Citations

• Google Scholar
  
• Scopus
  
• Web of Science
  
• Semantic Scholar
  
• ResearchGate
  
• Top 2% in a subfield worldwide:
  • John P. A. Ioannidis (2004). August 2024 data-update for "Updated science-wide author databases of standardized citation indicators", https://elsevier.digitalcommonsdata.com/datasets/btchxktzyw/7.
  • John P. A. Ioannidis, Kevin W. Boyack, Jeroen Baas (2023). "Updated science-wide author databases of standardized citation indicators", https://doi.org/10.1371/journal.pbio.3000918
  • Ioannidis, John P.A. (2022). September 2022 data-update for "Updated science-wide author databases of standardized citation indicators", Mendeley Data, V5, doi: 10.17632/btchxktzyw.5 [spreadsheet] --- on the career-long list.
  • Baas, Jeroen; Boyack, Kevin; Ioannidis, John P.A. (2021). August 2021 data-update for "Updated science-wide author databases of standardized citation indicators", Mendeley Data, V3, doi: 10.17632/btchxktzyw.3 [Spreadsheet] --- on the career-long list.
  • Ioannidis JPA, Boyack KW, Baas J (2020) Updated science-wide author databases of standardized citation indicators. PLoS Biol 18(10): e3000918. [Spreadsheet] --- on the career-long list.

Publications

Advances in Distributed and Parallel Knowledge Discovery Hillol Kargupta and Philip Chan (editors) AAAI/MIT Press, 2000 ISBN: 0-262-61155-4

Available at: AAAI/MIT Press, amazon.com, barnesandnoble.com

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• GII: A Unified Approach to Representation Learning in Open Set Recognition with Novel Category Discovery.
  J. Jia and P. Chan
  Proc. Intl. Conf. Artificial Neural Networks, Part VIII, pp. 243-254, 2023.
• Feature Decoupling in Self-Supervised Representation Learning for Open Set Recognition.
  J. Jia and P. Chan
  Proc. Intl. Joint Conf. Neural Networks, 2023.
• Representation learning with function call graph transformations for malware open set recognition.
  J. Jia and P. Chan
  Proc. Intl. Joint Conf. Neural Networks, 2022.
• Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition.
  J. Jia and P. Chan
  Proc. Intl. Conf. Artificial Neural Networks, Part IV, pp. 471-483, 2022.
• A Machine Learning Approach to Predicting SEP Events Using Properties of Coronal Mass Ejections.
  J. Torres, L. Zhao, P. Chan, and M. Zhang
  Space Weather, 20(7), 2022.
• MMF: A loss extension for feature learning in open set recognition. [longer version]
  J. Jia and P. Chan
  Proc. Intl. Conf. Artificial Neural Networks, pp. 319-331, 2021.
• Unsupervised Open Set Recognition using Adversarial Autoencoders. [longer version]
  M. Hassen and P. Chan
  Proc. IEEE Intl. Conf. Machine Learning and Applications, pp. 360-365, 2020.
