Publications

2023

Conferences and Workshops

  1. CLAWSAT: Towards Both Robust and Accurate Code Models. Jia*, J., Srikant*, S., Mitrovska, T., Chang, S., Gan, C., Liu, S., and O'Reilly, U. M. (2022). SANER 2023. [arXiv pre-print]
  2. Genetic Programming and Coevolution to Play the Bomberman™ Video Game. Gold, R., Branquinho, H., Hemberg, E., O’Reilly, UM., García-Sánchez, P. (2023). Genetic Programming and Coevolution to Play the Bomberman™ Video Game. In: Correia, J., Smith, S., Qaddoura, R. (eds) Applications of Evolutionary Computation. EvoApplications 2023. Lecture Notes in Computer Science, vol 13989. Springer, Cham. https://doi.org/10.1007/978-3-031-30229-9_49 2023.
  3. Semi-Supervised Learning with Coevolutionary Generative Adversarial Networks, Jamal Toutouh, Subhash Nalluru, Erik Hemberg, Una-May O’Reilly, GECCO, 2023.
  4. Analysis of a Pairwise Dominance Coevolutionary Algorithm And DefendIt, Per Kristian Lehre, Mario Hevia Fajardo, Erik Hemberg, Jamal Toutouh, Una-May O’Reilly, GECCO, 2023.
  5. Investigating Student’s Problem-solving Approaches in MOOCs using Natural Language Processing, ByeongJo Kong, Erik Hemberg, and Una-May O’Reilly, International Conference on Learning Analytics & Knowledge, 2023.
  6. Assessing Large Language Model’s knowledge of threat behavior in MITRE ATT&CK, Garza, Ethan, and Hemberg, Erik, and Moskal, Stephen, and O’Reilly, Una-May. AI4Cyber Workshop at KDD 2023

Journals and Bookchapters

  1. Adversarial Agent-Learning for Cybersecurity: A Comparison of Algorithms, Alexander Shashkov, Erik Hemberg, Miguel Tulla and Una-May O’Reilly, The Knowledge Engineering Review, 2023
  2. Enhancements to Threat, Vulnerability, and Mitigation Knowledge For Cyber Analytics, Hunting, and Simulations, Erik Hemberg, Matthew Turner, Nick Rutar, Una-May O'Reilly, ACM Digital Threats: Research and Practice, 2023
  3. Semi-Supervised Generative Adversarial Networks with Spatial Co-evolution for Image Generation and Classification, Jamal Toutouh, Subhash Nalluru, Erik Hemberg, Una-May O’Reilly, Applied Soft Computing Journal, 2023.
  4. Analysis of a Pairwise Dominance Coevolutionary Algorithm with Spatial Topology, Mario Hevia Fajardo, Per Kristian Lehre, Erik Hemberg, Jamal Toutouh, Una-May O’Reilly,. Genetic Programming Theory and Practice XXX. Genetic and Evolutionary Computation. 2023, In production.
  5. Adversarial Evolutionary Learning With Distributed Spatial Coevolution, Jamal Toutouh, Erik Hemberg, Una-May O'Reilly, in Handbook of Evolutionary Machine Learning, Springer, 2023

ArXiv Reports

  1. LLMs Killed the Script Kiddie: How Agents Supported by Large Language Models Change the Landscape of Network Threat Testing S Moskal, S Laney, E Hemberg, UM O'Reilly, arXiv preprint arXiv:2310.06936

Theses

  1. Remote Sensing, Inference, and Intelligence in the Information Environment, Thomas Galligani, S.M in Technology and Policy, MIT, June 2023, Advisors: Una-May O'Reilly and Erik Hemberg
  2. Inference of Cyber Threats Vulnerabilities and Mitigations to Enhance Cybersecurity Simulations, Kyle Liu, M.Eng, MIT-EECS, June 2023, Advisors: Erik Hemberg
  3. Coevolving Cybersecurity Adversaries for Industrial Control Systems in Failure-Prone Environments, Kathryn Wicks, M.Eng, MIT-EECS, June 2023, Advisors: Erik Hemberg and Una-May-O'Reilly
  4. Analysing Climate Change Information Campaigns on Online Social Networks, Thomas Benchetrit, MSc in Data Science EPFL, August 2023, Advisors: Jerome Baudry (EPFL) and Erik Hemberg
  5. Describing information influence in social media with coupling inference methods, Cyril Vallez, MSc in Computational Science and Engineering EPFL, February 2023, Advisors Robert West (EPFL), Erik Hemberg and Una-May O'Reilly

