AI 원천기술연구 | 서울대학교AI연구원(AIIS)

연구

AI 원천기술연구

서울대학교 AI연구원은 AI의 난제를 해결할 수 있는 세계 최고 수준의 AI 원천기술 인력을
대규모로 보유하고 있는 국내 최대의 AI 연구기관입니다.

AI 플랫폼 (AI Platform)

머신러닝이 가능하기 위해서는 최적화된 고성능 플랫폼이 갖추어져 있어야 합니다.
AIIS에서는 인공지능의 학습을 가능하게 하는 최적화된 플랫폼을 연구하고 있습니다.

박성호경영대학 경영학과

  • 연구실/전공분야계량마케팅
  • 연구분야(AI 원천기술)AI Platform, Data Intelligence
  • 연구분야(X+AI)Online Advertising, Digital Marketing, Retailing

대표논문

Christopher, Ranjit, Sungho Park, Sang Pil Han, Min Kyu Kim (2022), "Bypassing Performance Optimizers of Real Time Bidding Systems in Display Ad Valuation," Information Systems Research, 33(2), 399-412.

Lee, Mi Hyun, Su Jung Kim, Sang-Hyeak Yoon, Sungho Park (2022), "An Integrative Approach to Determinants of Pre-Roll Ad Acceptance and Their Relative Impact: Evidence from Big Data," Journal of Advertising, 51(1), 76-84.

Son, Yoonseock, Wonseok Oh, San-Pil Han, Sungho Park (2020), “When Loyalty Goes Mobile: Effects of Mobile Loyalty Apps on Purchase, Redemption, and Competition,” Information Systems Research, 31(3), 835-847.

Park, Sungho, Elliot Rabinovich, Christopher Tang, Rui Yin (2020), “The Impact of Disclosing Inventory Scarcity Messages on Sales in Online Retailing,” Journal of Operations Management, 66(5), 534-552.

Han, Sang-Pil, Sungho Park, Wonseok Oh (2016), “Mobile App Analytics: A Multiple Discrete-Continuous Choice Framework,” MIS Quarterly, 40(4).

Park, Sungho and Sachin Gupta (2012), “Handling Endogenous Regressors by Joint Estimation Using Copulas,” Marketing Science, 31(4), 567-586.

임재현경영대학 경영학과

  • 연구실/전공분야Sustainable Operations Management, Supply Chain and Logistics Management, Data Analytics
  • 연구분야(AI 원천기술)Learning & Reasoning, AI Platform, Data Intelligence
  • 연구분야(X+AI)Commerce, Energy, Manufacturing, Logistics

대표논문

Optimal Ratcheting in Executive Compensation. Decision Analysis, accepted. with I. Hwang, Y. Kim.

Got Organic Milk? Joint Inventory Model with Supply Uncertainties and Partial Substitution. Operations Research Letters, 49(5), 2021. with D. Jeon, Z. Peng, Y. Rong.

Why Have Voluntary Time-of-Use Tariffs Fallen Short in the Residential Sector? Production & Operations Management, 29(3), 2020. with D.G. Choi, K. Murali, V. Thomas

Money Well Spent? Operations, Mainstreaming, and Fairness of Fair Trade. Production & Operations Management, 28(12), 2019. with H.Y. Mak, S.J. Park

The Effects of Ecolabels and Environmental Regulation on Green Product Development. Manufacturing & Service Operations Management, 21(3), 2019. with K. Murali, N.C. Petruzzi

Promoting Clean Technology Products: To Subsidize Products or Service Infrastructure? Service Science, 11(2), 2019. with G. Ma, H.Y. Mak, Z. Wan

Beyond the Speed-Price Tradeoff. MIT Sloan Management Review, 59(4), Summer, 2018. with J. Acimovic, H.Y. Mak

김진수공과대학 컴퓨터공학부

  • 연구실/전공분야시스템 소프트웨어 및 구조 연구실
  • 연구분야(AI 원천기술)AI Platform, AI Chip
  • 연구분야(X+AI)

대표논문

  • 연구실/전공분야생명정보 및 생물정보 연구실
  • 연구분야(AI 원천기술)Learning & Reasoning, AI Platform, Data Intelligence
  • 연구분야(X+AI)Bio, Medicine, Pharma

