Fellows
/Senior Distinguished
Researchers
/Distinguished
Researchers

Awards

1. Best Paper Award, The International International Conference on High Performance Computing in Asia-Pacific Region (HPC Asia 2022), 2022
2. Best Paper Award Honorable Mention, Eighth Workshop on Accelerator Programming Using Directives (WACCPD21), 2021
3. Best Paper Award, Advanced Geospatial Applications for Smart Cities and Regions, International Society for Photogrammetry and Remote Sensing, 2019
4. Magazine Dissertation Award, IECE Communication Society, 2019
5. Best Paper Award, Dicomo (Multimedia, Distributed, Cooperative, and Mobile Symposium), 2018
6. Prizes for Science and Technology, （Research Category), The Commendation for Science and Technology by MEXT,2018
7. Industrial Distinguished Leader Award, APSIPA(Asia-Pacific Signal and Information Processing Association), 2018
8. Best Poster Award, SC2017, The International Conference for High Performance Computing, Networking, Storage and Analysis (Supercomputing), 2017
9. Achievement Award, IEICE (The Institute of Electronics, Information, and Communication Engineers), 2016
10. Best Paper Award, IPSJ (Infomation Processing Society of Japan) SIGUBI, 2015
11. Best Paper Award, Dicomo (Multimedia, Distributed, Cooperative, and Mobile Symposium), 2014
12. SIGKDD Best Research Paper Award Honorable Mention, 2010
13. Best Paper Award, ICONIP (International Conference on Neural Information Processing), 2009
14. Best Paper Award, IPS (Information Processing Society), 2009
15. Best Paper Award, Funai Foundation Award, 2007
16. Telecommunication Advanced Foundation Award, 2005
17. Yamashita SIG Research Award, IPS (Information Processing Society), 2005
18. Paper Award, Funai Foundation Award, 2005
19. Research Award, JSAI (Japanese Society for Artificial Intelligence), 2005
20. Meritorious Award, IEICE (Institute of Electronics, Information, and Communication Engineers), 2004
21. Best Paper Award, Funai Foundation Award, 2004
22. Best Paper Award, IEICE (Institute of Electronics, Information, and Communication Engineers), 2004
23. Research Award, JNNS(Japanese Neural Network Society), 2003
24. Best Paper Award, IEICE (Institute of Electronics, Information, and Communication Engineers), 2000
25. Telecommunication Advanced Foundation Award, 1997
26. Research Award, JNNS (Japanese Neural Network Society), 1995

Academic Activities

Committee member(Ministries related)

• Evaluation Committee member, Grant-in-Aid for Transformative Research Areas(B), KAKENHI, MEXT, May 2021-August 2021.
• Member of the Advisory Council for Science and Technology, MEXT, March 2021-present.
• Panel member, ERATO 2021, Japan Science and Technology Agency (JST), March 2021-February 2022.
• Review Committee member, the University Fellowship Foundation Project for the Creation of Science and Technology Innovation, MEXT, February 2021-March 2021, June 2021-present.
• Evaluation Committee member, Grant-in-Aid for Transformative Research Areas(A), KAKENHI, MEXT, August 2020-September 2020.
• Committee member, the Institutional Evaluation / Medium-Term Plan Review Committee, NISTEP, MEXT, July 2020-March 2021.
  ※National Institute of Science and Technology Policy
• Member of Next Generation Computational Infrastructure Study Group, Science and Technology Council Information Committee, MEXT, April 2020-present.
• Evaluation committee member, Maeda PJ, ERATO, JST, April 2020-Present.
• Subproject Director (Subproject 3), Japan Science and Technology Agency (JST), Moonshot R&D Project,April-2020-present.
  *JST: Japan Science and Technology Agency
• Members of the 2020 Japan Prize Selection Committee, Selection Subcommittee for the "Electronics, Information, Communication"field, March 2019-March 2020. Working Group member, Moonshot International Symposium, JST, November 2019-March 2020.
  *JST: Japan Science and Technology Agency
• Review Committee member, "FUGAKU" Achievement Creation Acceleration Program, MEXT, October 2019-March 2020.
  *MEXT: Ministry of Education, Culture, Sports, Science and Technology
• Advisory Committee member, JST-Mirai Program: Realization of super smart society, JST, August 2019- October 2019.
  *JST: Japan Science and Technology Agency
• Research Supervisor, CREST (Mathematical Information Platform), Japan Science and Technology Agency (JST), April 2019-present.
• Review Committee member, Security Technology Research Promotion System, Defense Equipment Agency, July 2019- present.
• Member of Security Technology Research Promotion Committee, ATLA, June 2019-present.
  *ATLA：Acquisition Technology & Logistics Agency
• Member of National Research and Development Corporation Council Ocean Research and Development Organization, MEXT,April 2019-present.
  *MEXT: Ministry of Education, Culture, Sports, Science and Technology
• Member of HPCI Plan Promotion Committee, MEXT, March 2019-present.
  *MEXT: Ministry of Education, Culture, Sports, Science and Technology
• R&D Evaluation Committee member for Strategic Information and Communications R&D Promotion Programme(SCOPE), MIC, November 2018-present. *MIC: Ministry of Internal Affairs and Communications Selection Panel Member, JST Strategic Basic Research Programs, ERATO, 2018-present.
  *JST: Japan Science and Technology Agency
  *ERATO: Exploratory Research for Advanced Technology
• Cordinator for AI study group, Osaka Prefectual Manufacturing & Industrial association, September 2018-March 2019.
• Member of HPCI Planning Promotion Committee, MEXT, August 2017-present.
  * MEXT: Ministry of Education, Culture, Sports, Science and Technology
• Member of Artficial Intelligence Working Group, Information and Communications Council, MIC, January 2017-July 2017.
• Committee member, Data Science and Mathematical Education Enhancement Meeting, MEXT, Augaust 2016-March 2017.
• Big Data Analysis Contest Jury, "IoT Acceleration Lab," METI, July 2016-October 2016.
  * METI: Ministry of Economy, Trade and Inustory
• Committee member, Strategic Conference on Artificial Intelligence Technologies: "Task Force for Human Resource Development," MEXT, July 2016‐March 2018.
• Member of Scientific research fund committee, Japan Society for the Promotion of Science(JSPS), January 2016-December 2016.
• Committee member, AI/Brain Research Working Group, Information and Communications Council, MIC, January 2016-present.
• Evaluation Committee member in special fields for Strategic Information and Communications R&D Promotion Programme(SCOPE), MIC, November 2016-present.
• Reserch Area Adviser, JST Strategic Basic Research Programs, PRESTO, "Innovational technical basis for cultivation in cooperation with information science,"August 2015-March 2020.
  * PRESTO: Funding program for promoting individual research to nurture the seeds of future innovation and organizing unique, innovative network.
• NEDO Technical Committee member, New Energy and Industrial Technology Development Organization, June 2015-present.
• Committee member, Patent application technical trends surveys(Artficial Intelligence), January 2015-March 2015.
• Member of the Advisory Council for Science and Technology, MEXT, February 2013-present.
• Reserch Area Adviser, JST Strategic Basic Research Programs, CREST/PRESTO, "Advanced Core Technologies for Big Data Integration," 2013-present.
  * CREST: Funding program for team-oriented research with the aim of strategic goals by the government.
• Research member, AMED Strategic International Brain Science Research Promotion Program, "Research and development on next generation AI and fundamental technology based on nonlinear dynamics," Febtruary 2018-March 2019.
  * AMED: Japan Agency for Medical Research and Development
• Research member, JST Strategic Basic Research Programs, CREST, "Advanced application technologies to boost big data utilization for multiple-filed scientific discovery and social problem solving. -Statistical Computational Cosmology with Big Astronomical Imaging Data," October 2014-March 2020.
• Research Representative at NTT, SODA: An Infrastructure for Leveraging Social Big Data to Enable Open Smart Cities, Funding program by NICT: July 2014-March 2016.
  * NICT: National Institute of Information and Communications Technology
• Sub-Project Leader, Funding Program for World-Leading Innovative R&D on Science and Technology (First Program),
• Cabinet Office, Government of Japan, March 2010-February 2014.
• Principal Investigator, Scientific Research (C), MEXT, Government of Japan, April 2008-March 2011.

Committee member(Society related)

