Publications / Academic Works

Snyder, S., Risbud, S. and Parsa, M. (2023). "Neuromorphic Bayesian Optimization in Lava". In Proceedings of the International Conference on Neuromorphic Systems 2023 (pp. 1-5).

Snyder, S., Zhu, K., Vega, R., Nowzari, C. and Parsa, M. (2023). "Zespol: A Lightweight Environment for Training Swarming Agents". In Proceedings of the International Conference on Neuromorphic Systems 2023 (pp. 1-5).

Parsa, M., Khasawneh, K.N. and Alouani, I., (2023). "A Brain-inspired Approach for Malware Detection using Sub-semantic Hardware Features". In Proceedings of the Great Lakes Symposium on VLSI 2023 (pp. 139-142).

Scott, E.O., Coletti, M., Schuman, C.D., Kay, B., Kulkarni, S.R., Parsa, M., Gunaratne, C. and De Jong, K.A., (2023). "Avoiding excess computation in asynchronous evolutionary algorithms". Expert Systems, 40(5), p.e13100.

Snyder, S., Thompson, H., Kaiser, M., Schwartz, G., Jaiswal, A., Parsa, M. (2023). "Object Motion Sensitivity: A Bio-inspired Solution to the Ego-motion Problem for Event-based Cameras". arXiv preprint arXiv:2303.14114.

Vega, R., Zhu, K., Luke, S., Parsa, M., & Nowzari, C. (2023). "Simulate Less, Expect More: Bringing Robot Swarms to Life via Low-Fidelity Simulations". arXiv preprint arXiv:2301.09018.

Schuman, C. D., Plank, J. S., Rose, G. S., & Parsa, M. (2022, November). "Lessons Learned in Omnidirectional Co-Design of Neuromorphic Systems". In 2022 IEEE International Meeting for Future of Electron Devices, Kansai (IMFEDK) (pp. 1-5). IEEE.

Schmidgall, S., Schuman, C., & Parsa, M. (2022). "Biological connectomes as a representation for the architecture of artificial neural networks" bioRxiv, 2022-09.

Aimone, J., Date, P., Fonseca-Guerra, G., Hamilton, K., Henke, K., Kay, B., ... & Smith, J. D. (2022). "A review of non-cognitive applications for neuromorphic computing". Neuromorphic Computing and Engineering.

Cong, G., Lim, S. H., Kulkarni, S., Date, P., Potok, T., Snyder, S., ... & Schuman, C. (2022, July). "Semi-Supervised Graph Structure Learning on Neuromorphic Computers". In Proceedings of the International Conference on Neuromorphic Systems 2022 (pp. 1-4).

Schuman, C. D., Kulkarni, S. R., Parsa, M., Mitchell, J. P., Date, P., & Kay, B. (2022). "Opportunities for neuromorphic computing algorithms and applications". Nature Computational Science, 2(1), 10-19.

Yin, Z., Kaiser, M. A. A., Camara, L. O., Camarena, M., Parsa, M., Jacob, A., ... & Jaiswal, A. (2022). "IRIS: Integrated Retinal Functionality in Image Sensors". bioRxiv, 2022-08.

Catherine Schuman, Robert Patton, Shruti Kulkarni, Maryam Parsa, Christopher Stahl, Nicholas Quentin Haas, John Parker Mitchell, Shay Snyder, Amelie Nagle, Alexandra Shanafield, Thomas Potok, "Evolutionary vs. Imitation Learning for Neuromorphic Control at the Edge", Neuromorphic Computing and Engineering, 2021

Maryam Parsa, Catherine D. Schuman, Amir K. Ziabari, Derek C. Rose, J. Parker Mitchell, Bill Kay, Steven R. Young, J. Travis Johnston, Nitin Rathi, and Kaushik Roy, "Accurate and Accelerated Neuromorphic Network Design Leveraging A Bayesian Hyperparameter Pareto Optimization Approach", International Conference on Neuromorphic Systems (ICONS), pp. 1-8, 2021, Best Paper Award

Eric O. Scott, Mark Coletti, Catherine D. Schuman, Bill Kay, Shruti R. Kulkarni, Maryam Parsa, and Kenneth A. De Jong, "Avoiding Excess Computation in Asynchronous Evolutionary Algorithms", UK Workshop on Computational Intelligence (UKCI), 2021, Best Paper Award

Maryam Parsa, Shruti R. Kulkarni, and Catherine D. Schuman, "Multi-Objective Hyperparameter Optimization for Spiking Neural Network Neuroevolution", IEEE Congress on Evolutionary Computation (CEC), pp. 1225-1232, 2021

Shruti R. Kulkarni, Maryam Parsa, J. Parker Mitchell, and Catherine D. Schuman "Benchmarking the Performance of Neuromorphic and Spiking Neural Network Simulators", Neurocomputing, vol.44, pp. 145-160, 2021

Catherine D. Schuman, James S. Plank, Maryam Parsa, Shruti R. Kulkarni, Nicholas Skuda, and J. Parker Mitchell, "A Software Framework for Comparing Training Approaches for Spiking Neuromorphic Systems", International Joint Conference on Neural Networks (IJCNN), pp. 1-10, 2021

