Yung-Kyun Noh

Work address:

Department of Mechanical and Aerospace Engineering,

Seoul National University,

Gwanak-gu, Gwanak-ro 1, Seoul 08826,

Republic of Korea

Phone: +82-2-880-7149

E-mail: nohyung@snu.ac.kr

 

I am currently a BK assistant professor in the department of Mechanical and Aerospace Engineering at Seoul National University (SNU). My research interests are metric learning and dimensionality reduction in machine learning, and I am especially interested in applying statistical theory of nearest neighbors to real and large datasets. I received B.S. in Physics from POSTECH and Ph.D. in Computer Science from Interdisciplinary Program in Cognitive Science at SNU. I was a postdoctoral fellow in the same department I am now affiliated with at SNU and a research professor in the department of Computer Science at KAIST. I was a visiting scholar in the Sugiyama Lab at the Tokyo Institute of Technology and worked with Prof. Masashi Sugiyama and in the GRASP Robotics Laboratory at the University of Pennsylvania where I worked with Prof. Daniel D. Lee.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Education


August 2007 ~           General Robotics, Automation, Sensing, Perception (GRASP) Lab. University of Pennsylvania,

July 2012                   Philadelphia, U.S.A.

                                   Visiting Scholar

                                   Research titles:

                                                   - Generative local metric learning for nearest neighbor classification

                                                   - Information estimation using nearest neighbors

                                                   - Unification of cognitive science models and machine learning algorithms

                                                   - Physics-based model for dimensionality reduction

                                                   - Implementation of learning algorithm using biology molecules

                                   Advisor: Prof. Daniel D. Lee

 

September 2001 ~      Biointelligence (BI) Lab. Seoul National University, Seoul, Korea

August 2011              Ph.D. degree in Computer Science (Interdisciplinary Program in Cognitive Science)

                                   Dissertation: Generative metric learning and dimensionality reduction with f-divergences

                                   Advisor: Prof. Byoung-Tak Zhang

 

March 1994 ~            Vacuum Physics Lab. (VPL) Pohang University of Science and Technology (POSTECH), Pohang,

February 1998           Korea

                                   B.S. degree in Physics

                                   Dissertation: The Effect of Mechanical Polishing on Gas Emission from Surface in Vacuum

                                   Advisor: Prof. Sukmin Chung

 

 

Short Term Education


July 2006 ~                Machine Learning Summer School (MLSS), National Taiwan University of Sci & Tech, Taiwan

August 2006              About Statistical Machine Learning theory and applications

                                   http://www.mlss.cc

                                   Participated with fellowship

 

August 2005              Institute for Pure & Applied Mathematics (IPAM), UCLA, USA

                                   Intelligent Extraction of Information from Graphs and High Dimensional Data

                                   http://www.ipam.ucla.edu

                                   Participated with fellowship

 

September 2004         Machine Learning Summer School (MLSS), Berder, France

                                   About Statistical Machine Learning theory and applications

                                   http://www.mlss.cc

                                   Participated with fellowship

 

Work Experience


January 2015 ~          Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Korea

Present                       BK Contract Assistant Professor (working with Prof. Frank Chongwoo Park)

 

April 2013 ~              Artificial Intelligence Pattern Recognition (AiPR) Lab. KAIST, Daejeon, Korea

December 2014          Research Professor (worked with Prof. Kee-Eung Kim)

 

October 2013 &        Sugiyama Laboratory (Machine Learning Lab.), Tokyo Institute of Technology, Tokyo, Japan

July 2014                   Visiting Researcher (worked with Prof. Masashi Sugiyama)

 

September 2011 ~      Robotics Lab. Seoul National University, Seoul, Korea

February 2013           Postdoctoral Fellow (worked with Prof. Frank Chongwoo Park)

 

June 2010 ~               Samsung SDS, Seoul, Korea

August 2010              Internship, Healthcare service group, Incubation center

                                   Gene expression data analysis for biological module selection

 

March 1998 ~            MPC Co., Ltd., Seoul, Korea (Marketing company)

June 2003                  Sectional chief:

-          Data consulting and system integration for many companies (Shinhan bank, Prudential life insurance Korea, Kyobo life insurance, SK DtoD, Daekyo Zibro, and others)

                                   Project manager:

-          Natural language processing

-          Motorola, Inc., Korea, Automatic ERMS (E-mail Response Management System) (2001)

-          ERMS development with KAIST, Korea. (Prof. Jong Cheol Park) (2000~2001)

 

 

Teaching


Fall 2015                    Machine Learning and Robotics, Seoul National University

                                      – M2794.009700 Machine Learning and Robotics

 

Summer 2015            Machine Learning, LG Electronics

                                      – 10 machine learning lecture series for Engineers.