• Area Chair, International Conference on Machine Learning (ICML), 2023-present.
• Vice President, Information Processing Society of Japan(IPSJ）,June 2021-May 2023.
• Program Committee, Asian Conference on Machine Learning (ACML), 2019-present.
• Japanese Association for Medical Artificial Intelligence, Councillor, April 2018-present.
• Special Session Organizer, The 50th ISCIE International Symposium on Stochastic Systems Theory and Its Applications(SSS), 2018.
• Japanese Association for Medical Artificial Intelligence, Councillor, April 2018-present.
• Program Committee, 3rd EAI International Conference on IoT in Urban Space(Urb-IoT 2018), 2018.
• Reviewer, Advances in Neural Information Processing Systems (NIPS), 2017.
• Program Committee, Twentieth International Conference on Artificial Intelligence and Statistics(AISTATS 2017), 2017.
• Program Committee, The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 2017.
• Program Committee, The 2nd EAI International Conference on IoT in Urban Space2016, 2016.
• Program Committee, The 2nd International Workshop on Smart Cities: People, Technology and Data(IWSC'16), 2016.
• Reviewer, International Conference on Machine Learning (ICML), 2016-present.
• Organizing Committee, The 1st International Workshop on Smart Cities: People, Technology and Data, The 2015 ACM International Joint Conferenceon Pervasive and Ubiquitous Computing (Ubicomp 2015), 2015.
• Organizing Committee, The 47th ISCIE International Symposium on Stochastic Systems Theory and Its Applications (ISSS2015), 2015.
• Senior Program Committee, International Conference on Artificial Intelligence (IJCAI)-European Conference on Artificial Intelligence (ECAI), Machine Learning Track, 2015-present.
• Area Chair, The Neural Information Processing Systems Conference (NIPS), 2015, 2019-present.
• Guest Editor for Special Issue "Machine Learning for Media Processing," The Institute of Image Information and Television Engineers of Japan, 2014.
• Coordinator, Mathematical Statistics and Committee member, E-Science Data Science Division, Science Council of Japan (SCJ), October 2014-present.
• Vice Chair, IEEE Kansai section, January 2013-January 2015.
• Member, The Institute of Statistical Mathematics(ISM) Steering Committee of the Cooperation with Mathematics Program, December 2012-March 2017.
• Chair, Information-Based Induction Science and Machine Learning(IBISML) Steering Committee member, The Institute of Electronics Information and Communication Engineers(IEICE), April 2012-March 2014.
• Vice Chair, IBISML Steering Committee member, IEICE, April 2010-March 2012.
• Program Committee (Research), Association for Computing Machinery(ACM) SIG-KDD, 2010-present.
• Fellow, IEICE, 2009.
• Associate Editor, Neurocomputing Journal, 2007-2012.
• Senior Program Committee, International Conference on Machine Learning (ICML), 2007.
• Associate Editor, Mathematical Modeling and Problem Solving (MPS), Information Processing Society (IPS) 2006-2018.
• Technical Committee, MPS, IPS 2006-March 2010.
• Part-time Researcher, RIKEN, The Institute of Physical and Chemical Research, 2003-September 2012.
• Associate Editor, Neural Networks Journal, 2003-2010.
• General Chair, IBIS Technical Group, IEICE, 2003-2004.
• General Chair, IBIS, 2003.
• Program Committee, IEEE Neural Networks for Signal Processing (NNSP), 2001-2002.
• General Conference Board, Japanese Neural Network Society (JNNS), 2001.
• Editor in Chief, IEICE Transactions on Infomation and System (Special Issue on IBIS), 2001.
• Board Member, Japanese Neural Network Society (JNNS), 2000-2001.
• Program Commitee, IBIS Workshop, 2000.
• Reviewer, Neural Information Processing Systems Conference (NIPS), 1999-2002, 2005-2014.
• Paper Editorial Committee Member, IEICE, 1999-2002.
• Special Issue, Editorial board, IEICE, 1998, 2002-2003.
• Paper Review Member, The institute of Electrical Engineers (IEE), 1997-2000.
• Paper Review Member, The institute of Electronics, Information,and Communication Engineers (IEICE), 1996-present.
• Editorial board, IEICE, 1996-1998.
• Commitee Member, Audio and Visual Information Research Group (AVIRG), 1992-1993.

Visiting Professor

• Guest Professor, School of Interdisciplinary Mathematical Sciences, Meiji University, April 2020- present.
• Advisory Board Member of Mathematical and Data Science Center, Kobe University, April 2019- present.
• Advisory board member, Graduate School of Infomatics, Kyoto University, October 2017.
• J-Node Platforms Evaluation Committee member, RIKEN, The Institute of Physical and Chemical Research, July 2016‐March 2017.
• NIJC Steering Committee member, September 2015-March 2017, July 2017-March 2018.
• Off-campus Program Member, Collaborative Graduate Program in Design, Kyoto University Design School, September 2013.
• NIJC Steering Committee Chair, July 2013-March 2015.
• Member of the Education Council, Unit of Design, Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, June 2013-March 2018.
• Member, ISM Steering Committee of the Cooperation with Mathematics Program, December 2012-March 2017.
• Outside Evaluation Committee member, Nara Women's University, 2012.
• Outside Evaluation Committee member, NAIST Graduate School of Information Science, 2010-2013.
  * NAIST: Nara Institute of Science and Technology
• Outside Evaluation Committee member, ISM , 2007, 2013.
• Visiting Researcher, Geoffrey Hinton Lab., University College London (UCL), UK, April 1999, August 2000.
• Visiting Researcher, Geoffrey Hinton Lab., University of Toronto, CANADA, October 1997.
• Visiting Scholar, Purdue University, USA, 1993-1994.
• Interview, Appier, December,3.
• Opinion hearing committee member, KYOTO Industorial Support Organization 21, Public Interest Incorporated Foundation, November 2018-present.
• The 7th Special talk, JBpress, The forefront academia of real world data utilization-x IQVIA supports healthcare with human data science, October 19, 2018.
• Cordinator, AI Seminar, Osaka Industrial Association, September 2018-present.
• Seminar lecturer, Seminar Info '' Machine learning technology in the age of AI and IoT, examples of practical application, future prospects '' August 27, 2018.
• Supervisor, Keihanna"Edison Society,"International Institute for Advanced Studies, April 2018-present.
• NHK Radio First Broadcast, End-of-year special number "How artificial intelligence changes society -Happiness in the AI era-" broadcast on December 31, 2017.