2022

Conferences and Workshops

  1. Convergent representations of computer programs in human and artificial neural networks. Srikant*, S., Lipkin*, B., Ivanova, A. A., Fedorenko, E., and O'Reilly, U. M., NeurIPS 2022
  2. Synthesizing Programs from Program Pieces using Genetic Programming and Refinement Type Checking, Sabrina Tseng, Erik Hemberg and Una-May O'Reilly. EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming, pages 187-201, Madrid, Spain, 2022. Springer Verlag.
  3. Exploiting Knowledge from Code to Guide Program Search, Dirk Schweim, Erik Hemberg, Dominik Sobania and Una-May O'Reilly. EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming, pages 250-255, Madrid, Spain, 2022. Springer Verlag.
  4. Coevolutionary Generative Adversarial Networks for Medical Image Augmentation at Scale, Diana Flores, Jamal Toutouh, Erik Hemberg, Una-May O’Reilly. GECCO, 2022.
  5. Analyzing Multi-Agent Reinforcement Learning and Coevolution in Cybersecurity, Matthew Turner, Erik Hemberg, Una-May O’Reilly. GECCO, 2022.
  6. GUI-based, Efficient Genetic Programming for Unity3D, Robert Gold, Andrew Hayden Grant, Erik Hemberg, Chathika Gunaratne, Una-May O'Reilly. GECCO, 2022.
  7. Using Machine Learning to Infer Plausible and Undetected Cyber Threat, Vulnerability and Mitigation Relationships, Erik Hemberg, Ashwin Srinivasan, Nick Rutar, and Una-May O’Reilly ICML- ML4Cyber Workshop, 2022.
  8. Sourcing Language Models and Text Information for Inferring Cyber Threat, Vulnerability and Mitigation Relationships, Erik Hemberg, Ashwin Srinivasan, Nick Rutar, and Una-May O’Reilly, AI4Cyber/MLHat: AI-enabled Cybersecurity Analytics and Deployable Defense Workshop, 2022.

Journals and Book Chapters

  1. GUI-based, Efficient Genetic Programming and AI Planning for Unity3D, Gold, R., Grant, A.H., Hemberg, E., Gunaratne, C., O’Reilly, UM. (2023). In: Trujillo, L., Winkler, S.M., Silva, S., Banzhaf, W. (eds) Genetic Programming Theory and Practice XIX. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-19-8460-0_3 2022.

Abstracts and Posters

  1. Data Science and Natural Language Processing to Understand Tactics, Techniques, and Procedures of Climate Change Disinformation in the European Union, Helen Landwehr and Una-May O'Reilly, WiDS, Cambridge, 2022. 
  2. Using Domain Knowledge in Coevolution and Reinforcement Learning to Simulate a Logistics Enterprise,Ying Zhao, Erik Hemberg, Nate Derbinsky, Gabino Mata, Una-May O’Reilly, GECCO, 2022
     

2021

Conferences and Workshops

  1. Automating Cyber Threat Hunting Using NLP, Automated Query Generation, and Genetic Perturbation, Prakruthi Karuna, Erik Hemberg, Una-May O'Reilly, Nick Rutar. AI4CSec, 2021.
  2.  AI/ML for Cybersecurity: Challenges, Solutions, and Novel Ideas, John Emanuello, Kimberly Ferguson-Walter, Erik Hemberg, Una-May O Reilly, Ahmad Ridley, Dennis Ross, Diane Staheli and William Streilein. Proceedings -  SIAM Data Mining 2021 axXiv: 2104.13254, 2021.
  3. Using a Collated Cybersecurity Dataset for Machine Learning and Artificial Intelligence, Erik Hemberg, Una-May O'Reilly. 1st KDD Workshop on AI-enabled Cybersecurity Analytics, 2021.
  4. Generating Adversarial Computer Programs using Optimized Obfuscations, Srikant, Shashank, Liu, Sijia, Mitrovska, Tamara, Chang, Shiyu, Fan, Quanfu, Zhang, Gaoyuan, and O’Reilly, Una-May,  ICLR 2021.
  5. Getting a Head Start on Program Synthesis with Genetic Programming, Jordan Wick, Erik Hemberg, and Una-May O’Reilly, EuroGP 2021.
  6. Signal Propagation in a Gradient-Based and Evolutionary Learning System, Jamal Toutouh, Una-May O'Reilly, - arXiv preprint arXiv:2102.08929, GECCO, 2021. Nominated for Best Paper Award. 
  7. Co-evolutionary Modeling of Cyber Attack Patterns and Mitigations Using Public Datasets, Michal Shlapentokh-Rothman, Jonathan Kelly, Avital Baral, Erik Hemberg, Una-May O’Reilly. GECCO, 2021.
  8. Can Cognitive Neuroscience inform Neuro-Symbolic Inference Models?, Shashank Srikant, Una-May O'Reilly. NSNLI Workshop, International Joint Conference on Artificial Intelligence (IJCAI), 2021. 
  9. STRATA: Simple, Gradient-Free Attacks for Models of Code, Jacob Springer, Bryn Reinstadler, Una-May O'Reilly, KDD, 2021. 
     

