Assistant Professor
Director of Active Intelligence Research Group
Graduate School of Information Systems
University of Electro-Communications
Tokyo, Japan
Affiliations: AI Lab (Prof. Okamoto/ Prof. Ueno), Center for Developing E-Learning, Center for Frontier Science and Engineering
Research Interests
Artificial Intelligence, Machine Learning (especially unsupervised learning, semi-supervised learning, meta learning, data mining, heuristics).
Applications: e-Learning (especially collaborative learning, informal learning), Information Retrieval (especially information extraction, natural language processing NLP), Personalization (collaborative filtering, recommender systems, user modeling, peer modeling).
Publications
Book Chapters
N. Rubens, D. Kaplan, M. Sugiyama. Recommender Systems Handbook: Active Learning (eds. P.B. Kantor, F. Ricci, L. Rokach, B. Shapira). Springer. [to be published Jul.2010]. web
M. Sugiyama, N. Rubens, and K.-R. Müller. Dataset Shift in Machine Learning: A conditional expectation approach to model selection and active learning under covariate shift. MIT Press, Cambridge, 2009. pdf web
Journal Articles
N. Rubens, D. Kaplan, M. Villenius, and T. Okamoto, “CAFE: Collaboration Aimed at Finding Experts,” International Journal of Knowledge and Web Intelligence (IJKWI), 2010 [to be published].
N. Rubens, D. Kaplan, and T. Okamoto, “ELIxIR: Expertise Learning and Identification x Information Retrieval,” International Journal of Information Systems and Social Change (IJISSC), 2010 [to be published].
M. Vilenius, N. Rubens, F. Anma, and T. Okamoto, “Supporting collaborative activities in informal, ill-constructed learning,” Journal of Information and Systems in Education, 2010 [to be published].
N. Rubens, R. Tomioka, and M. Sugiyama, “Output divergence criterion for active learning in collaborative settings,” IPSJ Transactions on Mathematical Modeling and Its Applications, vol. 2, pp. 87 — 96, 12 2009.
M. Sugiyama and N. Rubens. A batch ensemble approach to active learning with model selection. Neural Networks, 2008. pdf
N. Rubens. The application of fuzzy logic to the construction of the ranking function of information retrieval systems. Computer Modelling and New Technologies, 10(1):20–27, 2006. pdf bib code
Papers / Presentations
N. Rubens, D. Kaplan, T. Okamoto, "WE: Web of Experts", Computers and Advanced Technology in Education (CATE 2010), 2010.
R. P. Mendoza, N. Rubens, T. Okamoto, "Hierarchical Aggregation Prediction Method", KDD Cup 2010 Workshop, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), Jul.2010.
N. Rubens, T. Okamoto, and M. G. Russell, “Journalistic research tools: How to spot trends in innovation ecosystems by visualizing data,” in The Seventh Conference on Innovation Journalism (IJ-7), Stanford University, Jun.2010. [invited presentation]
N. Rubens, T. Okamoto, and M. G. Russell, “New Tools for Analysis: Innovation Ecosystems DataSet,” in Media X: Social Network Analysis: New Tools and Data, Stanford University, Jun.2010.
N. Rubens and T. Okamoto, “Getting answers to questions we haven’t yet asked,” in Media X: Innovation Ecosystem Networks, Stanford University, Mar. 2010.
N. Rubens, K. Still, J. Huhtamaki, and M. G. Russell, “Leveraging social media for analysis of innovation players and their moves,” tech. rep., Media X, Stanford University, Feb. 2010.
N. Rubens, M. Vilenius, T. Okamoto. Automatic Group Formation for Informal Collaborative Learning. SPeL 2009. In Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2009. WI-IAT '09.
N. Rubens, M. Vilenius, T. Okamoto. Data-driven Group Formation for Informal Collaborative Learning. E-Learn 2009. AACE. 2009
N. Rubens, M. Vilenius, and T. Okamoto, “Automatic group formation for informal collaborative learning,” in 34th Conference of Japanese Society for Information and Systems in Education (JSiSE), 2009.
N. Rubens, M. Vilenius, and T. Okamoto, “Mashup-based group formation for informal learning,” in 25th Conference of the Japan Society for Educational Technology (JSET), 2009.
M. V. Jankovic and N. Rubens, “A new probabilistic approach to on-line learning in artificial neural networks,” in ASMCSS’09: Proceedings of the 3rd International Conference on Applied Mathematics, Simulation, Modeling, Circuits, Systems and Signals, 2009.
N. Rubens, T. Okamoto. Social Mobile Services: Japanese Perspective. MediaX 2009: New Metrics for New Media. Stanford Univesity. 2009.
M. Russell, N. Rubens, J. Huhtamäki, C. Duan. Strategies & Practices in International Alliances for National and Regional Information Communication Technologies Industries. MediaX 2009: Visualization for Collective, Connective and Distributed Intelligence. Stanford Univesity. 2009.
N. Rubens, T. Okamoto, and M. Ueno, “Model-agnostic active learning,” in Proceedings of the Japanese Society for Artificial Intelligence 69th Meeting of Special Interest Group on Fundamental Problem in Artificial Intelligence, 2009.
N. Rubens and M. Ueno, “Estimate response-based active learning,” in Annual Workshop of the Behaviormetric Society of Japan (BSJ 2009), 2009.