• Learning a Neural-network-based Representation for Open Set Recognition. [supplement]
  M. Hassen and P. Chan
  Proc. SIAM Intl. Conf. Data Mining, pp. 154-162, 2020.
• Identifying Pros and Cons of Product Aspects Based on Customer Reviews. [longer version]
  E. Ahmadzadeh and P. Chan
  Proc. IEEE Intl. Conf. Big Data, pp. 931-936, 2018.
• Learning to Identify Known and Unknown Classes: A Case Study in Open World Malware Classification. [longer version]
  M. Hassen and P. Chan
  Proc. Intl. FLAIRS Conf., pp. 26-31, 2018.
• Using a Personalized Anomaly Detection Approach with Machine Learning to Detect Stolen Phones.
  H. Hu and P. Chan
  Proc. Intl. FLAIRS Conf., pp. 323-328, 2018.
• Detecting Harmful Hand Behaviors with Machine Learning from Wearable Motion Sensor Data.
  L. Zhang and P. Chan
  Proc. Intl. FLAIRS Conf., pp. 410-415, 2018.
• Estimating Effectiveness of Twitter Messages with a Personalized Machine Learning Approach.
  X. Sun and P. Chan.
  Knowledge and Information Systems, 56(1):27-53, 2018. https://doi.org/10.1007/s10115-017-1088-3.
• Mining Pros and Cons of Actions from Social Media for Decision Support. [longer version]
  E. Ahmadzadeh and P. Chan
  Proc. IEEE Intl. Conf. Big Data, pp. 877-882, 2017.
• Malware Classification Using Static Analysis Based Features.
  M. Hassen, M. Carvalho and P. Chan
  IEEE Symposium on Computational Intelligence in Cyber Security (IEEE Symposium Series on Computational Intelligence), pp. 734-740, 2017.
• Using demographics to understand better the students' behavior, improving the performance prediction in online courses.
  A. Kuroswiski and P. Chan.
  28th Brazilian Symposium on Informatics in Education (SBIE), Vol 29, p. 1577-1586, 2017.
• Scalable Function-Call Graph based Malware Classification.
  M. Hassen and P. Chan
  ACM Conference on Data and Applications Security and Privacy (CODASPY), pp. 239-248, 2017.
• Improving Efficiency of Maximizing Spread in the Flow Authority Model for Large Sparse Networks. [longer version]
  P. Chan and E. Ahmadzadeh
  Proc. IEEE Intl. Conf. Big Data, pp. 863-868, 2016.
• Identifying Student Behaviors Early in the Term for Improving Online Course Performance. [longer version]
  M. Mori and P. Chan.
  Intl. Conf. Educational Data Mining, pp. 611-612, 2016.
• An Analysis of Instance Selection for Neural Networks to Improve Training Speed.
  X. Sun and P. Chan.
  Intl. Conf. Machine Learning and Applications (ICMLA), pp. 288-293, 2014.
• Semantic Search Techniques for Learning Smaller Boolean Expression Trees in Genetic Programming.
  N. Miller and P. Chan.
  Intl. J. Computational Intelligence and Applications, 13(3), 2014.
  
