Publications

Peer-Reviewed Publications (Conferences and Workshops organized by year)

    2015

  1. Dynamic Hierarchical Classification for Patient Risk-of-Readmission, Senjuti Basu Roy, Ankur Teredesai, Kiyana Zolfaghar, Rui Liu, David Hazel, Stacey Newman, Albert Marinez, (to appear) ACM KDD 2015
  2. Selecting Robust Strategies in RTS Games via Concurrent Plan Augmentation. Abdelrahman Elogeel, Andrey Kolobov, Ankur Teredesai, Mathew Alden, 14th International Conference on Autonomous Agents & Multiagent Systems 2015.
  3. Coma: Road Network Compression for Map Matching,.Abdeltawab Hendawi, Amruta Khot, Aqeel Rustum, Anas Basalamah, Ankur Teredesai and Mohamed Ali, International Conference on Mobile Data Management (MDM), 2015.
  4. A Map-Matching Aware Framework For Road Network Compression,. Abdeltawab Hendawi, Amruta Khot, Aqeel Rustum, Anas Basalamah, Ankur Teredesai and Mohamed Ali, International Conference on Mobile Data Management (MDM), 2015. (Demo)
  5. Population Cost Prediction on Public Healthcare Datasets,. S. Sushmita, S. Newman, J. Marquardt, P. Ram, V. Prasad, M. De Cock, A. Teredesai in Proceedings of ACM Digital Health 2015 (5th International Conference on Digital Health), 2015
  6. Fuzzy Rough Set Prototype Selection for Regression, S. Vluymans, Y. Saeys, C. Cornelis, A. Teredesai, M. De Cock to appear in: Proceedings of FUZZ-IEEE 2015 (2015 IEEE International Conference on Fuzzy Systems), 2015
  7. 2014

