Conference Proceedings
  1. Hong Yang, Qi Yu and Travis Desell. When Does Restricting a Coding Agent to execute_code Help? A Regime x Agent-Design Ablation. Agentic Software Engineering (SE 3.0) Workshop at KDD 2026. Jeju ICC, Korea. August 2026. [pdf]
  2. Abhishek Singh, Zimeng Lyu and Travis Desell. Repopulation Frequency as a Driving Factor in Bias Correction in Asynchronous Parallel Evolutionary Neural Architecture Search. Parallel Problem Solving Through Nature (PPSN 2026). Trento, Italy. September 2026. [pdf]
  3. Devroop Kar, Daniel Krutz and Travis Desell. Evolutionary Design of Parameterized Quantum Circuits for Classification and Reinforcement Learning Tasks. Parallel Problem Solving Through Nature (PPSN 2026). Trento, Italy. September 2026. [pdf]
  4. Onkar Shelar and Travis Desell. Diversifying Toxicity Search in Large Language Models Through Speciation. The Genetic and Evolutionary Computation Conference Companion (GECCO 2026). San José, Costa Rica. July 2026. [pdf]
  5. Diana Velychko and Travis Desell. Investigating Memetic Scheduling Strategies for Distributed Neuroevolution. The Genetic and Evolutionary Computation Conference Companion (GECCO 2026). San José, Costa Rica. July 2026. [pdf]
  6. Steven Szachara, Sheeraja Rajakrishnan, Dylan Jay Van Allen, Jason Pollack, Travis Desell and Daniel Krutz. GSC-QEMit: A Telemetry-Driven Hierarchical Forecast-and-Bandit Framework for Adaptive Quantum Error Mitigation. The International Joint Conference on Neural Networks (IJCNN 2026). Maastricht, The Netherlands. June 2026. [pdf]
  7. Abhishek Singh, Zimeng Lyu and Travis Desell. Biologically-Inspired Homeostasis for Neuroevolution: Alternating Growth and Pruning Phases. The 29th International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2026). Toulouse, France. April 2026. [pdf]
  8. Onkar Shelar and Travis Desell. ToxSearch: Evolving Prompts for Toxicity Search in Large Language Models. The 29th International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2026). Toulouse, France. April 2026. [pdf]
  9. Zimeng Lyu, Alexander Ororbia, Rui Li and Travis Desell. Minimally Supervised Regression using Topological Projections in Self-Organizing Maps. The International Joint Conference on Neural Networks (IJCNN 2025). Rome, Italy. July 2025. [pdf]
  10. AbdElRahman ElSaid and Travis Desell. CG-CANTS-N: A Versatile Graph-Based Framework for Scalable and Adaptive Problem Solving Across Domains. The Genetic and Evolutionary Computation Conference Companion (GECCO 2025). Malaga, Spain. July 2025. [pdf]
  11. Joshua Karns and Travis Desell. Evaluation Time Bias in Asynchronous Evolutionary Algorithms: A Replication Study and a Novel Mitigation Strategy. The Genetic and Evolutionary Computation Conference (GECCO 2025). Malaga, Spain. July 2025. [pdf]
  12. Evan Patterson, Joshua Karns, Zimeng Lyu and Travis Desell. Visualizing the Dynamics of Neuroevolution with Genetic Distance Projections. The Genetic and Evolutionary Computation Conference (GECCO 2025). Malaga, Spain. July 2025. Best Paper Nominee. [pdf]
  13. Hong Yang, Qi Yu and Travis Desell. Can We Ignore Labels in Out-of-Distribution Detection?. The Thirteenth International Conference on Learning Representations (ICLR 2025).. Singapore. April 2025. [pdf]
  14. Zimeng Lyu, Devroop Kar, Matthew Simoni, Rohaan Nadeem, Avinash Bhojanapalli, Hao Zhang and Travis Desell. Evolving RNNs for Stock Forecasting: A Low Parameter Efficient Alternative to Transformers. The 28th International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2025). Trieste, Italy. April 23-25, 2025. [pdf]
  15. Zimeng Lyu, Pujan Thapa, and Travis Desell. Minimally Supervised Topological Projections of Self-Organizing Maps for Phase of Flight Identification. The International Joint Conference on Neural Networks (IJCNN). Yokohama, Japan. June 30-July 5, 2024. [pdf]
  16. Devroop Kar, Zimeng Lyu, Alexander G. Ororibia, Travis Desell, and Daniel Krutz. Enabling An Informed Contextual Multi-Armed Bandit Framework For Stock Trading With Neuroevolution. Proceedings of the Genetic and Evolutionary Computation Conference Companion. Melbourne, Australia. July 14-18, 2024. [pdf]
  17. Jared Murphy, Devroop Kar, Joshua Karns, and Travis Desell. EXA-GP: Unifying Graph-Based Genetic Programming and Neuroevolution for Explainable Time Series Forecasting. Proceedings of the Genetic and Evolutionary Computation Conference Companion. Melbourne, Australia. July 14-18, 2024. [pdf]