Journals and Book Chapters

  1. Spatial Coevolution for Generative Adversarial Network Training, Erik Hemberg, Jamal Toutouh, Abdullah Al-Dujaili, Tom Schmiedlechner, and Una-May O'Reilly. 2021.  ACM Transactions On Evolutionary Learning and Optimization,1, 2, Article 6, https://doi.org/10.1145/3458845 2021. 
  2. Exploring Adversarial Artificial Intelligence for Autonomous Adaptive Cyber Defense, Hemberg, E., Zhang L., and O'Reilly, Una-May. In: Jajodia, S., Cybenko, G., Subrahmanian, V., Swarup, V., Wang, C., Wellman, M. (eds) Adaptive Autonomous Secure Cyber Systems. Springer, Cham.
  3. From Biological to Computational Arms Races - Studying Cyber Security Dynamics, O'Reilly, Una-May,  and Hemberg, Erik. In: Banzhaf, W., et al. Evolution in Action: Past, Present and Future. Genetic and Evolutionary Computation. Springer, Cham.

Abstracts and Posters

  1. Analyzing Student Reection Sentiments and Problem-Solving Procedures in MOOCs, Alexander Shashkov, Robert Gold, Erik Hemberg, Byeongjo Kong, Ana Bell and Una-May O'Reilly. Learning At Scale, 2021. (Work-In-Progress)
  2. Using Knowledge of Human-Generated Code to Bias the Search in Program Synthesis with Grammatical Evolution, Dirk Schweim, Dominik Sobania, Erik Hemberg, Una-May O'Reilly, Franz Rothlauf. GECCO, 2021.
     

Tutorials

  1. Genetic Programming: A Tutorial Introduction, Una-May O'Reilly. GECCO 2021.
  2. Coevolutionary Computation for Adversarial Deep Learning, Jamal Toutouh, Una-May O'Reilly. GECCO, 2021.
  3. Coevolutionary Computation for Adversarial Deep Learning, Jamal Toutouh, Una-May O'Reilly. CEC, 2021.
     

arXiv Reports

  1. arXiv: 2102.08929. Signal Propagation in a Gradient-Based and Evolutionary Learning System, Jamal Toutouh, Una-May O'Reilly, 2021. 
  2. arXiv: 2104.11576. Automating Cyber Threat Hunting Using NLP, Automated Query Generation, and Genetic Perturbation, Prakruthi Karuna, Erik Hemberg, Una-May O'Reilly, Nick Rutar, 2021. 
  3. arXiv: 2009.13562. STRATA: Building Robustness with a Simple Method for Generating Black-box Adversarial Attacks, Jacob Springer, Bryn Reinstadler, Una-May O'Reilly, 2021. 

Theses

  1. Investigating System Resilience in Distributed Evolutionary GAN Training, Urmi Mustafi. M.Eng,  MIT-EECS, January 2021. Advisors: Erik Hemberg, Jamal Toutouh.
  2. AI Attack Planning for Emulated Networks, Bryn Reinstadler. M.Sc. MIT-EECS, February 2021. Advisor: Una-May O'Reilly.
  3. Using High-Performance Computing to Scale Generative Adversarial Networks, Diana Flores. M.Eng, MIT-EECS, June 2021. Advisors: Erik Hemberg, Una-May O'Reilly.
  4. Analyzing the Usability of Natural Language Processing for Detecting Disinformation Tactics, Techniques, and Procedures, Masters Thesis, 2021, Massachusetts Institute of Technology,  Dept of Political Science and Dept of Electrical Engineering and Computer Science.

 

2020

Conferences and Workshops

  1. The Robust Malware Detection Challenge and Greedy Random Accelerated Multi-Bit Search, Sicco Verwer, Azqa Nadeem, Christian Hammerschmidt, Laurens Bliek, Abdullah Al-Dujaili, Una-May O'Reilly. AISec'20: Proceedings of the 13th ACM Workshop on Artificial Intelligence and Security, November 2020. Pages 61–70. https://doi.org/10.1145/3411508.3421374. 
  2. Analyzing the Components of Distributed Co-evolutionary GANs Training, Jamal Toutouh, Erik Hemberg, Una-May O'Reilly, PPSN, 2020.
  3. Understanding Learner Behavior Through Learning Design Informed Learning Analytics,  Shen. H, Hemberg. E, Leming. L, Law. N and O'Reilly, U.-M., Learning At Scale 2020.
  4. Sign Bits Are All You Need for Black-Box Attacks. Al-Dujaili, A. and O'Reilly, U.-M. ICLR 2020. 
  5. Dependency-Based Neural Representations for Classifying Lines of Programs, Shashank Srikant, Nicolas Lesimple, Una-May O'Reilly, https://arxiv.org/abs/2004.10166v1 2020.
  6. Computational Intelligence for Evaluating the Air Quality in the Center of Madrid, Spain, J. Toutouh, I. Lebrusan, S. Nesmachnow. In International Conference in Optimization and Learning (OLA2020), 2020.
  7. Re-purposing Heterogeneous Generative Ensembles with Evolutionary Computation, J. Toutouh, E. Hemberg, U. O’Reilly. In Genetic and Evolutionary Computation Conference (GECCO ’20), pages. 10, 2020. DOI: 10.1145/3377930.3390229
  8. Parallel/Distributed Implementation of Cellular Training For Generative Adversarial Neural Networks, E. Perez, S. Nesmachnow, J. Toutouh, E. Hemberg, U. O’Reilly. In 10th IEEE Workshop Parallel/Distributed Combinatorics and Optimization (PDCO 2020), pages 7, 2020.
  9. Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks, Sijia Liu, Songtao Lu, Xiangyi Chen, Yao Feng, Kaidi Xu, Abdullah Al-Dujaili, Mingyi Hong, Una-May O'Reilly. ICML 2020.
  10.  Parallel/distributed Generative Adversarial Neural Networks for Data Augmentation of COVID-19 Training Images, J. Toutouh, M. Esteban, S. Nesmachnow. Latin America High Performance Computing Conference 2020. Pages: 15.
     