  8. A Framework to Recommend Interventions for 30-Day Heart Failure Readmission Risk, Rui Liu, Kiyana Zolfaghar, SC Chin, Senjuti Basu Roy, Ankur Teredesai, Data Mining (ICDM), 2014 IEEE International Conference on DOI: 10.1109/ICDM.2014.89 Publication Year: 2014, Page(s): 911 - 916
  9. HealthSCOPE: An Interactive Distributed Data Mining Framework for Scalable Prediction of Healthcare Costs , Marquardt James, Newman Stacey, Hattarki Deepa, Srinivasan Rajagopalan, Sushmita Shanu, Ram Prabhu, Prasad Viren, Hazel David, Ramesh Archana, De Cock Martine, Teredesai Ankur, IEEE Data Mining Conference Demo Track, 2014 IEEE International Conference on DOI: 10.1109/ICDMW.2014.45 Publication Year: 2014 , Page(s): 1227 - 1230
  10. Road network compression techniques in spatiotemporal embedded systems: a survey, Amruta Khot, Abdeltawab Hendawi, Anderson Nascimento, Raj Katti, Ankur Teredesai, and Mohamed Ali. In Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS '14), Chengyang Zhang, Anas Basalamah, and Abdeltawab Hendawi (Eds.). ACM, New York, NY, USA, 33-36. DOI=10.1145/2676552.2676645 http://doi.acm.org/10.1145/2676552.2676645
  11. Computing fuzzy rough approximations in large scale information systems.Hasan Asfoor, Rajagopalan Srinivasan, Gayathri Vasudevan, Nele Verbiest, Chris Cornelis, Matthew E. Tolentino, Ankur Teredesai, Martine De Cock, 2nd Workshop on Scalable Machine Learning - Theory and Applications at 2014 IEEE Conference on Big Data: 9-16
  12. Divide-n-Discover - Discretization based Data Exploration Framework for Healthcare Analytics. Si-Chi Chin, Kiyana Zolfaghar, Senjuti Basu Roy, Ankur Teredesai, Paul Amoroso: HEALTHINF 2014: 329-333
  13. Age and Gender Identification in Social Media. James Marquardt, Golnoosh Farnadi, Gayathri Vasudevan, Marie-Francine Moens, Sergio Davalos, Ankur Teredesai, Martine De Cock, CLEF (Working Notes) 2014: 1129-1136
  14. Readmission Score as a Service(RaaS), Vivek Rao, Kiyana Zolfaghar, Vani Mandava, Senjuti Basu Roy, Ankur Teredesai, Data Science for Social Good, in conjunction with KDD 2014.
  15. Divide-n-Discover : Discretization based Data Exploration Framework for Healthcare Analytics, Si-chi Chin, Kiyana Zolfaghar, Senjuti Basu Roy, Ankur Teredesai, Paul Amoroso, HealthInf 2014.
  16. Minimizing Makespan and Total Completion Time in MapReduce-like Systems, Yuqing Zhu, Yiwei Jiang, Weili Wu, Ling Ding, Ankur Teredesai, Deying Li, and Wonjun Lee, IEEE INFOCOM 2014
  17. Visualization in Educational Datasets Using a Rule-Based Inference System. Aniruddha Desai, Muaz Mian, David Hazel, Ankur Teredesai, Gregory Benner: Data BigData Congress 2014: 462-469
  18. Work in Progress - In-Memory Analysis for Healthcare Big Data. Muaz Mian, Ankur Teredesai, David Hazel, Sreenivasulu Pokuri, Krishna Uppala BigData Congress 2014: 778-779
  19. AMADEUS: A System for Monitoring Water Quality Parameters and Predicting Contaminant Paths. Abdeltawab M Hendawi, David Hazel, Joel Larson, YiRu Li, Dwaine Trummert, Mohamed Ali, Ankur Teredesai, 7th International Congress on Environmental Modelling and Software (iEMSs), San Diego, California, USA June 15-19, 2014.
  20. Azure Marketplace of Applications for Diverse Environmental Use As-A-Service . Mohamed Ali, Abdeltawab Hendawi, Ankur Teredesai, David Hazel, Geological Society Assembly (GSA) October 19-22, 2014, Vancouver, BC, Canada.
  21. Routing Service With Real World Severe Weather. Yiru Li, Sarah George, Craig Apfelbeck, Abdeltawab Hendawi, David Hazel, Ankur Teredesai, Mohamed Ali, ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2014) November 4-7, 2014 — Dallas, Texas, USA. (Best Demo paper award)
  22. Predictive Spatial Search. Mohamed Ali , Abdeltawab M. Hendawi, Ankur Teredesai, Spatial Search Specialist Meeting, Santa Barbara, December 8-9, 2014. (Position paper)
  23. Road Network Compression Techniques in Spatiotemporal Embedded Systems: A Survey. Amruta Khot, Abdeltawab Hendawi, Raj Katti, Anderson Nascimento, Ankur Teredesai, Mohamed Ali,International ACM SIGSPATIAL Workshop on Geostreaming (IWGS), Dallas, Texas, November, 2014
  24. An Effective Message Forwarding Algorithm for Delay Tolerant Network with Cyclic Probabilistic Influences, Yitao Li, Ling Ding, Jie Sheng, Ankur Teredesai, Mobile Ad-hoc and Sensor Networks, 2014 IEEE International Conference on DOI 10.1109/MSN.2014.31 Publication Year: 2014, Page(s): 179-185.
  25. Basic Micro-Aerial Vehicle (MAV) Object Avoidance using Monocular Computer Vision, Lim Kwan Kong, Jie Sheng, Ankur Teredesai, Proc. of the 13th International Conference on Control, Automation, Robotics and Vision (ICARCV’14), Singapore, Dec. 10-12, 2014, Page(s): 1051 - 1056.
  26. Visual Tracking via Supervised Similarity Matching, Ji Zhang, Jie Sheng, Ankur Teredesai, POSTER presentation at the 12th Asian Conference on Computer Vision (ACCV’14), Singapore, Nov. 1-5, 2014, and in Springer's Lecture Notes on Computer Science (LNCS).
  27. 2013