대표논문

PRISM: Methylation Pattern-based, Reference-free Inference of Subclonal Makeup (ISMB 2019, Bioinformatics)
mirTime: Identifying Condition-Specific Targets of MicroRNA in Time-series Transcript Data using Gaussian Process Model and Spherical Vector Clustering (Bioinformatics 2019)
GABA-stimulated adipose-derived stem cells suppress subcutaneous adipose inflammation in obesity (PNAS, 2019)
DeepFam: Deep learning based alignment-free method for protein family modeling and prediction (ISMB 2018, Bioinformatics)
Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification (IJCAI 2017)
과제명: 멀티오믹스 분석 알고리즘 및 플랫폼 개발
연구비 지원기관: 한국연구재단 (과학기술정보통신부)
연구수행 기간: 2014.11.01 - 2022.10.31 (96개월)
과제명: 실험 및 문헌정보 거대 복잡형 데이터 통합분석추론 연구
연구비 지원기관: 한국연구재단 (과학기술정보통신부)
연구기간: 2017.9.1 - 2020.12.31 (40개월)

이창건공과대학 컴퓨터공학부

  • 연구실/전공분야실시간 유비쿼터스 시스템 연구실
  • 연구분야(AI 원천기술)Robotics & Action, AI Platform, Autonomous Driving
  • 연구분야(X+AI)Manufacturing

대표논문

Kang-Wook Kim, Youngeun Cho, Jeongyoon Eo, Chang-Gun Lee, and Junghee Han, System-wide Time vs. Density Tradeoff in Real-Time Multicore Fluid Scheduling, in IEEE Transactions on Computers (TC), Vol. 67, Issue 7, pp. 7, July 2018.
Youngeun Cho, Do Hyung Kim, Daechul Park, Seungsu Lee, and Chang-Gun Lee, Conditionally Optimal Task Parallelization for Global EDF on Multi-core Systems, in IEEE Real-Time Systems Symposium (RTSS), Dec. 2019.
Kyoung-Soo We, Seunggon Kim, Wonseok Lee, and Chang-Gun Lee, Functionally and Temporally Correct Simulation of Cyber-Systems for Automotive Systems, in IEEE Real-Time Systems Symposium (RTSS), Paris, France, Dec. 2017.
Duhee Lee, Chang-Gun Lee, and Kanghee Kim, A Generic Framework for Soft Real-Time Program Executions on NAND Flash Memory in Multi-Tasking Embedded Systems, in 30th IEEE Real-Time Systems Symposium, Washington D.C., U.S., Dec. 2009.
E. Felemban, Chang-Gun Lee, E. Ekici, R. Boder, and S. Vural, Probabilistic QoS Guarantee in Reliability and Timeliness Domains in Wireless Sensor Networks, in IEEE INFOCOM, Vol. 4, Mar. 2005.
[2015. 03. ~ 2023. 02.] (SW Star Lab) Real-Time System SW on Multicore and GPGPU for Unmanned Vehicles, IITP
[2011. 07. ~ 2018. 10.] Development of Techniques for ECU-Level Real-Time Verification/Implementation, Hyundai/Kia Motors

전병곤공과대학 컴퓨터공학부

  • 연구실/전공분야소프트웨어 플랫폼 연구실
  • 연구분야(AI 원천기술)Learning & Reasoning, AI Platform, AI Chip
  • 연구분야(X+AI)

대표논문

Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs. NSDI 2019.
Parallax: Sparsity-aware Data Parallel Training of Deep Neural Networks. EuroSys 2019.
Apache Nemo: A Framework for Building Distributed Dataflow Optimization Policies. ATC 2019.
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems. OSDI 2018.
Improving the Expressiveness of Deep Learning Frameworks with Recursion. EuroSys 2018.
(SW 스타랩) 다양한 분석을 고속 수행하는 단일화된 빅데이터 스택 개발
[뉴럴 프로세싱 시스템 연구/17세부] 대규모 클러스터에서 딥러닝 학습을 자동 분산하는 시스템
비디오 튜링 테스트를 통과할 수준의 비디오 스토리 이해 기반의 질의응답 기술 개발