 

Spring 2015               Engineering Mathematics 1, Seoul National University

                                      – 033.014-002 Engineering Mathematics 1

                                      – Solving differential equation for undergraduate students

 

Fall 2014                    Machine Learning and Stochastic Estimation in Robotics, Seoul National University

                                      – M2794.0057 Advanced Topics in Dynamics, Control, and Robotics

                                      – Co-teaching with Prof. Frank Chongwoo Park

 

Spring 2014               Software Graduate Program, KAIST

                                      – SEP.532 Machine Learning and Data Mining

                                      – Co-teaching with Prof. Kee-Eung Kim

 

February 2014           Pattern Recognition and Machine Learning Winter School (PRMLWS), Yonsei University

                                      – Title: Supervised Learning (2-hour lecture among 12 lecture series in 3-day winter school), http://prml.yonsei.ac.kr/

                                      Size of audience: Over 400

 

Fall 2013                    Department of Computer Science, KAIST

                                      – CS.774 Advanced Topic in Artificial Intelligence – Advanced Concepts in Machine Learning

                                      – Co-teaching with Prof. Kee-Eung Kim

                                      Student evaluation: 4.21/5.00 (Dept. Avg: 4.13, College Avg: 4.16)

 

Fall 2013                    Software Graduate Program, KAIST

                                      – SEP.544 Internet Service and Infra

                                      – Co-teaching with Prof. Kee-Eung Kim

                                      Student evaluation: 4.60/5.00 (Dept. Avg: 4.21, College Avg: 4.16)

 

Spring 2013               School of Mechanical and Aerospace Engineering, Seoul National University

                                      – 446.672 Machine Learning and Robotics

                                      Student evaluation: 4.23/5.00 (Dept. Avg: 4.21, College Avg: 4.22)

 

February 2013           Pattern Recognition and Machine Learning Winter School (PRMLWS), Seoul National University

                                      – Title: Nearest Neighbor Methods (2-hour lecture among 10 lecture series in 3-day winter school), http://bi.snu.ac.kr/PRMLWS2013/

                                      Size of audience: Over 400

 

Fall 2012                    Interdisciplinary Program in Cognitive Science, Seoul National University

                                      – 132.650 Studies in Artificial Intelligence and Cognitive Processes

                                      Student evaluation: 4.63/5.00 (Dept. Avg: 4.57, College Avg: 4.44)

 

Spring 2012               School of Mechanical and Aerospace Engineering, Seoul National University

                                      – 446.672 Machine Learning and Robotics

                                      Student evaluation: 4.45/5.00 (Dept. Avg: 4.20, College Avg: 4.22)

 

February 2012           Pattern Recognition and Machine Learning Winter School (PRMLWS), Seoul National University

                                      – Title: Supervised Learning (2-hour lecture among 10 lecture series in 3-day winter school), http://bi.snu.ac.kr/PRMLWS2012/

                                      Size of audience: Over 300

 

 

Research Interests


                                   Addressing theoretical and statistical issues in machine learning for big data analysis

                                   Exploiting nearest neighbor information for big data applications – metric learning and information estimation for classification, feature selection, and feature extraction

                                   Conducting theoretical study of nearest neighbor information in a metric space and its applications to exemplar decision making models

                                   Developing a unified mathematical framework for understanding cognitive processes from various disciplines, such as machine learning, psychology, and neuroscience

 

 

Selected Publications


1. Noh, Y.K., Sugiyama, M., Liu, S., Marthinus, C., Park, F.C., and Lee, D.D. (2014) Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence, Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS)

