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Publications

Contact a member of the group to request a publication.

Name note: Articles by Dr. O'Reilly

Dr. O'Reilly consistently publishes under the name Una-May O'Reilly with the abbreviation U.M. O'Reilly. However there are numerous citations of her papers which are inconsistent in citing her name. Popular errors (try to spot them!) are Una May O'Reilly, Una-May O Reilly, OReilly, Una M, U-M or U.-M. or U-M. with many combinations of the preceding errors which mangle the hyphen or apostrophe. We have even found that different fonts have different apostrophes so that two equivalent citations are not attributed to the same paper!

A few sources have incomplete but somewhat extensive lists of my publications:


2017

Conferences and Workshops

  1. Distributed Stratified. Locality Sensitive Hashing for Critical Event Prediction in the Cloud. De Palma, Alessandro and Hemberg, Erik and O'Reilly, Una-May. NIPS ML4H, 2017.
  2. Towards Formalizing Statute Law as Default Logic through Automatic Semantic Parsing. Pertierra, Marcos and Lawsky, Sarah and Hemberg, Erik and O’Reilly, Una-May. ISAIL, 2017.

Image 3.Towards evolutionary machine learning comparison, competition, and collaboration with a multi-cloud platform.Pasquale Salza, Erik Hemberg,
             Filomena  Ferrucci, Una-May O'Reilly. GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017.
Image 4. Investigating coevolutionary archive based genetic algorithms on cyber defense networks. Dennis Garcia, Anthony Erb Lugo, Erik Hemberg, Una-May
              O'Reilly.
GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017

          5. Machine Learning or Discrete Choice Models for Car Ownership Demand Estimation and Prediction. Miguel Paredes, Erik Hemberg, Una-May O'Reilly, and  
              Chris Zegras. Conference Proceedings of the 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS),
              2017. Naples, Italy. (in presss)


Image 6. If You Can't Measure It, You Can't Improve It: Moving Target Defense Metrics. Stjepan Picek, Erik Hemberg, Una-May O'Reilly. Proceedings of the 2017
             Workshop on Moving Target Defense, 2017.
Image 7. CryptoBench: benchmarking evolutionary algorithms with cryptographic problems. Stjepan Picek, Domagoj Jakobovic, Una-May O'Reilly.
              GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017
Image 8. Developing proactive defenses for computer networks with coevolutionary genetic algorithms.Anthony Erb Lugo, Dennis Garcia, Erik Hemberg, Una-May
              O'Reilly.
GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017
Image 9. cCube: a cloud microservices architecture for evolutionary machine learning classification.Pasquale Salza, Erik Hemberg, Filomena Ferrucci, Una-May
              O'Reilly.
GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017

         10. Collision frequency locality-sensitive hashing for prediction of critical events.Yongwook Bryce Kim, Erik Hemberg, Una-My O'Reilly.  EMBC2017
                3088-3093.

         11. Proceedings of the 9th International Joint Conference on Computational Intelligence. Christophe Sabourin, Juan Julián Merelo Guervós, Una-May O'Reilly,
                Kurosh Madani, Kevin Warwick. IJCCI 2017, Funchal, Madeira, Portugal, November 1-3, 2017.SciTePress2017, ISBN 978-989-758-274-5 [[contents]].

         12. Distributed Stratified Locality Sensitive Hashing for Critical Event Prediction in the Cloud. Alessandro De Palma, Erik Hemberg, Una-May O'Reilly.
                CoRRabs/1712.00206 (2017).


Abstracts and Posters

  1. Maintaining and Extending MOOC Clickstream Curation in the MOOC-Learner-Project. Austin Liew, Erik Hemberg, Una-May O’Reilly Learning with MOOCs Conference, 2017. Austin, TX.
  2. Exploring Stopout Prediction and Transfer Learning in MOOCs. Alex Huang, Erik Hemberg, Una-May O’Reilly. Learning with MOOCs Conference, 2017. Austin, TX.