  28. Big Data Solutions for Predicting Risk-of-Readmission for Congestive Heart Failure Patients, Kiyana Zolfaghar, Naren Meadem, Ankur Teredesai, Senjuti Basu Roy, Si-Chi Chin, Brian Muckian, BigData'13, Big Data in Bioinformatics and Health Informatics.
  29. ChronoZoom: Travel Through Time for Education, Information, and Information Technology Research, Bob Walter, Ankur Teredesai, Ling Ding, Joel Larson, Yitao Li, Charu Chandiram, et al., SIGITE/RIIT 2013, October 2013, Orlando, Fl.
  30. Predicting Risk-of-Readmission of the Congestive Heart Failure Patients, Jayshree Agarwal, Kiyana Zolfaghar, Si-Chi Chin, Senjuti Basu-Roy, Ankur Teredesai, ACM SIGKDD Workshop on Data Mining in Healthcare DMH 2013, August 2013.
  31. Audience Segment Expansion Using Distributed In-Database K-Means Clustering, Archana Ramesh, Ankur Teredesai, Ashish Bindra, Sreenivasulu Pokuri, Krishna Uppala, ACM SIGKDD Workshop on Advertising KDD,ADKDD 2013, August 2013.
  32. Risk-O-Meter: An Intelligent Clinical Risk Calculator, Kiyana Zolfaghar, Jayshree Agarwal, Deepthi Sistla, Si-Chi Chin, Senjuti Basu Roy, Nele Verbiest, Ankur Teredesai, David Hazel, Paul Amoroso, Lester Reed, ACM SIGKDD 2013 – Selected for Demonstration, August 2013.
  33. Exploring Preprocessing Techniques for Prediction of Risk of Readmission for Congestive Heart Failure Patients, Naren Meadem, Nele Verbiest, Kiyana Zolfaghar, Jayshree Agarwal, Si-chi Chin, Senjuti Basu Roy, Ankur Teredesai, Data Mining in Healthcare, in conjunction with KDD 2013
  34. Predicting Risk-of-Readmission for Congestive Heart Failure Patients: A Multi-Layer Approach, Kiyana Zolfaghar, Nele Verbiest, Jayshree Agarwal, Naren Meadem, Si-Chi Chin, Senjuti Basu Roy, Ankur Teredesai, David Hazel, Paul Amoroso, Lester Reed, American Medical Informatics Association (AMIA) Conference 2013, November 2013.
  35. Finding Approachable Experts in Social Network, Swapna Savvana, Senjuti Basu Roy, Vani Mandava, and Ankur Teredesai, IEEE International Conference on Big Data 2013.
  36. 2012

  37. Discovering meaningful cut-points to predict high HbA1c variation, S.-C. Chin, W. N. Street, and Ankur Teredesai, 7th INFORMS Workshop on Data Mining and Health Informatics, Phoenix, AZ, 2012.
  38. Distributed Big Advertiser Data Mining, Ashish Bindra , Sreenivasulu Pokuri, Krishna Uppala, Ankur Teredesai, (Industry track) IEEE International Conference on Data Mining 2012 (IEEE-ICDM)Brussels, December 2012.
  39. Map-Matching as a problem for the 1st ACM SIGSPATIAL CUP, Mohamed Ali, Travis Rautman, John Krumm, Ankur Teredesai, Proceedings of the 20th ACM SIGSPATIAL Conference on Advanced in Geographic Information Systems (GIS), Redondo Beach, November 2012.
  40. Dietary Intake Assessment using Integrated Sensors and Software, Junqing Shang, Eric Pepin, Eric Johnson, Ankur Teredesai, Alan Kristal, and Alexander Mamishev, Proceedings of IS&T/SPIE Electronic Imaging Conference, SPIE Volume 8304A 2012.
  41. 2011

  42. An Extensibility Approach for Spatio-temporal Stream Processing using Microsoft StreamInsight, Jeremiah Miller, Miles Raymond, Josh Archer, Seid Adem, Leo Hansel, Sushma Konda, Malik Luti, Yao Zhao, Ankur Teredesai and Mohamed Ali, Demo Track at 10th International Symposium on Spatial and Temporal Databases 2011, Minneapolis, August 2011.
  43. A Pervasive Dietary Data Recording System, Junqing Shang, Kishore Sundara-Rajan, Levi Lindsey, Alexander V. Mamishev, Eric Johnson, Ankur Teredesai, Alan Kristal, IEEE International Conference on Pervasive Computing, PerCom Demo Track, March 2011.
  44. 2010

  45. Ranking in Microblog Search, Rinkesh Nagmoti, Ankur Teredesai and Martine De Cock, 2010 IEEE/WIC/ACM International Conference on Web Intelligence (WI-10) August 31 - September 3, 2010, York University, Toronto, Canada.
  46. Born to Trade: a Genetically Evolved Keyword Bidder for Sponsored Search, Michael Munsey, Jonathan Veilleux, Sindhura Bikkani, Ankur Teredesai, and Martine De Cock, Congress for Evolutionary Computing 2010 (CEC 2010), Barcelona, Spain, July, 2010.
  47. A RapidMiner Framework for Protein Interaction Extraction,
 T. Fayruzov, G. Dittmar, N. Spence, M. De Cock, A. Teredesai
in: Proceedings of RCOMM2010 (RapidMiner Community Meeting), p.58-63, 2010.
  48. 2009