강유공과대학 컴퓨터공학부

  • 연구실/전공분야데이터 마이닝 연구실
  • 연구분야(AI 원천기술)Learning & Reasoning, AI Platform, Data Intelligence
  • 연구분야(X+AI)Finance, Commerce, Manufacturing

대표논문

Jaemin Yoo, Minyong Cho, Taebum Kim, and U Kang, Knowledge Extraction with No Observable Data, NeurIPS 2019, Vancouver, Canada.
Jaemin Yoo, Hyunsik Jeon, and U Kang, Belief Propagation Network for Hard Inductive Semi-supervised Learning, 28th International Joint Conference on Artificial Intelligence (IJCAI) 2019, Macao, China.
Junghwan Kim, Haekyu Park, Ji-Eun Lee, and U Kang, SIDE: Representation Learning in Signed Directed Networks, The Web Conference (WWW) 2018, Lyon, France.
Minji Yoon, Woojeong Jin, and U Kang, Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees, The Web Conference (WWW) 2018, Lyon, France.
Jun-gi Jang, Dongjin Choi, Jinhong Jung, and U Kang, Zoom-SVD: Fast and Memory Efficient Method for Extracting Key Patterns in an Arbitrary Time Range, ACM International Conference on Information and Knowledge Management (CIKM) 2018, Lingotto, Turin, Italy.
초고속 텐서 스트림 분석을 통한 실시간 경량 다차원 데이터 마이닝, 과학기술정보통신부, 2019 - 2022
시청이력기반 콘텐츠 추천 기술, SK Telecom, 2018
Real-time Anomaly Detection in High-Speed Time-evolving Graphs, AOARD, 2017 - 2018

이재욱공과대학 컴퓨터공학부

  • 연구실/전공분야아키텍처 및 코드 최적화 연구실
  • 연구분야(AI 원천기술)AI Platform, AI Chip
  • 연구분야(X+AI)

대표논문

Tae Jun Ham, Seonghak Kim, and Sung Jun Jung, Young H. Oh, Yeonhong Park, Yoon Ho Song, Junghoon Park, Sanghee Lee, Kyoung Park, Jae W. Lee, and Deog-Kyoon Jeong, "A3: Accelerating Neural Network Attention Mechanism with Approximation", 26th IEEE International Symposium on High Performance Computer Architecture (HPCA), San Diego, California, February 2020.
Shine Kim, Jonghyun Bae, Hakbeom Jang, Wenjing Jin, Jeonghun Gong, Seungyeon Lee, Tae Jun Ham, and Jae W. Lee, "SSDStreamer: Specializing I/O Stack for Large-Scale Machine Learning", IEEE Micro, September/October 2019.
Young H. Oh, Quan Quan, Daeyeon Kim, Seonghak Kim, Jun Heo, Jaeyoung Jang, Sung Jun Jung, and Jae W. Lee, "A Portable, Automatic Data Quantizer for Deep Neural Networks", IEEE International Conference on Parallel Architectures and Compilation Techniques (PACT-27), Limassol, Cyprus, November 2018.
Channoh Kim, Jaehyeok Kim, Sungmin Kim, Dooyoung Kim, Namho Kim, Gitae Na, Young H. Oh, Hyeon Gyu Cho, and Jae W. Lee, "Typed Architectures: Architectural Support for Lightweight Scripting", 22nd ACM Architectural Support for Programming Languages and Operating Systems (ASPLOS), Xi'an, China, April 2017.
Doo Young Kim, Jin Min Kim, Hakbeom Jang, Jinkyu Jeong, and Jae W. Lee, "A Neural Network Accelerator for Mobile Application Processors", IEEE Transactions on Consumer Electronics, 61(4), November 2015.
NAND 플래시 기반 심층신경망 학습 시스템, 연구재단, 2020.3-2023.2
뉴럴 프로세싱 시스템 연구, 삼성전자, 2017.11-2020.10
Beyond Limit, 삼성전자, 2018.11-2021.10