Theses

  1. Cohort Selection and Sampling Techniques to Balance Time-Series Retrospective Studies. Brian Bell Jr, M.E. thesis, MIT Dept of EECS, February 2017. Advisor: Una-May O'Reilly.
  2. Extensions to Behavioral Genetic Programming. Steven B. Fine, M.E. thesis, MIT Dept of EECS, February 2017. Advisor: Una-May O'Reilly.
  3. Coevolutionary Genetic Algorithms for Proactive Computer Network Defenses. Anthony Erb Lugo, M.E. thesis, MIT Dept of EECS, June 2017. Advisor: Una-May O'Reilly.
  4. BeatDB v3: A Framework for the Creation of Predictive Datasets from Physiological Signals.Steven Rivera, M.E. thesis, MIT Dept of EECS, June 2017. Advisor: Una-May O'Reilly.
  5. Overcoming Code Rot in Legacy Software Projects. Austin Liew, M.E. thesis, MIT Dept of EECS, June 2017. Advisor: Una-May O'Reilly.
  6. Long Short-term Memory Recurrent Neural Networks for Classification of Acute Hypotensive Episodes.Alexander Jaffe, M.E. thesis, MIT Dept of EECS, June 2017. Advisor: Una-May O'Reilly.
  7. Peer-to-Peer Network Modeling for Adversarial Proactive Cyber Defenses. Dennis GArcia, M.E. thesis, MIT Dept of EECS, June 2017. Advisor: Una-May O'Reilly.
  8. Physiological Time Series Retrieval and Prediction with Locality-Sensitive Hashing. Y. Bryce Kim, PhD thesis, MIT Dept of EECS, June 2017. Advisor: Una-May O'Reilly.


Journals and Book Chapters

  1. Exploiting Subprograms in Genetic Programming. Fine, Steven and Hemberg, Erik and Krawiec, Krzysztof and O'Reilly, Una-May. Genetic Programming Theory and Practice XV (Genetic and Evolutionary Computation). 2017

 


 

2016

Conferences

  1. Analysis of Locality-Sensitive Hashing for Fast Critical Event Prediction on Physiological Time Series. Y. Bryce Kim and Una-May O'Reilly. 38th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016: 783-787.  NSF Award for Young Professionals Contributing to Smart and Connected Health.
  2. Stratified locality-sensitive hashing for accelerated physiological time series retrieval. Yongwook Bryce Kim, Erik Hemberg, Una-May O'Reilly. EMBC2016: 24792483.
  3. STEALTH: Modeling Coevolutionary Dynamics of Tax Evasion and Auditing.[http://dblp.uni-trier.de/db/conf/ic3k/kdir2016.html#OReilly16|]Una-May O'Reilly. KDIR2016: 9.
  4. STEALTH: Modeling Coevolutionary Dynamics of Tax Evasion and Auditing. Una-May O'Reilly.[http://dblp.uni-trier.de/db/conf/ic3k/kdir2016.html#OReilly16|]IJCCI (ECTA)2016: 11

Image 5.Multi-Line Batch Scheduling by Similarity. Ignacio Arnaldo, Erik Hemberg, Una-May O'Reilly. GECCO '16 Companion Proceedings of the 2016 on Genetic and
             Evolutionary Computation Conference Companion, 2016.

Image 6.Dynamics of Adversarial Co-evolution in Tax Non-Compliance Detection. Jacob Rosen, Erik Hemberg, Una-May O'Reilly. GECCO '16 Companion Proceedings
             of the 2016 on Genetic and Evolutionary Computation Conference Companion, 2016.

Image 7.Genetic Programming: A Tutorial Introduction. Una-May O'Reilly. GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation
             Conference Companion, 2016.



Abstracts and Posters

  1. Multi-Resolution Histogram Representation for Physiological Time Series and its Integration into Locality-Sensitive Hashing. Y. Bryce Kim and Una-May O'Reilly. Poster in 38th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016.



