XAIENCE 2019

Speaker List

• 정 연준
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    Delfos: deep learning model for prediction of solvation free energies in generic organic solvents

• Jeongho Bang KIAS
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    Quantum Algorithms and Quantum Machine Learning

• Jung Hee Cheon Seoul National University
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    데이터의 프라이버시를 보존하는 기계학습 (Safe Machine Learning toward Private AI)

• Dongpyo Chi Seoul National University
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    Quantum AI, Quantum Machine Learning

• Won Sang Cho Seoul National University
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    복소 산술 신경망을 통한 물리적 기계학습모형의 건설과 물리적 불변량의 추출

• Yoo-Geun Ham Chonnam National University
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    Deep learning for multi-year ENSO forecasts

• Jungho Im UNIST
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    AI Applications for Satellite-based Disaster Monitoring and Prediction

• Doo Seok Jeong Hanynag University
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    Architecture of Neuromorphic Hardware with Embedded Learning

• Junghyo Jo Seoul National University
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    Information in Unsupervised Learning

• Sunghoon Jung Seoul National University
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    What Can We Learn with ML in Particle Physics, and How?

• beomchang kang
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    Prediction and Taking Insight of Molecular Quantum Property by Random Forest Regression

• Jung-Hoon Kim Seoul National University
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    Application of Artificial Intelligence techniques to Weather Forecasting

• Duk-jin Kim Seoul National University
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    Deep Learning for Sea Ice Classification and Ship Detection using Satellite Synthetic Aperture Radar

• Hyung Do Kim Seoul National University
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    Learning QCD Jet Flavors in search for Invisible Higgs Decays

• Yongdai Kim
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    Theoretical Advantages of Deep Neural Networks

• Ji-hoon Kim SNU
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    Machine Learning In Astrophysics: Estimating Galactic Baryonic Properties from Their Dark Matter

• Woo Youn Kim Chemistry, KAIST
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    AI-based Smart Molecular Design

• Dongha Kim
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    On Casting Importance Weighted Autoencoder to an EM Algorithm to Learn Deep Generative Models

• Junsu Ko © Arontier
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    Fleming : AI-driven Integrated Drug Discovery Platform

• Woong Kook Seoul National University
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    Harmonic Data Analysis for Shape and Centrality

• Sven Krippendorf Ludwig Maximilian University of Munich
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    Searching for Axions and new concepts in fundamental physics with Machine Learning

• Inah Lee Seoul National University
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    Hoping for Better AI: Advice from the Brain

• Junho Lee Seoul National University
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    Connectome Project & Neuroscience with AI

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    Welcome remarks on the 1st XAIENCE Conference

• Sanghoon Lee
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    Application of Neural Network Model to Predict and to Evaluate the Groundwater Level Fluctuation

• Juyong Lee
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    Discovering Novel Fluorescent Molecules by Combining Machine-learning and Global Optimization

• Hye-Lim Lee
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    Predicting location of suitable groundwater for brewing coffee using tree-based ensemble machine learning

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

• Ilsang Ohn
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    Nonconvex Sparse Regularization For Deep Neural Networks and its Optimal Property

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

• Chaok Seok Seoul National University
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    Prediction of Protein Structure and Interaction by Physics and Informatics

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

• Seok-Woo Son Seoul National University
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    Clustering weather maps for high PM10 events in Seoul using Self Organization Maps

• Joong-Ho Won Seoul National University
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    Projection onto Minkowski Sums with Application to Constrained Learning

• Hwidong Yoo Seoul National University
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    Recent Developments of Deep Learning in Experimental High Energy Physics

• Jaejun Yu Seoul National University
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    Exploration of energy surfaces and conformation of molecules and solids by utilizing a machine-learning technique