제원호자연과학대학 물리천문학부

  • 연구실/전공분야원자물리 및 광학실험
  • 연구분야(AI 원천기술)Learning & Reasoning, AI Platform, Data Intelligence
  • 연구분야(X+AI)Manufacturing, AI-based instruments

대표논문

Interfacial thermodynamics of spherical nanodroplets: Molecular understanding of surface tension via hydrogen bond network
GCIceNet: A Graph Convolution Network For Deep Learning Of Ice Phases
AI-based atomic force microscopy
리더연구과제 0차원 나노플루이딕스

전동석융합과학기술대학원 지능정보융합학과

  • 연구실/전공분야Mobile Multimedia Systems Group
  • 연구분야(AI 원천기술)AI Platform, AI Chip
  • 연구분야(X+AI)Bio, Brain

대표논문

J. Park, J. Lee, and D. Jeon, “A 65-nm Neuromorphic Image Classification Processor With Energy-Efficient Training Through Direct Spike-Only Feedback,” IEEE Journal of Solid-State Circuits (JSSC), 2020.
S. Moon, K. Shin, and D. Jeon, “Enhancing Reliability of Analog Neural Network Processors,” IEEE Transactions on VLSI Systems (TVLSI), 2019.
J. Park, Y. Kwon, Y. Park, and D. Jeon, “Microarchitecture-Aware Code Generation for Deep Learning on Single-ISA Heterogeneous Multi-Core Mobile Processors,” IEEE Access, 2019.
D. Jeon, Q. Dong, Y. Kim, X. Wang, S. Chen, H. Yu, D. Blaauw, and D. Sylvester, “A 23-mW Face Recognition Processor with Mostly-Read 5T Memory in 40-nm CMOS,” IEEE Journal of Solid-State Circuits (JSSC), 2017.
D. Jeon, N. Ickes, P. Raina, H.-C. Wang, and A. P. Chandrakasan, “A 0.6V, 8mW 3D Vision Processor for a Navigation Device for the Visually Impaired,” IEEE International Solid-State Circuits Conference (ISSCC), 2016.
자가 학습이 가능한 초저전력 혼성신호 뉴로모픽 프로세서 설계, 과학기술정보통신부, 2019~2022.
고효율 딥러닝 하드웨어 가속기 개발, 한국과학기술연구원, 2019~2021.
모바일 시스템을 위한 저전력 머신 러닝 하드웨어 가속기 개발, 과학기술정보통신부, 2016~2019.

김장우공과대학 전기정보공학부

  • 연구실/전공분야고성능 컴퓨터 시스템 연구실
  • 연구분야(AI 원천기술)AI Platform, AI Chip
  • 연구분야(X+AI)Brain, Medicine, Energy

대표논문

"FlexLearn: Fast and Highly Efficient Brain Simulations Using Flexible On-Chip Learning", Eunjin Baek, Hunjun Lee, Youngsok Kim, and Jangwoo Kim, ACM/IEEE International Symposium on Microarchitecture (MICRO), Oct 2019
"MnnFast: A Fast and Scalable System Architecture for Memory-Augmented Neural Networks", Hanhwi Jang, Joonsung Kim, Jae-Eon Jo, Jaewon Lee, and Jangwoo Kim, ACM/IEEE International Symposium on Computer Architecture (ISCA), Jun 2019
"Flexon: A Flexible Digital Neuron for Efficient Spiking Neural Network Simulations"
Dayeol Lee, Gwangmu Lee, Dongup Kwon, Sunghwa Lee, Youngsok Kim, and Jangwoo Kim, ACM/IEEE International Symposium on Computer Architecture (ISCA), Jun 2018
"μLayer: Low Latency On-Device Inference Using Cooperative Single-Layer Acceleration and Processor-Friendly Quantization", Youngsok Kim, Joonsung Kim, Dongju Chae, Daehyun Kim, and Jangwoo Kim, ACM European Conference on Computer Systems (EuroSys), Mar 2019
"FIDR: A Scalable Storage System for Fine-Grain Inline Data Reduction with Efficient Memory Handling", Mohammadamin Ajdari, Wonsik Lee, Pyeongsu Park, Joonsung Kim, and Jangwoo Kim, ACM/IEEE International Symposium on Microarchitecture (MICRO), Oct 2019
인공지능 가상머신: 이종 인공지능의 동시, 고속, 독립 실행을 위한 컴퓨터 구조/삼성미래기술육성사업/2019.6-2022.5
이종 스파이크 뉴런 기반의 인간 두뇌 규모 시뮬레이션을 위한 프로세서 및 시스템 개발/한국연구재단/2017.3-2021.2
대규모 뉴럴 프로세싱을 위한 FPGA 기반의 확장형 시스템 개발/삼성전자/2017.11-2020.10