Workshops

  1. Dynamics of Adversarial Co-evolution in Tax Non-Compliance Detection. Jacob Rosen, Erik Hemberg, Una-May O’Reilly. GECCO Security & Risk Workshop, 2016. DOI:http://dx.doi.org/10.1145/2908961.2931680
  2. Multi-Line Batch Scheduling by Similarity. Ignacio Arnaldo, Erik Hemberg, Una-May O’Reilly. GECCO Industrial Applications of Metaheuristics Workshop, 2016. DOI:http://dx.doi.org/10.1145/2908961.2931647
  3. Stratified Locality-Sensitive Hashing for Sublinear Time Critical Event Prediction. Y. Bryce Kim, Erik Hemberg, and Una-May O'Reilly. Conference on Neural Information Processing Systems (NIPS) Machine Learning in Healthcare Workshop, 2016. NSF Best Paper Award.


Theses

  1. Towards An Automatic Predictive Question Formulations. Benjamin J. Schreck, M.E. thesis, MIT Dept of EECS, June 2016. Advisor: Kalyan Veeramachaneni.
  2. Model Factory: A New Way to Look at Data Through Models. Yonglin Wu, M.E. thesis, MIT Dept of EECS, June 2016. Advisor: Kalyan Veeramachaneni.
  3. Transfer Learning for Predictive Models in MOOCs.Sebastien Boyer, M.S. thesis, MIT Dept of EECS, IDSS, June 2016. Advisor: Kalyan Veeramachaneni.
  4. The Synthetic Data Vault: Generative Modeling for Relational Databases. Neha Patki, M.E. thesis, MIT Dept of EECS, June 2016. Advisor: Kalyan Veeramachaneni.
  5. Articial Intelligence Opportunities and an End-To-End Data-Driven Solution for Predicting Hardware Failures. Mario Orozco Gabriel, M.S., MBA thesis, MIT Dept of Mechanical Engineering, Sloan School of Management, June 2016. Advisors: Kalyan Veeramachaneni, Tauhid Zaman, John J. Leonard.


Journals and Book Chapters

  1. Detecting Tax Evasion: A Co-Evolutionary Approach. Erik Hemberg, Jacob Rosen, Geoff Warner, Sanith Wijesinghe, Una-May O'Reilly. Artificial Intelligence and Law, 1-34, 2016. DOI 10.1007/s10506-016-9181-6.
  2. Investigating Multi-Population Competitive Coevolution for Anticipating Tax Evasion. Erik Hemberg, Jacob Rosen, Una-May O'Reilly. Genetic Programming: Theory to Practice, 2016.

 


 

2015

Conferences

  1. Data Science Foundry for MOOCs. Sebastien Boyer, Ben U. Gelman, Benjamin Schreck, Kalyan Veeramachaneni. IEEE/ACM Data Science and Advanced Analytics Conference.
  2. Deep Feature Synthesis: Towards Automating Data Science Endeavors. James Max Kanter, Kalyan Veeramachaneni. Accepted, IEEE/ACM Data Science and Advanced Analytics Conference.
  3. Large-Scale Physiological Waveform Retrieval via Locality-Sensitive Hashing. Y. Bryce Kim and Una-May O'Reilly. 37th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015.
  4. Gaussian Process-based Feature Selection for Wavelet Parameters: Predicting Acute Hypotensive Episodes from Physiological Signals. Franck Dernoncourt, Kalyan Veeramachaneni and Una-May O'Reilly. IEEE 28th International Symposium on Computer-Based Medical Systems. IEEE Computer Society, 2015.
  5. Computer Aided Tax Evasion Policy Analysis: Directed Search using Autonomous Agents. Jacob Rosen, Erik Hemberg, Geoffrey Warner, Sanith Wijesinghe, Una-May O'Reilly, Shadow 2015 , Munster, Germany.

Image 6. Tax non-compliance detection using co-evolution of tax evasion risk and audit likelihood. Erik Hemberg, Jacob Rosen, Geoff Warner, Sanith Wijesinghe,
             Una-May O'Reilly.
ICAIL '15 Proceedings of the 15th International Conference on Artificial Intelligence and Law, 2015.
Image 7.Feature Factory: Crowd Sourced Feature Discovery. Kalyan Veeramachaneni, Kiarash Adl, Una-May O'Reilly. L@S '15 Proceedings of the Second (2015) ACM
             Conference on Learning @ Scale, 2015.
         8. Copula Graphical Models for Wind Resource Estimation. Kalyan Veeramachaneni, Alfredo Cuesta-Infante, Una-May O'Reilly, IJCAI 2015: 2646-265.