Professional Activities

Invited Academic Talks

• "Simulation-based machine learning for digital twin computing," ICSCP symposium, December 10, 2021.
• "Simulation-based machine learning that combines data-driven and model-driven approaches," HPCI Achievement Report Meeting, October 29, 2021.
• "AI Research on Moonshot Goal3," Carnegie Corporation Workshop, July 21, 2021.
• "Latest Trends in AI Technology: Introducing Specific Applications," RIKEN Innovation Seminar, June 21, 2021.
• "Artificial intelligence and machine learning technology that drives the third AI boom," Seminar, School of Interdisciplinary Mathematical Science, MEIJI UNIVERSITY, September 15, 2021.
• "AI and medical care," KOBE UNIVERSITY「Fundamentals of Computational Life Science」，December 16, 2020.
• "Relational data mining based on nonparametric Bayesian method," JST/CRDS seminar 'Collaboration between mathematics, natural sciences and engineering', December 9, 2020.
• "Mathematical understanding of phenomena by neural networks," Meiji Non-Linear Mathematical Seminar:Autmn School, November 24, 2020.
• "Introduction of data-driven scientific research at RIKEN AIP," Center for Mathmaticsal Modeling and Data Science, Osaka University, October 6, 2020.
• "Technology trends of AI and application to the medical field," The 67th Annual Meeting of the Japanese Society of Anesthesiologist, June 5, 2020.
• "AI strategy and its implementation in precision medicine utilizing medical data," Japanese Association for Medical Artificial Intelligence 2020, Tokyo Big Sight, February 1, 2020.
• "Introduction to AI utilization," in-house seminar, Hamura Techinical Center of CASIO Computer co., ltd., December 24, 2019.
• "AI technologies for disaster prevention and mitigation," Institute for Future City Studies seminar, Tokyo City University, December 20, 2019.
• "Group optimal navigation technology via spatio-temporal statistical analysis and multi-agent simulation learning," IPSJ-seminar2019, November 15,2019.
  *IPSJ: Information Processing Society of Japan
• "Integration of Inductive and Deductive Inferences - Simulation-based Machine Learning -," The 1st AIRC-ISIR International Symposium "Challenges for Introducing AI to Industrial Sciences," Osaka International Convention Center, October 26, 2019.
• "ICT☓AI," Japan IT Week Autumn, October 23, 2019.
• "Latest AI technology for spatio-temporal data analysis," Japanese Society of Breeding - AI Symposium, Kindai university, September 6, 2019.
• "HPC☓AI," Scientific System Study Group HPC Forum, FUJITSU Digital Transformation Center, August 20, 2019.
• "A Future society created by artificial intelligence," Life Science Forum, Coop Inn Kyoto, July 20, 2019.
• "A Future society created by AI technology," Hankyu Hanshin Holdings Group President's Meeting, Hotel Hankyu International, July 19, 2019.
• "Introduction of disaster prevention research by AI technology," Highway Technology Research Center -Technical class-, June 11, 2019.
• "Astronomical time-series classification by machine learning," Technical Committee on Data Science in Astronomy, The Institute of Statistical Mathematics, May 27, 2019.
• "A future image brought by artificial intelligence society and AI business set up by NTT," The 3nd AI EXPO, Tokyo Big Sight, April 3, 2019.
• "The future of AI ～Future outlook of AI utilization～," AI seminar, Osaka Prefectural Manufacturing & Industrial Association, March 5, 2019.
• "A future society brought by artificial intelligence," Osaka University Kick-off Symposium of Initiative for Life Design Innovation, Grand Front Osaka, February 21, 2019.
• "Current and future outlook of machine learning technologies for supporting artificial intelligence," Data and Intelligence Education Research Division Commemorative Symposium, Wakayama University, November 21, 2018.
• "New development of the artificial intelligence technology," The Institute of Electrical Engineers of Japan, Kindai University, November 12, 2018.
• "Future society utilizing artificial intelligence technology," The 61st National Convention of Japan Architects and Building Engineers Association, Omiya Sonic City Hall, October 26, 2018.
• "Technical scenario of the 4th industrial revolution and its social impact," Study Group of Adaptation to the 4th Industrial Revolution, International Institute for Advanced Studies, September 14, 2018.
• "Simulation-based machine learning," The 2nd International Workshop for symbolic-Neural Learning (SNL2018) Keynote Talk, Nagoya Congress Center, July 6, 2018.
• "Application to social and natural sciences of artificial intelligence," The 32th Annual Conference of the Japanese Society for Artificial Intelligence, Shiroyama Hotel Kagoshima, June 8, 2018.
• "Artificial intelligence technology for complex and advanced social system design," The 2nd AI EXPO, Tokyo Big Sight, April 4, 2018.
• "Future AI technologies and technological aspect for disaster prevention," The 17th Symposium for Safety Net of Land, Pacifico Yokohama, Feburary 8, 2018.
• "Simulation science by fusion of super computer and artificial intelligence technologies," Commemoration ceremony for completion of the new supercomputer ITO, Inamori Hall, Kyushu University, February 5, 2018.
• "New development of cancer research by the artificial intelligence technologies," Symposium "The way of health care in the Society 5.0 era," Ito Hall, Tokyo University, January 25, 2018.
• "Utilization of artificial intelligence in disaster prevention field -Introduction of the Disaster Resilience Science Team of RIKEN Center for Advanced Intelligence Project-,"Edion no Kai, International Institute for Advanced Studies, December 26, 2017.
• "Basic technology of artificial intelligence and its applications -Introduction of Research and Development at RIKEN AIP-," Mitsui Interbusiness Reseach Institute, December 18, 2017.
• "Artificial intelligence technologies in IoT era and its social implementation," Chugoku Economic Federation, ANA Crown Plaza Hotel Hirosima, November 30, 2017.
• "Latest trends and future possibilities of AI," Factory Management Workshop, Osaka Prefectural Manufacturing & Industrial Association, November 21, 2017.
• "Machine learning techniques leading the artificial intelligence era," The Institute of Electrical Engineers of Japan, Kansai University, November 16, 2017.
• "New development of the artificial intelligence -For realization of ambient intelligence-,"Public Works Research Institute Lecture, Hitotsubashi Auditorium, October 19, 2017.
• "Introduction of RIKEN, AIP," Canada Japan AI collaboration, Embassy of Canada, October 4, 2017.
• "The future society poiduced by AI technologies," Asahi World Forum 2017, Imperial Hotel Tokyo, October 3, 2017.
• "New development of AI technologies for IoT era," 100th anniversary founding technology forum, Nara Prefectural Industry Promotion Center, September 4, 2017.
• "New development of machine learning ～Simulation science～," K computer X data science sympoisum, Marunouchi Building, August 25, 2017.
• "Machine learning techniques for IoT era -For realization of ambient intelligence-," Tohoku Forum for Creativity, Tohoku Univesity, Augusut 24, 2017.
• "Machine learning techniques from wide variety of data ～New development of machine learning technologies for AI era～," Mitsubishi Electric Corpration, July 31, 2017.
• "For front of AI research," Kick-off symoisium of Big Data Activation Research Center, Niigata University, July 21, 2017.
• "For realization of the ambient intelligence," Ultra Smart Community Study Group hosted by Nikken Sekkei Research Institute, Kobe University, July 13, 2017.
• "Data-driven science ～Machine learning technologies in IoT era," Technical development seminar of Central Japan Railway Company, Komaki Lab, May 29, 2017.
• "AI research in natural science and social science," 'Current and future of data science' symposium hosted by Kobe Univesity, Kobe University, May 15, 2017.
• "For realization of advanced artificial intelligence,"Advanced science and technology study session, Diet Members' No. 2 Office Building of the Lower House, April 27, 2017.
• "Disaster Manegement Research in RIKEN Center for Advanced Intelligence Project," Yasuda Auditorium, University of Tokyo, March 14, 2017.
• "New Development of Machine Learning Research -Technical Contributions to the Various Science Fields -" DEIM2017, Takayama Green Hotel, March 6, 2017.
• "New development of the artificial intelligence by machine learning technologies," The Japan Institute of Marine engineering, The Sasakawa Hall, March 2, 2017.
• "Recent novel applications based on advanced machine learning technologies," Meet the Data hosted by Ecole Polytechnique, Maison De La Chimie, February 23, 2017.
• "Industrial applications of machine learning technonogies," Latest AI Symposium hosted by Monozukuri Nippon Confernce, Osaka Science & Tschnology Center, February 10, 2017.
• "New development of machine learning research toward artificial intelligence society ,"IEICE Technical Committee on Communication Quality Workshop, Osaka University Nakanoshima Center, January 21, 2017.
• "Machine learning technology for big data analysis in the field of natural science and social science," The Japan Society of Mechanical Engineers, Osaka Science & Tschnology Center, October 24, 2016.
• "Recent Advancement in Machine Learning Research," IEICE-HCG Informatics Science on Cognition and Behaviors Workshop, Kishu-Minabe Royal Hotel, October 6, 2016.
• "RIKEN Center for Advanced Intelligence Project," The Chem-Bio Infomatics Society Seminar, Grand Front Osaka, September 1, 2016.
• Panel Discussion "What is data science?," Data Science Symposium hosted by Shiga University, Osaka International Convention Center, July 23, 2016.
• "New trend of IoT brought by ambient artificial intelligence," Interop Tokyo Premium Conference, Makuhari Messe, June 8, 2016.
• "Big data analysis in the field of natural science and social science," Japanese Journal of Applied Statistics Fontier Seminar, May 28, 2016.
• "Data-driven science," Celebration of the 10th Anniversary of the Establishment of Faculty of Informatics, Kogakuin University, November 28, 2015.
• "Spatio-temporal prediction technique and its application for IoT/Bigdata analytics," Keihanna Information and Communications Fair 2015, Technical Talk, Keihanna Plaza, November 6, 2015.
• "Future of machine learning -Spatio-temporal analysis in IoT/Bigdata era," MATLAB EXPO 2015 Japan, Grand Pacific LE DAIBA, October 16, 2015.
• "Spatio-temporal statistical collective data analysis for IoT/Big data," The 33rd Annual Conference of the Robot Society of Japan(RSJ), Tokyo Denki University, September4, .2015.
• "Data-driven science - Machine learning techniques for IoT," IEICE Kyushu Section, Fukuoka Institute of Technology, June 9, 2015.
• "Big data challenge by machine learning techniques," The 16th Symposium on Informatics, Big data and Human Science, March 17, 2015.
• "Constructing IoT foundation by machine learning technologies," The Japan Society for Industrial and Applied Mathematics, University of Tsukuba, February 9, 2015.
• "Machine learning for healthcare," French-Japanese Conference, November 18, 2014.
• "Healthcare data analysis by machine learning techniques," Technical Seminar sponsored by the Institute of Electrical Engineers, the Institute of Electronics, Information and Communication Engineers, and the Institute of Image Information and Television Engineers, Central Electric Club, October 21, 2014.
• "Big data and its introduction in business," Nagoya Lecture, Industrial Division, The Telecommunications Association, Nagoya, September 9, 2014.
• "Service creation by using big data," IEICE General Conference, March 20, 2014.
• "Machine learning techniques for big data analysis," The Cooperation with Mathematics Program, Industry/Academic Tutorial Seminar, The Institute of Statistical Mathematics, March 12, 2014.
• "Machine leaning research - Current and outlook for future," Academic Lecture, The Institute of Science and Engineering, Ritsumeikan University, December 16, 2013.
• "Bayesian meta-ｌearning and its application to high-level real nursing activity recognition using accelerometers," Math-for-industry Forum, November 5, 2013.
• "Basics of Bayesian modeling in machine learning," MLMI 2013, A MICCAI 2013 Workshop, Nagoya, September 22, 2013.
• "Big data analysis by Bayes theorem," Consecutive Seminar 2013, Information Processing Society of Japan, June 26, 2013.
• "Machine learning techniques for creating new values from big data," 2013 Symposium, Graduate School/Faculty of Information Science and Electrical Engineering, Kyushu University, May 14, 2013.
• "Statistical machine learning techniques in big data era," IMI Colloquium, Institute of Mathematics for Industry, Kyushu University, January 16, 2013.
• "Bayesian Relational Data Analysis," KDD2012, Beijing, August 14, 2012.
• "Statistical Machine Learning Techniques for the Era of Big Data," MIRU2012, Fukuoka International Congress Center, August 7, 2012.
• "New Developments on Machine Learning Techniques," DCP Business Seeds Workshop for Digital Information Appliance Industry, Kansai Institute of Information Systems,July 2012.
• "Statistical Machine Learning Techniques for Big Data analysis," Toyota Central R&D LABS.,INC., July 2012.
• "The Era of Big Data: New Developments on Information Science," Osaka University School of Engineering, July 2012.
• "Bayesian Relational Data Analysis," Mext Workshop Cryptographic Technologies suitable for Cloud Computing, The Institute of Statistical Mathematics, February 2012.
• "Arrival of the Era of Big Data," IEEE Kansai Section Lecture Meeting, TKP Honmachi Business Center, Osaka, January 2012.
• "Machine Learning for Pattern Recognition," IBISML Tutorial, Tokyo University, January 2012.
• "Business Challenges for the 21st Century," Academic Center for Computing and Media Studies, Kyoto University, November 2011.
• "Introduction to Statistical Machine Learning," NII Karuizawa Saturday Salon, International Seminar House For Advanced Studies, November 2011.
• "Statistical Machine Learning for Intelligent Computing" IPSJ SIGMUS, October, 2010.
• "Introduction to Nonparametric Bayesian Models," IPSJ CVIM/IEICE PRMU, March 2009.
• "Nonparametric Bayesian Learning," NHK Science and Technical Research Labs, August 2007.
• "Inference Methods for Nonparametric Bayes models, Workshop on Bayesian Inference," (The Institute of Statistical Mathematics), August. 2007.
• "Multimedia Signal Processing," Osaka University, May 2007.
• "Nonparametric Bayesian Theory and its Application to Data Mining," SIG-DMSM, July 2006.
• "Mathematical MOodeling for Multiple Topics," Information ProcessingSociety of Japan (IPSJ), Yamashita SIG Award Memorial Lecture, March 2006
• "Ensemble Learning," IEICE PRMU, September 2004.
• "New Development of Text Modeling," NLP, March 2003.
• "Variational Bayes/Parametric Mixture Models," ATR Spoken Language Translation Research Laboratories, July 2002.
• "Ensemble Learning," The Institute of Systems, Control, and Information Engineers, May 2002.
• "New Development of the EM Algorithm. -Variational Bayes- ," Meeting of Technical Group on Neural Computation, IEICE, January 2002.
• "Theory and Applications of Variatiaonal Bayes," The Institute of Statistical Mathematics, February 2001.
• "New Development of the EM Algorithm," Japanese Society of Computational Statistics, October 2000.
• "The Front of Statistical Learning Theory," Kyoto University, April 2000.
• "Optimal Model Search based on Bayesian Approach," Biometric Analysis Dept., SHIONOGI & CO., LTD, March 2000.
• "Latent Variable Models and Probabilistic Dimensionality Reduction," The Institute of Statistical Mathematics, September 1998.
• "A Feature Extraction Method Based on Latent Variable Models," Meeting of Technical Group on Pattern Recognition and Media Understanding, IEICE, June 1998.
• "Probabilistic Neural Networks Based on Latent Variable Models," Yukawa Institute for Theoretical Physics, Kyoto University, January 1998.
• "Mathematical Morphology," Meeting of Technical Group on Precision Engineering, August 1996.
• "EM Algorithm," Bio-infomatics Research Meeting, October 1995.
• "Deterministic Annealing EM Algorithm," ATR Interpreting Telecommunications Research Laboratories, May 1995.
• "Deterministic Annealing EM Algorithm," GA Research Meeting, April 1995.
• "Ensemble Learning," Osaka University, January 1995.