Journals and Book Chapters

  1. Data Dieting in GAN Training. J. Toutouh, E. Hemberg, U. O’Reilly. H. Iba, N. Noman (Eds.), Deep Neural Evolution - Deep Learning with Evolutionary Computation, pages 19, Springer, 2020, Springer.
  2. Adversarial Genetic Programming for Cyber Security: A Rising Application Domain Where GP Matters, O’Reilly, U., Toutouh, J., Pertierra, M. et al. Genet Program Evolvable Mach 21, 219–250 (2020).
  3. Exact and Heuristic Approaches for Multi-Objective Garbage Accumulation Points Location in Real Scenarios, D. G. Rossit, J. Toutouh, S. Nesmachnow. Waste Management. Vol. 105, pp. 467-481, 2020.
  4. Random Error Sampling-based Recurrent Neural Network Architecture Optimization, A. Camero, J Toutouh, E. Alba.  Engineering Applications of Artificial Intelligence, Volume 96, 2020, https://doi.org/10.1016/j.engappai.2020.103946
  5. Car Restriction Policies for Better Urban Health: A Low Emission Zone in Madrid, Spain, I. Lebrusán, J. Toutouh.  Air Qual Atmos Health (2020). https://doi.org/10.1007/s11869-020-00938-z 
  6. Optimizing household energy planning in smart cities: A multiobjective Approach, S. Nesmachnow, G. Colacurcio, D. Gabriel Rossit, J. Toutouh, F. Luna. Revista Facultad de Ingeniería Universidad de Antioquia (2020) https://doi.org/10.17533/udea.redin.20200587 
  7. Comprehension of Computer Code Relies Primarily on Domain-general Executive Brain Regions, Ivanova, Anna A., Shashank Srikant, Yotaro Sueoka, Hope H. Kean, Riva Dhamala, Una-May O'reilly, Marina U. Bers, and Evelina Fedorenko. Elife 9 (2020): e58906.
     

Abstracts and Posters

  1. Analyzing K-12 Blended MOOC Learning Behaviors, Robert Gold, Erik Hemberg and Una-May O'Reilly, Learning At Scale, 2020.
  2. Analyzing Pre-Existing Knowledge and Performance in a Programming MOOC, Hannah Burd, Erik Hemberg, Ana Bell and Una-May O'Reilly, Learning At Scale, 2020.
     

Tutorials

  1. Lipizzaner: Distributed Coevolution for Resilient Generative Adversarial Networks (GAN) Training, Jamal Toutouh, Universidad de la Republica, Montevideo Uruguay, April 2020.
  2. Deep Neuroevolution applied to Generative Adversarial Networks, Jamal Toutouh, Spain AI, April 2020.
  3. Navigating to Generative Adversarial Networks (GANs), A Friendly Introduction, Jamal Toutouh, Spain AI, April 2020.
     

Theses

  1. Exploring Deep Learning Models for Vulnerabilities Detection in Smart Contracts. Nicolas Lesimple. Master Project in Computational Science and Engineering, Mathematics. EPFL, January 2020. Advisors: Prof Martin Jaggi, EPFL, Dr. Una-May O'Reilly, MIT.
  2. Vulcan: Classifying Vulnerabilities in Solidity Smart Contracts Using Dependency-Based Deep Program Representations. Shashank Srikant. M.Sc. MIT-EECS, May 2020. Advisor: Una-May O'Reilly.
  3.  Adaptive Defense Against Adversarial Artificial Intelligence at the Edge of the Cloud using Evolutionary Algorithms. Sofiane Djeffal. M.Sc. EM, MIT-SDM, May 2020. Advisors: Erik Hemberg, Una-May O’Reilly.
  4. Unifying Public Threat Knowledge for Cyber Hunting. Michal Shlapentokh-Rothman. M.Eng, MIT- EECS, May 2020. Advisors: Erik Hemberg, Una-May O’Reilly.
  5. Using Existing Knowledge for Transfer and Regularization for Program Synthesis with Genetic Programming. Jordan Wick. M.Eng, MIT- EECS, May 2020. Advisors: Erik Hemberg, Una-May O’Reilly.  
  6. Using Machine Learning for Analysis of Neuronal Network Activity. Srilaya Bhavaraju. M.Eng, MIT- EECS, September 2020. Advisors: Erik Hemberg, Una-May O’Reilly.  
     