         9. Transfer Learning for Predictive Models in Massive Open Online Courses. Sebastien Boyer and Kalyan Veeramachaneni, 17th International Conference in  
             Artificial Intelligence in Education, 2015.

        10. Autotuning Algorithmic Choice for Input Sensitivity. Yufei Ding, Jason Ansel, Kalyan Veeramachaneni, Xipeng Shen, Una-May O’Reilly, Saman Amarasinghe,
               PLDI 2015.

Image 11.Building Predictive Models via Feature Synthesis. Ignacio Arnaldo, Una-May O'Reilly, Kalyan Veeramachaneni. GECCO '15 Proceedings of the 2015 Annual
                Conference on Genetic and Evolutionary Computation, 2015

Image 12.Genetic Programming: A Tutorial Introduction. Una-May O'Reilly. GECCO Companion '15 Proceedings of the Companion Publication of the 2015 Annual    
                Conference on Genetic and Evolutionary Computation, 2015.

 


Abstracts and Posters

  1. Computer Aided Tax Evasion Policy Analysis: Directed Search using Autonomous Agents. Jacob Rosen, Erik Hemberg, Geoffrey Warner, Sanith Wijesinghe, Una-May O'Reilly, Extended Abstract, AAMAS 2015, Istanbul.
  2. Large-Scale Prediction of Acute Hypotensive Episodes via Locality-Sensitive Hashing on Physiological Waveform Time Series. Y. Bryce Kim and Una-May O'Reilly. Poster in 37th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015.


Workshops

  1. Analysis of Data-Driven Event Prediction based on Sublinear Time Retrieval of Physiological Waveforms. Y. Bryce Kim and Una-May O'Reilly. Conference on Neural Information Processing Systems (NIPS) Machine Learning in Healthcare Workshop, 2015.

Theses

  1. BeatDB: An End-to-end Approach to Unveil Saliencies from Massive Signal Data Sets. Franck Dernoncourt, S.M, thesis, MIT Dept of EECS, February 2015. Advisors: Una-May O'Reilly, Kalyan Veeramachaneni.
  2. Computer Aided Tax Avoidance Policy Analysis. Jacob Rosen, S.M Thesis, MIT Dept of EDS, 2015. Advisors: Una-May O'Reilly, Erik Hemberg.
  3. Deep Mining: Copula-Based Hyper-Parameter Optimization for Machine Learning Pipelines. Sebastien Dubois M.E. Thesis, Ecole Polytechnique, MIT, 2015. Advisor: Kalyan Veeramachaneni.
  4. Machine Learning Blocks. Bryan Omar Collazo Santiago, M.E. Thesis MIT Dept of EECS, 2015. Advisor: Kalyan Veeramachaneni.
  5. The Data Science Machine: Emulating Human Intelligence in Data Science Endeavors.James Max Kanter, MIT Dept of EECS, 2015. Advisor: Kalyan Veeramachaneni.


Journals and Book Chapters

  1. Learning a Goal-Oriented Model for Energy Efficient Adaptive Applications in Data Centers. Monica Vitali, Barbara Pernici, Una-May O'Reilly. Inf. Sci. 319: 152-170 (2015)
  2. FlexGP: Cloud-Based Ensemble Learning with Genetic Programming for Large Regression Problems. Kalyan Veeramachaneni, Ignacio Arnaldo, Owen Derby, Una-May O’Reilly. Journal Of Grid Computing 13(3): 391-407, (Mar 2015).
  3. Bring Your Own Learner! A cloud-based, data-parallel commons for machine learning. Ignacio Arnaldo, Kalyan Veeramachaneni, Andrew Song, Una-May O’Reilly. Special Issue on Computational Intelligence for Cloud Computing, IEEE Comp. Int. Mag. 10(1): 20-32 (2015).
  4. Genetic Programming. James McDermott and Una-May O'Reilly. Handbook of Computational Intelligence, 2015: 845-869 (Part E Evolutionary Computation with Topic Editors: Dr. F. Neumann and Dr. K Witt, Editors in Chief: Prof. Janusz Kacprzyk and Prof. Witold Pedrycz).