Bernhard Egger공과대학 컴퓨터공학부

  • 연구실/전공분야컴퓨터 시스템 및 플랫폼 연구실
  • 연구분야(AI 원천기술)Learning & Reasoning, AI Platform, AI Chip
  • 연구분야(X+AI)

대표논문

Barend Harris, Inpyo Bae, and Bernhard Egger. "Architectures and algorithms for on-device user customization of CNNs." In Integration, the VLSI Journal, Volume 67, July 2019.
Inpyo Bae, Barend Harris, Hyemi Min, and Bernhard Egger. "Auto-Tuning CNNs for Coarse-Grained Reconfigurable Array-based Accelerators." Presented at the 2018 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES'18) and in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), Volume 37, Issue, 11; November 2018.
Younghyun Cho, Surim Oh, and Bernhard Egger. "Performance Modeling of Parallel Loops on Multi-Socket Platforms using Queueing Systems." In IEEE Transactions on Parallel and Distributed Systems (TPDS), Volume 31, Issue 2; February 2020.
Younghyun Cho, Camilo A.C. Guzman, and Bernhard Egger. "Maximizing System Utilization via Parallelism Management for Co-Located Parallel Applications." In Proceedings of the the 2018 International Conference on Parallel Architectures and Compilation (PACT'18), Limassol, Cyprus, November 2018.
Changyeon Jo, Youngsu Cho, and Bernhard Egger. "A Machine Learning Approach to Live Migration Modeling." In Proceedings of the 2017 ACM Symposium on Cloud Computing (SoCC'17), Santa Clara, USA, September 2017.
Efficient Mapping and Scheduling of Resource and Dataflow for NPU Architecture Search, 삼성전자, 2020
Exploring the Effect of Data Compression on Runtime and Accuracy of DNNs, SK텔레콤, 2018-2020
H/W–컴파일러 수직적 통합 최적화된 임베디드 DNN 프로세서연구, 삼성전자, 2017-2020

윤성로공과대학 전기정보공학부

  • 연구실/전공분야인공지능 연구실
  • 연구분야(AI 원천기술)Learning & Reasoning, Vision & Perception, Language & Cognition, AI Platform, AI Chip, Data Intelligence, AI Security
  • 연구분야(X+AI)Bio, Medicine, Pharma, Finance, Manufacturing, Energy

대표논문

이광근공과대학 컴퓨터공학부

  • 연구실/전공분야프로그래밍 연구실
  • 연구분야(AI 원천기술)AI Platform, AI Security, ai verification, security, safety
  • 연구분야(X+AI)

대표논문

[book] Introduction to Static Analysis: an abstract interpretation perspective, MIT Press, 2020
Adaptive Static Analysis via Learning with Bayesian Optimization, TOPLAS 40(4), no.14, 2018
Global Sparse Analysis Framework, TOPLAS 36(3), no.8, 2014
Optimizing Homomorphic Evaluation Circuits by Program Synthesis and Term Rewriting, PLDI 2020
Selective Context-Sensitivity Guided by Impact Pre-Analysis, PLDI 2014
과기부 연구재단 선도연구센터(ERC), 소프트웨어무결점 연구센터, 센터장, 2008-2015
과기부 연구재단 창의연구단(CRI), 프로그램분석시스템 연구단, 연구단장, 1998-2003

정진호의과대학 피부과학교실

  • 연구실/전공분야피부면역학, 피부노화학, 광생물학, 노인피부과
  • 연구분야(AI 원천기술)Learning & Reasoning, AI Platform, Data Intelligence
  • 연구분야(X+AI)Bio, Humanities/Social Sciences, Medicine