arXiv Reports

  1. arXiv:2004.10166. Dependency-Based Neural Representations for Classifying Lines of Programs. Shashank Srikant, Nicolas Lesimple, Una-May O'Reilly, 2020.
  2. arXiv: 2009.13562. STRATA: Building Robustness with a Simple Method for Generating Black-box Adversarial Attacks for Models of Code. Jacob M. Springer. Bryn Marie Reinstadler, Una-May O'Reilly, 2020. 
  3. arXiv: 2010.0053. BRON -- Linking Attack Tactics, Techniques, and Patterns with Defensive Weaknesses, Vulnerabilities and Affected Platform Configurations. Erik Hemberg, Jonathan Kelly, Michal Shlapentokh-Rothman, Bryn Reinstadler, Katherine Xu, Nick Rutar, Una-May O'Reilly, 2020.
  4. bioRxiv: 2020.04.16.045732. Comprehension of Computer Code Relies Primarily on Domain-General Resources. Anna A. Ivanova, Shashank Srikant, Yotaro Sueoka, Hope H. Kean, Riva Dhamala, Una-May O’Reilly, Marina U. Bers, Evelina Fedorenko. doi: https://doi.org/10.1101/2020.04.16.045732. 2020.

 

2019

Conferences and Workshops

  1. Transfer Learning using Representation Learning in Massive Online Open Courses. John Mucong Ding, Erik Hemberg, Una-May O'Reilly, International Learning Analytics and Knowledge Conference, 2019.  
  2. Using Detailed Access Trajectories for Learning Behavior Analysis. Yanbang Wang, Nancy Law, Erik Hemberg, Una-May O'Reilly,  International Learning Analytics and Knowledge Conference, 2019.  
  3. Adversarially Adapting Deceptive Views and Reconnaissance Scans on a Software Defined Network. Jonathan Kelly, Michael DeLaus, Erik Hemberg, Una-May O'Reilly. 4th IEEE/IFIP International Workshop on Analytics for Network and Service Management (AnNet 2019), 2019.
  4. Improving Genetic Programming with Novel Exploration - Exploitation Control. Jonathan Kelly, Erik Hemberg, Una-May O'Reilly. 22nd European Conference on Genetic Programming (EuroGP), 2019.
  5. On Domain Knowledge and Novelty to Improve Program Synthesis Performance with Grammatical Evolution. Erik Hemberg, Jonathan Kelly, and Una-May O'Reilly. GECCO, 2019.
  6. Spatial Evolutionary Generative Adversarial Networks. Jamal Toutouh, Erik Hemberg and Una-May O'Reilly. GECCO, 2019.
  7. Investigating Algorithms for Finding Nash Equilibria in Cybersecurity Problems. Linda Zhang, Erik Hemberg, SecDef Workshop at GECCO, 2019.
  8. Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML. Liu, S., Lu, S., Chen, X., Feng, Y., Xu, K., Al-Dujaili, A., Hong, M. and O'Reilly, U.-M. ICLR, 2019.
  9. Categorizing Resources Workshop Papers and Learners for a Finer-Grained Analysis of MOOC Viewing & Doing. Erik Hemberg, Sagar Biswas, Ayesha Bajwa, Nancy Law, Una-May O'Reilly. Learning With MOOCs, 2019.
  10. Analyzing Student Code Trajectories in an Introductory Programming MOOC. Ayesha Bajwa, Erik Hemberg, Ana Bell, Una-May O'Reilly. Learning With MOOCs, 2019.
  11. The Influence of Grades on Learning Behavior in MOOCs: Certification vs. Continued Participation. Li Wang, Erik Hemberg, Una-May O'Reilly. Learning With MOOCs, 2019.
  12. How Student Back- ground and Topic Impact the Doer Effect in Computational Thinking MOOCs. Jitesh Maiyuran, Ayesha Bajwa, Erik Hemberg, Ana Bell, Una-May O'Reilly. Learning With MOOCs, 2019. Best Student Paper Award
  13. Multi-Objective Household Energy Planning Using Evolutionary Algorithms. G. Colacurcio, S. Nesmachnow, J. Toutouh, F. Luna, D. Rossit. In II Ibero-American Congress of Smart Cities (ICSC-CITIES 2019), pages 15, 2019.
  14. Assessing the Environmental Impact of Car Restrictions Policies: Madrid Central Case. I. Lebrusan, J. Toutouh. In II Ibero-American Congress of Smart Cities (ICSC-CITIES 2019), pages 15, 2019.
  15. Smart City Tools to Evaluate the Impact of Car Restrictions Policies in Urban Areas: Madrid Central Case. J. Toutouh, I. Lebrusan. I ECUSA Congress, Boston MA, 2019. *Best Oral Communication Award
     

Tutorials

  1. Practical Data Science Seminar on practical Data Science. Erik Hemberg. CoLAB Workshop, Uruguay, May 2019.
  2. Spatial Coevolutionary Deep Neural Networks Training. Jamal Toutouh. Universidad de la Republica, Montevideo Uruguay, May 2019.
  3. An Artificial Coevolutionary Framework for Adversarial AI. Jamal Toutouh. Universidad de la Republica, Montevideo Uruguay, May 2019.
  4. Introduction to Genetic Programming. Una-May O'Reilly, Erik Hemberg.GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO, 2019.
  5. Introduction to Genetic Programming Tutorial on Genetic Programming. Erik Hemberg. GECCO Tutorial, July 2019
  6. Spatial Coevolutionary Generative Adversarial Networks Hackathon. Erik Hemberg. Oak Ridge National Laboratory, August 2019.
     

