| Publications |
|---|
| E. Yeats, F. Liu and H. Li, "Disentangling Learning Representations with Density Estimation", International Conference on Learning Representations(ICLR), 2023 |
| G. Nair, H. Suh, M. Halappanavar, F. Liu, J. Seo and Y. Cao, "FPGA acceleration of GCN in light of the symmetry of graph adjacency matrix", IEEE Design Automation and Test Europe (DATE), 2023 |
| AD. Gaidhane, Z. Yang and Y. Cao, "Graph-based compact modeling of CMOS transistors for efficient parameter extraction: a machine learning approach", Solid-state electronics, 2023 |
| M. Fan, D. Lu and S. Liu, "A deep learning-based direct forecasting of CO2 plume migration", Geoenergy Science and Engineering, 2023 |
| Y Du, S. Chatterjee, A. Bhattacharya, A. Dutta, and M. Halappanavar, "Role of Reinforcement Learning for Risk-based Robust Control of Cyber-Physical Energy Systems", Risk Analysis, 2023 |
| K. Rajput, M. Schram and K. Somayaji, "Uncertainty aware deep learning for particle accelerators", NeurIPS Machine learning and physical sciences workshop 2022 |
| M. Fan, D. Lu, D. Rastog and E.M. Pierce, "A spatiotemporal-aware climate model ensembling method for improving precipitation predictability", Journal of machine learning in modeling and simulation, 2022 |
| M. Fan, L. Zhang, S. Liu, T. Yang and D. Lu, "Identifying the hydrometeorological decision factors influeing reservoir release over the Upper Colorado Region", AGU Fall Meetings 2022 |
| S. Liu, D. Lu, D. Ricciuto and A. Walker, "Improving net ecosystem CO2 flux prediction using memory-based interpretable machine learning", IEEE International Conference on Data Mining Workshops (ICDMW), 2022 |
| X. Liu, J. Zhang and Z. Pei, "Machine Learning for High Entropy Alloys: Progress, Challenges and Opportunities", Progress in materials science, 2022 |
| C. Wei, J. Zhang, K. Liechti and C. Wu, "Deep-green inversion to extract traction-separation relations at material interfaces", Journal of Solids and Structures, 2022 |
| W. Shaw Cortez, J. Drgona, A. Tuor, M. Halappanavar and D. Vrabie, "Differentiable predictive control with safety guarantees: a control barrier function approach", IEEE Conference on Decision and Control (CDC), 2022 |
| S. Mukherjee, J. Drgona, A. Tuor, M. Halappanavar and D. Vrabie, "Neural Lyapunov differential predictive control", IEEE Conference on Decision and Control (CDC), 2022 |
| J. Drgona, S. Mukherjee, A. Tuor and M. Halappanavar, "Learning Stochastic Parametric Diferentiable Predictive Control Policies", IFAC Symposium on Robust Control Design (RONCON), 2022 |
| E. Yeats, F. Liu, D. Womble and H. Li, "NashAE: Disentangling Representations Through Adversarial Covariance Minimization", European Conference on Computer Vision (ECCV), 2022 |
| P. Fan, D. Lu and D. Rastogi, "Multimodel Ensemble Predictions of Precipitation using Bayesian Neural Networks", International Conference on Learning Representations(ICLR) Workshops 2022 |
| D. Liu, DM Ricciuto, and S Liu, "An interpretable machine learning model for advancing terrestrial ecosystem predictions", ICLR Workshops 2022 |
| D. Lu, DM Ricciuto, and J. Zhang, "Invertible neural networks for E3SM land model calibration and simution", ICLR Workshops, 2022 |
| J. Sun, L. Yang, J. Zhang, F. Liu, M. Halappanavar, D. Fang and Y. Cao, "Gradient-Based Novelty Detection Boosted by Self-Supervised Binary Classification", AAAI Conference on Artificial Intelligence, 2022 |
| J. Zhang, K. Saleeby, T. Feldhausen, S. Bi, A. Plotkowski, D. Womble. Self-Supervised Anomaly Detection via Neural Autoregressive Flows with Active Learning. In NeurIPS 2021 Workshop Deep Generative Models and Downstream Applications, 2021 |
| V. Fung, J. Zhang, G. Hu, P. Ganesh, B. G. Sumpter. Inverse design of two-dimensional materials with invertible neural networks. In npj Computational Materials - Nature, 2021 |
| J. Sun, L. Yang, J. Zhang, F. Liu, M. M. Halappanavar, D. Fan, Y. Cao, "Self-supervised Novelty Detection for Continual Learning: A Gradient-based Approach Boosted by Binary Classification", IJCAI, Workshop on Continual Learning, 2021 |
| P. Ramuhalli, V. Chandan, M. Schram, J. Drgona, M. Halappanavar, F. Liu, “Overview of drivers for AI-informed decision and control in energy applications,” Presented at the SIAM AN21 Minisymposium, July 2021. |
| F. Liu, M. Halappanavar, Y. Cao and P. Li and D. Womble, "Data-Driven Framework for Decision and Control of Dynamic Systems", Presented at the SIAM AN21 Minisymposium, 2021 |
| V. Fung, J. Zhang, E. Juarez, B. Sumpter. Benchmarking graph neural networks for materials chemistry. In npj Computational Materials - Nature, 7, 84, 2021. |
| J. Zhang, J. Drgona, S. Mukherjee, M. Halappanavar, F. Liu, Variational Generative Flows for Reconstruction Uncertainty Estimation. International Conference on Machine Learning (ICML) Workshop on Uncertainty & Robustness in Deep Learning, 2021 |
| J. Zhang, V. Fung, Efficient Inverse Learning for Materials Design and Discovery. International Conference on Learning Representations(ICLR) Workshop on Science and Engineering of Deep Learning, 2021 |
| J. Drgona, S. Mukherjee, J. Zhang, F. Liu, M. Halappanavar, "On the Stochastic Stability of Deep Markov Models", NeurIPS 2021 |
| D. Lu, E. Pierce, SC Kao, D. Womble, "Machine learning-enabled model-data integration for predicting subsurvace water storage", Tackling Climate Change with Machine Learning workshop at NeurIPS 2021 |