대표논문

유승주공과대학 컴퓨터공학부

  • 연구실/전공분야컴퓨터 구조 메모리 연구소
  • 연구분야(AI 원천기술)AI Platform
  • 연구분야(X+AI)

대표논문

최현용자연과학대학 물리천문학부

  • 연구실/전공분야Ultrafast Quantum Photonics Laboratory
  • 연구분야(AI 원천기술)AI Platform
  • 연구분야(X+AI)Bio, Energy, Optical system, materials sciences

대표논문

Nano Letters 19, 7464−7469 (2019)
Nature Nanotechnology 13, 910-914 (2018)
Nature Communications 9, 351 (2018)
Nano Letters 18, 734 (2018)
Nature Communications 7, 13569 (2016)

이상형의과대학 신경외과학교실

  • 연구실/전공분야뇌종양, 경동맥질환, 퇴행성뇌질환
  • 연구분야(AI 원천기술)Learning & Reasoning, AI Platform, Human-AI Interaction, Data Intelligence
  • 연구분야(X+AI)Brain, Medicine, Pharma

대표논문

하순회공과대학 컴퓨터공학부

  • 연구실/전공분야통합설계 및 병렬 처리 연구실
  • 연구분야(AI 원천기술)AI Platform, AI Chip
  • 연구분야(X+AI)embedded systems, electronic systems

대표논문

" Tensor Virtualization Technique to Support Efficient Data Reorganization for CNN Accelerators," DAC 2020 (to appear)
"A Novel CNN(Convolutional Neural Network) Accelerator That Enables Fully-pipelined Execution of Layers," ICCD 2019
"Fast Performance Estimation and Design Space Exploration of Manycore-based Neural Processors," DAC 2019
"C-GOOD: C-code Generation Framework for Optimized On-device Deep Learning," ICCAD 2018
"Joint Optimization of Speed, Accuracy, and Energy for Embedded Image Recognition Systems," DATE 2018
단말용 뉴럴 프로세서 시뮬레이션 및 소프트웨어 최적화 기술,  삼성종합기술원, 2016. 12 - 2020. 10
MIDAP (Memory-In-the DAtapath Processor) 뉴럴 프로세서의 성능 개선 연구, 삼성전자, 2018.5 - 2020.4
이종 하드웨어 가속기를 포함하는 모바일 플랫폼을 위한 시스템 수준의 딥 러닝 추론 최적화 기법, 삼성전자, 2018.3 - 2018.12

김홍기치의학대학원 치의학과

  • 연구실/전공분야의료정보학
  • 연구분야(AI 원천기술)Language & Cognition, AI Platform, Data Intelligence
  • 연구분야(X+AI)Bio, Humanities/Social Sciences, Medicine

대표논문

Multi-channel PINN: investigating scalable and transferable neural networks for drug discovery. Journal of Chemical Informatics 11.1 (2019): 46
Cognitive Profiling Related to Cerebral Amyloid Beta Burden Using Machine Learning Approaches. Frontiers in Aging Neuroscience. 10.3389 (2019)
The Disturbance in Dynamic Property in the Reconstructed State Space during Nitrous Oxide Administration. Neuroreport 30.3(2019):162–68
Differential Diagnosis of Jaw Pain using Informatics Technology. Journal of Oral Rehabilitation (2018)
A dynamic and parallel approach for repetitive prime labeling of XML with MapReduce, The Journal of Supercomputing, 73:2 (2017)

박건수공과대학 산업공학과

  • 연구실/전공분야Operations Management, Inventory control, Global supply chain
  • 연구분야(AI 원천기술)Learning & Reasoning, AI Platform, Data Intelligence
  • 연구분야(X+AI)Finance, Logistics, Manufacturing

대표논문

안정호융합과학기술대학원 지능정보융합학과

  • 연구실/전공분야SCALable Computer Architecture Laboratory
  • 연구분야(AI 원천기술)AI Platform, AI Chip
  • 연구분야(X+AI)