Journals and Book Chapters

  1. Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury. Edilberto Amorim, Michellevan der Stoel, Sunil B. Nagaraj, Mohammad M.Ghassemi, JinJing, Una-May O'Reilly,Benjamin M.Scirica, Jong Woo Lee, Sydney S.Cash, M. Brandon Westover. Clinical Neurophysiology. Volume 130, Ussue 10. Oct, 2019. Pg 1908-1916.
  2. Soft Computing Methods for Multi-Objective Location of Garbage Accumulation Points in Smart Cities. J. Toutouh, D. G. Rossit, S. Nesmachnow. Annals of Mathematics and Artificial Intelligence. pp. 1-27. 2019.
  3. Waste Generation Prediction Under Uncertainty in Smart Cities Through Deep Neuroevolution. A. Camero, J. Toutouh, J. Ferrer, E. Alba. Revista de Ingeniería, Universidad de Antioquia, No.93, pp. 128-138, 2019.
  4. A Bi-Objective Integer Programming Model for Locating Garbage Accumulation Points: A Case Study. D. G. Rossit, S. Nesmachnow, J. Toutouh. Revista de Ingeniería, Universidad de Antioquia, No.93, pp. 70-81, 2019.
  5. A Review of Dynamic Verification of Security and Dependability Properties. A. Muñoz, J. Toutouh, F. Jaime.  Ryma Abassi (Eds.), Artificial Intelligence and Security Challenges in Emerging Networks, pp. 162-187, 2019, IGI Global. ISBN: 1522573534.
     

Abstracts and Posters

  1. On the Use of Context Sensitive Grammars in Genetic Programming For Legal Non-Compliance Detection. Carl Im, Erik Hemberg. GECCO, 2019
  2. On the Influence of Grades on Learning Behavior of Students in MOOCs. Li Wang, Erik Hemberg and Una-May O'Reilly, Learning At Scale, 2019.
  3. Investigating Learning Design Categorization and Learning Behaviour in Computational MOOCS. Sagar Biswas, Erik Hemberg, Nancy Law and Una-May Oreilly, Learning At Scale, 2019.
  4. Student Code Trajectories in an Introductory Programming MOOC. Ayesha Bajwa, Ana Bell, Erik Hemberg and Una-May O'Reilly, Learning At Scale, 2019. (Work-In-Progress)
  5. Representation Learning for Code Malware. Sanja Simonovikj, Abdullah Al-Dujaili, Shashank Srikant, Erik Hemberg, Una-May O'Reilly. IBM AI Research Week, AI Horizons Colloquium, 2019.
     

Theses

  1. Analyzing Student Learning Trajectories in an Introductory Programming MOOC. Ayesha Bajwa, M.Eng., MIT-EECS, June 2019. Advisors: Erik Hemberg, Una-May O'Reilly.
  2. Investigating Genetic Prigramming with Novelty and Domain Knowledge for Program Synthesis. Jonathan Kelly, M.Eng., MIT-EECS, June 2019. Advisors: Erik Hemberg, Una-May O'Reilly.
  3. Investigating Coevolutionary Algorithms for Finding Nash Equilibria in Cybersecurity Problems. Linda Zhang, M.Eng., MIT-EECS, June 2019. Advisors: Erik Hemberg, Una-May O'Reilly.
  4. The Influence of Grades on Learning Behavior of Students in MOOCs. Li Wang, M.Eng., MIT-EECS, June 2019. Advisors: Erik Hemberg, Una-May O'Reilly.
  5. Structured Grammatical Evolution Applied to Program Synthesis. Andrew Zhang, M.Eng., MIT-EECS, June 2019. Advisors: Erik Hemberg, Una-May O'Reilly.
  6. Master of Engineering Thesis. Carolyn Holz, M.Eng., MIT-EECS, June 2019. Advisors: Erik Hemberg, Una-May O'Reilly.
     

arXiv Reports

  1. arXiv:1812.05043. Transfer Learning using Representation Learning in Massive Online Open Courses. John Mucong Ding, Erik Hemberg, Una-May O'Reilly, 2019.  
  2. arXiv:1812.05767. Using Detailed Access Trajectories for Learning Behavior Analysis. Yanbang Wang, Nancy Law, Erik Hemberg, Una-May O'Reilly, 2019. 

