Name: XAIENCE 2019
Start: 2019-11-07T08:00:00+09:00
End: 2019-11-08T20:00:00+09:00
Location: Seoul National University, College of Natural Sciences, Building 25-1

XAIENCE 2019

from Thursday, 7 November 2019 (08:00) to Friday, 8 November 2019 (20:00)

Monday, 4 November 2019
Tuesday, 5 November 2019
Wednesday, 6 November 2019
Thursday, 7 November 2019

08:00 Registration and reception
Registration and reception
08:00 - 09:00
Room: 국제회의실 (International Conference Hall)
09:00
09:00 - 09:20
Room: 국제회의실 (International Conference Hall)
Contributions

09:00 Welcome remarks on the 1st XAIENCE Conference - Junho Lee (Seoul National University)
09:20
09:20 - 10:00
Room: 국제회의실 (International Conference Hall)
Contributions

09:20 Theoretical Advantages of Deep Neural Networks - Yongdai Kim (Seoul National University)

09:40 Projection onto Minkowski Sums with Application to Constrained Learning - Joong-Ho Won (Seoul National University)
10:00 Coffee break
Coffee break
10:00 - 10:20
Room: 국제회의실 (International Conference Hall)
10:20
10:20 - 11:50
Room: 국제회의실 (International Conference Hall)
Contributions

10:20 Lessons Learned from Running Interdisciplinary Computational Sciences Program for Undergraduate at Seoul National University on the Future of Scientific Computing Education - Sang-Mook Lee (Seoul National University)

10:50 Deep Learning for Sea Ice Classification and Ship Detection using Satellite Synthetic Aperture Radar - Duk-jin Kim (Seoul National University)

11:10 Deep learning for multi-year ENSO forecasts - Yoo-Geun Ham (Chonnam National University)

11:30 AI Applications for Satellite-based Disaster Monitoring and Prediction - Jungho Im (UNIST)
11:50 Lunch
Lunch
11:50 - 13:30
13:30
13:30 - 14:30
Room: 국제회의실 (International Conference Hall)
Contributions

13:30 Prediction of Protein Structure and Interaction by Physics and Informatics - Chaok Seok (Seoul National University)

13:50 Discovering Novel Fluorescent Molecules by Combining Machine-learning and Global Optimization - Juyong Lee (Kangwon National University)

14:10 Fleming : AI-driven Integrated Drug Discovery Platform - Junsu Ko (© Arontier)
14:30 Coffee break
Coffee break
14:30 - 14:50
Room: 국제회의실 (International Conference Hall)
14:50
14:50 - 15:30
Room: 국제회의실 (International Conference Hall)
Contributions

14:50 Application of Machine Learning for Big Data Analysis in Astronomy (천문학 자료분석에서의 기계학습의 활용) - Min-Su Shin (KASI)

15:10 Machine Learning In Astrophysics: Estimating Galactic Baryonic Properties from Their Dark Matter - Ji-hoon Kim (SNU)
15:30 Coffee break
Coffee break
15:30 - 15:45
Room: 국제회의실 (International Conference Hall)
15:45
15:45 - 17:15
Room: 국제회의실 (International Conference Hall)
Contributions

15:45 What Can We Learn with ML in Particle Physics, and How? - Sunghoon Jung (Seoul National University)

16:05 Searching for Axions and new concepts in fundamental physics with Machine Learning - Sven Krippendorf (Ludwig Maximilian University of Munich)

16:35 Learning QCD Jet Flavors in search for Invisible Higgs Decays - Hyung Do Kim (Seoul National University)

16:55 Recent Developments of Deep Learning in Experimental High Energy Physics - Hwidong Yoo (Seoul National University)
17:15
17:15 - 17:45
Room: 국제회의실 (International Conference Hall)
Contributions

17:15 On Casting Importance Weighted Autoencoder to an EM Algorithm to Learn Deep Generative Models - Dongha Kim (Seoul National University)