Career

• Part-time Lecturer, (Kobe University, Introduction to Data Science A), 2020.
• Guest Professor, (Meiji University,Graduate School of Advanced Mathematical Sciences), 2020-present.
• Guest Professor, (Kobe University, Graduate School of System Informatics), 2018-present.
• Part-time Lecturer, (Nagoya University, Graduate School of Information Science), 2016-present.
• Part-time Lecturer, (Graduate School of Informatics, Kyoto University, Bayesian Learning & Data Mining), 2010-present
• Part-time Lecturer, (Yamagata University, Advanced machine learning), 2010
• Part-time Lecturer, (Tokyo University, Bayesian Learning Theory), 2007
• Guest Professor, (Nara Advanced Institute of Science and Technology (NAIST), 2004-2013.
• Guest Associate Professor, (Nara Advanced Institute of Science and Technology (NAIST), 1998-2004
• Part-time Lecturer, (Osaka University), 2003-2011.
• Part-time Lecturer, (Kyoto University, Pattern Recognition and Bayesian Learning), 2003-present.
• Part-time Lecturer, (Waseda University, Statistical Learning Theory), 2003.
• Part-time Lecturer, (Okayama University, Statistical Pattern Recognition), 1999.
• Part-time Lecturer, (Nagoya Institute of Technology, Electrical and Computer Engineering), 2002.
• Part-time Lecturer, (Tsukuba University, Statistical Learning Theory), 1999-2002. "