N. Rubens, V. Sheinman, T. Tokunaga, and M. Sugiyama. Order retrieval. In Proccedings of the 3rd International Conference on Large-scale Knowledge Resources (LKR 2008), Lecture Notes in Artificial Intelligence (LNAI). Springer-Verlag, 2008. pdf web
M. Sugiyama and N. Rubens. Active learning with model selection in linear regression. In SIAM International Conference on Data Mining (SDM 2008), 2008. pdf
N. Rubens and M. Sugiyama. Influence-based collaborative active learning. In Proceedings of the 2007 ACM conference on Recommender systems (RecSys 2007). ACM, 2007. pdf
N. Rubens and M. Sugiyama. Explorative active learning for collaborative filtering. In Proceedings of the Japanese Society for Artificial Intelligence 67th Meeting of Special Interest Group on Fundamental Problem in Artificial Intelligence, 2007. pdf
V. Sheinman, N. Rubens, and T. Tokunaga. Commonly perceived order within a category. In Proceeding of OntoLex Workshop at 6th International Semantic Web Conference (ISWC 07 ), 2007. pdf
V. Sheinman, N. Rubens, and T. Tokunaga. Word sequences for second language acquisition. MAPLL Workshop. Technical report of IEICE. Thought and language, 107(138), 2007.
N. Rubens and M. Sugiyama. Coping with active learning with model selection dilemma: Minimizing expected generalization error. In Proceedings of 2006 Workshop on Information-Based Induction Sciences (IBIS 2006), 2006. pdf bib
Other
N. Rubens. Active Mining. Research Meeting. Nagaoka University of Technology, 2009 [invited speaker].
N. Rubens. DNA sequence alignment with the use of reinforcement learning. Technical report, 2004.
N. Rubens. Application of fuzzy logic to the refinement of an answer set of information retrieval systems. Technical report, 2004.
N. Rubens. Detecting network intrusion with the use of belief networks. Technical report, 2003.
Awards
Best Paper Award JSiSE 2009.
Research Fellowship Apr.2007 – Oct.2008
Japanese Government (MEXT (Monbukagasho) / Tokyo Institute of Technology) $50,000
Research Fellowship Oct.2005 – Apr.2007
Japanese Government (MEXT (Monbukagasho) / Tokyo Institute of Technology) $50,000
Research Fellowship Oct.2005 – Oct.2008
Japanese Government (MEXT (Monbukagasho) / JAIST) $100,000
Declined due to conflicting offer
Travel / Conference Grant Oct.2006
Information-Based Induction Sciences (IBIS)
Students
Mikko Vilenius (PhD) (co-supervised with Prof. Okamoto)
Rafael Perez (MS & PhD) (co-supervised with Prof. Okamoto)
Service
Reviewer: Neurocomputing, Behaviormetrika
Publication Co-Chair: ISECS 2010 International Symposium on Electronic Commerce and Security (IEEE)
Education
Tokyo Institute of Technology
PhD Computer Science
Thesis: Collaborative Active Learning
Machine Learning Lab
Supervisor: Prof. Sugiyama
University of Massachusetts
MSc Computer Science
Thesis: Fuzzy Ranking for Information Retrieval Systems
Supervisors: Prof. Alan, Prof. Kilmer
Brigham Young University
BSc Computer Science
Professional Experience
University of Electro Communications, Graduate School of Information Science
Feb.2009 – present
Assistant Professor
Affiliations: Center for Frontier Science and Engineering, AI Lab (Okamoto/Ueno), Center for Developing E-Learning
Tokyo Institute of Technology, Machine Learning Lab (Sugiyama) Oct.2005 – Feb.2009
Research Fellow
Artificial Intelligence/Machine Learning: development of fundamental theories and practical algorithms
Fincross May.2003 – Aug.2005
Senior Software Engineer (Consultant)
Application of Artificial Intelligence and Information Retrieval to Financial Analysis
CTS / Bell Dec.2001–May.2003
Software Engineer
Analysis and processing of telecommunication data
NIC Jan.2000–Sep.2001
Software Engineer
Development of server side applications with high reliability/scalability requirements
ETS May.1999–Jan.2000
Software Engineer
Database Engine optimization, Analysis and processing of standardized assessment data
Brigham Young University / Nissan Sep.1998–May.1999
Research Assistant
R&D of neuro-fuzzy controller for Accumulative Cruise Control
Corel Apr.1998–Sep.1998
Software Engineer
Primarily responsible for maintaining and improving Word Perfect Expert module – combines the features from QuickTasks, templates, Help, and coaches user to help accomplish work more efficiently
Frank Phillips College Aug.1996–May.1997
Lab Supervisor
Developing and maintaining IT infrastructure
Training/Supervising teaching assistants
Projects
E-Business
Software Engineer primarily responsible for (NIC):
▪ DMV Renewal (revenue: $10 Million / year)
▪ Payment Processing Framework (revenue: $20 Million / year)
▪ Shopping Cart (used by several US state agencies)
Data Analysis / Processing
▪ financial data (Fincross)
▪ standardized assessment data (ETS)
▪ telecommunication data (CTS)
Skills
▪ Numerical Analysis: Matlab, R, SAS, SPSS, RapidMiner, Weka, Tableau
▪ Programming Languages: Java EE (Enterprise Edition), Python, C#, C++
▪ Database: design and implementation, Oracle, MySQL, SQL
▪ Information Retrieval: Apache Lucene, Solr, Lemur
▪ Frameworks: Grid/Cloud Computing (Hadoop, Google App Engine [GAE], Amazon Elastic Cloud [EC]), Design Patterns, UML, Unit Testing, Extreme Programming