2. Noh, Y.K., Park, F.C., and Lee, D.D. (2012) Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification, Advances in Neural Information Processing Systems 25 (NIPS)  <travel awards>

3. Noh, Y.K., Zhang, B.T., and Lee, D.D. (2010) Generative Local Metric Learning for Nearest Neighbor Classification, Advances in Neural Information Processing Systems 23 (NIPS)  <travel awards supported by Google>

4. Noh, Y.K., Zhang, B.T., and Lee, D.D. (2010) Fluid Dynamics Models for Low Rank Discriminant Analysis, Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS) (oral 7.8%)  <registration waiver>

 

 

Other Publications


                      Book translation

1. Author: Sugiyama, M., Translator: Noh, Y.K., Nam, H., and Kim, E.S. (2015) Statistical Machine Learning – Pattern Recognition Based on Generative Models (in Korean), SNU Press (in press)

 

                      Journals

1. Noh, Y.K., Hamm, J.H., Park, F.C., Zhang, B.T., and Lee, D.D. (2015) Approximating Bhattacharyya-based Discriminant Analysis, IEEE Transactions in Pattern Analysis and Machine Intelligence (submitted)

2. Noh, Y.K., Lee, D.D., Yang, K.A., Kim, C.T., and Zhang, B.T. (2015) Molecular Learning with DNA Kernel Machines, Biosystems (accepted)

3. Park, J., Rha, S., Choi, B., Choi, J., Ryu, S. Kim, S. Noh, Y.K., Choi, S. Akkala, R., Li, H. Ali, J., Xu, S., Ngow, H., Lee, J., Lee, G., Kim, J., Lee, S., Na, J., Choi, C., Lim, H., Kim, J., Kim, E., Park, C., Seo, H., and Ho, D. (2015) Impact of low dose atorvastatin on development of new-onset diabetes mellitus in Asian population: Three-year clinical outcomes, International Journal of Cardiology, 184:502-506

4. Kim, E.S., Noh, Y.K., Zhang, B.T. (2015) Locally Linear Embedding for Face Recognition with Simultaneous Diagonalization, Journal of KIISE: Computing Practices, 21(2):235-241

5. Park, J., Ryu, S., Choi, J., Kim, M., Jun, J., Rha, S., Park, S., Kim, H., Choi, B., Noh, Y.K., and Kim., S. (2014) Association of inflammation, myocardial fibrosis and cardiac remodelling in patients with mild aortic stenosis as assessed by biomarkers and echocardiography, Clinical and Experimental Pharmacology and Physiology, 41:185-191

6. Noh, Y.K., Park, F.C., Lee, D.D. (2012) Model Dependency of the Performance in Generative Local Metric Learning, Journal of KIISE: Software and Applications, 39(5):347-354

7. Kim, J.S., Lee, J.W., Noh, Y.K., Park, J.Y., Lee, D.Y., Yang, K.A., Chai, Y.G., Kim, J.C., and Zhang, B.T. (2008) An Evolutionary Monte Carlo Algorithm for Predicting DNA Hybridization, Biosystems, 91(1):69-75

                     

                      Conferences

1.        Shi, Y., Noh, Y.K., Sha, F., and Lee, D.D. (2015) Learning Discriminative Metrics via Generative Models and Kernel Learning (in preparation)

2.        Sasaki, H., Noh, Y.K., and Sugiyama, M. (2015) Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation, Eighteenth International Conference on Artificial Intelligence and Statistics (AISTATS)

3. Kim, E.S., Noh, Y.K., and Zhang, B.T. (2014) Locally Linear Embedding for Face Recognition with Simultaneous Diagonalization, Korea Computer Congress <Outstanding Paper Awards, Women in Computer Science Awards>

4. Noh, Y.K., Park, F.C., and Lee, D.D. (2013) k-Nearest Neighbor Classification Algorithm for Multiple Choice Sequential Sampling, Proceedings of the Thirty-Fifth Annual Conference of the Cognitive Science Society (CogSci)

5. Lee, Y.S., Noh, Y.K., Kimberg, D., Coslett, Branch, and Schwartz, M. (2012) Predicting language performance using multivariate lesion pattern-based analysis, Human Brain Mapping