Journal Papers

• Saeidi,V.,Seydi,S.T.,Kalantar,B.,Ueda,N.,Tajfirooz,B.,& Shabani,F.,"Water depth estimation from Sentinel-2 imagery using advanced machine learning methods and explainable artificial intelligence",Geomatics,Natural Hazards and Risk,14(1),2225691,2023.
• Takahashi,A.,Hokari,H.,Doi,M.,Yoshikawa,N.,Mariyama,T.,Ueda,N.,and Hirai,N.,"Using active cooling/heating for 1C1R gray-box model parameter identification in actual environment: a proof-of-concept study," Building Services Engineering Research & Technology (Sage Journals) (in preparation)
• Mulia,I.E.,Ueda,N.,Miyoshi,T.,Iwamoto,T.& Heidarzadeh,M.A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields. Scientific Reports 13,7918,2023. doi:10.1038/s41598-023-35093-9.
• Hachiya,H.,Nagayoshi,K.,Iwaki,A.,Maeda,T.,Ueda,N.,Fujiwara,H.,"Position-dependent partial convolutions for supervised spatial interpolation,"Machine Learning with Applications,100514-100514,2023.
• Hachiya,H.,Masumoto,Y.,Kudo,A.,and Ueda,N.,"Encoder-decoder-based image transformation approach for integrating multiple spatial forecasts,"Machine Learning with Applications 12(100473) 1-11,2023.
• Murakami,S.,Fujita,K.,Ichimura,T.,Hori,T.,Hori,M.,Lalith,M.,and Ueda,N.,"Development of 3D viscoelastic crustal deformation analysis solver with data-driven method on GPU, Lecture Notes in Computer Science, vol 14074,2023, https://doi.org/10.1007/978-3-031-36021-3_45
• Mulia, I. E., Ueda, N., Miyoshi, T., Iwamoto, T. & Heidarzadeh, M. A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields. Scientific Reports 13, 7918 (2023). doi: 10.1038/s41598-023-35093-9
• Hachiya, H., Masumoto, Y., Kudo, A., and Ueda,N.,"Encoder-decoder-based image transformation approach for integrating multiple spatial forecasts,," Machine Learning with Applications, Vol.12, No.5, 2023.
• Okazaki, T., Ito, T., Hirahara., and Ueda, N., "Physics-informed deep learning approach for modeling crustal deformation," Nature Communications, 13, 7092, 2022.
• Mulia, I., Ueda, N., Miyoshi, T., Gusman, A.R. and Satake, K., "Machine learning-based tsunami inundation prediction derived from offshore observations," Nature Communications, 13, 5489, 2022.
• Okazaki, T., Fukuhata, Y., and Ueda, N., "Time variable stress inversion of centroid moment tensor using Gaussian processes," Journal of Geophysical Research (JGR): Solid Earth, 2022.
• Takahashi, I., Hamasaki, R., Ueda, N., Tanaka, M., Tominaga, N., Sako, Shigeyuki, Ohsawa, R., and Yoshida, N., "Deep-learning real/bogus classification for the Tomo-e Gozen transient survey," Publication of the Astronomical Society of Japan, Vol.74, Issue 4, pp.946--960, 2022.
• Saed, F. G., Noori, A. M., Kalantar, B., Oader, W. M., and Ueda, N., "Earthquake-induced ground deformation assenment via sentinel-1 rader aided at Darbandikhan town," Journal of Sensors, Vol. 2022, Article ID 2020069, 2022.
• Seydi, S. T., Saeidi, V., Kalantar, B., Ueda., N Genderen, V., Maskouni, F. H., and Aria, F. A., "Fusion of the multisource datasets for flood extent mapping based on ensemble convolutional neural network (CNN) model," Journal of Sensors, Vol.2022, ID 2887502, 2022.
• Okazaki, T., N. Morikawa, A. Iwaki, H. Fujiwara, T. Iwata, N. Ueda,. "Ground-Motion Prediction Model Based on Neural Networks to Extract Site Properties from Observational Records," Bulletin of the Seismological Society of America, 2021.
• Okazaki, T., H. Hachiya, A. Iwaki, T. Maeda, H. Fujiwara, N. Ueda,." Broad-band ground motions with consistent long-period and short-period components using Wasserstein interpolation of acceleration envelopes," Geophysical Journal International, 2021.
• Kalantar, B., Ueda, N., Saeidi, V., Janizadeh, S., Shabani, F., Ahmadi, K., & Shabani, F., "Deep neural network utilizing remote sensing datasets for flood hazard susceptibility mapping in Brisbane," Australia. Remote Sensing, 13(13), 2021.
• Ojogbane, S. S., Mansor, S., Kalantar, B., Khuzaimah, Z. B., Shafri, H. Z. M., & Ueda, N., "Automated Building Detection from Airborne LiDAR and Very High-Resolution Aerial Imagery with Deep Neural Network. Remote Sensing," 13(23), 2021.
• Al-Dogom, D., Al-Ruzouq, R., Kalantar, B., Schuckman, K., Al-Mansoori, S., Mukherjee, S., & Ueda, N., "Geospatial multicriteria analysis for earthquake risk assessment: Case Study of Fujairah City in the UAE," Journal of Sensors, 2021.
• Jumaah, H. J., Kalantar, B., Halin, A. A., Mansor, S., Ueda, N., & Jumaah, S. J., "Development of UAV-based PM2. 5 monitoring system. Drones," 5(3), 2021.
• Tehrany, M. S., Özener, H., Kalantar, B., Ueda, N., Habibi, M. R., Shabani, F., & Shabani, F., "Application of an ensemble statistical approach in spatial predictions of bushfire probability and risk mapping," Journal of Sensors, 2021.
• Ameen, M. H., Jumaah, H. J., Kalantar, B., Ueda, N., Halin, A. A., Tais, A. S., & Jumaah, S. J., "Evaluation of PM2. 5 particulate matter and noise pollution in Tikrit University based on GIS and statistical modeling," Sustainability, 13(17), 2021.
• Hamed, H. H., Jumaah, H. J., Kalantar, B., Ueda, N., Saeidi, V., Mansor, S., & Khalaf, Z. A., "Predicting PM2. 5 levels over the north of Iraq using regression analysis and geographical information system (GIS) techniques. Geomatics, Natural Hazards and Risk," 12(1), pp.1778-1796, 2021.
• Futami, F., Iwata, T., Ueda, N., and Sato, I., "Accelerated diffusion-based sampling by the non-reversible dynamics with skew-symmetric matrices," Special Issue "Approximate Bayesian Inference," Entropy, 2021.
• Okazaki, T., Morikawa, N., Fujiwara, H., and Ueda, N., "Monotonic neural network for ground ｍotion predictions to avoid overfitting to recorded site, " Seismological Research Letters, 2021.
• Okawa, M., Owata, T., Kurashima, T., Tanaka, Y., Toda, H., and Ueda, N., "Deep mixture point processes, " Transaction of the Japanese Society for Artificial Intelligence, 2021.
• Fujiwara, Y., Kanai, S., Ida, Y., Kumagai, A., and Ueda, N.,"Fast algorithm for anchor graph hashing," Proc. of the VLDB　Endowment, Vol.14, Issue 6, 2021.
• Tanaka, Y., Iwata、T., Kurashima、T., Ueda. N., Tanaka、T.,"Time-delayed collective flow diffusion models for inferring latent people flow from aggregated data at limited locations," Artificial Intelligence, Vol.292, 103430, 2021.
• Kalantar, B., Ueda, N.,Saeidi, V.,Ahmadi, K.,Halin, A.A., and Shabani, F.,"Landslide susceptibility mapping: Machine and ensemble learning based on remote sensing big data," Remote Sensing, 12(11), 1737, 2020.
• Takahashi, I, Suzuki, Nao, Yasuda, N., Kimura, A., Ueda, N., Tanaka, M., Tominaga, N., Yoshida, N.,"Photometric classification of hyper suprime-cam transients using machine learning," Publications of the Astronomical Society of Japan, Vol.72, Issue 5, 89, pp.1-22, 2020.
• Iwata, T., Toyoda,M., Tora,S., and Ueda,N., "Anomaly Detection with Inexact Labels," Machine Learning, Vol.109, Issue. 8, pp.1617-1633, 2020.
• Gibril, M. B. A., Kalantar, B., Al-Ruzouq, R., Ueda, N., Saeidi, V., Shanableh, A., Mansor, S., and Shafri, H. Z. M., "Mapping heterogeneous urban landscapes from the fusion of digital surface model and unmanned aerial vehicle-based images using adaptive multiscale image segmentation and classification," Remote Sensing, 2020,12(7), 1081; https://doi.org/10.3390/rs12071081, 2020.(to appear).
• Yamamoto, Y., Tsuzuki, T., Akatsuka, J., Ueki, M., Morikawa, H., Numata, Y., Takahara, T., Tsuyuki, T., Shimizu, A., Maeda, K., Tsuchiya, S., Kanno, H., Kondo, Y., Tamiya, G., Ueda, N., and Kimura, G., "Automated acquisition of explainable knowledge from unannotated," Nature Communications, 10, 5642 2019.
• Kalantar, B., Al-Najjar, H.A.H., Pradhan, B., Saeidi, V., Halin, A.A, Ueda, N., Naghibi., S.A.,"Optimized conditioning factors and machine learning for groundwater potential mapping," Water Journal, 2019.
• Al-Najjar, H. A., Kalantar, B., Pradhan, B., Saeidi, V., Halin, A. A., Ueda, N., and Mansor, S.,"Land cover classification from fused DSM and UAV images using convolutional neural networks", Remote Sensing, 11(12), 1461, 2019.
• Yasuda, N., Tanaka, M., Tominaga, N., Jiang, J., Moriya, T., Morokuma, T., Suzuki, N., Takahashi, I., Yamaguchi, M., Maeda, K., Sako, M., Ikeda, S.,Kimura, A., Morii, M., Ueda, N., Yoshida, N., Lee, C., Suyu, S., Komiyama, Y., Regnault, N., and Rubin, D., "The Hyper Suprime-cam SSP transient survey in COSMOS: Overview," Publications of the Astronomical Society of Japan, vol.71, No.4, pp.1--16, 2019.