  49. Multi-dimensional Phenomenon-aware Stream Query Processing, Ashish Bindra, Ankur Teredesai, Mohamed Ali, and Walid Aref, ACM SIGSPATIAL GIS 2009 (short paper), Bellevue, WA, Nov 2009. (Best short paper award)
  50. Maximizing Ridesharing matches for Dynamic Carpooling, David Thaler and Ankur Teredesai, Intelligent Environments 2009, Barcelona, Spain, July 20-21, 2009.
  51. An Evolved Fuzzy Logic System for Fire Size Prediction, Alan Fowler, Martine De Cock and Ankur Teredesai, The 28th North American Fuzzy Information Processing Society Annual Conference, June 14-17th, 2009, Cincinnati, OH.
  52. A Comparative Analysis of Trust-Enhanced Recommenders for Controversial Items, P Victor, C Cornelius, M De Cock, A Teredesai, Third International Conference on Weblogs and Social Media (ICWSM 2009), May 2009, San Jose, CA. (Acceptance Rate 20%)
  53. Trust and Distrust based Recommendations for Controversial Items, P Victor, C Cornelius, M De Cock, and A Teredesai, Web Science 09, March 2009, Athens, Greece. (Acceptance Rate 20%)
  54. 2008

  55. Getting Cold Start Users Connected in a Recommender System’s Trust Network, P Victor, C Cornelius, M De Cock, and A Teredesai, The 8th International FLINS Conference on Computational Intelligence in Decision and Control, Sept 2008, Madrid, Spain. (Acceptance Rate 33%)
  56. Whom Should I Trust? The Impact of Key Figures on Cold Start Recommendations, P Victor, C Cornelius, A Teredesai and M De Cock, ACM 2008 Symposium on Applied Computing (SAC), March 2008, Fortaleza, Brazil. (Acceptance Rate 30%)
  57. 2007

  58. GPSENSE: An Algorithmic Framework for Intelligent Sensing at Node Level in WSN, S Soni and A. Teredesai, Proceedings of the International Symposium on Data, Information & Knowledge Spectrum, Dec 13 - 15, 2007, Kerala, India. (Acceptance Rate 28%)
  59. 2006

  60. Modeling Proliferation of Ideas in Online Social Networks, M. Ahmad and A. Teredesai, Proceedings of the 5th Australasian Data Mining Conference, November 29-30 2006, in conjunction with the 19th Australian Joint Conference on Artificial Intelligence, Sydney, Australia. (Oral presentation- acceptance Rate 24%)
  61. Towards Enhancing Undergraduate Pervasive Computing Skills: An Innovative Multi-Disciplinary Approach, F. Hu and A. Teredesai, International Conference on Engineering Education (ICEE) (peer-reviewed abstract), July 23-28 2006, San Juan, Puerto Rice, USA. (Acceptance Rate 40%)
  62. SoNEA: Sensing Online Novelty Using Event Archives, A. Teredesai and Yuan-Feng Zhu, First IEEE International Workshop on Intelligent Systems Techniques for Wireless Sensor Networks, October 2006, Vancouver, BC, Canada. (Acceptance Rate 25%)
  63. AquaGP: Approximate Query Processing using Genetic Programming, J. Peltzer, A. Teredesai, and G. Reinard, European Conference on Genetic Programming (EuroGP06), Budapest, Hungary, April 2006. (Acceptance Rate 35%)
  64. 2005

  65. Revisiting the Connectionist vs. Classicist Debate using Image Annotation as a Paradigm for Critique, M. Ahmad and A. Teredesai, 3rd International Conference on Philosophical Foundations of Artificial Intelligence, Berlin, Germany, March 2005. (Acceptance rate 30%)
  66. Timing-controlled Data Query in Wireless Sensor Networks: Towards A Cross-layer Optimization Approach, F. Hu, A. Teredesai, and H. Wu, IEEE International Conference On Networking, Sensing and Control, ICNSC 05, March 2005. (Acceptance Rate 47%)
  67. Genetic Programming in Wireless Sensor Networks, D. Johnson, A. Teredesai and R. Saltarelli, European Conference on Genetic Programming EuroGP 2005, Lausanne, Switzerland, April 2005. (Acceptance Rate 31%)
  68. Portable Image Archiving: Annotation, Search and Data Retrieval, V. Misic, J. Kang, A. Teredesai and J. Sissel, IS&T Archiving Conference, 114-118, Washington, DC., April, 2005. (Acceptance Rate 70%)
  69. 2004