6. Noh, Y.K., Park, F.C., and Lee, D.D. (2011) Model Dependency of the Performance in Generative Local Metric Learning, Korea Computer Congress <Outstanding Paper Awards>

7. Noh, Y.K., Lee, D.D., and Park, F.C. (2011) Parametric Model-Based Local Metric Learning for Jensen-Shannon Divergence Estimation, Korea Computer Congress

8. Noh, Y.K., Hamm, J.H., and Lee, D.D. (2008) Regularized Discriminant Analysis for Transformation-Invariant Object Recognition, International Conference on Pattern Recognition (ICPR) (oral 18.0%)  <travel awards>

9. Noh, Y.K., Kim, C.T., and Zhang, B.T. (2007) Design of Temperature Regulation for DNA Kernel to Satisfy Positive Definiteness, Korea Computer Congress

10. Noh, Y.K., Kim, C.T., and Zhang, B.T. (2006) Modeling of Classifiers by Simple Kernel Update, Korea Computer Congress

11. Noh, Y.K., Kim, S.K., Kim, C.T., and Zhang, B.T. (2005) MicroRNA Target Prediction using DNA Kernels, Korea Computer Congress

12. Noh, Y.K., Kim, C.T., and Zhang, B.T. (2005) Design of Kernels Based on DNA Computing for Concept Learning, Korean Society for Cognitive Science Annual Spring Conference   <Outstanding Paper Awards>

13. Noh, Y.K., Kang, Y.J., Kim, C.T., and Zhang, B.T. (2005) Design of DNA Computing- Based Kernels for Assigning Metric between DNA Sequences, Computational Intelligence

14. Kim, J.J., Kwon, O.S., Lee, H.D., Noh, Y.K., Park, J.Y., and Park, J.C. (2001) Design and Implementation of E-mail Response Management System for Call Center, Korea Computer Congress

 

                      Peer Reviewed Workshop publications

1.Noh, Y.K., Park, F.C., Hamm J.H. and Lee, D.D. (2015) Feature Selection for Robot Learning Using Nearest Neighbor Information, The 14th Advanced Mechanism Control Symposium, The University of Tokyo, Tokyo, Japan

2.Noh, Y.K., Park, F.C., Kim, K.E., and Lee, D.D. (2014) Machine Learning Approach for Sequential Sampling – k-Nearest Neighbor Classification and Metric Learning, Asia-Pacific Conference on Computational Behavioral Sciences‎ (APCCBS), Seoul National University, Korea

3.Kim, J., Noh, Y.K., Fific M., and Zhang, B.T. (2014) Closed-Form Approximation of Drift Diffusion Response Time for Parameter Estimation, Asia-Pacific Conference on Computational Behavioral Sciences‎ (APCCBS), Seoul National University, Korea

4.Kim, E.S., Noh, Y.K., Sugiyama, M., and Zhang B.T. (2014) Locally Linear Embedding for Face Recognition with Simultaneous Diagonalization, Asia-Pacific Conference on Computational Behavioral Sciences‎ (APCCBS), Seoul National University, Korea

5.Noh, Y.K., Min, B.K. (2014) Feature Selection for Brain-Computer Interface Using Nearest Neighbor Information, The 2nd IEEE International Winter Workshop on Brain-Computer Interface (BCI), High1 Resort, Korea

6. Kim, E.S., Noh, Y.K., and Zhang, B.T. (2012) Learning-style recognition from eye-hand movement using a dynamic Bayesian network, NIPS 2012 Workshop: Personalizing Education with Machine Learning, Lake Tahoe, NE, U.S.A

7. Noh, Y.K., Park, F.C., and Lee, D.D. (2012) Bayesian Diffusion Decision Model for Adaptive k-Nearest Neighbor Classification, Snowbird Learning Workshop, Snowbird, UT, U.S.A

8. Noh, Y.K., Park, F.C., Hamm J.H. and Lee, D.D. (2012) Machine Learning Algorithms for Transformation-Invariant Object Recognition‒–Regularized Discriminant Analysis, The 11th Advanced Mechanism Control Symposium, The University of Tokyo, Tokyo, Japan

9. Shi, Y., Noh, Y.K., Sha, F., and Lee, D.D. (2011) Learning Discriminative Metrics via Generative Models and Kernel Learning, NIPS 2011 Workshop: Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity, Granada, Spain

10. Noh, Y.K. and Lee, D.D. (2011) Learning Metrics for Nearest Neighbor Classification, Information Theory and Applications, San Diego, CA, U.S.A.