Robert Patton, Catherine D. Schuman, Shruti Kulkarni, Maryam Parsa, J. Parker Mitchell, N Quentin Haas, Christopher Stahl, Spencer Paulissen, Prasanna Date, Thomas E. Potok, and Shay Snyder, "Neuromorphic Computing for Autonomous Racing", International Conference on Neuromorphic Systems (ICONS), pp. 1-5, 2021

Shrui R. Kulkarni, Maryam Parsa, J. Parker Mitchell, and Catherine D. Schuman, "Training Spiking Neural Networks with Synaptic Plasticity under Integer Representation", International Conference on Neuromorphic Systems (ICONS), pp. 1-7, 2021

Maryam Parsa, J. Parker Mitchell, Catherine D. Schuman, Robert M. Patton, Thomas E. Potok, and Kaushik Roy, "Bayesian Multi-Objective Hyperparameter Optimization for Accurate, Fast, and Efficient Neural Network Accelerator Design", Frontiers in Neuroscience, vol.14, p. 667, 2020

Maryam Parsa, Catherine D. Schuman, Bill Kay, Prasanna Date, Robert M. Patton, Thomas E. Potok, and Kaushik Roy, "Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment", International Joint Conference on Neural Networks (IJCNN), pp. 1-9, 2020

Catherine D. Schuman, J. Parker Mitchell, J. Travis Johnston, Maryam Parsa, Bill Kay, Prasanna Date, Robert M. Patton, and Thomas E. Potok, "Resilience and Robustness of Spiking Neural Networks for Neuromorphic Systems", International Joint Conference on Neural Networks (IJCNN), pp. 1-10, 2020

Catherine D. Schuman, J. Parker Mitchell, Maryam Parsa, James S. Plank, Samuel D. Brown, Garret S. Rose, Robert M. Patton, and Thomas E. Potok, "Automated Design of Neuromorphic Networks for Scientific Applications at the Edge", International Joint Conference on Neural Networks (IJCNN), pp.1-7, 2020

Daniel Elbrecht, Maryam Parsa, Shruti R. Kulkarni, J. Parker Mitchell, and Catherine D. Schuman, "Training Spiking Neural Networks Using Combined Learning Approaches", IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1995-2001, 2020

Daniel Elbrecht, Shruti R. Kulkarni, Maryam Parsa, J. Parker Mitchell, and Catherine D. Schuman, "Evolving Ensembles of Spiking Neural Networks for Neuromorphic Systems", IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1989-1994, 2020.

Amir K. Ziabari, Maryam Parsa, Yi Xuan, Je-Hyeong Bahk, Kazauaki Yazawa, Xavier Alvarez, and Ali Shakouri, "Far-field Thermal Imaging Below Diffraction Limit", Optics Express, 28 (5), 7036-7050, 2020.

Maryam Parsa, Aayush Ankit, Amir K. Ziabari, and Kaushik Roy, "PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design", IEEE/ACM International Conference on Computer-Aided Design (ICCAD), Westminster, CO, USA, pp. 1-8, 2019,

Maryam Parsa, J. Parker Mitchell, Catherine D. Schuman, Robert M. Patton, Thomas E. Potok, and Kaushik Roy, "Bayesian-based Hyperparameter Optimization for Spiking Neuromorphic Systems", IEEE International Conference on Big Data (Big Data), 4472-4478, 2019

Steven R. Young, Pravallika Devineni, Maryam Parsa, J. Travis Johnston, Bill Kay, Robert M. Patton, Catherine D. Schuman, Derek C. Rose, and Thomas E. Potok, "Evolving Energy Efficient Convolutional Neural Networks", IEEE International Conference on Big Data (Big Data), 4479-4485, 2019

Amir K. Ziabari, Pol Torres, Bjorn Vermeersch, Yi Xuan, Xavier Cartoixa, Alvar Torello, Y. Xuan, Je-Hyeong Bahk, Yee Rui Koh, Maryam Parsa, Peide Ye, F. Xavier Alvarez, and Ali Shakouri, "Full-field thermal imaging of quasiballistic crosstalk reduction in nanoscale devices", Nature Communications, Vol. 9 No. 1, 2018

Maryam Parsa, Priyadarshini Panda, Shreyas Sen, and Kaushik Roy, "Staged Inference Using Conditional Deep Learning for Energy Efficient Real-Time Smart Diagnosis", International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 78--81, 2017

Amir Ziabari, Yi Xuan, Je-Hyeong Bahk, Maryam Parsa, Peide Ye, and Ali Shakouri, "Sub-Diffraction Thermoreflectance Thermal Imaging using Image Reconstruction", 16th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), pp. 122-127, 2017

Abhronil Sengupta, Maryam Parsa, Bing Han, and Kaushik Roy, "Probabilistic Deep Spiking Neural Systems Enabled by Magnetic Tunnel Junction", IEEE Transactions on Electron Devices, Vol. 63, Issue. 7, pp. 2963 - 2970, 2016