Publications:

1. 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]
2. 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]
3. 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]
4. 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]
5. 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]

Publications:

1. 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]
2. 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]

Publications:

1. 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]

Publications:

1. 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]

Publications:

1. 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]
2. 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]

Dr. Zimeng Lyu is now an Assistant Professor in the Department of Computer Science and Technology at Kean University.
Personal Website
Publications:

1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
6. 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]
7. 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]
8. 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]
9. 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]
10. 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]
11. 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]
12. 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]
13. 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]
14. 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]
15. 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]
16. 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]
17. 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]
18. 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]
19. 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]
20. 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]
21. 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]
22. 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]
23. 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]
24. 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]

Josh Kars is now a research scientist at Meta.
Personal Website
Publications:

1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
6. 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]
7. 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]
8. 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]
9. 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]
10. 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]
11. 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]
12. 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]
13. 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]
14. 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]
15. 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]

Dr. Hong Yang is a Machine Learning Engineer at Stand Insurance.
Personal Website
Publications:

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. 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]
3. 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]
4. Hong Yang, Aidan LaBella and Travis Desell. Predictive Maintenance for General Aviation Using Convolutional Transformers. arXiv: Machine Learning (cs.LG). October, 2021. [pdf]

Dr. AbdElRahman ElSaid is now an Assistant Professor in the Department of Computer Science at University of North Carolina Wilmington.
Personal Website
Publications:

1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
6. 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]
7. 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]
8. 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]
9. 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]
10. 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]
11. 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]
12. 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]
13. 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]
14. 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]
15. 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]
16. 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]
17. 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]
18. 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]
19. 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]
20. 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]
21. 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]
22. 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]
23. 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]
24. 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]
25. 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]
26. 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]
27. AbdElRahman ElSaid. Nature-Inspired Topology Optimization of Recurrent Neural Networks. PhD Thesis. Rochester Institute of Technology. December 2020. [pdf]
28. 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]
29. 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.

Publications:

1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
6. 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]

Aidan graduated the D2S2 Lab in May 2023 with a BSc in Computer Science. He is now pursuing his Ph.D. in Computer Science at Brown University in Providence, RI where he is advised by Stephen Bach in the BATS research group and AI 'superlab'. His interests revolve around making sense of large real-world datasets using machine learning and data science techniques.
Personal Website
Publications:

1. 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]
2. 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]
3. Hong Yang, Aidan LaBella and Travis Desell. Predictive Maintenance for General Aviation Using Convolutional Transformers. arXiv: Machine Learning (cs.LG). October, 2021. [pdf]