• Ueda, N., asd Fujino, A., "Partial auc maximization via nonlinear scoring functions," Xiv submit/2294250, 2018.
• Ueda, N., and Naya, F., "Spatio-temporal multidimensional collective data analysis for providing comfortable living anytime and anywhere," APSIPA Transactions on Signal and Information Processing, Vol.7, No.4, 2018.
• Iwata, T., Hirao, T., Ueda, N.,"Topic models for unsupervised cluster matching," IEEE Transactions on Knowledge and Data Engineering, Volume:30, Issue:4, pages 786--795, 2018.
• Iwata, T., Shimizu, H., Naya, F. and Ueda, N., "Estimating people flow from spatio-temporal population data via collective graphical mixture models," ACM Transactions on Spatial Algorithms and Systems, Vol. 3, Issue 1, Article 39. 2017.
• Ishiguro, K., Sato, I. and Ueda, N., "Averaged collapsed variational Bayes inference," Journal of Machine Learning Resaerch (JMLR), Volume 18, Number 1, pp.1--29, 2017.
• Ueda, N., "Proactive People-flow Navigation Based on Spatio-temporal Prediction," Japanese Journal Applied Statistics, Vol.45, No.3, pp.89-104, 2016, (invited).
• Morii, M., Ikeda, S., Tominaga, N., Tanaka, M., Morokuma, T., Ishiguro, K., Yamato, J., Ueda, N., Suzuki, N., Yasuda, N. and Yoshida, N., "Machine-learning selection of optical transients in Subaru/hyper suprime-cam survey," Publication of Astronomical Society of Japan, Vol.68, No.6, pp.104-112, 2016.
• Inoue, S., Ueda, N., Nohara, Y. and Nakashima, N., "Recognizing and understanding nursing activities for a whole day with a big data set," Journal of Information Processing, Vol.57, No.10, 2016.
• Ueda, N., "Spatio-temporal prediction and its application to proactive people-flow naviation," Journal of the Institute of Image Electronics Engeneers of Japan (in Japanese), Vol.45, No.1, pp4-11, 2016.
• Iwata, T., Hirao T. and Ueda, N., "Unsupervised many-to-many object matching via probabilistic latent variable models," Information Processing & Management, Volume 52, Issue 4, pp682-697, July 2016.
• Blondel, M., Onogi, A., Iwata, H. and Ueda, N., "A Ranking Approach to Genomic Selection," PLOS ONE (peer-reviewed open acces journal), Public Library of Science, 2015.
• Nohara, Y., Kai, E., Ghosh, P., Islam, R., Ahmed, A., Kuroda, M., Inoue, S., Hiramatsu, T., Kimura, M., Shimizu, S., Kobayashi, K., Baba, Y., Kashima, H., Tsuda, K., Sugiyama, M., Blondel, M., Ueda, N., Kitsuregawa, M. and Nakashima, N., "Health Checkup and Telemedical Intervention Program for Preventive Medicine in Developing Countries: Verification Study," Journal of Medical Internet Research, Vol.17, No.1 January 2015.
• Tanaka, Y., Ueda, N. and Tanaka, T., "Bayesian classifier based on class-specific feature selection," Transactions of IEICEJ, Vol.J96-D-DII, No.11, pp.2755-2764, 2013, (in Japanese).
• Sun, X., Kashima, H. and Ueda, N., "Large-Scale Personalized Human Activity Recognition using Online Multi-Task Learning," IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol.25, No.11, pp.2551-2563, 2013. [IEEE Copyright Notice]
• Sawada, H., Kameoka, H., Araki, S. and Ueda, N., "Multichannel Extensions of Non-negative Matrix Factorization with Complex-valued Data," IEEE Transactions on Audio, Speech, and Language Processing, Vol.21, No.5, pp.971-982, 2013.[IEEE Copyright Notice]
• Iwata, T., Yamada, T. and Ueda, N., "Modeling Noisy Annotated Data with Application to Social Annotation," IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol.25. No.7, pp.1601-1613, 2013.[IEEE Copyright Notice]
• Fujino, A., Ueda, N. and Nagata, M., "Adaptive semi-supervised learning on labeled and unlabeled data with different distributions," Knowledge and Information Systems(KAIS), Vol. 37, Issue 1, pp. 129-154, Springer, 2013, (invited paper).
• Iwata, T., Yamada, T., Sakurai, Y. and Ueda, N., "Sequential Modeling of Topics Dynamics with Multiple Timescales," ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 5 Issue 4, 19:1-19:27, 2012.
• Hachiya, H., Sugiyama, M. and Ueda, N., "Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition," Neurocomputing, Vol. 80, pp 93-101, 2012.
• Fujino, A., Ueda, N. and Nagata, M., "Robust Semi-supervised Learning for Labeled Data Selection Bias," Transaction of Information Processing Society of Japan, Vol.4, No.2, pp. 31-42, 2011, (in Japanese).
• Iwata, T., Tanaka, T., Yamada, T. and Ueda, N., "Improving Classifier Performance Using Data with Different Taxonomies," IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol.23, No.11, pp. 1668-1677, 2011. .[IEEE Xplore] [DOI link] [IEEE Copyright Notice]
• Fujino, A., Ueda, N. and Nagata, M., "Robust semisupervised learning for data selection bias," Transaction of Information Processing Society of Japan, Vol.2010-MPS-80 No.8, 2010, (in Japanese).
• Ishiguro, K., Iwata, T., and Ueda, N.,"Dynamic Infinite Relational Model for Time-dependent Relational Data Analysis," Transaction of Information Processing Society of Japan, Vol.3, No.1, 1-12, 2010, (in Japanese).
• Iwata, T., Watanabe, S., Yamada, T., and Ueda, N., "Topic Tracking Model for Purchase Behavior Analysis," Transactions of IEICEJ, Vol.J93-D, No.6, pp.978-987, 2010, (in Japanese).
• Iwata, T., Tanaka, T., Yamada, T., and Ueda, N., "Model Learning when Distributions Differ over Time," Transactions of IEICEJ, Vol.J92-D, No.3, 361-370, 2009, (in Japanese).
• Iwata, T., Yamada, T., and Ueda, N., "Visualizing Documents based on Topic Models," Journal of Information Processing Society of Japan, Vol.50, No.6,1649-1659, 2009, (in Japanese).
• Kawamae, N., Sakano, H., Yamada, T., and Ueda, N., "Collaborative filtering focusing on the dynamics and precedence of user preference," Transactions of IEICEJ, (D-II), Vol. J92-DII, No.6, pp. 767-776, 2009, (in Japanese).
• Fujino, A., Ueda, N., and Saito, K., "Semisupervised learning for a hybrid generative/discriminative classifier based on the maximum entropy principle," IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 30(3), 424-437,2008. [IEEE Xplore] [DOI link] [IEEE Copyright Notice]
• Ueda, N., Yamada, T., and Kuwata, S., "Co-clustering Discrete Data Based on the Dirichlet Process Mixture Model," Transaction of Information Processing Society of Japan, Vol.1 No.1 (pp. 60-73), 2008, [Transaction of Information Processing Society of Japan],(in Japanese).
• Iwata, T., Yamada, T., and Ueda, N., "Collaborative filtering efficiently using purchase orders," Transaction of Information Processing Society of Japan, Vol.49, No.SIG4 (TOM20), pp. 125-134, 2008, (in Japanese).
• Naud, A., Usui, S., Ueda, N., and Taniguchi. T., "Visualization of documents and concepts in Neuroinformatics with the 3D-SE Viewer," Neuroinformatics, 2007.
• Kuwata, S., and Ueda, N., "One-shot Collaborative Filtering," Transaction of Information Processing Society of Japan, Vol.48, No.SIG_15(TOM_18), pp. 153-162, 2007, [Transaction of Information Processing Society of Japan], (in Japanese).
• Kawamae, N., Yamada, T., and Ueda, N., "Personalized Ranking by Identifying, Relative Innovators," FIT2007 Letters, Vol.6, pp.99-102, 2007.
• Kuwata, S., and Ueda, N., "An efficient collaborative filtering algorithm based on marginal rating distributions," International Journal of IT & IC, IEEE CIS, Vol.2, No.1, 2007.
• Fujino, A., Ueda, N., and Saito, K., "Semi-supervised Learning of Multi-class Classifiers for Multi-component Data," Transaction of Information Processing Society of Japan, Vol.48, No.SIG_15(TOM_18), pp. 163-175, 2007, [Transactions of IPSJ.], (in Japanese)
• Fujino, A., Ueda, N., and Saito, K., "A hybrid generative/discriminative approach to text classification with additional information," Information Processing & Management, Elsevier, Vol.43, No.2, pp. 379-392, 2007.
• Usui, S., Plames, P., Nagata, K., Taniguchi, T., and Ueda, N., "Keyword extraction, ranking, and organization for the neuroinfomatics platform," Biosystems, Elsevier Science, Vol.88, Issue 3, pp. 334-342, 2007, [Biosystems].
• Iwata, T., Saito, K., Ueda, N., Stromsten, S., Griffiths, T., and Tenenbaum, J., "Parametric Embedding for Class Visualization," Neural Computation Vol. 19, No. 9, pp. 2536-2556: 2536-2556, 2007.
• Kawamae, N., Yamada, T., and Ueda, N., "Personalized Ranking by Identifying, RelativeInnovators," FIT2007 Letters, Vol.6, pp.99-102, 2007.
• Ueda, N. and Yamada, T., "Nonparametric Bayes," Journal of Japanese Applied Mathematics Vol.17, No.3, pp.196-214, 2007.
