Publications

(2024). Memristor-Spikelearn: A Spiking Neural Network Simulator for Studying Synaptic Plasticity under Realistic Memristor Behaviors. Proceedings of IEEE Design, Automation, and Test in Europe Conference (DATE).

(2024). Domain adaptation techniques for improved cross-domain study of galaxy mergers. Machine Learning and the Physical Sciences 2020 workshop held as a part of 34th Conference on Neural Information Processing Systems (NeurIPS).

(2024). Scaling transformer neural networks for skillful and reliable medium-range weather forecasting. Advances in Neural Information Processing Systems.

(2024). Quantifying Uncertainty in Large Language Models: Applications in Molecular Chemistry Tasks. Statistical Foundations of LLMs and Foundation Models Workshop at 38th Conference on Neural Information Processing Systems (NeurIPS 2024).

(2024). AstroMLab 1: Who wins astronomy jeopardy!?. Astronomy and Computing.

(2024). EFIT-Prime: Probabilistic and physics-constrained reduced-order neural network model for equilibrium reconstruction in DIII-D. Physics of Plasmas.

(2024). Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields. Journal of Mechanical Design.

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(2024). REMEDI: Corrective Transformations for Improved Neural Entropy Estimation. Forty-first International Conference on Machine Learning.

(2024). Thinking Bayesian for plasma physicists. Physics of Plasmas.

(2024). Sequential Bayesian Neural Subnetwork Ensembles.

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(2024). Scaling Transformers for Skillful and Reliable Medium-range Weather Forecasting.

(2024). Parametric Sensitivities of a Wind-driven Baroclinic Ocean Using Neural Surrogates. Proceedings of the Platform for Advanced Scientific Computing Conference.

(2024). BPNAS: Bayesian Progressive Neural Architecture Search.

(2024). Augmenting machine learning of Grad--Shafranov equilibrium reconstruction with Green's functions. Physics of Plasmas.

(2023). Improving performance in continual learning tasks using bio-inspired architectures. Conference on Lifelong Learning Agents.

(2023). Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck. arXiv preprint arXiv:2203.02592.

(2023). Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck. Proceedings of the 26th International Conference on Artificial Intelligence and Statistics.

(2023). DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection. Machine Learning: Science and Technology.

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(2023). Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures. 2nd Conference on Lifelong Learning Agents.

(2023). A domain-agnostic approach for characterization of lifelong learning systems. Neural Networks.

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(2022). HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization. 2022 IEEE International Conference on Cluster Computing (CLUSTER).

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(2022). Unified probabilistic neural architecture and weight ensembling improves model robustness.

(2022). Towards continually learning application performance models.

(2022). Single Gaussian process method for arbitrary tokamak regimes with a statistical analysis. Plasma Physics and Controlled Fusion.

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(2022). Single Gaussian Process Method for Arbitrary Tokamak Regimes with a Statistical Analysis. arXiv preprint arXiv:2202.11557.

(2022). General policy mapping: online continual reinforcement learning inspired on the insect brain.

(2022). DeepAdversaries: Examining the robustness of deep learning models for galaxy morphology classification. Machine Learning: Science and Technology.

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(2022). Biological Underpinnings for Lifelong Learning Machines: A Perspective. Nature Machine Intelligence.

(2022). AutoML for Neuromorphic Computing and Application-Driven Co-Design: Asynchronous, Massively Parallel Optimization of Spiking Architectures. 2022 IEEE International Conference on Rebooting Computing (ICRC).

(2022). Applications and techniques for fast machine learning in science. Frontiers in big Data.

(2022). Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction. *Plasma Physics and Controlled Fusion *.

(2022). Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction. Plasma Physics and Controlled Fusion.

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(2022). A Taxonomy of Error Sources in HPC I/O Machine Learning Models. arXiv preprint arXiv:2204.08180 (ACCEPTED at SC22).

(2021). DeepMerge – II. Building robust deep learning algorithms for merging galaxy identification across domains. Monthly Notices of the Royal Astronomical Society.

(2021). Robustness of deep learning algorithms in astronomy galaxy morphology studies.

(2021). Physical benchmarking for AI-generated cosmic web.

(2021). Physical Benchmarking for AI-Generated Cosmic Web. arXiv preprint arXiv:2112.05681.

(2021). Neuromorphic Architectures for Edge Computing Under Extreme Environments. 2021 IEEE Space Computing Conference (SCC).

(2021). IONET: Towards an Open Machine Learning Training Ground for I/O Performance Prediction.

(2021). DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification. arXiv preprint arXiv:2112.14299.

(2021). A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling.

(2021). $łess$i$greater$In situ$łess$/i$greater$ compression artifact removal in scientific data using deep transfer learning and experience replay. Machine Learning: Science and Technology.

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(2020). Towards Generalizable Models of I/O Throughput. Proceedings of the 10th International Workshop on Runtime and Operating Systems for Supercomputers.

(2020). Time-series learning of latent-space dynamics for reduced-order model closure. Physica D: Nonlinear Phenomena.

(2020). Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning. *4th Lifelong Learning Workshops held as a part of thirty-seventh International Conference on Machine Learning (ICML 2020) *.

(2020). In situ compression artifact removal in scientific data using deep transfer learning and experience replay. Machine Learning: Science and Technology.

(2020). HPC I/O Throughput Bottleneck Analysis with Explainable Local Models. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis.

(2020). Gauge: An Interactive Data-Driven Visualization Tool for HPC Application I/O Performance Analysis. 5th International Parallel Data Systems Workshop.

(2020). Domain adaptation techniques for improved cross-domain study of galaxy mergers. Machine Learning and the Physical Sciences 2020 workshop held as a part of 34th Conference on Neural Information Processing Systems (NeurIPS).

(2020). Calibration of hyperelastic constitutive models: the role of boundary conditions, search algorithms, and experimental variability. Biomechanics and Modeling in Mechanobiology.

(2019). Adaptive Learning for Concept Drift in Application Performance Modeling. 48th International Conference on Parallel Processing (ICPP 2019).

(2019). Value-Added Chemical Discovery Using Reinforcement Learning. Machine Learning and the Physical Sciences 2019 workshop held as a part of 33rd Conference on Neural Information Processing Systems (NeurIPS).

(2019). Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models. Machine Learning and the Physical Sciences 2019 workshop held as a part of 33rd Conference on Neural Information Processing Systems (NeurIPS).

(2019). Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection. Scientific reports.

(2019). On the inference of viscoelastic constants from stress relaxation experiments. Mechanics of Time-Dependent Materials.

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(2019). Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning. Proceedings of the International Conference on Neuromorphic Systems.

(2019). Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images. Machine Learning and the Physical Sciences 2019 workshop held as a part of 33rd Conference on Neural Information Processing Systems (NeurIPS).

(2019). Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time. 2019 IEEE International Conference on Big Data.

(2018). Modeling I/O Performance Variability Using Conditional Variational Autoencoders. 2018 IEEE International Conference on Cluster Computing (CLUSTER).

(2018). Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems. High Performance Computing.

(2017). Uncertainty Quantification Using the Nearest Neighbor Gaussian Process. New Advances in Statistics and Data Science.

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(2017). Analysis and Correlation of Application I/O Performance and System-Wide I/O Activity. 12th International Conference on Networking, Architecture, and Storage.

(2016). Bayesian calibration of hyperelastic constitutive models of soft tissue. Journal of the Mechanical Behavior of Biomedical Materials.

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(2015). A Bayesian approach to selecting hyperelastic constitutive models of soft tissue. Computer Methods in Applied Mechanics and Engineering.

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