  18. Jared Murphy, Travis Desell. Minimizing the EXA-GP Graph-Based Genetic Programming Algorithm for Interpretable Time Series Forecasting. Proceedings of the Genetic and Evolutionary Computation Conference Companion. Melbourne, Australia. July 14-18, 2024. [pdf]
  19. Aditya Shankar Thakur, Akshar Bajrang Awari, Zimeng Lyu, and Travis Desell. Efficient Neuroevolution using Island Repopulation and Simplex Hyperparameter Optimization. The 2023 IEEE Symposium Series on Computational Intelligence (SSCI 2023). Mexico City, Mexico. December 5-8, 2023. [pdf]
  20. Amit Dilip Kini∗, Swaraj Sambhaji Yadav∗, Aditya Shankar Thakur, Akshar Bajrang Awari, Zimeng Lyu, and Travis Desell. Co-evolving Recurrent Neural Networks and their Hyperparameters with Simplex Hyperparameter Optimization. The Genetic and Evolutionary Computation Conference Companion (GECCO '23 Companion). Lisbon, Portugal. July 15–19, 2023. *Indicates equal contribution. [pdf]
  21. Joshua Karns and Travis Desell. Local Stochastic Differentiable Architecture Search for Memetic Neuroevolution Algorithms. The Genetic and Evolutionary Computation Conference Companion (GECCO '23 Companion). Lisbon, Portugal. July 15–19, 2023. [pdf]
  22. Aizaz Ul Haq, Niranjana Deshpande, AbdElRahman ElSaid, Travis Desell and Daniel Krutz. Addressing Tactic Volatility in Self-Adaptive Systems Using Evolved Recurrent Neural Networks and Uncertainty Reduction Tactics. The Genetic and Evolutionary Computation Conference (GECCO 2022). Boston, USA. July 9-13, 2022. [pdf]
  23. Zimeng Lyu and Travis Desell. ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting. The Genetic and Evolutionary Computation Conference (GECCO 2022). Boston, USA. July 9-13, 2022. [pdf]
  24. Aidan LaBella, Joshua Karns, Farhad Akhbardeh, Andrew Walton, Zechariah Morgan, Brandon Wild, Mark Dusenbury and Travis Desell. Optimized Flight Safety Event Detection in the National General Aviation Flight Information Database. In The 37th ACM/SIGAPP Symposium on Applied Computing (SAC '22). Online. April 25-29, 2022. [pdf]
  25. Michael Kogan, Joshua Karns and Travis Desell. Self-Adaptation of Neuroevolution Algorithms using Reinforcement Learning. The 25th International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2021). Madrid, Spain. April 20-22, 2022. [pdf]
  26. Hong Yang, Aidan LaBella and Travis Desell. Predictive Maintenance for General Aviation Using Convolutional Transformers. The Thirty-Fourth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-22). Vancouver, British Columbia, Canada. February 24 - 26, 2022. [pdf]
  27. Hitesh Vaidya, Travis Desell* and Alexander Ororbia*. Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay (Student Abstract). The 36th AAAI Conference on Artificial Intelligence (AAAI 2022). Vancouver, British Columbia, Canada. February 22 - March 1, 2022. [pdf]
  28. Farhad Akhbardeh, Cecilia Ovesdotter Alm, Marcos Zampieri and Travis Desell. Handling Extreme Class Imbalance in Technical Logbook Datasets. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021). Bangkok, Thailand. August 2-4, 2021. [pdf]
  29. Zimeng Lyu, Shuchita Patwardhan, David Stadem, James Langfeld, Steve Benson, and Travis Desell. Neuroevolution of Recurrent Neural Networks for Time Series Forecasting of Coal-Fired Power Plant Data. ACM Workshop on NeuroEvolution@Work (NEWK@Work}, held in conjunction with ACM Genetic and Evolutionary Computation Conference (GECCO). pp. 1735-1743. Lille, France. July 10-14, 2021. [pdf]
  30. Joshua Karns and Travis Desell. Improving the Scalability of Distributed Neuroevolution Using Modular Congruence Class Generated Innovation Numbers. The 1st Workshop on Evolutionary Algorithms and High Performance Computing (EAHPC), held in conjunction with ACM Genetic and Evolutionary Computation Conference (GECCO). Lille, France. July 10-14, 2021. [pdf]
  31. AbdElRahman ElSaid, Joshua Karns, Zimeng Lyu, Alexander Ororbia and Travis Desell. Continuous Ant-Based Neural Topology Search. The 24th International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2021). Online. April 7-9, 2021. [pdf]
  32. Zimeng Lyu, AbdElRahman ElSaid, Joshua Karns, Mohamed Mkaouer and Travis Desell. An Experimental Study of Weight Initialization and Lamarckian Inheritance on Neuroevolution. The 24th International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2021). pp. 584-600. Online. April 7-9, 2021. [pdf]