 

2018

Conferences and Workshops

  1. Lipizzaner: A System That Scales Robust Generative Adversarial Network Training. Erik Hemberg, Abdullah Al-Dujaili, Tom Schmiedlechner, Una-May O'Reilly, Systems for Machine Learning workshop@ NeurIPS 2018.
  2. An Artificial Coevolutionary Framework for Adversarial AI. Una-May O’Reilly, Erik Hemberg. AAAI Fall Symposia, Washington DC, 2018.
  3. Towards Distributed Coevolutionary GANs. Abdullah Al-Dujaili, Tom Schmiedlechner, Erik Hemberg, Una-May O'Reilly. AAAI Fall Symposia, Washington DC, 2018.
  4. Adversarial Co-evolution of Attack and Defense in a Segmented Computer Network Environment. Erik Hemberg, Joe Zipkin, Richard Skowyra, Neal Wagner and Una-May O’Reilly. GECCO SECDEF Workshop, 2018.
  5. EuroGP 2018 Panel Debate: Genetic Programming in the Era of Deep Neural Networks. Machado, Penousal and O'Reilly, Una-May and Gori, Marco and Risi, Sebastian. SIGEVOlution, July 2018. Vol: 11, No 2. Aug 2018. Iss: 1931-8499, Pg 3-6. ACM, New York, NY, USA, 2018.
  6. Adversarial Deep Learning for Robust Detection of Binary Encoded Malware. Abdullah Al-Dujaili, Alex Huang, Erik Hemberg, and Una-May O'Reilly. Deep Learning and Security Workshop, IEEE Security and Privacy, 2018.
  7. Approximating Nash Equilibria for Black-Box Games: A Bayesian Optimization Approach. Abdullah Al-Dujaili, Erik Hemberg, and Una-May O'Reilly. International Workshop on Optimization in Multiagent Systems, @FAIM18. (AAMAS, ICML, IJCAI/ECAI, ICCBR, SoCS), 2018. 
  8. On Visual Hallmarks of Robustness to Adversarial Malware. Alex Huang, Abdullah Al-Dujaili, Erik Hemberg, and Una-May O'Reilly. IReDLiA@FAIM18 (AAMAS, ICML, IJCAI/ECAI, ICCBR, SoCS), 2018. 
  9. On the Application of Danskin's Theorem to Derivative-free Minimax Optimization. Abdullah Al-Dujaili, Shashank Srikant, Erik Hemberg, and Una-May O'Reilly. Int. Workshop on Global Optimization, 2018. 
  10. AST-Based Deep Learning for Detecting Malicious PowerShell.  Gili Rusak, Abdullah Al-Dujaili, Una-May O’Reilly. In Proceedings of 2018 ACM SIGSAC Conference on Computer & Communications Security (CCS ’18). ACM, New York, NY, USA, 3 pages.  
  11. Exploring the Use of Autoencoders for Botnets Traffic Representation. Ruggiero Dargenio, Shashank Srikant, Erik Hemberg, Una-May O'Reilly. IEEE Symposium on Security and Privacy Workshops 2018: 57-62.
  12. Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection. Jwala Dhamala, Emmanuel Azuh, Abdullah Al-Dujaili, Jonathan Rubin, Una-May O'Reilly. Machine Learning for Health (ML4H) Workshop at NeurIPS, 2018. 
  13. An Artificial Coevolutionary Framework for Adversarial AI. Una-May O'Reilly, Erik Hemberg. Proceedings of the AAAI Symposium on Adversary-Aware Learning Techniques and Trends in Cybersecurity (ALEC 2018), Association for the Advancement of Artificial Intelligence 2018 Fall Symposium Series (AAAI-FSS 2018) Arlington, Virginia, USA, October 18-20, 2018. Vol 2269 pp 50-55. 2018.
     

Abstracts and Posters

  1. Machine Learning Tools for Analyzing the Development of Neuronal Networks in Health and Disease. Raoul-Emil R. Khouri, Susanna B. Mierau, Przemyslaw Jarzebowski, Ole Paulsen, Erik Hemberg , Una-May O'Reilly. 2nd North East Computational Health Summit AI in Healthcare, 27 April 2018.
  2. Competitive Coevolutionary Algorithm Decision Support. Daniel Sanchez, Marcos Pertierra, Erik Hemberg and Una-May O’Reilly. Poster, GECCO, 2018.
  3. Machine Learning and Malware Arms Race. Abdullah Al-Dujaili, Alex Huang, Erik Hemberg, Una-May O'Reilly. IBM Research 2018 Horizons Colloquium. Cambridge MA. Oct, 2018 (Best poster Award)
  4. AST-Based Deep Learning for Detecting Malicious PowerShell.  Gili Rusak, Abdullah Al-Dujaili, Una-May O’Reilly. In Proceedings of 2018 ACM SIGSAC Conference on Computer & Communications Security (CCS ’18), ACM, New York, NY, USA.
     

