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