 


 

2014 

Conferences

  1. Building a Stage 1 Computer Aided Detector for Breast Cancer using Genetic Programming. Conor Ryan, Krzysztof Krawiec, Una-May O'Reilly, Jeannie Fitzgerald, David Medernach.17th European Conference on Genetic Programming, Springer LNCS 8599, pp 162-173.
  2. Behavioral Search Drivers for Genetic Programing. Krzysztof Krawiec, Una-May O'Reilly. 17th European Conference on Genetic Programming, Springer LNCS 8599, pp 210-221.
  3. Flash: A GP-GPU Ensemble Learning System for handling Large Datasets. Ignacio Arnaldo, Kalyan Veeramachaneni and Una-May O'Reilly. 17th European Conference on Genetic Programming, Springer LNCS 8599, pp 13-24.
  4. OpenTuner: An Extensible Framework for Program Autotuning. Jason Ansel, Shoaib Kamil, Kalyan Veeramachaneni, Jonathan Ragan-Kelley, Jeffrey Bosboom, Una-May O'Reilly and Saman P. Amarasinghe. International Conference on Parallel Architectures and Compilation, PACT '14, pp 303--316, DOI: 10.1145/2628071.2628092. Available free from the ACM Digital Library.
  5. Large-Scale Methodological Comparison of Acute Hypotensive Episode Forecasting Using MIMIC2 Physiological Waveforms. Y. Bryce Kim, Joohyun Seo, Una-May O'Reilly. 2014 Proceedings of the IEEE 27th International Symposium on Computer-Based Medical Systems (CBMS), New York, May 2014.
  6. Multiple Regression Genetic Programming. Ignacio Arnaldo, Krzysztof Krawiec, Una-May O'Reilly. GECCO '14, pp 879--886. Available free from the ACM Digital Library.
  7. Behavioral Programming: A Broader and More Detailed Take on Semantic GP.Krzysztof Krawiec, Una-May O'Reilly. GECCO 2014, pp 935-94, Awarded Best Paper in the Genetic Programming Track. Available free from the ACM Digital Library.
  8. A Continuous Developmental Model for Wind Farm Layout Optimization.Sylvain Cussat-Blanc, Hervé Luga, Una-May O'Reilly, Kalyan Veeramachaneni, Dennis Wilson. GECCO 2014, pp745-752. Available free from the ACM Digital Library.


Workshops

  1. Learning Decision Lists with Lags for Physiological Time Series. Erik Hemberg, Kalyan Veeramachaneni, Babak Hodjat, Prashan Wanigasekara, Hormoz Shahrzad, Una-May O'Reilly. 3rd Workshop on Data Mining for Medicine and Healthcare (2014), April 26, 2014, Philadelphia, PA, (held in conjunction with 14th SIAM International Conference on Data Mining (SDM 2014).


Theses

  1. PhysioMiner: A Scalable Cloud Based Framework for Physiological Waveform Mining. Vineet Gopal, M.Eng Thesis completed in MIT Dept of EECS, 2014. Advisors: Kalyan Veeramachaneni, Una-May O'Reilly.
  2. Stopout Prediction in Massive Open Online Courses. Colin Taylor, M.Eng Thesis completed in MIT Dept of EECS, 2014. Advisors: Kalyan Veeramachaneni, Una-May O'Reilly.
  3. Modeling Problem Solving in Massive Open Online Courses. Fang Han, M.Eng Thesis completed in MIT Dept of EECS, 2014. Advisors: Kalyan Veeramachaneni, Una-May O'Reilly.
  4. Using Distributed Machine Learning to Predict Arterial Blood Pressure. Ijeoma Emeagwali, M.Eng Thesis completed in MIT Dept of EECS, 2014. Advisors: Una-May O'Reilly, Erik Hemberg.
  5. Simulating Tax Evasion Using Agent Based Modelling And Evolutionary Search. Osama Badar, M.Eng Thesis completed in MIT Dept of EECS, 2014. Advisors: Una-May O'Reilly, Erik Hemberg