11. Noh, Y.K. and Lee, D.D. (2009) Classifying High-Dimensional Data: Bhattacharyya-based Discriminant Analysis, US-Korea Conference, Raleigh, NC, U.S.A.

12. Noh, Y.K., Zhang, B.T., and Lee, D.D. (2009) Kernel Machines Made of DNA Molecules, Learning Workshop, Clearwater, FL, U.S.A.

13. Noh, Y.K., Hamm, J.H., and Lee, D.D. (2009) Perturbation Methods for Discriminant Analysis, Learning Workshop, Clearwater, FL, U.S.A.

14. Noh, Y.K., Zhang, B.T., and Lee, D.D. (2008) Variational Bounds for Discriminant Analysis, Snowbird Workshop, Salt Lake City, UT, U.S.A.

15. Noh, Y.K., Zhang, B.T., and Lee, D.D. (2006) DNA Computing-Based Kernel Machines, MLSS Workshop, National Taiwan University of Sci & Tech, Taipei, Taiwan

 

 

Invited Talks


August 2015              Machine Learning with Nearest Neighbors, Department of Electrical, Electronic and Control Engineering, Hankyong National University, Korea

July 2015                   Introduction to Machine Learning and Research Trends, Open Standards and Internet Association (OSIA). Korea

July 2015                   Machine Learning with Nearest Neighbors, Naver Labs. Korea

May 2015                  Machine Learning with Nearest Neighbors, Department of Electrical and Computer Engineering, UC San Diego, San Diego, CA, USA

April 2015                 Machine Learning with Nearest Neighbors, CNSL Lab. KAIST, Korea

January 2015             Machine Learning with Nearest Neighbors, SNU Bioinformatics Institute, Seoul National University, Korea

December 2014          Diffusion Decision Model for Adaptive k-Nearest Neighbor Classification, Department of Psychology, Ohio State University, Columbus, OH, USA

December 2014          Machine Learning with Nearest Neighbors, Department of Computer Science and Operations Research, Université de Montréal, Montreal, Canada

September 2014         Machine Learning and Characteristics of Machine Learning Algorithms, School of Medicine, Yonsei University (Yongdong), Korea

July 2014                   Machine Learning with Nearest Neighbors, Department of Computer Science, Tokyo Institute of Technology, Japan

July 2014                   Machine Learning with Nearest Neighbors, Department of Computer Science and Engineering, POSTECH, Korea

June 2014                  Machine Learning with Nearest Neighbors, Division of Artificial Intelligence Society, Korea Computer Conference, Busan, Korea

June 2014                  Machine Learning and Characteristics of Machine Learning Algorithms, School of Medicine, Korea University (Anam), Korea

April 2014                 Machine Learning and Characteristics of Machine Learning Algorithms, School of Medicine, Kyunghee University, Korea

April 2014                 Machine Learning and Characteristics of Machine Learning Algorithms, School of Medicine, Korea University (Guro), Korea

March 2014               Machine Learning with Nearest Neighbors, Department of Electrical Engineering, KAIST, Korea

December 2013          Machine Learning with Nearest Neighbors, Department of Computer Science and Engineering, Seoul National University, Korea

May 2013                  Exploiting k-Nearest Neighbor Information with Many Data, Department of Computer Science, Tokyo Institute of Technology, Japan

April 2013                 Exploiting k-Nearest Neighbor Information with Many Data, School of Computer Science and Engineering, Kyungpook National University, Korea

April 2013                 Exploiting k-Nearest Neighbor Information with Many Data, Graduate School of Convergence Science and Technology, Seoul National University, Korea

February 2013           Leveraging Nearest Neighbor Information (Nearest Neighbor Surrogate Measure of Underlying Probability Density), Department of Brain and Cognitive Engineering, Korea University, Korea

February 2013           Exploiting k-Nearest Neighbor Information with Many Data, Brain-AI Symposium, High-One Resort, Gangwon-do, Korea

