1, Jan., 2017.

Patents

  1. E. Lee, H. Lee, D. Bae, J. Lee, and I. Byun, "Method for transmitting and receiving data in wireless communication system, and device therefor," U.S. Patent 10,609,696, Mar. 31, 2020
  2. H. Noh, J. Lee, Y. Kim, H. Lee, Y. Kwak, and S. Moon, "Interface device and method in wireless communication network," U.S. Patent 10,560,958, Feb. 11, 2020
  3. J.-W. Lee, D.-Y. Kim, H.-S. Lee, and K.-Y. Kim, "Apparatus and method for transmitting and receiving information and power in wireless communication system," U.S. Patent 10,411,764, Sep. 10, 2019
  4. K. Park, H. Lee, J. Lee, and H. Ko, "Method of performing scheduling for D2D multicast communication in wireless communication system and apparatus therefor," U.S. Patent 9,706,524, Jul. 11, 2017.
  5. J.-W. Lee and H.-S. Lee, "Apparatus and method for transmitting and receiving information and power in wireless communication system," U.S. Patent Application 16/727,354, Dec. 26, 2019
  6. J.-W. Lee, B.-H. Lee, S.-J. Moon, and H.-S. Lee, "Apparatus and method for random access in wireless communication system," U.S. Patent Application 16/565,812, Sep. 10, 2019

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