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AI로 인파 흐름 예측..."다중사고·감염병 위험 줄인다" 국내 연구진이 다중밀집사고를 예방하기 위해 인공지능을 활용해 인구 이동과 군중 밀집을 예측하는 기술을 개발했습니다. 대규모 축제나 행사, 도심 교통 혼잡 완화뿐만 아니라 감염병 확산 대응에도 활용될 것으로 기대됩니다. [링크] AI Predicts Crowd Flow: "Reducing Risks of Mass Accidents and Infectious Diseases" A Korean research team has developed an artificial intelligence technology that predicts population movement and crowd density to prevent mass crowd accidents. This technology is expected to be applied not only to managing large-scale fe

Prof. Lee and Dr. Hwanjun Song (jointly with Yuji Roh and Prof. Steven Whang) presented a tutorial on "Machine Learning Robustness,...

Our recent CIKM paper on adaptive batch selection for deep learning received media coverage in YTN as well as in many daily newspapers....

Our recent KDD paper on the prediction of inbound COVID-19 confirmed cases received considerable media coverage in KBS, YTN, MBC, and TJB...

Our work on parallel clustering was included in the spring issue of KAIST Breakthroughs (Biannual Engineering Magazine). #press

Prof. Lee appeared on KBS1 television for the Science Concert in Fourth Industrial Revolution. #press [YouTube link]

Hwanjun Song and Prof. Lee's KDD 2017 paper was introduced in Microsoft Research Blog. #press [Blog link]

Data Mining Lab was introduced in the summer issue of KAIST Vision (Quarterly KAIST Magazine). See pp. 22~25. #press

Our work on social network community detection was included in the spring issue of KAIST Breakthroughs (Biannual Engineering Magazine).

Our work on the location-based social search engine Glaucus was reported as one of this year's research results in KAIST Times. #press...

The Best Paper Award at AAAI ICWSM-13 received coverage in nine daily newspapers. #press News collection: