Design and fabrication of a smart electronic guide for museums. - Majed Rostamian; Maryam Parsa; Voicu Groza (SACI, 2012)

Probabilistic Deep Spiking Neural Systems Enabled by Magnetic Tunnel Junction. - Abhronil Sengupta; Maryam Parsa; Bing Han 006; Kaushik Roy 001 (CoRR, 2016)

Staged Inference using Conditional Deep Learning for energy efficient real-time smart diagnosis. - Maryam Parsa; Priyadarshini Panda; Shreyas Sen; Kaushik Roy 001 (EMBC, 2017)

Bayesian-based Hyperparameter Optimization for Spiking Neuromorphic Systems. - Maryam Parsa; J. Parker Mitchell; Catherine D. Schuman; Robert M. Patton; Thomas E. Potok; Kaushik Roy 001 (IEEE BigData, 2019)

Evolving Energy Efficient Convolutional Neural Networks. - Steven R. Young; Pravallika Devineni; Maryam Parsa; J. Travis Johnston; Bill Kay; Robert M. Patton; Catherine D. Schuman; Derek C. Rose; Thomas E. Potok (IEEE BigData, 2019)

PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design. - Maryam Parsa; Aayush Ankit; Amirkoushyar Ziabari; Kaushik Roy 001 (ICCAD, 2019)

Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment. - Maryam Parsa; Catherine D. Schuman; Prasanna Date; Derek C. Rose; Bill Kay; J. Parker Mitchell; Steven R. Young; Ryan Dellana; William Severa; Thomas E. Potok; Kaushik Roy 001 (IJCNN, 2020)

Resilience and Robustness of Spiking Neural Networks for Neuromorphic Systems. - Catherine D. Schuman; J. Parker Mitchell; J. Travis Johnston; Maryam Parsa; Bill Kay; Prasanna Date; Robert M. Patton (IJCNN, 2020)

Automated Design of Neuromorphic Networks for Scientific Applications at the Edge. - Catherine D. Schuman; J. Parker Mitchell; Maryam Parsa; James S. Plank; Samuel D. Brown; Garrett S. Rose; Robert M. Patton; Thomas E. Potok (IJCNN, 2020)

Evolving Ensembles of Spiking Neural Networks for Neuromorphic Systems. - Daniel Elbrecht; Shruti R. Kulkarni; Maryam Parsa; J. Parker Mitchell; Catherine D. Schuman (SSCI, 2020)

Training Spiking Neural Networks Using Combined Learning Approaches. - Daniel Elbrecht; Maryam Parsa; Shruti R. Kulkarni; J. Parker Mitchell; Catherine D. Schuman (SSCI, 2020)

Multi-Objective Hyperparameter Optimization for Spiking Neural Network Neuroevolution. - Maryam Parsa; Shruti R. Kulkarni; Mark Coletti; Jeffrey K. Bassett; J. Parker Mitchell; Catherine D. Schuman (CEC, 2021)

Training Spiking Neural Networks with Synaptic Plasticity under Integer Representation. - Shruti R. Kulkarni; Maryam Parsa; J. Parker Mitchell; Catherine D. Schuman (ICONS, 2021)

Accurate and Accelerated Neuromorphic Network Design Leveraging A Bayesian Hyperparameter Pareto Optimization Approach. - Maryam Parsa; Catherine D. Schuman; Nitin Rathi; Amirkoushyar Ziabari; Derek C. Rose; J. Parker Mitchell; J. Travis Johnston; Bill Kay; Steven R. Young; Kaushik Roy 001 (ICONS, 2021)

Neuromorphic Computing for Autonomous Racing. - Robert M. Patton; Catherine D. Schuman; Shruti R. Kulkarni; Maryam Parsa; J. Parker Mitchell; Nicholas Quentin Haas; Christopher G. Stahl; Spencer Paulissen; Prasanna Date; Thomas E. Potok; Shay Sneider (ICONS, 2021)

A Software Framework for Comparing Training Approaches for Spiking Neuromorphic Systems. - Catherine D. Schuman; James S. Plank; Maryam Parsa; Shruti R. Kulkarni; Nicholas D. Skuda; J. Parker Mitchell (IJCNN, 2021)

Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing. - Pravallika Devineni; Panchapakesan Ganesh; Nikhil Sivadas; Abhijeet Dhakane; Ketan Maheshwari; Drahomira Herrmannova; Ramakrishnan Kannan; Seung-Hwan Lim; Thomas E. Potok; Jordan B. Chipka; Priyantha Mudalige; Mark Coletti; et al. (SMC, 2021)

Avoiding Excess Computation in Asynchronous Evolutionary Algorithms. - Eric O. Scott; Mark Coletti; Catherine D. Schuman; Bill Kay; Shruti R. Kulkarni; Maryam Parsa; Kenneth A. De Jong (UKCI, 2021)

Benchmarking the performance of neuromorphic and spiking neural network simulators. - Shruti R. Kulkarni; Maryam Parsa; J. Parker Mitchell; Catherine D. Schuman (Neurocomputing, 2021)

Semi-Supervised Graph Structure Learning on Neuromorphic Computers. - Guojing Cong; Seung-Hwan Lim; Shruti R. Kulkarni; Prasanna Date; Thomas E. Potok; Shay Snyder; Maryam Parsa; Catherine D. Schuman (ICONS, 2022)

Biological connectomes as a representation for the architecture of artificial neural networks. - Samuel Schmidgall; Catherine D. Schuman; Maryam Parsa (CoRR, 2022)

Opportunities for neuromorphic computing algorithms and applications. - Catherine D. Schuman; Shruti R. Kulkarni; Maryam Parsa; J. Parker Mitchell; Prasanna Date; Bill Kay (Nat. Comput. Sci., 2022)

Publisher Correction: Opportunities for neuromorphic computing algorithms and applications. - Catherine D. Schuman; Shruti R. Kulkarni; Maryam Parsa; J. Parker Mitchell; Prasanna Date; Bill Kay (Nat. Comput. Sci., 2022)

Evolutionary vs imitation learning for neuromorphic control at the edge. - Catherine D. Schuman; Robert M. Patton; Shruti R. Kulkarni; Maryam Parsa; Christopher G. Stahl; Nicholas Quentin Haas; J. Parker Mitchell; Shay Snyder; Amelie Nagle; Alexandra Shanafield; Thomas E. Potok (Neuromorph. Comput. Eng., 2022)

A review of non-cognitive applications for neuromorphic computing. - James B. Aimone; Prasanna Date; Gabriel A. Fonseca Guerra; Kathleen E. Hamilton; Kyle Henke; Bill Kay; Garrett T. Kenyon; Shruti R. Kulkarni; Susan M. Mniszewski; Maryam Parsa; Sumedh R. Risbud; Catherine D. Schuman; et al. (Neuromorph. Comput. Eng., 2022)

A Brain-inspired Approach for Malware Detection using Sub-semantic Hardware Features. - Maryam Parsa; Khaled N. Khasawneh; Ihsen Alouani (ACM Great Lakes Symposium on VLSI, 2023)

Hyperparameter Optimization and Feature Inclusion in Graph Neural Networks for Spiking Implementation. - Guojing Cong; Shruti R. Kulkarni; Seung-Hwan Lim; Prasanna Date; Shay Snyder; Maryam Parsa; Dominic Kennedy; Catherine D. Schuman (ICMLA, 2023)

Neuromorphic Bayesian Optimization in Lava. - Shay Snyder; Sumedh R. Risbud; Maryam Parsa (ICONS, 2023)

Zespol: A Lightweight Environment for Training Swarming Agents. - Shay Snyder; Kevin Zhu; Ricardo Vega; Cameron Nowzari; Maryam Parsa (ICONS, 2023)

Simulate Less, Expect More: Bringing Robot Swarms to Life via Low-Fidelity Simulations. - Ricardo Vega; Kevin Zhu; Sean Luke; Maryam Parsa; Cameron Nowzari (CoRR, 2023)

Object Motion Sensitivity: A Bio-inspired Solution to the Ego-motion Problem for Event-based Cameras. - Shay Snyder; Hunter Thompson; Md. Abdullah-Al Kaiser; Gregory Schwartz; Akhilesh R. Jaiswal; Maryam Parsa (CoRR, 2023)

Avoiding excess computation in asynchronous evolutionary algorithms. - Eric O. Scott; Mark Coletti; Catherine D. Schuman; Bill Kay; Shruti R. Kulkarni; Maryam Parsa; Chathika Gunaratne; Kenneth A. De Jong (Expert Syst. J. Knowl. Eng., 2023)

Transductive Spiking Graph Neural Networks for Loihi. - Shay Snyder; Victoria Clerico; Guojing Cong; Shruti R. Kulkarni; Catherine D. Schuman; Sumedh R. Risbud; Maryam Parsa (ACM Great Lakes Symposium on VLSI, 2024)

Asynchronous Neuromorphic Optimization in Lava. - Shay Snyder; Sumedh R. Risbud; Maryam Parsa (ACM Great Lakes Symposium on VLSI, 2024)

BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attacks. - Hamed Poursiami; Ihsen Alouani; Maryam Parsa (ICMLA, 2024)

Exploration of Novel Neuromorphic Methodologies for Materials Applications. - Derek Gobin; Shay Snyder; Guojing Cong; Shruti R. Kulkarni; Catherine D. Schuman; Maryam Parsa (ICONS, 2024)

Watermarking Neuromorphic Brains: Intellectual Property Protection in Spiking Neural Networks. - Hamed Poursiami; Ihsen Alouani; Maryam Parsa (ICONS, 2024)

Learning to Estimate Regions of Attraction of Autonomous Dynamical Systems Using Bayesian Physics Informed Neural Networks. - Cody Scharzenberger; Shay Snyder; Sumedh R. Risbud; Joe Hays; Maryam Parsa (ICONS, 2024)

Hardware-Algorithm Re-Engineering of Retinal Circuit for Intelligent Object Motion Segmentation. - Jason Sinaga; Victoria Clerico; Md. Abdullah-Al Kaiser; Shay Snyder; Arya Lohia; Gregory Schwartz; Maryam Parsa; Akhilesh Jaiswal 001 (ICONS, 2024)

Parallelized Multi-Agent Bayesian Optimization in Lava. - Shay Snyder; Derek Gobin; Victoria Clerico; Sumedh R. Risbud; Maryam Parsa (ICONS, 2024)

Spiking Neural Networks as a Controller for Emergent Swarm Agents. - Kevin Zhu; Connor Mattson; Shay Snyder; Ricardo Vega; Daniel S. Brown; Maryam Parsa; Cameron Nowzari (ICONS, 2024)

Brain Inspired Probabilistic Occupancy Grid Mapping with Hyperdimensional Computing. - Shay Snyder; Andrew Capodieci; David J. Gorsich; Maryam Parsa (CoRR, 2024)

Retina-inspired Object Motion Segmentation. - Victoria Clerico; Shay Snyder; Arya Lohia; Md. Abdullah-Al Kaiser; Gregory Schwartz; Akhilesh Jaiswal 001; Maryam Parsa (CoRR, 2024)

Reconfigurable Retina-Inspired Looming Detection. - Jason Sinaga; Shay Snyder; Md. Abdullah-Al Kaiser; Dan Jinoy; Gregory Schwartz; Maryam Parsa; Akhilesh Jaiswal 001 (FCCM, 2025)

Retina-inspired Object Motion Segmentation for Event-Cameras. - Victoria Clerico; Shay Snyder; Arya Lohia; Md. Abdullah-Al Kaiser; Gregory Schwartz; Akhilesh Jaiswal 001; Maryam Parsa (NICE, 2025)

A Milling Swarm of Ground Robots using Spiking Neural Networks. - Kevin Zhu; Shay Snyder; Ricardo Vega; Maryam Parsa; Cameron Nowzari (NICE, 2025)

Are Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Study. - Ayana Moshruba; Ihsen Alouani; Maryam Parsa (Proc. Priv. Enhancing Technol., 2025)

Do Spikes Protect Privacy? Investigating Black-Box Model Inversion Attacks in Spiking Neural Networks. - Hamed Poursiami; Ayana Moshruba; Maryam Parsa (CoRR, 2025)

Generalizable Reinforcement Learning with Biologically Inspired Hyperdimensional Occupancy Grid Maps for Exploration and Goal-Directed Path Planning. - Shay Snyder; Ryan Shea; Andrew Capodieci; David J. Gorsich; Maryam Parsa (CoRR, 2025)

On the Privacy-Preserving Properties of Spiking Neural Networks with Unique Surrogate Gradients and Quantization Levels. - Ayana Moshruba; Shay Snyder; Hamed Poursiami; Maryam Parsa (CoRR, 2025)

A Retina-Inspired Pathway to Real-Time Motion Prediction inside Image Sensors for Extreme-Edge Intelligence. - Subhradip Chakraborty; Shay Snyder; Md. Abdullah-Al Kaiser; Maryam Parsa; Gregory Schwartz; Akhilesh R. Jaiswal (CoRR, 2025)

Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks. - Ayana Moshruba; Hamed Poursiami; Maryam Parsa (CoRR, 2025)