• Estimating Hospital Admissions with a Randomized Regression Approach.
  K. Garcia and P. Chan.
  Intl. Conf. Machine Learning and Applications (ICMLA), pp. 179-184, 2012.
• Incrementally Learning Rules for Anomaly Detection.
  D. Petrusenko and P. Chan.
  Intl. FLAIRS Conf., pp. 434-439, 2010.
• Increasing coverage to improve detection of network and host anomalies.
  G. Tandon and P. Chan.
  Machine Learning, 79(3):307-334, 2010.
• Machine Learning for IT Security, P. Chan, Encyclopedia of Machine Learning, pp 637-639, 2010.
• Tracking user mobility to detect suspicious behavior.
  G. Tandon and P. Chan.
  SIAM Intl. Conf. on Data Mining (SDM), pp. 871-882, 2009.
• Personalized Web Search by Using Learned User Profiles in Re-ranking.
  J. Hu and P. Chan.
  Workshop on Knowledge Discovery on the Web, KDD conf. pp. 84-97, 2008.
• Learning Implicit User Interest Hierarchy for Context in Personalization.
  H. Kim and P. Chan.
  Applied Intelligence, 28(2):153-166, 2008.
• Intelligent Systems at Florida Tech.
  P. Chan, R. Menezes, D. Mitra, E. Ribeiro, and M. Silaghi.
  IEEE Intelligent Informatics Bulletin, 8(1):5-6, 2007.
• Weighting versus Pruning in Rule Validation for Detecting Network and Host Anomalies.
  G. Tandon & P. Chan.
  Proc. ACM Intl. Conf. on Knowledge Discovery and Data Mining (KDD). pp. 697-706, 2007.
• Toward accurate dynamic time warping in linear time and space.
  S. Salvador and P. Chan.
  Intelligent Data Analysis, 11(5):561-580, 2007.
  [Source Code]
• Machine Learning for Computer Security.
  P. Chan & R. Lippmann.
  Journal of Machine Learning Research, 7:2669-2672, 2006.
• Personalized Ranking of Search Results with Learned User Interest Hierarchies from Bookmarks.
  H. Kim & P. Chan.
  In Advances in Web Mining and Web Usage Analysis (LNCS 4198), O. Nasraoui, O. Zaine, M. Spiliopolou, B. Mobasher, B. Masand & P. Yu (editors) pp 158-176, Springer, 2006.
• On the Learning of System Call Attributes for Host-based Anomaly Detection.
  G. Tandon and P. Chan.
  International Journal on Artificial Intelligence Tools, 15(6):875-892, 2006.
• Data cleaning and enriched representations for anomaly detection in system calls.
  G. Tandon, P. Chan, and D. Mitra.
  In Machine Learning and Data Mining for Computer Security: Methods and Applicatioins, M. Maloof (editor), Springer, pp. 137-156, 2006.
• Modeling Multiple Time Series for Anomaly Detection.
  P. Chan & M. Mahoney.
  Proc. IEEE Intl. Conf. on Data Mining, pp. 90-97, 2005.
• Trajectory Boundary Modeling of Time Series for Anomaly Detection.
  M. Mahoney and P. Chan.
  Workshop on Data Mining Methods for Anomaly Detection, KDD Conf., 2005.
• Personalized Ranking of Search Results with Learned User Interest Hierarchies from Bookmarks.
  H. Kim and P. Chan.
  WEBKDD Workshop, KDD Conf., 2005.
• Implicit Indicators for Interesting Web Pages.
  H. Kim and P. Chan.
  Proc. Intl. Conf. on Web Information Systems and Technologies, pp. 270-277, 2005.
• Project EMD-MLR: Educational Material Development and Research in Machine Learning for Undergraduate Students.
  G. Anagnostopoulos, M. Geogiopoulos, K. Ports, S. Richie, N. Cardinale, M. White, V. Kepuska, P. Chan, A. Wu & M. Kysilka.
  Proc. 2005 ASEE Annual Conf. and Exposition, 2005.
• Learning Useful System Call Attributes for Anomaly Detection.
  G. Tandon & P. Chan.
  Proc. 18th Intl. FLAIRS Conf., pp. 405-410, 2005.
• Learning States and Rules for Detecting Anomalies in Time Series.
  S. Salvador & P. Chan.
  Applied Intelligence, 23(3):241-255, 2005.
• Learning Rules and Clusters for Anomaly Detection in Network Traffic.
  P. Chan, M. Mahoney & M. Arshad.
  In Managing Cyber Threats: Issues, Approaches and Challenges, V. Kumar, J. Srivastava & A. Lazarevic (editors), Springer, pp. 81-99, 2005.
• Identifying Variable-Length Meaningful Phrases with Correlation Functions.
  H. Kim & P. Chan.