  33. Zimeng Lyu, AbdElRahman ElSaid, Joshua Karns, Mohamed Mkaouer and Travis Desell. Improving Distributed Neuroevolution Using Island Extinction and Repopulation. The 24th International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2021). pp. 568-583. Online. April 7-9, 2021. [pdf]
  34. Farhad Akhbardeh, Travis Desell, and Marcos Zampieri. NLP Tools for Predictive Maintenance Records in MaintNet. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations. Suzhou, China. December, 2020. [pdf]
  35. Farhad Akhbardeh, Travis Desell, and Marcos Zampieri. MaintNet: A Collaborative Open-Source Library for Predictive Maintenance Language Resources. In Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations. Barcelona, Spain. December, 2020. [pdf]
  36. AbdElRahman ElSaid, Joshua Karns, Zimeng Lyu, Daniel Krutz, Alexander Ororbia and Travis Desell. Improving Neuroevolutionary Transfer Learning of Deep Recurrent Neural Networks through Network-Aware Adaptation. The Genetic and Evolutionary Computation Conference (GECCO 2020). Cancun, Mexico. July 8-12, 2020. Best paper nominee. [pdf]
  37. Travis Desell, AbdElRahman ElSaid and Alexander G. Ororbia. An Empirical Exploration of Deep Recurrent Connections Using Neuro-Evolution. The 23nd International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2020). Seville, Spain. April 15-17, 2020. Best paper nominee. [pdf]
  38. AbdElRahman ElSaid, Joshua Karns, Zimeng Lyu, Daniel Krutz, Alexander G. Ororbia and Travis Desell. Neuro-Evolutionary Transfer Learning through Structural Adaptation. The 23nd International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2020). Seville, Spain. April 15-17, 2020. [pdf]
  39. AbdElRahman ElSaid, Alexander G. Ororbia* and Travis Desell*. Ant-based Neural Topology Search (ANTS) for Optimizing Recurrent Networks. The 23nd International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2020). Seville, Spain. April 15-17, 2020. *Indicates equal advising. [pdf]
  40. Jeffery Palmerino, Qi Yu, Travis Desell and Daniel Krutz. Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility. The 34th IEEE/ACM International Conference on Automated Software Engineering (ASE 2019). 949-961. San Diego, California, USA. November 10-15, 2019. [pdf]
  41. Alex Ororbia, AbdElRahman ElSaid, and Travis Desell. Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution. The Genetic and Evolutionary Computation Conference (GECCO 2019). Prague, Czech Republic. July 8-12, 2019. [pdf]
  42. AbdElRahman ElSaid, Steven Benson, Shuchita Patwardhan, David Stadem and Travis Desell. Evolving Recurrent Neural Networks for Time Series Data Prediction of Coal Plant Parameters. The 22nd International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2019). Leipzig, Germany. April 24-26, 2019. [pdf]
  43. AbdElRahman ElSaid, Travis Desell and Daniel Krutz. Is Adaptivity a Core Property of Intelligent Systems? It Depends. The 2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). Montreal, Canada. May 25-26, 2019. [pdf]
  44. Kelton Karboviak, Sophine Clachar, Travis Desell, Mark Dusenbury, Wyatt Hedrick, James Higgins, John Walberg, and Brandon Wild. Classifying Aircraft Approach Type in the National General Aviation Flight Information Database. The 2018 International Conference on Computational Science (ICCS 2018). Wuxi, China. June 11th-13th, 2018. [pdf]
  45. Connor Bowley, Marshall Mattingly, Andrew Barnas, Susan Ellis-Felege and Travis Desell. Detecting Wildlife in Unmanned Aerial Systems Imagery using Convolutional Neural Networks Trained with an Automated Feedback Loop. The 2018 International Conference on Computational Science (ICCS 2018). Wuxi, China. June 11th-13th 2018. [pdf]
  46. AbdElRahman ElSaid, Fatima El Jamiy, James Higgins, Brandon Wild and Travis Desell . Using Ant Colony Optimization to Optimize Long Short-Term Memory Recurrent Neural Networks. The 2018 Genetic and Evolutionary Computation Conference (GECCO 2018). Kyoto, Japan. July 15th-19th 2018. [pdf]
  47. Travis Desell. Accelerating the Evolution of Convolutional Neural Networks with Node-Level Mutations and Epigenetic Weight Initialization. The 2018 Genetic and Evolutionary Computation Conference (GECCO 2018) - Evolutionary Machine Learning Poster Session. Kyoto, Japan. July 15th-19th 2018. [pdf]
  48. Travis Desell. Developing a Volunteer Computing Project to Evolve Convolutional Neural Networks and Their Hyperparameters. The 13th IEEE International Conference on eScience (eScience 2017). Auckland, New Zealand. October 24-27 2017. [pdf]
  49. Connor Bowley, Marshall Mattingly III, Andrew Barnas, Susan Ellis-Felege and Travis Desell. Toward Using Citizen Scientists to Drive Automated Ecological Object Detection in Aerial Imagery. The 13th IEEE International Conference on eScience (eScience 2017). Auckland, New Zealand. October 24-27 2017. [pdf]
  50. Travis Desell. Large Scale Evolution of Convolutional Neural Networks Using Volunteer Computing. The 2017 Genetic and Evolutionary Computation Conference (GECCO) - Evolutionary Machine Learning Poster Session. Berlin, Germany. July 15th-19th 2017. [pdf]
  51. AbdElRahman ElSaid, Fatima ElJamiy, James Higgins, Brandon Wild, Travis Desell. Optimizing LSTM Recurrent Neural Networks Using Ant Colony Optimization to Predict Aircraft Engine Vibration. The 2017 Genetic and Evolutionary Computation Conference (GECCO) - Late Breaking Abstracts. Berlin, Germany. July 15th-19th 2017. [pdf]
  52. Marshall Mattingly III, Andrew Barnas, Susan Ellis-Felege, Robert Newman, David Iles and Travis Desell. Developing a Citizen Science Web Portal for Manual and Automated Ecological Image Detection. The IEEE 12th International Conference on eScience (eScience 2016). Baltimore, MD, USA. October 23-27, 2016. Best of Conference Award. [pdf]
  53. Connor Bowley, Alicia Andes, Susan Ellis-Felege and Travis Desell. Detecting Wildlife in Uncontrolled Outdoor Video using Convolutional Neural Networks. The IEEE 12th International Conference on eScience (eScience 2016). Baltimore, MD, USA. October 23-27, 2016. [pdf]
  54. AbdElRahman ElSaid, Brandon Wild, James Higgins and Travis Desell. Using LSTM Recurrent Neural Networks to Predict Excess Vibration Events in Aircraft Engines. The IEEE 12th International Conference on eScience (eScience 2016). Baltimore, MD, USA. October 23-27, 2016. [pdf]
  55. David Apostal, Sara Faraji Jalal Apostal, Ronald Marsh and Travis Desell. Towards Modeling a Complex Geological Simulation. The 2016 Spring Simulation Multi-Conference (SpringSim 2016). Pasadena, California, USA. April 3-6, 2016. [pdf]
  56. Travis Desell and Carlos A. Varela. Performance and Scalability Analysis of Actor Message Passing and Migration in SALSA Lite. The 5th International Workshop on Programming based on Actors, Agents, and Decentralized Control (AGERE!}, held in conjunction with ACM SIGPLAN conference on Systems, Programming, Languages and Applications: Software for Humanity (SPLASH). Pittsburgh, Pennslyvania, USA. October 26th, 2015. [pdf]
  57. Kyle Goehner, Rebecca Eckroad, Leila Mohsenian, Paul Burr, Nicholas Caswell, Alicia Andes, Susan Ellis-Felege, and Travis Desell. A Comparison of Background Subtraction Algorithms for Detecting Avian Nesting Events in Uncontrolled Outdoor Video. The 11th IEEE International Conference on eScience (eScience 2015). Munich, Germany. August 31 - September 4, 2015. [Data Release and Supplementary Material]. [pdf]
  58. Kris Zarns, Archana Dhasarathy, Sergei Nechaev and Travis Desell. Searching the Human Genome for Snail and Slug With DNA@Home. The 11th IEEE International Conference on eScience (eScience 2015). Munich, Germany. August 31 - September 4, 2015. [pdf]
  59. Thomas O'Neil and Travis Desell. Empirical Support for the High-Density Subset Sum Decision Threshold. In the 14th IEEE Canadian Workshop on Information Theory (CWIT'15). St. John's, Newfoundland, Canada. July 6-9, 2015. (Copyright IEEE 2015). [pdf]
  60. Travis Desell, Kyle Goehner, Alicia Andes, Rebecca Eckroad, and Susan Ellis-Felege. On the Effectiveness of Crowd Sourcing Avian Nesting Video Analysis at Wildlife@Home. In the 15th International Conference on Computational Science. Reykjavík, Iceland. June 1-3, 2015. [pdf]
  61. Travis Desell, Sophine Clachar, James Higgins and Brandon Wild. Evolving Deep Recurrent Neural Networks Using Ant Colony Optimization. In the 15th European Conference on Evolutionary Computation in Combinatorial Optimisation (Evo* 2015: EvoCOP). Copenhagen, Denmark. April 8-10, 2015. [Data Release and Supplementary Material].. [pdf]
  62. Travis Desell, Sophine Clachar, James Higgins and Brandon Wild. Evolving Neural Network Weights for Time-Series Prediction of General Aviation Flight Data. In the 13th International Conference on Parallel Problem Solving from Nature (PPSN 2014). Ljubljana, Slovenia. September 13-17, 2014. [Data Release and Supplementary Material]. [pdf]
  63. David Apostal, Kyle Foerster, Travis Desell and Will Gosnold. Performance Improvements for a Large-Scale Geological Simulation. In the 14th International Conference on Computational Science (ICCS 2014). Cairns, Australia. June 10-12, 2014.