Journals and Book Chapters

  1. Modeling tax evasion with genetic algorithms. Geoffrey Warner, Sanith Wijesinghe, Uma Marques, Osama Badar, Jacob Rosen, Erik Hemberg, and Una-May O’Reilly. Economics of Governance, pp 1-14, Nov 2014.DOI: 10.1007/s10101-014-0152-7
  2. Technology for Mining the Big Data of MOOCs. Una-May O'Reilly, Kalyan Veeramachaneni. Winter 2014, Research and Practice in Assessment.
  3. Using Reinforcement Learning to Optimize Occupant Comfort and Energy Usage in HVAC Systems. Pedro Fazenda, Kalyan Veeramachaneni, Pedro Lima, Una-May O'Reilly. November 2014. Journal of Ambient Intelligence and Smart Environments: JAISE 6(6): 675-690.
  4. The Max Problem Revisited: The Importance of Mutation in Genetic Programming. Timo Koetzing, Andrew M. Sutton, Frank Neumann and Una-May O'Reilly. Theoretical Computer Science, vol 545, pp 94-107, Aug 2014.


ArXiv Reports

  1. arXiv#1408.3382 Likely to stop? Predicting Stopout in Massive Open Online Courses. Colin Taylor, Kalyan Veeramachaneni, Una-May O'Reilly.
  2. arXiv#1407.5238 Towards Feature Engineering at Scale for Data from Massive Open Online Courses. Kalyan Veeramachaneni, Una-May O'Reilly, Colin Taylor.
  3. arXiv# 1406.201 MOOCdb: Developing Standards and Systems to Support MOOC Data Science. Kalyan Veeramachaneni, Sherif Halawa, Franck Dernoncourt, Una-May O'Reilly, Colin Taylor, Chuong Do.

 


 

2013 

Conferences

  1. Introducing Graphical Models to Analyze Genetic Programming Dynamics. Erik Hemberg, Constantin Berzan, Kalyan Veeramachaneni, Una-May O'Reilly. FOGA XII '13 Proceedings of the twelfth workshop on Foundations of genetic algorithms XII, 2013.
  2. Cloud Scale Distributed Evolutionary Strategies for High Dimensional Problems. Dennis Wilson, Kalyan Veeramachaneni, and Una-May O'Reilly. EVOPAR track, Applications of Evolutionary Computation, Lecture Notes in Computer Science Volume 7835, 2013, pp 519-528.
  3. Cloud Driven Design of a Distributed Genetic Programming Platform. Owen Derby, Kalyan Veeramachaneni, and Una-May O'Reilly. EVOPAR track, Applications of Evolutionary Computation, Lecture Notes in Computer Science Volume 7835, 2013, pp 509-518.
  4. Learning regression ensembles with genetic programming at scale. Kalyan Veeramachaneni, Owen Derby, Dylan Sherry, Una-May O'Reilly. GECCO '13, Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference, 2013.
  5. On Learning to Generate Wind Farm Layouts. Dennis Wilson, Emmanuel Awa, Sylvain Cussat-Blanc, Kalyan Veeramachaneni, Una-May O'Reilly. GECCO '13, Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference, 2013.
  6. Modeling Tax Evasion with Genetic Algorithms. Geoffrey Warner, Sanith Wijesinghe , Uma Marques, Una-May O'Reilly, Erik Hemberg, Osama Badar. Shadow 2013, Munster.