  Proc. 16th IEEE Intl. Conf. on Tools with AI, pp. 30-38, 2004.
• Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms.
  S. Salvador and P. Chan.
  Proc. 16th IEEE Intl. Conf. on Tools with AI, pp. 576-584, 2004.
• MORPHEUS: Motif Oriented Representations to Purge Hostile Events from Unlabeled Sequences.
  G. Tandon, P. Chan, and D. Mitra.
  In Workshop on Visualization and Data Mining for Computer Security (Viz/DMSEC), 11th ACM Conf. on Computer and Communications Security (CCS), pp. 16-25, 2004.
• FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space.
  S. Salvador and P. Chan.
  KDD Workshop on Mining Temporal and Sequential Data, pp. 70-80, 2004.
  [Source Code]
• Learning States and Rules for Time Series Anomaly Detection.
  S. Salvador, P. Chan & J. Brodie.
  Proc. 17th Intl. FLAIRS Conf., pp. 300-305, 2004.
• Motif-oriented Representation of Sequences for a Host-based Intrusion Detection System.
  G. Tandon, D. Mitra & P. Chan.
  17th Intl. Conf. on Industrial & Engineering Applications of AI & Expert Systems, pp. 605-615, 2004.
• Learning Rules for Anomaly Detection of Hostile Network Traffic.
  M. Mahoney & P. Chan.
  Proc. Third IEEE Intl. Conf. on Data Mining (ICDM), pp. 601-4, 2003.
  [Source Code]
• Learning Rules from System Call Arguments and Sequences for Anomaly Detection.
  G. Tandon & P. Chan.
  ICDM Workshop on Data Mining for Computer Security (DMSEC), pp. 20-29, 2003.
• Boundary Detection in Tokenizing Network Application Payload for Anomaly Detection.
  R. Vargiya & P. Chan.
  ICDM Workshop on Data Mining for Computer Security (DMSEC), pp. 50-59, 2003.
• An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection.
  M. Mahoney and P. Chan.
  Proc. 6th Intl. Symp. Recent Advances in Intrusion Detection, pp. 220-237, 2003.
  [Source Code]
• Learning Implicit User Interest Hierarchy for Context in Personalization.
  H. Kim and P. Chan.
  Proc. Intl. Conf. on Intelligent User Interfaces, pp. 101-108, 2003.
• Learning Nonstationary Models of Normal Network Traffic for Detecting Novel Attacks.
  M. Mahoney and P. Chan.
  Proc. Eighth Intl. Conf. Knowledge Discovery and Data Mining (KDD), pp. 376-385, 2002.
  [Source Code]
• Using Artificial Anomalies to Detect Unknown and Known Network Intrusions.
  W. Fan, M. Miller, S. Stolfo, W. Lee, P. Chan.
  IEEE Intl. Conf. Data Mining, pp. 123-130, 2001.
• A Protocol Language Approach to Generating Client-Server Software.
  M. Douglas and P. Chan.
  Proc. of Thirteenth Intl. Conf. Parallel and Distributed Computing and Systems, pp. 649-654, 2001.
• Distributed Communication for Highly Mobile Agents.
  M. Samarah and P. Chan.
  Fourth Pacific Rim Intl. Workshop on Multi-agents, 2001.
• Real Time Data Mining-based Intrusion Detection.
  W. Lee, S. Stolfo, P. Chan, E. Eskin, W. Fan, M. Miller, S. Hershkop, and J. Zhang.
  Proc. Second DARPA Information Survivability Conference and Exposition, pp. I85-100, 2001.
• Constructing web user profiles: A non-invasive learning approach.
  P. Chan, in Web Usage Analysis and User Profiling.
  LNAI 1836, Springer-Verlag, p39-55, 2000.
• Advances in Distributed and Parallel Knowledge Discovery.
  H. Kargupta and P. Chan (editors).
  AAAI/MIT Press, 2000.
• Cost-based Modeling for Fraud and Instrusion Detection: Results from the JAM Project.
  S. Stolfo, W. Fan, W. Lee, A. Prodromidis, and P. Chan,
  Proc. DARPA Information Survivability Conference and Exposition. IEEE Computer Press, pp. II 130-144, 2000.
• Meta-learning in distributed data mining systems: Issues and approaches.
  A. Prodromidis, P. Chan, and S. Stolfo.
  In Advances in Distributed and Parallel Knowledge Discovery, H. Kargupta and P. Chan (editors), Chapter 3, AAAI/MIT Press, 2000.
• Distributed data mining in credit card fraud detection.
  P. Chan, W. Fan, A. Prodromidis, and S. Stolfo.
  IEEE Intelligent Systems, 14(6):67-74, 1999.