  64. Travis Desell, Robert Bergman, Kyle Goehner, Ronald Marsh, Rebecca VanderClute, and Susan Ellis-Felege. Wildlife@Home: Combining Crowd Sourcing and Volunteer Computing to Analyze Avian Nesting Video. In the 2013 IEEE 9th International Conference on e-Science. Beijing, China. October 23-25, 2013. [pdf]
  65. Travis Desell. Using Actors and the SALSA Programming Language to Introduce Concurrency in Computer Science II. In the Third NSF/TCPP Workshop on Parallel and Distributed Computing Education (EduPar-13}, held in conjunction with the 27th IEEE International Parallel & Distributed Processing Symposium. Boston, Massachussets. May 20, 2013. [pdf]
  66. David Apostal, Kyle Foerster, Amrita Chatterjee and Travis Desell. Password Recovery Using MPI and CUDA. In the 19th Annual International Conference on High Performance Computing. Pune, India. December 18-21, 2012. [pdf]
  67. Travis Desell, Malik Magdon-Ismail, Heidi Newberg, Lee Newberg, Boleslaw K. Szymanski, and Carlos A. Varela. A Robust Asynchronous Newton Method for Massive Scale Computing Systems. In the 2011 IEEE International Conference on Computational Intelligence and Software Engineering (CiSE 2011). Wuhan, China. December 9-11, 2011. [pdf]
  68. Travis Desell, Lee A. Newberg, Malik Magdon-Ismail, Boleslaw K. Szymanski and William Thompson. Finding Protein Binding Sites Using Volunteer Computing Grids. In the 2011 2nd International Congress on Computer Applications and Computational Science (CACS 2011). Bali, Indonesia. November 15-17, 2011. [pdf]
  69. Travis Desell, Benjamin A. Willet, Matthew Arsenault, Heidi Newberg, Malik Magdon-Ismail, Boleslaw Szymanski and Carlos A. Varela. Evolving N-Body Simulations to Determine the Origin and Structure of the Milky Way Galaxy's Halo using Volunteer Computing. In the Proceedings of the IPDPS'11 Fifth Workshop on Desktop Grids and Volunteer Computing Systems (PCGrid 2011). Anchorage, Alaska, USA. May 20, 2011. [pdf]
  70. Travis Desell, David P. Anderson, Malik Magdon-Ismail, Heidi Newberg, Boleslaw Szymanski and Carlos A. Varela. An Analysis of Massively Distributed Evolutionary Algorithms. In the Proceedings of the 2010 IEEE Congress on Evolutionary Computation (IEEE CEC 2010). pages 1-8. Barcelona, Spain. July 2010. [pdf]
  71. Travis Desell, Malik Magdon-Ismail, Boleslaw Szymanski, Carlos A. Varela, Heidi Newberg and David P. Anderson. Validating Evolutionary Algorithms on Volunteer Computing Grids. In the Proceedings of the 10th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS 2010). pages 29-41. Amsterdam, Netherlands. June 2010. [pdf]
  72. Travis Desell, Malik Magdon-Ismail, Boleslaw Szymanski, Carlos Varela, Heidi Newberg and Nathan Cole. Robust Asynchronous Optimization for Volunteer Computing Grids. In the Proceedings of the 5th IEEE International Conference on e-Science (eScience2009). pages 263-270. Oxford, UK. December 2009. [pdf]
  73. Nathan Colen, Heidi Newberg, Malik Magdon-Ismail, Travis Desell, Kristopher Dawsey, Warren Hayashi, Xinyang Fred Liu, Jonathan Purnell, Boleslaw Szymanski, Carlos Varela, Benjamin Willett and James Wisniewski. A Study of the Sagittarius Tidal Stream Using Maximum Likelihood. In the Proceedings of the 18th Annual Conference on Astronomical Data Analysis Software and Systems. Quebec City, Quebec, Canada. November 2009.
  74. Travis Desell, Anthony Waters, Malik Magdon-Ismail, Boleslaw Szymanski, Carlos Varela, Matthew Newby, Heidi Newberg, Andreas Przystawik and Dave Anderson. Accelerating the MilkyWay@Home volunteer computing project with GPUs. In the 8th International Conference on Parallel Processing and Applied Mathematics (PPAM 2009). Wroclaw, Poland. September 2009. [pdf]
  75. Nathan Cole, Heidi Newberg, Malik Magdon-Ismail, Travis Desell, Boleslaw Szymanski, Carlos Varela. Tracing the Sagittarius Tidal Stream with Maximum Likelihood. In the Proceedings of the International Conference on Classification and Discovery in Large Astronomical Surveys. pages 216-220. Ringberg Castle, Germany. October 2008.