  70. Combing Multimedia Multi-relational Associations, A. Teredesai, J. Kanodia, M. Ahmad and R. Gaborski, 7th ACM SIGKDD Workshop on Multimedia Data Mining, Seattle, WA, USA, August 2004. (Track Acceptance Rate 12%) (ISI conference impact factor 1.28)
  71. Extracting Social Networks from Instant Messaging Populations, J. Resig, S. Dawara, C. M. Homan, and A. Teredesai, Proceedings of the 7th ACM SIGKDD Workshop on Link KDD, August 2004. (Track Acceptance Rate 20%) (ISI conference impact factor 1.28)
  72. Issues in Evolving GP based Classifiers for a Pattern Recognition Task, A. Teredesai, Congress for Evolutionary Computation 04 (CEC 04), Portland, OR, June 2004. (Acceptance Rate 70%)
  73. A Framework for Mining Instant Messaging Forests, A. Teredesai, and J. Resig, Workshop on Link Analysis, Counter-terrorism, and Privacy at the Fourth SIAM International Conference on Data Mining (SDM 2004), FL, April 2004.
  74. VENUS: A System for Novelty Detection in Video Streams with Learning, R. Gaborski, A. Teredesai, V. Vaingankar, V. Chaoji, A. Tentler, Proceedings of the 17th International FLAIRS Conference, South Beach, FL, 2004. (Acceptance rate 49%)
  75. Detection of Inconsistent regions in video streams, R. Gaborski, V. Vaingankar, A. Teredesai, V. Chaoji, Proceedings of the SPIE Electronic Imaging: Human Vision and Electronic Imaging IX Conference, San Jose, California 2004.
  76. 2003

  77. Cognitively Motivated Habituation for Novelty Detection in Video, V. Vaingankar, V. Chaoji, R. Gaborski, and A. Teredesai, Proceedings of the Seventeenth Annual Conference in Neural Information Processing Systems (NIPS 03) Open Problems in Cognitively Motivated Vision, December 2003, Whistler B.C., Canada. (Acceptance rate regular paper 27%)
  78. 2002

  79. Recurrent Genetic Programming, A. Teredesai, V. Govindaraju, E. Ratzlaf, and J. Subrahmonia, IEEE Conference on Systems, Man and Cybernetics 2002, October, 2002, Hammamet, Morocco. (Acceptance Rate 70%)
  80. On-Line Digit Recognition using Off-Line Features, A. Teredesai, E. Ratzlaf, J. Subrahmonia, V. Govindaraju, Indian Conference on Computer Vision, Graphics and Image Processing, Ahmadabad, India. December 2002.
  81. 2001

  82. Active Digit Classifiers: A Separability Optimization Approach to Emulate Cognition, A. Teredesai, and V. Govindaraju, Sixth International Conference on Document Analysis and Recognition (ICDAR01), Seattle, September 2001. (Acceptance rate 48%)
  83. Active Handwritten Character Recognition using Genetic Programming, A. Teredesai, J. Park and V. Govindaraju, European Conference on Genetic Programming (EuroGP01), Como Italy, April 2001. (Acceptance Rate 43%)
  84. Secondary Classification using Key Features, A. Teredesai, Z. Shi, and V. Govindaraju, SPIE01: Document Recognition and Retrieval VIII, Proceedings of the SPIE, San Jose, CA, January 2001.
  85. Regional Workshops (peer-reviewed)