• Fujino A., Ueda, N., and Saito, K., "Text Classification by Effectively Using Additional Information Based on Maximum Entropy Principle (Information Retrieval)," Transaction of Information Processing Society of Japan, Vol.47, No.10, pp. 2929-2937, 2006, [ Transactions of IPSJ.], (in Japanese).
• Fujino, A., Ueda, N., and Saito, K., "A hybrid generative/discriminative classifier design for semi-supervised learning," Journal of JSAI，Vol.21, No.3, pp.301-309, 2006, (in Japanese).
• Kimura, M., Saito, K., and Ueda, N., "Modeling network growth with directional attachment and communities," Systems and Computers in Japan, Vol. 35, No. 8, pp. 1-11, 2004, [Systems and Computers in Japan].Ueda, N. and Saito, K., "Parametric mixture models for multi-topic text," Systems and Computers in Japan, Vol.37, No.2, pp. 56-66, 2006, [Systems and Computers in Japan]
• Ueda, N., "Ensemble Learning," Transactions of IPSJ, CVIM-1036, (invited) Vol.46, No.SIG15(CVIM 12), pp. 11-20, 2005, [Transaction of Information Processing Society of Japan], (in Japanese)
• Iwata, T., Saito, K., Ueda, N., Stromsten, S., Griffiths, T., and Tenenbaum, J., "Parametric Embedding for Class Visualization," Neural Computation, vol.46,no.9, pp. 2337-2346, 2005, (in Japanese).
• Fujino, A., Ueda, N., and Saito, K., "Semi-supervised learning on hybrid generative/discriminative models," FIT2005 Letters, 2005, (in Japanese).
• Iwata, T., Saito, K., and Ueda, N., "Visualization via posterior preserving embedding," FIT2004 Letters, Vol. 3, pp. 119-120, 2004, (in Japanese).
• Kaneda, Y., Saito, K., and Ueda, N., "Automatic extraction of correspondences between document taxonomies," FIT2004 Letters, Vol. 3, pp.121-122, 2004, (in Japanese).
• Kaneda, Y. and Ueda, N., "A Robust text data clustering method for high-dimensional data," FIT2004 Letters, Vol. 3, pp. 123-124, 2004, (in Japanese).
• Fujino, A., Ueda, N., and Saito, K., "Relevance feedback with cross validation," FIT2004, Vol. 3, pp. 53-54, 2004, (in Japanese).
• Kimura, M., Saito, K., and Ueda, N., "Modeling share dynamics by extracting competition structure," Physica D, Vol.198, pp. 51-73, 2004.
• Watanabe, S., Minami, Y., Nakamura, A., and Ueda, N., "Variational Bayesian Estimation and Clustering for Speech Recognition," IEEE transaction on Speech and Audio Processing, Vol. 12, pp. 365-381, 2004.
• Kimura, M., Saito, K., and Ueda, N., "Modeling of growing networks with directional attachment and communities," Neural Networks, Vol. 17, No. 7, pp. 975-988, 2004.
• Ueda, N., and Saito, K., "Parametric Mixture Models for Multi-Topic Text," Transactions of IEICEJ, (D-II), Vol. J87-DII, No.3, pp. 872-883, 2004, (in Japanese)
• Ueda, N. and Inoue, M., "Extended Tied-Mixture HMMs for Both Labeled and Unlabeled Time Series Data," Journal of VLSI Signal Processing Systems, Vol. 37, pp. 189-197, 2004.
• Kimura, M., Saito, K., and Ueda, N., "Modeling of growing networks with directional attachment and communities," Transactions of IEICEJ, Vol. J86-DII, No, 10, pp. 1468-1479, 2003, (in Japanese).
• Ueda, N. and Saito, K., "Multi - topic Text Model for Topic - based Text Retrieval," Transaction of Information Processing Society of Japan Vol. 44, No. SIG14(TOM9), pp. 1-8, 2003, [Transaction of Information Processing Society of Japan] (in Japanese).
• Yamada, T., Saito, K., and Ueda, N., "Embedding networks data based on cross-entropy minimization," Transaction of Information Processing Society of Japan Vol. 44, No. 9, pp. 2401-2408, 2003, [Transaction of Information Processing Society of Japan] (in Japanese).
• Inoue, M. and Ueda, N., "Exploitation of unlabeled sequences in hidden markov models," IEEE Transaction on Pattern Analysis and Machine Intelligence (PAMI), Vol. 25, No. 12, pp1570-1581, 2003.
• Watanabe, S., Minami, Y., Nakamura, A., and Ueda, N., "Selection of Shared-States Hidden Markov Model Structure Using Bayesian Criterion," Transactions of IEICEJ, Vol. J86-DII, No. 6, pp. 776-786, 2003, (in Japanese).
• Ueda, N. and Ghahramani, Z., "Bayesian model search for mixture models based on optimizing variational bounds," Neural Networks, Vol.15, No.10, pp. 1223-1241, 2002.
• Inoue, M. and Ueda, N., "Use of Unlabeled Time Series Data in Hidden Markov Models," Transactions of IEICEJ, Vol. J86-DII, No. 2, pp. 173-183, 2003 (in Japanese).
• Ueda, N., "Variational Bayesian Learning for Optimal Model Search," Journal of Japanese Society for Artificial Intelligence, Vol.16, No.2, 2001, (in Japanese).
• Suzuki, S. and Ueda, N., "Adaptive clustering method using modular learning architecture," Transactions of IEICEJ, Vol. J83_DII, No. 6, pp. 1529-1538, 2000, (in Japanese).
• Ueda, N., "EM algorithm with split and merge operations for mixture models (invited)," Transactions of IEICE, Vol. E83-D, No. 12, pp. 2047-2055, 2000.
• Suzuki, S., and Ueda, N., "Adaptive clustering method using modular learning architecture," Transactions of IEICEJ, Vol. J83_DII, No. 6, pp. 1529-1538, 2000, (in Japanese).
• Ueda, N., Nakano, R., Ghahramani, Z., and Hinton, G. E., "SMEM Algorithm for Mixture Models," Neural Computation, Vol. 12, No. 9, pp. 2109-2128, 2000.
• Ueda, N., Nakano, R., Ghahramani, Z., and Hinton, G. E..,"Split and merge EM algorithm for improving Gaussian mixture density estimates (invited)," Journal of VLSI Signal Processing, Vol. 26, pp. 133-140, 2000.
• Ueda, N., "Optimal Linear Combination of Neural Networks for Improving Classification Performance," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). Vol. 22, No.2, pp. 207-215, 2000.
• Ueda, N. and Nakano, R., "Probabilistic Mixture Subspace Method," Transactions of IEICE, Transactions of IEICEJ, Vol. J82-DII, No. 12, pp. 2394-2401, 1999, (in Japanese).
• Ueda, N. and Nakano, R., "EM Algorithm with Split and Merge Operations for Mixture Models," Transactions of IEICE, Transactions of IEICEJ, Vol. J82-DII, No. 5, pp. 930-940, 1999, (in Japanese).
• Ueda, N., "Optimum Linear Combination of Neural Network Classifiers Based on the Minimum Classification Error Criterion," Transactions of IEICEJ, Vol. J82_DII, No. 3, pp. 522-530, 1999, (in Japanese).
• Ueda, N. and Nakano, R., "Deterministic Annealing EM Algorithm," Neural Networks, Vol. 11, No. 2, pp. 271-282, (1998).
• Ueda, N. and Nakano, R., "Analysis of Generalization Error on Ensemble Learning," Transactions of IEICEJ, Vol. J80-DII, No. 9, pp. 2512-2521, 1997, (in Japanese).
• Ueda, N. and Nakano, R., "Deterministic Annealing EM Algorithm," Transactions of IEICEJ, Vol. J80-DII, No. 1, pp. 267-276, 1997, (in Japanese).
• Ueda, N. and Mase, K., "Tracking Moving Contours Using Energy-minimizing Elastic Contour Models," International Journal of Pattern Recognition and Artificial Intelligence, Vol. 9, No. 3, pp. 465-484, 1995.
• Ueda, N. and Nakano, R., "A New Competitive Learning Approach Based on an Equidistortion Principle for Designing Optimal Vector Quantizers," Neural Networks, Vol.7, No.8, pp. 1211-1227, 1994.
• Ueda, N. and Nakano, R., "Competitive and Selective Learning method for Vector Quantizer Design - Equidistortion Principle and Its Algorithm -," Transactions of IEICEJ, Vol. J77-DII, No. 11, pp. 2265-2278, 1994, (in Japanese).
• Ueda, N. and Suzuki, S., "Learning Visual Models from Shape Contours Using Multiscale Convex/Concave Structure Matching," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 15, No. 4, pp. 337-352, 1993, [ IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI).
• Suzuki, S., Ueda, N. and Sklansky, J., "Graph-Based Thinning for Binary Images," International Journal of Pattern Recognition and Artificial Intelligence, Vol. 7, No. 5 pp. 1009-1030, 1993.
• Ueda, N., Mase K., and Suenaga Y., "A Contour Tracking Method Using Elastic Contour Model and Energy Minimization Approach," Transactions of IEICEJ, Vol. J75-DII, No. 1, pp. 111-120, 1992, (in Japanese).
• Ueda, N. and Suzuki, S., "Automatic Shape Model Acquisition Based on A Generalization of Convex/Concave Structure," Transactions of IEICEJ, Vol. J74-DII, No. 2, pp. 220-229, 1991, (in Japanese).
• Ueda, N. and Suzuki, S., "A Deformed Line-Drawing Matching Algorithm Using Multiscale Convex/Concave Structures," Transactions of IEICEJ, Vol. J73-DII, No. 7, pp. 992-1000, 1990, (in Japanese).
• Ueda, N., Nagura, M., Kosugi, M., and Mori, K., "Image Enhancemen Method for Law Quality Drawings," Transactions of the Institute of Television Engineers of Japan, Vol. 42, No. 8, pp. 831-836, 1988, (in Japanese).