  76. Travis Desell, Boleslaw Szymanski, and Carlos A. Varela.. An Asynchronous Hybrid Genetic-Simplex Search for Modeling the Milky Way Galaxy using Volunteer Computing. In the Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2008). pages 921-928. Atlanta, Georgia. July 2008. [pdf]
  77. Travis Desell, Boleslaw Szymanski, and Carlos A. Varela. Asynchronous Genetic Search for Scientific Modeling on Large-Scale Heterogeneous Environments. In the Proceedings of the 17th International Heterogeneity in Computing Workshop (HCW/IPDPS'08). IEEE. pages 12. Miami, FL. April 2008. [pdf]
  78. Boleslaw Szymanski, Travis Desell, and Carlos A. Varela. The Effect of Heterogeneity on Asynchronous Panmictic Genetic Search. In the Proceedings of the 7th International Conference on Parallel Processing and Applied Mathematics (PPAM'2007). LNCS. Gdansk, Poland. September 2007. [pdf]
  79. Travis Desell, Nathan Cole, Malik Magdon-Ismail, Heidi Newberg, Boleslaw Szymanski, and Carlos A. Varela. Distributed and Generic Maximum Likelihood Evaluation. In the Proceedings of the 3rd IEEE International Conference on e-Science and Grid Computing (eScience2007). pages 337-344. Bangalore, India. December 2007. Best paper finalist. [pdf]
  80. Kaoutar El Maghraoui, Travis Desell, Boleslaw K. Szymanski, and Carlos A. Varela. Dynamic Malleability in Iterative MPI Applications. In the Proceedings of 7th IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2007). pages 591-598. Rio de Janeiro, Brazil. May 2007. Best paper award nominee. [pdf]
  81. Travis Desell, Kaoutar El Maghraoui, and Carlos A. Varela. Malleable Components for Scalable High Performance Computing. In the Proceedings of the HPDC'15 Workshop on HPC Grid programming Environments and Components (HPC-GECO/CompFrame). IEEE. pages 37-44. Paris, France. June 2006. Best paper award. [pdf]
  82. Travis Desell, Harihar N. Iyer, Abe Stephens, and Carlos A. Varela. OverView: A Framework for Generic Online Visualization of Distributed Systems. In the Proceedings of the European Joint Conferences on Theory and Practice of Software (ETAPS 2004}, eclipse Technology eXchange (eTX) Workshop. Barcelona, Spain. March 2004. [pdf]
  83. Travis Desell, Kaoutar El Maghraoui, and Carlos A. Varela. Load Balancing of Autonomous Actors over Dynamic Networks. In the Proceedings of the Hawaii International Conference on System Sciences, HICSS-37 Software Technology Track. pages 1-10. January 2004. [pdf]
Journal Articles
  1. Devroop Kar, Sheeraja Rajakrishnan, Zimeng Lyu, Hao Zhang, Travis Desell, Alex Ororbia and Daniel Krutz. Directly Learning Stock Trading Strategies Through Profit Guided Loss Functions. Applied Soft Computing. August 2026. [pdf]
  2. AbdElRahman ElSaid, Karl Ricanek, Zimeng Lyu, Alexander Ororbia and Travis Desell. Backpropagation-free 4D continuous ant-based neural topology search. Applied Soft Computing. August, 2023. [pdf]
  3. Zimeng Lyu, Alexander Ororbia and Travis Desell. Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting. Applied Soft Computing. June, 2023. [pdf]
  4. Javier Lenzi, Andrew Barnas, AbdElRahman ElSaid, Travis Desell, Robert Rockwell and Susan Ellis-Felege. Artificial Intelligence for Automated Detection of Large Mammals Creates Path to Upscale Drone Surveys. Scientific Reports. January, 2023. [pdf]
  5. Deema Alshoaibi, Mohamed Wiem Mkaouer, Ali Ouni, AbdulMutalib Wahaishi, Travis Desell and Makram Soui. Search-based Detection of Code Changes Introducing Performance Regression. Swarm and Evolutionary Computation. August, 2022. [pdf]
  6. Aaron Bergstrom, John Nowatzki, Trevor Witt, Isaac Barnhart, Jordan Krueger, Mark Askelson, Kurt Barnhart, and Travis Desell. Protecting Farm Privacy while Researching Large-Scale UAS Platforms for Agricultural Applications. Agronomy Journal. March, 2022. [pdf]
  7. Travis Desell, AbdElRahman ElSaid, Zimeng Lyu, David Stadem, Shuchita Patwardhan and Steve Benson. Long Term Predictions of Coal Fired Power Plant Data Using Evolved Recurrent Neural Networks. at - Automatisierungstechnik. Volume 68: No 2, Pages 130-139. January, 2020. [pdf]
  8. Connor Bowley, Marshall Mattingly, Andrew Barnas, Susan Ellis-Felege and Travis Desell. An Analysis of Altitude, Citizen Science and a Convolutional Neural Network Feedback Loop on Object Detection in Unmanned Aerial Systems. Journal of Computational Science. May, 2019. [pdf]