  86. Image Search, Retrieval and Visualization in CoMMA, with M. Ahmad and J. Kanodia, Conference on Computing and Information Sciences (CCIS), Rochester, NY, January 2005.
  87. Intelligent Event Detection in Sensor Networks, A. Teredesai, Y. Zhu, 1st IEEE Upstate NY Workshop on Wireless Communication and Networking, Rochester, November 2004
  88. MAC-level Upstream (Sensor-to-Sink) QoS Support For Reliable Wireless Sensor Networks, F. Hu, A. Teredesai, and S. Kumar, 3rd IEEE Upstate NY Sensor Network Workshop, Syracuse, NY, October 2004.
  89. Quality of Query Measures in Sensor Networks, A. Teredesai and Chung-Liang Clark Hsu, 3rd IEEE Upstate NY Sensor Network Workshop, Syracuse, NY, October 2004.
  90. Event Detection in Video Sequences of Natural Scenes, A. Tentler, V. Vaingankar, R. Gaborski, A. Teredesai, IEEE Western New York Image Processing Workshop, October 2003, Rochester, NY.
  91. Feature Extraction and Classification in Machine Printed Korean OCR, D. Hollinger, P. Anderson, R. Gaborski, A. Teredesai, IEEE Western New York Image Processing Workshop, October 2003, Rochester, NY.

Journal

  1. ‘The good old days’: An examination of nostalgia in Facebook posts, Sergio Davalos, Altaf Merchant, Gregory M. Rose, Brenton J. Lessley, Ankur M. Teredesai, International Journal of Human-Computer Studies, Volume 83, November 2015, Pages 83-93, ISSN 1071-5819, http://dx.doi.org/10.1016/j.ijhcs.2015.05.009.
  2. Dimensionality Reduction Phenomenon-aware Stream Query Processing, Ashish Bindra, Mohamed Ali, Ankur Teredesai, International Journal of Data Engineering (IJDE), Volume 3, Issue 3 / 2012.
  3. Trust- and Distrust-Based Recommendations for Controversial Reviews
, P. Victor, C. Cornelis, M. De Cock, A. Teredesai,
IEEE Intelligent Systems 26(1), pp. 48-55, 2011. (ISI 2010 impact factor: 2.57)
  4. Key Figure Impact in Trust-Enhanced Recommender Systems, Patricia Victor, Chris Cornelis, Martine De Cock, Ankur M. Teredesai, AI Communications, Volume 21, Number 2-3/ 2008. (ISI impact factor: 0.57)
  5. A Pervasive Computing Curriculum for Engineering and Science Students, F. Hu and A. Teredesai, IEEE Pervasive Computing, Vol 6, Issue 1, 88-91, 2007. (ISI impact factor: 2.062)
  6. CoMMA: A Framework for Integrated Multimedia Mining using Multi-relational Associations, A. Teredesai, R. Gaborski, J. Kanodia, and M. Ahmad, Journal of Knowledge and Information Systems (KAIS), Vol 10(2): 135-162, 2006. (ISI impact factor: 0.833)
  7. Secondary Classification using Genetic Programming, A. Teredesai and V. Govindaraju, Journal of Pattern Recognition 38(4): 505-512, April 2005. (ISI impact factor: 1.822)

Book Chapters

  1. Evaluation of Classifiers, Ankur Teredesai and Nele Verbiest, Data Classification: Algorithms and Applications, CRC press. 633-656, Charu Aggarwal
  2. Forward by A. Teredesai, Trust Networks for Recommender Systems. P. Victor, C. Cornelis, M. De Cock, Atlantis Press, 2011.
  3. Cognitively Motivated Novelty Detection in Video Data Streams, J. Kang, M. Ahmad, A. Teredesai, and R. Gaborski. Multimedia Data Mining and Knowledge Discovery, Springer Publishers. Valery A. Petrushin and Latifur Khan (Eds.), pg. 209-233, December 2006.

Patents

  1. Apparatus and Method for Distributed In-Database Look-Alike Modeling for Audience Segmentation, Ashish Bindra, Ankur Teredesai, Sreenivasulu Pokuri, Krishna Uppala, U.S. Patent Application Serial Number 13/923,291.
  2. Augmented Audience Lift Measure for Consumer Intelligence, Archana Ramesh, Ankur Teredesai, Ashish Bindra, Sreenivasulu Pokuri, Ramesh Pabbati, Krishna Uppala, U.S. Provisional Patent Application No. 61/778,280.
  3. Distributed In-Database K-Means Clustering for Audience segment Expansion, Archana Ramesh, Ankur Teredesai, Ashish Bindra, Sreenivasulu Pokuri, Krishna Uppala, U.S. Provisional Patent Application No. 61/778,275.
  4. Real Time Conversion Tracking, Reporting, and Multi Data Provider Segment Insights, Ankur Teredesai, Fadi Obeid, Krishna Uppala, Mahmoud Alnahlawi, Bassel Ojjeh, Valter Sciarrillo, Tim Jones, Ramesh Pabbati, U.S. Provisional Patent Application No. 61/724,855.
  5. Real Time Efficient Segment Classification for Online Audience Intelligence, Ashish Bindra, Fadi Obeid, Dave Taniguchi, Ankur Teredesai, Krishna Uppala, U.S. Provisional Patent Application No. 61/679,020.