Workshops

  1. Building MultiClass Nonlinear Classifiers with GPUs. Ignacio Arnaldo, Kalyan Veeramachaneni and Una-May O'Reilly. 2014 NIPS Workshop on Big Learning.
  2. BeatDB : A Large Scale Waveform Feature Repository. Franck Dernoncourt, Kalyan Veeramachaneni and Una-May O'Reilly. MLCDA@NIPS 2013 : Machine Learning for Clinical Data Analysis and Healthcare.
  3. MoocViz: A Large Scale, Open Access, Collaborative Data Analytics Framework for MOOCs. Franck Dernoncourt,Choung Do, Sherif Halawa, Una-May O'Reilly, Colin Taylor, Kalyan Veeramachaneni and Sherwin Wu. DDE@NIPS 2013: Data Directed Education. Workshop website
  4. Analyzing Millions of Submissions to Help MOOC Instructors Understand Problem Solving. Fang Han, Kalyan Veeramachaneni and Una-May O'Reilly. DDE@NIPS 2013: Data Directed Education. Workshop website
  5. Copula-Based Wind Resource Assessment. Kalyan Veeramachaneni, Teasha Feldman-Fitzthum, Una-May O’Reilly, Alfredo Cuesta-Infante. Machine Learning for Sustainability Workshop@NIPS 2013.
  6. Efficient Training Set Use For Blood Pressure Prediction in a Large Scale Learning Classifier System. Erik Hemberg, Kalyan Veeramachaneni, Franck Dernoncourt, Mark Wagy and Una-May O'Reilly. Sixteenth International Workshop on Learning Classifiers Systems.
  7. Learning Blood Pressure Behavior from Large Physiological Waveform Repositories. Alexander Waldin, Kalyan Veeramachaneni, Una-May O'Reilly. ICML Workshop on Healthcare 2013.
  8. MOOCdb: Developing Data Standards for MOOC Datascience. Kalyan Veeramachaneni, Franck Dernoncourt, Colin Taylor, Zachary A. Pardos, Una-May O'Reilly. MOOCShop at Artificial Intelligence in Education, 2013. Workshop website


Theses

  1. Large-scale Consensus Clustering and Data Ownership Considerations for Medical Application. Chidube Ezeozue, S.M, thesis, MIT Dept of EECS, 2013. Advisors: Una-May O'Reilly, Kalyan Veeramachaneni.
  2. FlexGP: A Scalable System for Factored Learning in the Cloud. Owen Derby, M.Eng, thesis, completed in MIT Dept of EECS, 2013. Advisors: Kalyan Veeramachaneni, Una-May O'Reilly.
  3. Learning Blood Pressure Behavior from Large Blood Pressure Waveform Repositories and Building Predictive Models.Alexander Waldin, Masters Thesis, ETH-Zurich, 2013. Advisors: Kalyan Veeramachaneni, Una-May O'Reilly.
  4. FlexGP 2.0: Multiple Levels of Parallelism in Distributed Machine Learning via Genetic Programming. Dylan Sherry, 2013.  Advisors: Kalyan Veeramachaneni, Una-May O'Reilly.


Journals and Book Chapters

  1. Techniques for Accurate Wind Resource Estimation by Modeling Statistical Dependency. K. Veeramachaneni, Xiang Ye, U.M. O'Reilly. Ch 10, pp 303-330 in Computational Intelligent Data Analysis for Sustainable Development, Editors: Ting Yu, Nitesh Chawla, Simeon Simoff, Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, Taylor & Francis, 2013. ISBN 9781439895948. Link to Book
  2. Better GP Benchmarks: Community Survey Results and Proposals. David R. White, James McDermott, Mauro Castelli, Luca Manzoni, Brian W. Goldman, Gabriel Kronberger, Wojciech Jaśkowski, Una-May O'Reilly, Sean Luke. Genetic Programming and Evolvable Machines, Volume 14 Issue 1, March 2013, Pages 3-29. DOI: 10.1007/s10710-012-9177-2.
  3. Maintenance of a Long Running Distributed Genetic Programming System For Solving Problems Requiring Big Data. Babak Hodjat, Erik Hemberg, Hormoz Shahrzad and Una-May O'Reilly. Genetic Programming Theory and Practice XI, pp65-83. DOI: 10.1007/978-1-4939-0375-7_4.

 


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