• A non-invasive learning approach to building web user profiles.
  P. Chan.
  KDD-99 Workshop on Web Usage Analysis and User Profiling, pp. 7-12, 1999.
  [data used in the study]
• AdaCost: Misclassification Cost-sensitive Boosting.
  W. Fan, S. Stolfo, J. Zhang, and P. Chan.
  Proc. Sixteenth Intl. Conf. Machine Learning, pp. 99-105, 1999.
• Using Conflicts among Base Classifiers to Measure the Performance of Stacking.
  W. Fan, S. Stolfo, and P. Chan.
  ICML-99 Workshop on Recent Advances in Meta-learning and Future Work, pp. 10-17, 1999.
• Toward Scalable Learning with Non-uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection.
  P. Chan and S. Stolfo.
  Proc. Fourth Intl. Conf. Knowledge Discovery and Data Mining, p164-168, 1998. [PDF]
• Learning with Non-uniform Class and Cost Distributions: Effects and a Distributed Multi-classifier Approach.
  P. Chan and S. Stolfo.
  Work. Notes KDD-98 Workshop on Distributed Data Mining, p1-9, 1998.
• JAM: Java Agents for Meta-Learning over Distributed Databases.
  S. Stolfo, A. Prodromidis, S. Tselepis, W. Lee, W. Fan, and P. Chan.
  Proc. Third Intl. Conf. Knowledge Discovery and Data Mining, p74-81, 1997.
• Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results.
  S. Stolfo, W. Fan, W. Lee, A. Prodromidis, and P. Chan.
  Work. Notes AAAI-97 Workshop on AI Approaches to Fraud Detection and Risk Management, 1997.
• Learning Patterns from Unix Process Execution Traces for Intrusion Detection.
  W. Lee, S. Stolfo, and P. Chan.
  Work. Notes AAAI-97 Workshop on AI Approaches to Fraud Detection and Risk Management, 1997.
• P. Chan (1996). An Extensible Meta-Learning Approach for Scalable and Accurate Inductive Learning, PhD Thesis, Department of Computer Science, Columbia University, New York, NY (Technical Report CUCS-044-96). (gzip'd version)
• P. Chan and S. Stolfo (1997). On the Accuracy of Meta-learning for Scalable Data Mining, J. Intelligent Information Systems, 8:5-28.
• P. Chan and S. Stolfo (1996). Sharing Learned Models among Remote Database Partitions by Local Meta-learning, Proc. Second Intl. Conf. on Knowledge Discovery & Data Mining, p2-7.
• P. Chan and S. Stolfo (1996). Scaling Learning by Meta-Learning over Disjoint and Partially Replicated Data, Proc. Ninth Florida Artificial Intelligence Research Symposium, p151-155.
• P. Chan and S. Stolfo (1995). A Comparative Evaluation of Voting and Meta-learning on Partitioned Data, Proc. Twelfth Intl. Conf. on Machine Learning, p90-98.
• P. Chan and S. Stolfo (1995). Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning, Proc. Intl. Conf. on Knowledge Discovery and Data Mining, p39-44.
• P. Chan and S. Stolfo (1994). Toward Scalable and Parallel Learning: A Case Study in Splice Junction Prediction, ML94 Workshop on Machine Learning and Molecular Biology (Tech. Rep. CUCS-032-94, Department of Computer Science, Columbia U.).
• P. Chan and S. Stolfo (1993). Toward Parallel and Distributed Learning by Meta-learning, In Working Notes AAAI Work. Knowledge Discovery in Databases, p227-240.
• P. Chan and S. Stolfo (1993). Experiments on Multistrategy Learning by Meta-learning. In Proc. Second Intl. Conf. Info. Know. Manag., p314-323.
• S. Stolfo, O. Wolfson, P. Chan, H. Dewan, L. Woodbury, J. Glazier, and D. Ohsie (1991). PARULEL: Parallel Rule Processing Using Meta-rules for Redaction, Journal of Parallel and Distributed Computing, v13, n4, p366-382.
• P. Chan (1991). Machine Learning in Molecular Biology Sequence Analysis, Technical Report CUCS-041-91, Dept. of Computer Science, Columbia Univ., New York, NY.
• S. Stolfo, P. Chan, L. Woodbury, J. Glazier, and D. Ohsie (1991). The ALEXSYS Mortgage Pool Allocation System, In Proc. First Intl. Conf. Art. Intell. Appl. Wall Street, p79-84.
• P. Chan (1989). Inductive Learning with BCT, Intl. Workshop on Machine Learning, pp. 104-108.