대표논문

"Partitioning Compute Units in CNN Acceleration for Statistical Memory Traffic Shaping,"  D. Jung, S. Lee, W. Rhee, and J. Ahn, IEEE Computer Architecture Letters, Vol. 17, No. 1, 2018
뉴럴 프로세싱 시스템 연구, 삼성전자, 2017/11-2020/10
복합 심층학습 응용분야를 위한 가속기 구조 연구, 삼성미래기술육성재단, 2017/12-2019/11

정현훈의과대학 산부인과학교실

  • 연구실/전공분야산부인과
  • 연구분야(AI 원천기술)AI Platform, Human-AI Interaction
  • 연구분야(X+AI)Medicine

대표논문

허성욱법학전문대학원 법학과

  • 연구실/전공분야규제행정법/환경법/에너지법/법경제학/개인정보보호
  • 연구분야(AI 원천기술)AI Platform, Data Intelligence, AI Law & Ethics
  • 연구분야(X+AI)Arts, Humanities/Social Sciences, Energy

대표논문

한국에서 빅데이터를 둘러싼 법적 쟁점과 제도적 과제 경제규제와 법 서울대학교 법학연구소 단독  20141
스마트그리드와 개인정보보호 법정책 개인정보보호의 법과 정책 박영사 단독  201405
공법이론과 공공정책 (II) - 공공선택이론 관점에서 본 행정재판의 역할 - 공법연구 한국공법학회 단독  201812

이영기공과대학 컴퓨터공학부

  • 연구실/전공분야인간 중심 컴퓨터 시스템 연구실
  • 연구분야(AI 원천기술)AI Platform, Human-AI Interaction
  • 연구분야(X+AI)Humanities/Social Sciences, Commerce, Healthcare, Education

대표논문

서교국제농업기술대학원 그린에코시스템공학전공

  • 연구실/전공분야Green Resources Engineering Center
  • 연구분야(AI 원천기술)AI Platform, Data Intelligence
  • 연구분야(X+AI)Humanities/Social Sciences, Logistics, agriculture

대표논문

A Comparative Analysis of the Environmental Benefits of Drone-Based Delivery Services in Urban and Rural Areas
Leveraging Socially Networked Mobile ICT Platforms for the Last-Mile Delivery Problem
공간빅데이터 기반의 농산물 스마트 로지스틱스 파일럿 시스템 구축

심병효공과대학 전기정보공학부

  • 연구실/전공분야정보시스템 연구실
  • 연구분야(AI 원천기술)Learning & Reasoning, AI Platform, Data Intelligence
  • 연구분야(X+AI)Finance, Manufacturing, Wireless Communications

대표논문

W. Kim, Y. Ahn and B. Shim, "Deep Neural Network Based Active User Detection for Grant-free NOMA Systems," IEEE Transactions on Communications, 2020.
W. Kim, H. Ji, H. Lee, Y. Kim, J. Lee and B. Shim, "Sparse Vector Transmission: An Idea Whose Time Has Come," IEEE Vehicular Technology Magazine, 2020.
L. Nguyen, J. Kim and B. Shim, "Low-Rank Matrix Completion: A Contemporary Survey," IEEE Access, vol. 7, no. 1, pp. 94215-94237, Jul. 2019.
H. Ji, S. Park, J. Yeo, Y. Kim, J. Lee and B. Shim, "Ultra Reliable and Low Latency Communications in 5G Downlink: Physical Layer Aspects," IEEE Wireless Communications, vol. 25, no. 3, pp. 124-130 , June. 2018.
J. Choi, B. Shim, Y. Ding, B. Rao and D. Kim, "Compressed sensing for wireless communications: useful tips and tricks," IEEE Communications Surveys and Tutorials, vol. 19, no. 3, pp. 1527-1550, 2017.

염헌영공과대학 컴퓨터공학부

  • 연구실/전공분야분산 시스템 연구실
  • 연구분야(AI 원천기술)AI Platform, Data Intelligence
  • 연구분야(X+AI)Logistics, Manufacturing

대표논문

허충길공과대학 컴퓨터공학부

  • 연구실/전공분야소프트웨어 원리 연구실
  • 연구분야(AI 원천기술)AI Platform, AI Security
  • 연구분야(X+AI)Medicine

대표논문