17:30 Nonconvex Sparse Regularization For Deep Neural Networks and its Optimal Property - Ilsang Ohn (Department of Statistics, Seoul National University)
17:45 Banquet
Banquet
17:45 - 19:00
Room: 국제회의실 로비 (Lobby of International Conference Hall)
Friday, 8 November 2019

08:00 Registration and reception
Registration and reception
08:00 - 09:00
Room: 국제회의실 (International Conference Hall)
09:00
09:00 - 09:40
Room: 국제회의실 (International Conference Hall)
Contributions

09:00 Connectome Project & Neuroscience with AI - Junho Lee (Seoul National University)

09:20 Hoping for Better AI: Advice from the Brain - Inah Lee (Seoul National University)
09:40 Coffee break
Coffee break
09:40 - 10:00
Room: 국제회의실 (International Conference Hall)
10:00
10:00 - 11:00
Room: 국제회의실 (International Conference Hall)
Contributions

10:00 데이터의 프라이버시를 보존하는 기계학습 (Safe Machine Learning toward Private AI) - Jung Hee Cheon (Seoul National University)

10:20 Quantum AI, Quantum Machine Learning - Dongpyo Chi (Seoul National University)

10:40 Harmonic Data Analysis for Shape and Centrality - Woong Kook (Seoul National University)
11:00
11:00 - 11:40
Room: 국제회의실 (International Conference Hall)
Contributions

11:00 Application of Artificial Intelligence techniques to Weather Forecasting - Jung-Hoon Kim (Seoul National University)

11:20 Clustering weather maps for high PM10 events in Seoul using Self Organization Maps - Seok-Woo Son (Seoul National University)
11:40 Lunch
Lunch
11:40 - 13:00
13:00
13:00 - 13:40
Room: 국제회의실 (International Conference Hall)
Contributions

13:00 Delfos: deep learning model for prediction of solvation free energies in generic organic solvents - YounJoun Jung (Seoul National University)

13:20 AI-based Smart Molecular Design - Woo Youn Kim (Chemistry, KAIST)
13:40 Coffee break
Coffee break
13:40 - 14:00
Room: 국제회의실 (International Conference Hall)
14:00
14:00 - 15:20
Room: 국제회의실 (International Conference Hall)
Contributions

14:00 Architecture of Neuromorphic Hardware with Embedded Learning - Doo Seok Jeong (Hanynag University)

14:20 Information in Unsupervised Learning - Junghyo Jo (Seoul National University)

14:40 Exploration of energy surfaces and conformation of molecules and solids by utilizing a machine-learning technique - Jaejun Yu (Seoul National University)

15:00 Quantum Algorithms and Quantum Machine Learning - Jeongho Bang (KIAS)
15:20 Coffee break
Coffee break
15:20 - 15:40
Room: 국제회의실 (International Conference Hall)
15:40
15:40 - 16:55
Room: 국제회의실 (International Conference Hall)
Contributions

15:40 Probing Trilinear Higgs Self–coupling at the HL-LHC with Machine Learning - Jubin Park (Institute for Universe and Elementary Particles, Chonnam National University)

15:55 Prediction and Taking Insight of Molecular Quantum Property by Random Forest Regression - Beomchang Kang (Seoul National University)

16:10 Predicting location of suitable groundwater for brewing coffee using tree-based ensemble machine learning - Hye-Lim Lee (School of Earth and Environmental Sciences, Seoul National University)

16:25 Application of Neural Network Model to Predict and to Evaluate the Groundwater Level Fluctuation - Sanghoon Lee (Seoul National University)

16:40 복소 산술 신경망을 통한 물리적 기계학습모형의 건설과 물리적 불변량의 추출 - Won Sang Cho (Seoul National University)
16:55
16:55 - 17:20
Room: 국제회의실 (International Conference Hall)
Contributions

16:55 Adjourn