International Conference Papers

• Kalantar,B.,Ueda,N.,Zand,M.,& Al-Najjar,H.,"Moving object detection by low-rank analysis of region-based correlated motion fields," GARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, (pp. 5874-5877), 2023.
• Fujiwara, Y., Nakano, M., Kumagai, A., Ida, Y., Kimura, A., and Ueda, N., "Fast binary network hashing via graph clustering," Proc. of IEEE Conference on Bigdata, 2022.
• Ichimura, T., Fujita, K., Koyama, K., Kusakabe, R., Kikuchi, Y., Hori, T., Hori, M., Maddegedara, L., Ohi, N., Nishiki, T., Inoue, H., Minami, K., Nishizawa, S., Tsuji, M., and Ueda, N., "152K-computer-node parallel scalable implicit solver for dynamic nonlinear earthquake simulation, " Proc. of the International Conference on High Performance Computing in Asia-Pacific Region, HPC Asia 2022. (Best Paper Finalists)
• Tanaka, Y., Iwata, T., and Ueda, N., "Symplectic spectrum Gaussian processes: Learning a Hamiltonian from Noisy and sparse data, " Proc. of Neural Information Processing Systems, NeurIPS2022.
• Mulia, I. E., Ueda, N., Miyoshi, T., Gusman, A. R., Satake, K., "Method for real-time prediction of tsunami inundation directly from offshore observations using machine learning," AGU Fall Meeting 2021, virtual meeting, 13-17 December 2021.
• Hachiya, H., Nagayoshi, K., Iwasaki, A., Maeda, T., Ueda, N., and Fujiwara, H, "Position-dependent partial convolutions for supervised spatial interpolation, " Proc. of The 14th Asian Conference on Machine Learning (ACML), 2022.
• Tanaka, Y., Iwata, T., and Ueda, N., "Symplectic Spectrum Gaussian Processes: Learning a Hamiltonian from Noisy and Sparse Data," Proc of Neural Information Processing Systems, NeurIPS2022.
• Kalantar, B., Ojogbane,S. S., Seydi,S. T., Halin, A., Mansor, S., Ueda,N, "A deep learning approach for automated building outlines extraction in compact urban environments," Proc. of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2022.
• Kalantar, B., Seydi, S. T., Ueda,N., Saeidi, V., Halin, A. A., Shabani, F.,"Deep ensemble learning for land cover classification based on hyperspectral prisma image," Proc. of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2022.
• Nakano, M., Nishikimi, R., Fujiwara, Y., Kimura, A., Yamada, T., and Ueda, N., "Nonparametric relational models with superrectangulation," Proc. of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS2022), 2022.
• Fujiwara, Y., Ida, Y., Kumagai, A., kanai, S., and Ueda, N., "Fast and accurate anchor graph-based label prediction,"
  Proc of the 30th ACM International Conference on Information and Knowledge Management (CIKM), pp.504--513, 2021.
• Jumaah, H. J., Kalantar, B., Ueda, N., Sani, O. S., Ajaj, Q. M., & Jumaah, S. J., "The effect of war on land use dynamics in mosul Iraq using remote sensing and GIS techniques," In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 6476-6479), 2021.
• Fujita, K., Kikuchi, Y., Ichimura, T., Hori, M., Maddegedara, L., and Ueda, N., "GPU porting of scalable implicit solver with Green's function-based neural networks by open ACC," Proc. of Eighth Workshop on Accelerator Programming using Directives (WACCPD), 2021.
• Futami, F., Iwata, T., Ueda, N., Sato, I., and Sugiyama, M., "Loss function based second-order Jensen inequality and its application to particle variational inference, " Proc. of Neural Information Processing Systems, NeurIPS 2021.
• Nakano, M., Fujiwara, Y., Kimura, A., Yamada, T., and Ueda, N., "Permuton-induced Chinese restaurant process," Proc. of Neural Information Processing Systems, NeurIPS 2021.
• Yamagishi, Y., Saito, K., Hirahara, K., and Ueda, N., "Constructing weighted networks of earthquakes with multiple-parent nodes based on correlation-metric," Proc. of International Conference on Complex Networks and their Applications, COMLEX NETWROKS2021.
• Nakano, M., Fujiwara, Y., Kimura, A., Yamada, T., and Ueda, N., "Bayesian nonparametric model for arbitrary cubic partitioning," Proc. of Asian Conference on Machine Learning (ACML2021), 2021.
• Futami, F., Iwata, T., Sato, I,, and Ueda, N., "Skew symmetrically perturbed gradient flow for convex optimization," Proc. of Asian Conference on Machine Learning (ACML2021), 2021.
• Hachiya, H., Masamoto, Y., Mori, Y., and Ueda, N., "Encoder-decoder-based image transformation approach for integrating precipitation forecasts," Proc. of Asian Conference on Machine Learning (ACML2021), 2021.
• Yamagishi, Y., Saito,K., Hirahara, K., and Ueda, N., "Magnitude-weighted mean-shift clustering with leave-one-out bandwidth estimation," Proc. of Pacific Rim International Conference on Artificial Intelligence (PRICAI2021), 2021.
• Nakano, M., Kimura, A., Yamada, T, and Ueda, N., "Baxter permutation process, " Proc. of Neural Information Processing Systems, NeurIPS 2020.
• Yamaguchi, Y., Saito, K., Hirahara, K., and Ueda, N., "Spatio-temporal clustering of earthquakes based on average magnitudes," Proc. of International Conference on Complex Networks and their Applications, 2020.
• Yamaguchi, T., Ichimura, T., Fujita, K., Hori, M., Wijerathne, L., and Ueda, N., "Data-driven approach to inversion analysis of three-dimensional inner soil structure via wave propagation analysis," Proc. of International Conference on Computational Science (ICCS-2020).
• Fujiwara、Y., Kumagai, A., Kanai, S., Ida, Y., and Ueda, N.,"Efficient algorithm for the b-matching graph," proc. of ACM SIG-KDD 2020.
• Miyoshi, T., Honda,T., Otsuka,S., Amemiya, A., Maejima,Y., Ishikawa, Y., Seko, H.,Yoshizaki,Y., Ueda,N., Tomita, H., Ishikawa,Y., Satoh,S., Ushio,T., Koike,K., and Nakada, Y., "Big data assimilation: Real-time workflow for 30-second-update forecasting and perspectives toward DA-AI integration," Proc. of EGU General Assembly, EGU2020-2483, 2020.
• Kalantar, B., Ueda, N., Al-Najjar, H. A. H.Saeidi, V., Gibril, M. B. A, Halin, A.,"A comparison between three conditioning factors dataset for landslide prediction in the Sajadrood Catchment of Iran," Proc. of ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS),2020 (to appear).
• Yamaguchi, T., Ichimura, T., Fujita, K., Naruse,A., Wells,J.C., Zimmer, C. J., Straatsma,T.P., Hori,M., Lalith, W., and Ueda, N., "Implicit low-order finite element solver with small matrix-matrix multiplication accelerated by AI-specific hardware," Proc. Of Platform for Advanced Scientific Computing Conference (PASC2020), 2020 (accepted)
• Ichimura, T., Fujita, K., Yamaguchi, T., Hori, M., Wijerathne, L., and Ueda, N, "Fast multi-step optimization with deep learning for data-centric supercomputing," The 4th International Conference on High Performance Compilation, Computing and Communications, 2020 (accepted)
• Iwata, T., Fujino, A., Ueda, N., "Semi-supervised Learning for maximizing the partial AUC," Proc. of Association for the Advancement of Artificial Intelligence (AAAI2020), 2020.
• Hachiya, H., Hirahara,K.,and Ueda, N.,"Machine learning approach for adaptive integration of multiple relative intensity models toward improved earthquake forecasts in Japan," International Union of Geodesy and Geophysics (IUGG2019),2019.
• Hachiya, H., Yamamoto, Y., Hirahara, K, and Ueda, N., "Adaptive truncated residual regression for fine-grained regression problems," Proc. of Asian Conference on Machine Learning (ACML), 2019.
• Miyoshi, T., Otsuka, S., Honda, T., Lien, G., Maejima, Y., Ohhigashi, M., Yoshizaki, Y., Seko, H., Tomita, H., Satoh, S., Ushio, T., Gerofi, B., Ishikawa, Y., Ueda, N., Koike, K., Nakada, Y., "Big data assimilation: Past 6 years and future plans," AMS 39th Conference on Radar Meteorology, 2019. *AMS: American Meteorological Society
• Otsuka,T.,Shimizu,H.,Iwata,T.,Naya,F.,Sawada,H., and Ueda,N., "Bayesian optimization for crowd traffic control using multi-agent simulation," Proc. Intelligent transportation systems conference (ITSC), 2019
• Omi, T, Ueda, N, and Aihara, K,"Fully neural based model for general temporal point processes," Proc. Neural Information Processing Systems, NeuriPS2019.
• Okazaki, T, Hachiya, H, Ueda, N., Iwaki, A., Maeda, T. and Fujiwara, H.,"Synthesis of broadband ground motions using embedding and neural networks," Geophysical Research Abstracts, Vol. 21, EGU2019-4590, EGU General Assembly 2019.
• Ichimura,T., Fujita, K.,Yamaguchi, T., Naruse,A., Wells, J.C., Zimmer, C. J.,Straatsma,T.,Hori, T., Puel,S., Becker, T.W., Hori,M., and Ueda, T,"2416-PFLOPS fast scalable implicit solver on low-ordered unstructured finite elements accelerated by 1.10-ExaFLOPS kernel with reformulated AI-like algorithm: For equation-based earthquake modeling," Proc. of International Conference for High Performance Computing, Networking, Storage, and Analysis (SC2019). 2019.
• Kalantar, B., Ueda, N., Al-Najjar, H.A.H., Gibril M. B. A., Lay, U.S., Motevalli, A.,"An evaluation of landslide susceptibility mapping using remote sensing data and machine learning algorithms in Iran. ISPRS Annals of the Photogrammetry", Remote Sensing and Spatial Information Sciences, 2019.
• Kalantar, B., Ueda, N., Al-Najjar, H.A.H., Moayedi. H., Halin, A.A., Mansor, S.,"UAV and LiDAR image registration: A surf-based approach for ground control points selection", International Archives of the Photogrammetry, ‐Remote Sensing and Spatial Information Sciences - ISPRS Archives, 2019.
• Kalantar, B., Ueda, N., Lay, U.S., Al-Najjar, A.H.A., Halin, A.A., "Conditioning factors determination for landslide susceptibility mapping using support vector machine learning",IEEE International Geoscience and Remote Sensing Symposium, 2019.
• Fujiwara,Y., Ida, Y., Kanai,S., Kumagai,A., Arai, J., and Ueda, N., "Fast random forest algorithm via incremental upper bound," Proc. of the 28th ACM International Conference on Information and Knowledge Management (CIKM2019) , 2019.
• Okita, T., Hachiya,H.,Inoue, S.,and Ueda, N.,"Translation between waves, wave2wave," Proc. of the 22nd International Conference on Discovery Science (DS2019) ,2019.
• Fujiwara, Y., Kanai, S., Arai, J., Ida, Y., and Ueda, N., "Efficient data point pruning for one-class SVM," Proc. of Association for the Advancement of Artificial Intelligence (AAAI2019), 2019 (to appear)
• Shimizu, H., Matsubayashi, T., Tanaka, Y., Iwata1, T., Ueda, N., and Sawada, H.,"Improving route traffic estimation by considering staying population," The 21st International Conference on Principles and Practice of Multi-Agent Systems (PRIMA), 2018.
• Kalantar, B., Mansor, S., Halin, A. A., Ueda, N., Shafri, H. Z. M. and Zand, M., "A graph-based approach for moving objects detection from UAV videos," Proc. of SPIE Image and Signal Processing for Remote Sensing, Vol.10789, 2018.
• Kalantar, B., Ueda, N., AL-Najjar, H. A. H., Idrees, M. O., Motevalli, A. and Pradhan, B., "Landslide susceptibility mapping at dodangeh watershed, Iran, using LR and ANN models in GIS," Proc. of SPIE Earth Resources and Environmental Remote Sensing, Vlo.10790, 2018.
• zeez, O. S., Kalantar, B., Al-Najjar, H. A. H., Halin, A. A., Ueda, N. and Mansor, S., "Object boundaries regularization using the dynamic polyline compression algorithm," The International Archives of the Photogrammetry, ‐Remote Sensing and Spatial Information Science (ISPRS2018), Vol.XLII-4, pp. 541-546, 2018.
• Yonezawa, T., Takeuchi, K., Itoh, T., Sakamura, N., Kishino, Y., Naya, F, Ueda, N. and Nakazawa, J., "Accelerating urban science by crowdsensing with civil officers," Proc. of ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp2018), 2018.
• Kishino, Y., Shirai, Y., Takeuchi, K., Suyama, T., Naya, F. and Ueda, N., "Regional Garbage Amount Estimation and Analysis using Car-Mounted Motion Sensor," Proc. of ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp2018), 2018.
• Fujiwara, Y., Arai, J., Kanai, S., Ida, Y. and Ueda, N., "Adaptive data pruning for support vector machines," Proc. of IEEE International Conference on Big Data, 2018.
• Chffin, B., and Ueda, N.,"Scaling Bayesian optimization up to higher dimensions: A review and comparison of recent algorithms," Proc. of IEEE International Workshop on Machine Learning for Signal Processing (MLSP2018), 2018.
• Kimura, A., Gharamani, Z., Takeuchi, K., Iwata, T., and Ueda, N., "Few-shot learning of neural networks from scratch by pseudo example optimization, " Proc. of 29th British Machine Vison Confernece（BMVC), 2018.
• Tanaka, Y., Iwata, T, Kurashima, T., Toda, H., and Ueda, N., "Estimating latent people flow without tracking individuals," International Conference on Artificial Intelligence (IJCAI), July 2018.
• Kimura, A., Takahashi, I., Tanaka, M., Yasuda, N., Ueda, N., and Yoshida, N., "Single-epoch supernova classification with deep convolutional neural networks," Proc. US-Japan Workshop on Collaborative Global Research on Applying Information Technology, in conjunction with ICDCS2017.
• Blondel, M., Niculae, V., Otsuka, T., and Ueda, N., "Multi-output polynomial networks and factorization machine, "Proc. Neural Information Processing Systems (NIPS2017), 2017.
• Kishino, Y., Takeuchi, K., Shirai, Y., Naya, F., and Ueda, N., "Datafying city: detecting and accumulating sptio-temporal events by vehicle-mounted sensors, "Proc of International Workshop on Smart Cities (IWSC2017), 2017.
• Takeuchi, K., Kashima, H., and Ueda, N., "Autoregressive tensor factorization for spatio-temporal predicitons," Proc. of IEEE Ineternationl Conference on Data Mining (ICDM2017), 2017.
• Fujiwara, Y., Marumo, N., Blondel, M., Takeuchi, K., Kim, H., Iwata, T. and Ueda, N., "Scaling Locally Linear Embedding," In Proc. SIGMOD 2017, pp. 1479-1492, 2017.
• Kim, H., Iwata, T., Fujiwara, Y. and Ueda, N., "Read the Silence: Well-Timed Recommendation via Admixture Marked Point Processes," In Proc. AAAI 2017, pp. 132-139, 2017.
• Ichimura1, T., Fujita1, K., Yamaguchi, T., Hori1, M., Lalith1, M. and Ueda, N., "AI with Super-computed Data for Monte Carlo Earthquake Hazard Classification," Proc. of the international conference for high performance computing, networking, storage and analysis (SC2017), 2017.
• Kimura, A., Takahashi, I., Tanaka, M., Yasuda, N., Ueda, N. and Yoshida, N., "Single-epoch supernova classification with deep convolutional neural networks," The 1st US-Japan Workshop 2017, 2017.
• Toda, T., Inoue, S. and Ueda, N., "Mobile activity recognition through training labelswith inaccurate activity segments," 13th Annucal International Conference on Mobile and Ubiquitous Systems 2016 (MobiQuious2016), 2016.
• Blondel, M., Ishihata, M., Fujino, A. and Ueda, N., "Higher-order factorization machines," Advances in Neural Information Processing Systems (NIPS2016), 2016.
• ujino, A. and Ueda, N., "A semi-supervised AUC optimization method with generative models," IEEE International Conference on Data Mining (ICDM2016), 2016.
• Takeuchi, K. and Ueda, N., "Graph regularized non-negative tensor completion for spatio-temporal data analysis," The Second International Workshop on Smart Cities, 2016.
• M. Blondel, Fujino, A. and Ueda, N., "Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms," International Conference on Machine Learning (ICML2016), 2016.
• Ishiguro, K., Sato, I., Ueda, N., Nakano, M. and Kimura, S., "Infinite plaid models for infinite bi-clustering," Proc. the 27th AAAI Conference on Artificial Intelligence (AAAI2016), 2016.
• Ueda, N., Naya, F., Shimizu, H., Iwata, T., Okawa, M. and Sawada, H., "Real-time and proactive navigation via spatio-temporal prediction, "Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers(UbiComp 2015), pp. 1559-1566, 2015.
• Inoue, S., Ueda., N., Nohara, Y. and Nakashima, N., "Mobile activity recognition for a whole day: recognizing real nursing activities with big dataset," Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing(UbiComp2015), pp. 1269-1280, 2015.
• Baba, Y., Kashima, H., Nohara, Y., Kai, E., Ghosh, P., Islam, R., Ahmed, A., Kuroda, M., Inoue, S., Hiramatsu, T., Kimura, M., Shimizu, S., Kobayashi, K., Tsuda, K., Sugiyama, M., Blondel, M., Ueda, N., Kitsuregawa, M. and Nakashima, N., "Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries," Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2015), pp. 1681-1690, 2015.
• Blondel, M., Fujino, A. and Ueda, N., "Convex Factorization Machines," Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECML PKDD), Part II, LNAI 9285, pp. 19-35, 2015.
• Matsubara, Y., Sakurai, Y., Ueda, N. and Yoshikawa M., "Fast and Exact Monitoring of Co-Evolving Data Streams," 2014 IEEE International Conference on Data Mining(ICDM), pp. 390-399, 2014.
• Blondel, M., Fujino, A. and Ueda, N., "Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex," 22nd International Conference on Pattern Recognition (ICPR2014), pp. 1289-1294, 2014.
• Nakano, M., Ishiguro, K., Kimura, A., Yamada, T. and Ueda, N., "Rectangular tiling process," Proceedings of The 31st International Conference on Machine Learnin (ICML2014), pp. 361-369, 2014.
• Blondel, M., Kubota, Y. and Ueda, N., "Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion," Proc. 17th International Conference on Artificial Intelligence and Statistics (AISTATS2014), Vol.33, pp. 96-104, 2014.