Research Panels

  1. How can Big Data bring out Deep Knowledge?, Bharat Rao (Siemens Healthcare), Vivekanand Gopalkrishnan (Deloitte), Charles Parker (BigML), Ankur Teredesai (University of Washington Tacoma), Anjan Goswami (eBay and UC Davis), KDD 2012, Beijing, China, August 2012.
  2. What’s in a Link, Lada Adamic (Moderator), Ankur Teredesai, and Bernie Hogan, Microsoft Faculty Summit, July 2008.
  3. Challenges and Trends: Intelligent Environments and Wireless Sensor Applications, Sajid Hussain (Moderator), Ankur Teredesai, Peter Graham, Alexandru Coman, First IEEE International Workshop on Intelligent Systems Techniques for Wireless Sensor Networks, organized in conjunction with the Third IEEE International Conference on Mobile Ad-hoc and Sensor Systems, MASS'06, Vancouver, Canada, October 9, 2006.

Abstracts and Editorials

  1. Augmenting Microblog Search with Social Authority Ranking, R. Nagmoti, A. Teredesai, M. De Cock in: ISNA2010 (30th International Sunbelt Social Network Conference), July 2010 (extended abstract)
  2. PRISE - a PRotein Interaction Search Engine, T. Fayruzov, M. De Cock, C. Cornelis, V. Hoste, R. Hill, S. Lynch, A. Teredesai in: LTCI2009 (Third AUGent Workshop on Language Technology and Computational Intelligence), 2009

Technical Reports

  1. Auto-Indexing WebPages using Common Nodes in Decision Trees, A. Teredesai, and S. Sharma, RIT LAC Technical Report. January 2003.
  2. Constraint Based Frequent Itemset Mining of Web Access Logs, A. Teredesai, and F. Sultan, RIT LAC Technical Report. December 2002.
  3. Co-Training with SVM and Naïve Bayes Classifiers, A. Teredesai, V. Chaoji, S. Dawara, and R. Gaborski, RIT LAC Technical Report. October 2002.

Technology and Outreach Panels

  1. Cloud Computing: The Next Technology Revolution, A. Teredesai, Mike Marzano, Aaron Kimbell (moderator), South Sound Technology Conference, November 2008.
  2. Symbiosis of Globalization and Technology, A. Teredesai (organizer), Andrew Fry (moderator), George Mobus, Samir Manjure, Nitin Ahuja, Lewis McMurran, ACM Panel Discussion on Computers, Society and Ethics, October 2008.
  3. Why Software Engineers Have the Best Job in the U.S., A. Teredesai (moderator), Career Discovery Week, UW Tacoma, January 2008.
  4. How Non-Profits Use Current Technology, A. Teredesai (moderator), ACM Panel Discussion on Computers, Society and Ethics, Tacoma, March 2007.

Research Software Contributions and Community Contributed Artifacts

  1. Congestive Heart Failure Risk of Rehospitalization Predictive Modeling Tool, Developed at the Center for Web and Data Science, University of Washington, in collaboration with Multicare Health System. Under deployment to be used by clinicians for improving quality of care for Multicare Patients.
  2. Handwritten Zip-Code Recognizers, Developed at Center for Excellence in Document Analysis and Recognition (CEDAR-SUNY Buffalo) and the United States Postal Service. Used by the USPS for zip-code recognition of millions of handwritten mail-pieces everyday.
  3. Individuality of Handwriting. Developed tools to statistically validate the Individuality of Handwriting at Center for Excellence in Document Analysis and Recognition (CEDAR-SUNY Buffalo) in sponsorship from the National Institutes of Justice (NIJ).
  4. Active Genetic Programming for N-Way Binary Classification, Available in a GP plugin for WEKA Machine Learning Toolkit used widely by the machine learning and data mining community.
  5. Grouping Related Attributes, Public domain software module used by the data mining research community to perform co-clustering and grouping of relevant features for feature selection and dimensionality reduction.