December 2012         Exploiting k-Nearest Neighbor Information with Many Data, Tech Talk, Google, Mountain View, CA, USA

December 2012         Exploiting k-Nearest Neighbor Information with Many Data, Media Analytics Department, NEC Labs, Cupertino, CA, USA

October 2012             Nonparametric Estimation of Information-theoretic Measures with Nearest Neighbors (From the Perspective of Feature Selection), Department of Personalized Medicine, Hanyang University, Korea

October 2012             Diffusion Decision Model for Adaptive k-Nearest Neighbor Classification, Department of Psychology, Seoul National University, Korea

June 2012                  Introduction to Machine Learning and Characteristics of Data Analysis Algorithms in Machine Learning, College of Medicine, Eulji University, Korea

June 2012                  Diffusion Decision Model for Adaptive k-Nearest Neighbor Classification, Interdisciplinary Program in Cognitive Science, Seoul National University, Korea

December 2011          Leveraging Nearest Neighbor Information (Nearest Neighbor Surrogate Measure of Underlying Probability Density), Department of Computer Science, KAIST, Korea

April 2011                 Generative metric learning for nearest neighbor methods with f-divergences, School of Computer Science and Engineering, Soongsil University, Korea

April 2011                 Generative metric learning for nearest neighbor methods with f-divergences, Department of Computer Science and Engineering, POSTECH, Korea

April 2011                 Generative metric learning for nearest neighbor methods with f-divergences, Department of Bio and Brain Engineering, KAIST, Korea

 

 

Professional Activities


                                   Services

1.        Organizing committee, Neural Information Processing Systems (NIPS 2015) main conference

2.        Organizing committee, Asia-Pacific Conference on Computational Behavioral Sciences‎ (APCCBS*2014)

3.        Organizing committee, Asian Conference on Machine Learning (ACML 2017)

4.        Education director, Computational Intelligence Society, KIISE (The Korean Institute of Information Scientists and Engineers) – 2013, 2014

 

                                   Conference/Journal reviewer

1.        Neural Information Processing Systems (NIPS) – 2013, 2014

2.        Asian Conference on Machine Learning (ACML) – 2013, 2014

3.        SIAM International Conference on Data Mining (SDM) – 2014

4.        Journal of Machine Learning Research (JMLR) – 2013, 2014

5.        IEEE Transactions on Image Processing (TIP) – 2014

6.        IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) – 2009, 2010, 2011

7.        Journal of Korean Institute of Information Scientists and Engineers (KIISE) – 2013, 2014

8.        Neural Networks – 2012, 2014

9.        Neurocomputing – 2013

 

 

Grants


                                   1. Air Force Office of Scientific Research (AFOSR) U.S. Grant (2015-2017)

                                      - Title: Understanding cognitive decision making via nearest neighbor algorithms in machine learning

                                      - $450,000 for three years

 

                                   2. National Security Research (NSR) Institute Grant (2015)

                                      - Title: Data classification research using machine learning algorithms

                                      - $30,000 for seven months

 

                                   3. KAIST Dr. M Project (2014)

                                       - Title: Machine learning algorithm research for bio-signal processing (Acute Coronary Syndrome classification)

                                      - $50,000 for one year

 

 

References


Frank Chongwoo Park, Professor, School of Mechanical & Aerospace Engineering

Seoul National University

Tel: +82-2-880-7133, Email: fcp@snu.ac.kr

Kee-Eung Kim, Associate Professor, Department of Computer Science

Korea Advanced Institute of Science and Technology

Tel: +82-42-350-3536, Email: kekim@cs.kaist.ac.kr

Daniel D. Lee, Professor, Department of Electrical and Systems Engineering

University of Pennsylvania

Tel: +1-215-898-8112, Email: ddlee@seas.upenn.edu

Byoung-Tak Zhang, Professor, Department of Computer Science and Engineering

Seoul National University

Tel: +82-2-880-8133, Email: btzhang@snu.ac.kr

Cheongtag Kim, Professor, Department of Psychology

Seoul National University

Tel: +82-2-880-6076, Email: ctkim@snu.ac.kr