  9. Jake Weiss, Heidi Jo Newberg, Matthew Newby, and Travis Desell. Fitting the Density Substructure of the Stellar Halo with MilkyWay@Home. The Astrophysical Journal Supplement Series, Volume 238, Number 2. September, 2018. [pdf]
  10. AbdElRahman ElSaid, Travis Desell, Fatima El Jamiy, James Higgins and Brandon Wild. Optimizing Long Short-Term Memory Recurrent Neural Networks Using Ant Colony Optimization to Predict Turbine Engine Vibration. Applied Soft Computing. Volume 73, Pages 969-991. December, 2018. [pdf]
  11. Andrei P. Kirilenko, Travis Desell, Hany Kim, and Svetlana Stepchenkova. Crowdsourcing Analysis of Twitter Data on Climate Change: Paid Workers vs. Volunteers. Sustainability. November 3, 2017. [pdf]
  12. Susan N. Ellis-Felege, Travis Desell and Christopher J. Felege. A Bird's Eye View of... Birds: Combining Technology and Citizen Science for Conservation. Wildlife Professional 8: 27-30. Spring 2014. PDF courtsey of The Wildlife Professional. [pdf]
  13. Travis Desell and Carlos Varela. SALSA Lite: A Hash-Based Actor Runtime for Efficient Local Concurrency. Springer Lecture Notes in Computer Science: Concurrent Objects and Beyond. 23 pages. 2013. [pdf]
  14. Matthew Newby, Nathan Cole, Heidi Jo Newberg, Travis Desell, Malik Magdon-Ismail, Boleslaw Szymanski, Carlos Varela, Benjamin Willett, and Brian Yanny. A Spatial Characterization of the Sagittarius Dwarf Galaxy Tidal Tails. The Astronomical Journal. Volume 145, Number 6. May 2013. [pdf]
  15. Travis Desell, Jason LaPorte, Carlos A. Varela, and Gustavo Guevara. Modular Visualization of Distributed Systems. CLEI (Latin-american Center for Informatics Studies) Electronic Journal: Special issue of best papers presented at CLEI'2010. Volume 14, Number 1, Paper 7. April 2011. [pdf]
  16. Kaoutar El Maghraoui, Travis Desell, Boleslaw K. Szymanski, and Carlos A. Varela. Malleable Iterative MPI Applications. Concurrency and Computation: Practice and Experience. Volume 21, Issue 3, pages 393-413. March 2009. [pdf]
  17. Nathan Cole, Heidi Newberg, Malik Magdon-Ismail, Travis Desell, Kristopher Dawsey, Warren Hayashi, Jonathan Purnell, Boleslaw Szymanski, Carlos A. Varela, Benjamin Willett, and James Wisniewski. Maximum Likelihood Fitting of Tidal Streams with Application to the Sagittarius Dwarf Tidal Tails. Astrophysical Journal. Volume 683, pages 750-766. 2008. [pdf]
  18. Travis Desell, Kaoutar El Maghraoui, and Carlos A. Varela. Malleable Applications for Scalable High Performance Computing. Cluster Computing. pages 323-337. June 2007. [pdf]
  19. Kaoutar El Maghraoui, Travis Desell, Boleslaw K. Szymanski, and Carlos A. Varela. The Internet Operating System: Middleware for Adaptive Distributed Computing. The International Journal of High Performance Computing Applications (IJHPCA): Special Issue on Scheduling Techniques for Large-Scale Distributed Platforms. Volume 20, Issue 4, pages 467-480. 2006. [pdf]
Preprints
  1. Hong Yang, Aidan LaBella and Travis Desell. Predictive Maintenance for General Aviation Using Convolutional Transformers. arXiv: Machine Learning (cs.LG). October, 2021. [pdf]
  2. Zimeng Lyu, AbdElRahman ElSaid, Joshua Karns, Alexander Ororbia and Travis Desell. Continuous Ant-Based Neural Topology Search. arXiv: Computation and Language (cs.CL). November, 2020. [pdf]
  3. Zimeng Lyu, AbdElRahman ElSaid, Joshua Karns, Mohamed Mkaouer and Travis Desell. A Experimental Study of Weight Initialization and Weight Inheritance Effects on Neuroevolution. arXiv: Computation and Language (cs.CL). September, 2020. [pdf]
  4. Farhad Akhbardeh, Travis Desell and Marcos Zampieri. MaintNet: A Collaborative Open-Source Library for Predictive Maintenance Language Resources. arXiv: Computation and Language (cs.CL). May, 2020. [pdf]
  5. Zimeng Lyu, Joshua Karns, AbdElRahman ElSaid and Travis Desell. Improving Neuroevolution Using Island Extinction and Repopulation. arXiv: Neural and Evolutionary Computing (cs.NE). May, 2020. [pdf]
  6. AbdElRahman ElSaid, Alexander G. Ororbia and Travis Desell. The Ant Swarm Neuro-Evolution Procedure for Optimizing Recurrent Networks. arXiv: Neural and Evolutionary Computing (cs.NE). September, 2019. [pdf]