Theses

  1. Investigating EEG Burst Suppression for Coma Outcome Prediction.Tiange Zhan, M.Eng., MIT EECS, June 2018. Advisors: Erik Hemberg, Una-May O'Reilly.
  2. Investigating Coevolutionary Algorithms for Expensive Fitness Evaluations In Cybersecurity. Marcos Pertierra Arrojo, M.Eng., MIT EECS, June 2018. Advisors: Erik Hemberg, Una-May O'Reilly.
  3. Two-Photon Calcium Imaging Sequence Analysis Pipeline: A Method for Analyzing Neuronal Network Activity. Raoul-Emil Roger Khouri, M.Eng., MIT EECS, June 2018. Advisors: Erik Hemberg, Una-May O'Reilly.
  4. Understanding the Doer Effect for Computational Subjects with MOOCs. Jitesh Maiyuran, M.Eng., MIT EECS, June 2018. Advisors: Erik Hemberg, Una-May O'Reilly.
  5. Visualizing Adversaries - Transparent Pooling Approaches for decision Support in Cybersecurity. Daniel Prado Sanchez, M.Eng., MIT EECS, June 2018. Advisors: Erik Hemberg, Una-May O'Reilly
  6. An Integrative Data Science  Framework to Jointly Address Prediction, Quasi-Causation, and Causation. Miguel Paredes, PhD, DUSP, MIT, 2018. Avisor: Una-may O'Reilly
     

Journals and Book Chapters

  1. Grammatical Evolution with Coevolutionary Algorithms in Cyber Security. Erik Hemberg, Anthony Erb Lugo, Dennis Garcia, Una-May O’Reilly. pp 407-431, Handbook of Grammatical Evolution, Springer 2018.
     

arXiv Reports

  1. arXiv:1811.07216. Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection. Jwala Dhamala, Emmanuel Azuh, Abdullah Al-Dujaili, Jonathan Rubin, Una-May O'Reilly, 2018.
  2. arXiv: 1810.09230. AST-Based Deep Learning for Detecting Malicious PowerShell.  Gili Rusak, Abdullah Al-Dujaili, Una-May O’Reilly, 2018.
  3. arXiv:1801.02950. Adversarial Deep Learning for Robust Detection of Binary Encoded Malware. Abdullah Al-Dujaili, Alex Huang, Erik Hemberg, and Una-May O'Reilly, 2018.
  4. arXiv: 1804.10586. Approximating Nash Equilibria for Black-Box Games: A Bayesian Optimization Approach. Abdullah Al-Dujaili, Erik Hemberg, and Una-May O'Reilly, 2018.
  5. arXiv: 1805.03553. On Visual Hallmarks of Robustness to Adversarial Malware. Alex Huang, Abdullah Al-Dujaili, Erik Hemberg, and Una-May O'Reilly, 2018. 
  6. arXiv: 1805.06322. On the Application of Danskin's Theorem to Derivative-free Minimax Optimization. Abdullah Al-Dujaili, Shashank Srikant, Erik Hemberg, and Una-May O'Reilly, 2018. 
  7. arXiv: 1811.12843 Lipizzaner: A System That Scales Robust Generative Adversarial Network Training. Erik Hemberg, Abdullah Al-Dujaili, Tom Schmiedlechner, Una-May O'Reilly, 2018.

 

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.
  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.
  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. 
  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. 
  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.
  8. Developing proactive defenses for computer networks with coevolutionary genetic algorithms. Anthony Erb Lugo, Dennis Garcia, Erik Hemberg, Una-May O'Reilly. SecDEF GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017.
  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. Y. Bryce Kim, Erik Hemberg, Una-My O'Reilly.  EMBC2017: 3088-3093.
  11. 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).
  12. One-Class Classification of Low Volumes DoS Attacks with Genetic Programming. Stjepan Picek and Erik Hemberg and Una-May O'Reilly. Genetic Programming Theory & Practice. 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.Eng., MIT EECS, February 2017. Advisor: Una-May O'Reilly.
  2. Extensions to Behavioral Genetic Programming. Steven B. Fine, M.Eng., MIT EECS, February 2017. Advisor: Una-May O'Reilly.
  3. Coevolutionary Genetic Algorithms for Proactive Computer Network Defenses. Anthony Erb Lugo, M.Eng., MIT 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.Eng., MIT EECS, June 2017. Advisor: Una-May O'Reilly.
  5. Overcoming Code Rot in Legacy Software Projects. Austin Liew, M.Eng., MIT 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.Eng., MIT EECS, June 2017. Advisor: Una-May O'Reilly.
  7. Peer-to-Peer Network Modeling for Adversarial Proactive Cyber Defenses. Dennis Garcia, M.Eng., MIT EECS, June 2017. Advisor: Una-May O'Reilly.
  8. Physiological Time Series Retrieval and Prediction with Locality-Sensitive Hashing. Y. Bryce Kim, PhD, MIT 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.
     

Edited Volumes

  1. 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.

     

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. Y. Bryce Kim, Erik Hemberg, Una-May O'Reilly. EMBC2016: 24792483.
  3. STEALTH: Modeling Coevolutionary Dynamics of Tax Evasion and Auditing. Una-May O'Reilly. KDIR2016: 9.
  4. STEALTH: Modeling Coevolutionary Dynamics of Tax Evasion and Auditing. Una-May O'Reilly. IJCCI (ECTA)2016: 11.
  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.
  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.
     

Tutorials

  1. 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. Artificial 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.
  6. Program Auto-tuning Through Population-based Stochastic Optimization, Minshu Zhan. M.E. thesis, MIT Dept of EECS, June 2016. Advisor: Kalyan Veeramachaneni.
     

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.

     

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:

Older Publications...