  7. Travis Desell, AbdElRahman ElSaid and Alexander G. Ororbia. An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution. arXiv: Neural and Evolutionary Computing (cs.NE). September, 2019. [pdf]
  8. Alex Ororbia, AbdElRahman ElSaid and Travis Desell. Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution. arXiv: Neural and Evolutionary Computing (cs.NE). February, 2019. [pdf]
  9. Travis Desell. Accelerating the Evolution of Convolutional Neural Networks with Node-Level Mutations and Epigenetic Weight Initialization. arXiv: Neural and Evolutionary Computing (cs.NE). November, 2018. [pdf]
  10. AbdElRahman ElSaid, Travis Desell, Fatima El Jamiy, James Higgins and Brandon Wild. Optimizing Long Short-Term Memory Recurrent Neural Networks Using Ant Colony Optimization to Predict Turbine Engine Vibration. arXiv: Neural and Evolutionary Computing (cs.NE). October 10, 2017. [pdf]
  11. Travis Desell. Large Scale Evolution of Convolutional Neural Networks Using Volunteer Computing. arXiv: Neural and Evolutionary Computing (cs.NE). March 15, 2017. [pdf]
Theses
  1. Zimeng Lyu. Online and Offline Multi-Variate Time Series Forecasting with NeuroEvolution Based Neural Architecture Search. PhD Thesis. Rochester Institute of Technology. July 2025. [pdf]
  2. Farhad Akhbardeh. NLP and ML Methods for Pre-processing, Clustering and Classification of Technical Logbook Datasets. PhD Thesis. Rochester Institute of Technology. July 2022. [pdf]
  3. AbdElRahman ElSaid. Nature-Inspired Topology Optimization of Recurrent Neural Networks. PhD Thesis. Rochester Institute of Technology. December 2020. [pdf]
  4. Travis Desell. Asynchronous Global Optimization for Massive-Scale Computing. PhD Thesis. Rensselaer Polytechnic Institute. December 2009. [pdf]
  5. Travis Desell. Autonomic Grid Computing using Malleability and Migration: An Actor-Oriented Software Framework. Master's Thesis. Rensselaer Polytechnic Institute. May 2007. [pdf]
Book Chapters
  1. AbdElRahman ElSaid and Travis Desell. CANTS-GP: A Nature-Inspired Metaheuristic for Graph Based Genetic Programs. In B. Burlacu, F. Olivetti de França, A. Lalejini, s. Kelly, and W. Banzhaf (Eds.): Genetic Programming Theory and Practice XXII. 23 pages. Springer. 2026. [html]
  2. Travis Desell, AbdElRahman ElSaid and Alexander G. Ororbia II. Investigating Deep Recurrent Connections and Recurrent Memory Cells Using Neuro-Evolution. In H. Iba and N. Noman: Deep Neural Evolution – Deep Learning with Evolutionary Computation. 36 pages. Springer. 2020.
  3. Nathan Cole, Travis Desell, Daniel Lombranaa Gonzalez, Francisco Fernandez de Vega, Malik Magdon-Ismail, Heidi Newberg, Boleslaw Szymanski and Carlos A. Varela. Evolutionary Algorithms on Volunteer Computing Platforms: The MilkyWay@Home Project. In F. Fernandez de Vega, E. Cantu-Paz (Eds.): Parallel and Distributed Computational Intelligence. SCI 269, pages 63-90. Springer-Verlag Berlin Heidelberg. 2010. [pdf]
  4. Kaoutar El Maghraoui, Travis Desell, Boleslaw K. Szymanski, James D. Teresco, and Carlos A. Varela. Towards a Middleware Framework for Dynamically Reconfigurable Scientific Computing. In L. Grandinetti, editor, Grid Computing and New Frontiers of High Performance Processing. Advances in Parallel Computing, Volume 14, pages 275-301. Elsevier. 2005. [pdf]
Books
  1. Sima Noghanian, Abas Sabouni, Travis Desell and Ali Ashtari. Microwave Tomography - Global Optimization, Parallelization and Performance Evaluation. Springer. 2014. [html]
Technical Reports
  1. Carlos A. Varela, Gul Agha, Wei-Jen Wang, Travis Desell, Kaoutar El Maghraoui, Jason LaPorte, and Abe Stephens. The SALSA Programming Language: 1.1.2 Release Tutorial. Technical report 07-12. Department of Computer Science, RPI, Troy, NY, USA. February 2007. [pdf]
  2. Kaoutar El Maghraoui, Travis J. Desell, and Carlos A. Varela. Network Sensitive Reconfiguration of Distributed Applications. Technical report 05-03. Department of Computer Science, RPI, Troy, NY USA. 2005. [pdf]
  3. Harihar N. Iyer, Abe Stephens, Travis Desell, and Carlos A. Varela. OverView - Dynamic Visualization of Java-Based Highly Reconfigurable Distributed Systems. Technical report. Worldwide Computing Laboratory, RPI, Troy, NY USA. August 2003. [pdf]